.devcontainer/devcontainer.json

@@ -1,29 +1,18 @@
 {
     "name": "jpawlowski/hass.tibber_prices",
-    "image": "mcr.microsoft.com/devcontainers/python:3.14",
+    "image": "mcr.microsoft.com/devcontainers/python:3.13",
-    "postCreateCommand": "bash .devcontainer/setup-git.sh && scripts/setup/setup",
+    "postCreateCommand": "scripts/setup",
     "postStartCommand": "scripts/motd",
     "containerEnv": {
-        "PYTHONASYNCIODEBUG": "1",
+        "PYTHONASYNCIODEBUG": "1"
-        "TIBBER_PRICES_DEV": "1"
     },
     "forwardPorts": [
-        8123,
+        8123
-        3000,
-        3001
     ],
     "portsAttributes": {
         "8123": {
             "label": "Home Assistant",
             "onAutoForward": "notify"
-        },
-        "3000": {
-            "label": "Docusaurus User Docs",
-            "onAutoForward": "notify"
-        },
-        "3001": {
-            "label": "Docusaurus Developer Docs",
-            "onAutoForward": "notify"
         }
     },
     "customizations": {
@@ -70,7 +59,7 @@
                 ],
                 "python.defaultInterpreterPath": "${workspaceFolder}/.venv/bin/python",
                 "python.analysis.extraPaths": [
-                    "${workspaceFolder}/.venv/lib/python3.14/site-packages"
+                    "${workspaceFolder}/.venv/lib/python3.13/site-packages"
                 ],
                 "python.terminal.activateEnvironment": true,
                 "python.terminal.activateEnvInCurrentTerminal": true,
@@ -105,30 +94,23 @@
                         "fileMatch": [
                             "homeassistant/components/*/manifest.json"
                         ],
-                        "url": "${containerWorkspaceFolder}/schemas/json/manifest_schema.json"
+                        "url": "${containerWorkspaceFolder}/scripts/json_schemas/manifest_schema.json"
                     },
                     {
                         "fileMatch": [
                             "homeassistant/components/*/translations/*.json"
                         ],
-                        "url": "${containerWorkspaceFolder}/schemas/json/translation_schema.json"
+                        "url": "${containerWorkspaceFolder}/scripts/json_schemas/translation_schema.json"
                     }
-                ],
+                ]
-                "git.useConfigOnly": false
             }
         }
     },
-    "mounts": [
-        "source=${localEnv:HOME}${localEnv:USERPROFILE}/.gitconfig,target=/home/vscode/.gitconfig.host,type=bind,consistency=cached"
-    ],
     "remoteUser": "vscode",
     "features": {
         "ghcr.io/devcontainers/features/github-cli:1": {},
-        "ghcr.io/flexwie/devcontainer-features/op:1": {
-            "version": "latest"
-        },
         "ghcr.io/devcontainers/features/node:1": {
-            "version": "24"
+            "version": "22"
         },
         "ghcr.io/devcontainers/features/rust:1": {
             "version": "latest",

.devcontainer/setup-git.sh

@@ -1,99 +0,0 @@
-#!/bin/bash
-# Setup Git configuration from host
-# This script is idempotent and can be run multiple times safely.
-
-# Exit on error
-set -e
-
-# Check if host gitconfig exists
-if [ ! -f ~/.gitconfig.host ]; then
-    echo "No host .gitconfig found, skipping Git setup"
-    exit 0
-fi
-
-echo "Setting up Git configuration from host..."
-
-# Extract and set user info
-USER_NAME=$(grep -A 2 '^\[user\]' ~/.gitconfig.host | grep 'name' | sed 's/.*= //' | xargs)
-USER_EMAIL=$(grep -A 2 '^\[user\]' ~/.gitconfig.host | grep 'email' | sed 's/.*= //' | xargs)
-
-if [ -n "$USER_NAME" ]; then
-    CURRENT_NAME=$(git config --global user.name 2>/dev/null || echo "")
-    if [ "$CURRENT_NAME" != "$USER_NAME" ]; then
-        git config --global user.name "$USER_NAME"
-        echo "✓ Set user.name: $USER_NAME"
-    else
-        echo "  user.name already set: $USER_NAME"
-    fi
-fi
-
-if [ -n "$USER_EMAIL" ]; then
-    CURRENT_EMAIL=$(git config --global user.email 2>/dev/null || echo "")
-    if [ "$CURRENT_EMAIL" != "$USER_EMAIL" ]; then
-        git config --global user.email "$USER_EMAIL"
-        echo "✓ Set user.email: $USER_EMAIL"
-    else
-        echo "  user.email already set: $USER_EMAIL"
-    fi
-fi
-
-# Set safe defaults for container
-git config --global init.defaultBranch main
-git config --global pull.rebase false
-git config --global merge.conflictStyle diff3
-git config --global submodule.recurse true
-git config --global color.ui true
-echo "✓ Set Git defaults"
-
-# Copy useful aliases (skip if they have macOS-specific paths)
-if grep -q '^\[alias\]' ~/.gitconfig.host; then
-    echo "✓ Syncing Git aliases..."
-
-    # First, collect all aliases from host config
-    TEMP_ALIASES=$(mktemp)
-    sed -n '/^\[alias\]/,/^\[/p' ~/.gitconfig.host | \
-        grep -v '^\[' | \
-        grep -v '^$' | \
-        while IFS= read -r line; do
-            # Skip aliases with macOS-specific paths
-            if echo "$line" | grep -q -E '/(Applications|usr/local)'; then
-                continue
-            fi
-            echo "$line" >> "$TEMP_ALIASES"
-        done
-
-    # Apply each alias (git config --global overwrites existing values = idempotent)
-    if [ -s "$TEMP_ALIASES" ]; then
-        while IFS= read -r line; do
-            ALIAS_NAME=$(echo "$line" | awk '{print $1}')
-            ALIAS_VALUE=$(echo "$line" | sed "s/^$ALIAS_NAME = //")
-            git config --global "alias.$ALIAS_NAME" "$ALIAS_VALUE" 2>/dev/null || true
-        done < "$TEMP_ALIASES"
-        echo "  Synced $(wc -l < "$TEMP_ALIASES") aliases"
-    fi
-
-    rm -f "$TEMP_ALIASES"
-fi
-
-# Disable GPG signing in container (1Password SSH signing doesn't work in DevContainers)
-# SSH agent forwarding works for git push/pull, but SSH signing requires direct
-# access to 1Password app which isn't available in the container.
-#
-# For signed commits: Make final commits on host macOS where 1Password is available.
-# The container is for development/testing - pre-commit hooks will still run.
-CURRENT_SIGNING=$(git config --global commit.gpgsign 2>/dev/null || echo "false")
-if [ "$CURRENT_SIGNING" != "false" ]; then
-    echo "ℹ Disabling commit signing in container (1Password not accessible)"
-    echo "  → For signed commits, commit from macOS terminal outside container"
-    git config --global commit.gpgsign false
-else
-    echo "  Commit signing already disabled"
-fi
-
-# Keep the signing key info for reference, but don't use it
-SIGNING_KEY=$(grep 'signingkey' ~/.gitconfig.host 2>/dev/null | sed 's/.*= //' | xargs || echo "")
-if [ -n "$SIGNING_KEY" ]; then
-    echo "  → Your signing key: ${SIGNING_KEY:0:20}... (available on host)"
-fi
-
-echo "✓ Git configuration complete"

.github/FUNDING.yml

@@ -1,4 +0,0 @@
-# These are supported funding model platforms
-
-github: [ jpawlowski ]
-buy_me_a_coffee: jpawlowski

.github/workflows/auto-tag.yml

@@ -20,7 +20,7 @@ jobs:
     runs-on: ubuntu-latest
     steps:
       - name: Checkout repository
-        uses: actions/checkout@v6
+        uses: actions/checkout@v6.0.0
         with:
           fetch-depth: 0  # Need full history for git describe
 
@@ -43,13 +43,13 @@ jobs:
             echo "✗ Tag v${{ steps.manifest.outputs.version }} does not exist yet"
           fi
 
-      - name: Validate version format (stable or beta)
+      - name: Validate version format
         if: steps.tag_check.outputs.exists == 'false'
         run: |
           VERSION="${{ steps.manifest.outputs.version }}"
-          if ! echo "$VERSION" | grep -qE '^[0-9]+\.[0-9]+\.[0-9]+(b[0-9]+)?$'; then
+          if ! echo "$VERSION" | grep -qE '^[0-9]+\.[0-9]+\.[0-9]+$'; then
             echo "❌ Invalid version format: $VERSION"
-            echo "Expected format: X.Y.Z or X.Y.ZbN (e.g., 1.0.0, 0.25.0b0)"
+            echo "Expected format: X.Y.Z (e.g., 1.0.0)"
             exit 1
           fi
           echo "✓ Version format valid: $VERSION"

.github/workflows/docusaurus.yml

@@ -1,163 +0,0 @@
-name: Deploy Docusaurus Documentation (Dual Sites)
-
-on:
-  push:
-    branches: [main]
-    paths:
-      - 'docs/**'
-      - '.github/workflows/docusaurus.yml'
-    tags:
-      - 'v*.*.*'
-  workflow_dispatch:
-
-# Concurrency control: cancel in-progress deployments
-# Pattern from GitHub Actions best practices for deployment workflows
-concurrency:
-  group: ${{ github.workflow }}-${{ github.ref }}
-  cancel-in-progress: true
-
-permissions:
-  contents: write
-  pages: write
-  id-token: write
-
-jobs:
-  deploy:
-    name: Build and Deploy Documentation Sites
-    runs-on: ubuntu-latest
-    environment:
-      name: github-pages
-      url: ${{ steps.deployment.outputs.page_url }}
-    steps:
-      - uses: actions/checkout@v6
-        with:
-          fetch-depth: 0  # Needed for version timestamps
-
-      - name: Detect prerelease tag (beta/rc)
-        id: taginfo
-        run: |
-          if [[ "${GITHUB_REF}" =~ ^refs/tags/v[0-9]+\.[0-9]+\.[0-9]+(b[0-9]+|rc[0-9]+)$ ]]; then
-            echo "is_prerelease=true" >> "$GITHUB_OUTPUT"
-            echo "Detected prerelease tag: ${GITHUB_REF}"
-          else
-            echo "is_prerelease=false" >> "$GITHUB_OUTPUT"
-            echo "Stable tag or branch: ${GITHUB_REF}"
-          fi
-
-      - uses: actions/setup-node@v6
-        with:
-          node-version: 24
-          cache: 'npm'
-          cache-dependency-path: |
-            docs/user/package-lock.json
-            docs/developer/package-lock.json
-
-      # USER DOCS BUILD
-      - name: Install user docs dependencies
-        working-directory: docs/user
-        run: npm ci
-
-      - name: Create user docs version snapshot on tag
-        if: startsWith(github.ref, 'refs/tags/v') && steps.taginfo.outputs.is_prerelease != 'true'
-        working-directory: docs/user
-        run: |
-          TAG_VERSION=${GITHUB_REF#refs/tags/}
-          echo "Creating user documentation version: $TAG_VERSION"
-
-          npm run docusaurus docs:version $TAG_VERSION || echo "Version already exists"
-
-          # Update GitHub links in versioned docs
-          if [ -d "versioned_docs/version-$TAG_VERSION" ]; then
-            find versioned_docs/version-$TAG_VERSION -name "*.md" -type f -exec sed -i "s|github.com/jpawlowski/hass.tibber_prices/blob/main/|github.com/jpawlowski/hass.tibber_prices/blob/$TAG_VERSION/|g" {} \; || true
-          fi
-
-      - name: Cleanup old user docs versions
-        if: startsWith(github.ref, 'refs/tags/v') && steps.taginfo.outputs.is_prerelease != 'true'
-        working-directory: docs/user
-        run: |
-          chmod +x ../cleanup-old-versions.sh
-          # Adapt script for single-instance mode (versioned_docs/ instead of user_versioned_docs/)
-          sed 's/user_versioned_docs/versioned_docs/g; s/user_versions.json/versions.json/g; s/developer_versioned_docs/versioned_docs/g; s/developer_versions.json/versions.json/g' ../cleanup-old-versions.sh > cleanup-single.sh
-          chmod +x cleanup-single.sh
-          ./cleanup-single.sh
-
-      - name: Build user docs website
-        working-directory: docs/user
-        run: npm run build
-
-      # DEVELOPER DOCS BUILD
-      - name: Install developer docs dependencies
-        working-directory: docs/developer
-        run: npm ci
-
-      - name: Create developer docs version snapshot on tag
-        if: startsWith(github.ref, 'refs/tags/v') && steps.taginfo.outputs.is_prerelease != 'true'
-        working-directory: docs/developer
-        run: |
-          TAG_VERSION=${GITHUB_REF#refs/tags/}
-          echo "Creating developer documentation version: $TAG_VERSION"
-
-          npm run docusaurus docs:version $TAG_VERSION || echo "Version already exists"
-
-          # Update GitHub links in versioned docs
-          if [ -d "versioned_docs/version-$TAG_VERSION" ]; then
-            find versioned_docs/version-$TAG_VERSION -name "*.md" -type f -exec sed -i "s|github.com/jpawlowski/hass.tibber_prices/blob/main/|github.com/jpawlowski/hass.tibber_prices/blob/$TAG_VERSION/|g" {} \; || true
-          fi
-
-      - name: Cleanup old developer docs versions
-        if: startsWith(github.ref, 'refs/tags/v') && steps.taginfo.outputs.is_prerelease != 'true'
-        working-directory: docs/developer
-        run: |
-          chmod +x ../cleanup-old-versions.sh
-          # Adapt script for single-instance mode
-          sed 's/user_versioned_docs/versioned_docs/g; s/user_versions.json/versions.json/g; s/developer_versioned_docs/versioned_docs/g; s/developer_versions.json/versions.json/g' ../cleanup-old-versions.sh > cleanup-single.sh
-          chmod +x cleanup-single.sh
-          ./cleanup-single.sh
-
-      - name: Build developer docs website
-        working-directory: docs/developer
-        run: npm run build
-
-      # MERGE BUILDS
-      - name: Merge both documentation sites
-        run: |
-          mkdir -p deploy-root/user
-          mkdir -p deploy-root/developer
-          cp docs/index.html deploy-root/
-          cp -r docs/user/build/* deploy-root/user/
-          cp -r docs/developer/build/* deploy-root/developer/
-
-      # COMMIT VERSION SNAPSHOTS
-      - name: Commit version snapshots back to repository
-        if: startsWith(github.ref, 'refs/tags/v') && steps.taginfo.outputs.is_prerelease != 'true'
-        run: |
-          TAG_VERSION=${GITHUB_REF#refs/tags/}
-
-          git config user.name "github-actions[bot]"
-          git config user.email "github-actions[bot]@users.noreply.github.com"
-
-          # Add version files from both docs
-          git add docs/user/versioned_docs/ docs/user/versions.json 2>/dev/null || true
-          git add docs/developer/versioned_docs/ docs/developer/versions.json 2>/dev/null || true
-
-          # Commit if there are changes
-          if git diff --staged --quiet; then
-            echo "No version snapshot changes to commit"
-          else
-            git commit -m "docs: add version snapshot $TAG_VERSION and cleanup old versions [skip ci]"
-            git push origin HEAD:main
-            echo "Version snapshots committed and pushed to main"
-          fi
-
-      # DEPLOY TO GITHUB PAGES
-      - name: Setup Pages
-        uses: actions/configure-pages@v6
-
-      - name: Upload artifact
-        uses: actions/upload-pages-artifact@v4
-        with:
-          path: ./deploy-root
-
-      - name: Deploy to GitHub Pages
-        id: deployment
-        uses: actions/deploy-pages@v5

.github/workflows/lint.yml

@@ -4,15 +4,9 @@ on:
   push:
     branches:
       - "main"
-    paths-ignore:
-      - 'docs/**'
-      - '.github/workflows/docusaurus.yml'
   pull_request:
     branches:
       - "main"
-    paths-ignore:
-      - 'docs/**'
-      - '.github/workflows/docusaurus.yml'
 
 permissions: {}
 
@@ -26,20 +20,20 @@ jobs:
     runs-on: "ubuntu-latest"
     steps:
       - name: Checkout the repository
-        uses: actions/checkout@8e8c483db84b4bee98b60c0593521ed34d9990e8 # v6.0.1
+        uses: actions/checkout@93cb6efe18208431cddfb8368fd83d5badbf9bfd # v5.0.1
 
       - name: Set up Python
-        uses: actions/setup-python@a309ff8b426b58ec0e2a45f0f869d46889d02405 # v6.2.0
+        uses: actions/setup-python@e797f83bcb11b83ae66e0230d6156d7c80228e7c # v6.0.0
         with:
-          python-version: "3.14"
+          python-version: "3.13"
 
       - name: Install uv
-        uses: astral-sh/setup-uv@37802adc94f370d6bfd71619e3f0bf239e1f3b78  # v7.6.0
+        uses: astral-sh/setup-uv@5a7eac68fb9809dea845d802897dc5c723910fa3  # v7.1.3
         with:
           version: "0.9.3"
 
       - name: Install requirements
-        run: scripts/setup/bootstrap
+        run: scripts/bootstrap
 
       - name: Lint check
         run: scripts/lint-check

.github/workflows/release.yml

@@ -27,7 +27,7 @@ jobs:
       version: ${{ steps.tag.outputs.version }}
     steps:
       - name: Checkout repository
-        uses: actions/checkout@v6
+        uses: actions/checkout@v6.0.0
         with:
           fetch-depth: 0
           token: ${{ secrets.GITHUB_TOKEN }}
@@ -77,36 +77,22 @@ jobs:
       - name: Commit and push manifest.json update
         if: steps.update.outputs.updated == 'true'
         run: |
-          TAG_VERSION="v${{ steps.tag.outputs.version }}"
-
           git config user.name "github-actions[bot]"
           git config user.email "github-actions[bot]@users.noreply.github.com"
 
           git add custom_components/tibber_prices/manifest.json
-          git commit -m "chore(release): sync manifest.json with tag ${TAG_VERSION}"
+          git commit -m "chore(release): sync manifest.json with tag v${{ steps.tag.outputs.version }}"
 
           # Push to main branch
           git push origin HEAD:main
 
-          # Delete and recreate tag on new commit
-          echo "::notice::Recreating tag ${TAG_VERSION} on updated commit"
-          git tag -d "${TAG_VERSION}"
-          git push origin :refs/tags/"${TAG_VERSION}"
-          git tag -a "${TAG_VERSION}" -m "Release ${TAG_VERSION}"
-          git push origin "${TAG_VERSION}"
-
-          # Delete existing release if present (will be recreated by release-notes job)
-          gh release delete "${TAG_VERSION}" --yes --cleanup-tag=false || echo "No existing release to delete"
-        env:
-          GH_TOKEN: ${{ secrets.GITHUB_TOKEN }}
-
   release-notes:
     name: Generate and publish release notes
     runs-on: ubuntu-latest
     needs: sync-manifest  # Wait for manifest sync to complete
     steps:
       - name: Checkout repository
-        uses: actions/checkout@v6
+        uses: actions/checkout@v6.0.0
         with:
           fetch-depth: 0  # Fetch all history for git-cliff
           ref: main  # Use updated main branch if manifest was synced
@@ -135,20 +121,10 @@ jobs:
           FEAT=$(echo "$COMMITS" | grep -cE "^feat(\(.+\))?:" || true)
           FIX=$(echo "$COMMITS" | grep -cE "^fix(\(.+\))?:" || true)
 
-          parse_version() {
+          # Parse versions
-            local version="$1"
-            if [[ $version =~ ^([0-9]+)\.([0-9]+)\.([0-9]+)(b[0-9]+)?$ ]]; then
-              echo "${BASH_REMATCH[1]} ${BASH_REMATCH[2]} ${BASH_REMATCH[3]} ${BASH_REMATCH[4]}"
-            else
-              echo "Invalid version format: $version" >&2
-              exit 1
-            fi
-          }
-
-          # Parse versions (support beta/prerelease suffix like 0.25.0b0)
           PREV_VERSION="${PREV_TAG#v}"
-          read -r PREV_MAJOR PREV_MINOR PREV_PATCH PREV_PRERELEASE <<< "$(parse_version "$PREV_VERSION")"
+          IFS='.' read -r PREV_MAJOR PREV_MINOR PREV_PATCH <<< "$PREV_VERSION"
-          read -r MAJOR MINOR PATCH PRERELEASE <<< "$(parse_version "$TAG_VERSION")"
+          IFS='.' read -r MAJOR MINOR PATCH <<< "$TAG_VERSION"
 
           WARNING=""
           SUGGESTION=""
@@ -190,11 +166,9 @@ jobs:
               echo "**Commits analyzed:** Breaking=$BREAKING, Features=$FEAT, Fixes=$FIX"
               echo ""
               echo "**To fix:**"
-              echo "1. Run locally: \`./scripts/release/suggest-version\`"
+              echo "1. Delete the tag: \`git tag -d v$TAG_VERSION && git push origin :refs/tags/v$TAG_VERSION\`"
-              echo "2. Create correct tag: \`./scripts/release/prepare <suggested-version>\`"
+              echo "2. Run locally: \`./scripts/suggest-version\`"
-              echo "3. Push the corrected tag: \`git push origin v<suggested-version>\`"
+              echo "3. Create correct tag: \`./scripts/prepare-release X.Y.Z\`"
-              echo ""
-              echo "**This tag will be automatically deleted in the next step.**"
               echo "EOF"
             } >> $GITHUB_OUTPUT
           else
@@ -202,19 +176,7 @@ jobs:
             echo "warning=" >> $GITHUB_OUTPUT
           fi
 
-      - name: Delete inappropriate version tag
-        if: steps.version_check.outputs.warning != ''
-        run: |
-          TAG_NAME="${GITHUB_REF#refs/tags/}"
-          echo "❌ Deleting tag $TAG_NAME (version not appropriate for changes)"
-          echo ""
-          echo "${{ steps.version_check.outputs.warning }}"
-          echo ""
-          git push origin --delete "$TAG_NAME"
-          exit 1
-
       - name: Install git-cliff
-        if: steps.version_check.outputs.warning == ''
         run: |
           wget https://github.com/orhun/git-cliff/releases/download/v2.4.0/git-cliff-2.4.0-x86_64-unknown-linux-gnu.tar.gz
           tar -xzf git-cliff-2.4.0-x86_64-unknown-linux-gnu.tar.gz
@@ -222,7 +184,6 @@ jobs:
           git-cliff --version
 
       - name: Generate release notes
-        if: steps.version_check.outputs.warning == ''
         id: release_notes
         run: |
           FROM_TAG="${{ steps.previoustag.outputs.previous_tag }}"
@@ -232,7 +193,7 @@ jobs:
 
           # Use our script with git-cliff backend (AI disabled in CI)
           # git-cliff will handle filtering via cliff.toml
-          USE_AI=false ./scripts/release/generate-notes "${FROM_TAG}" "${TO_TAG}" > release-notes.md
+          USE_AI=false ./scripts/generate-release-notes "${FROM_TAG}" "${TO_TAG}" > release-notes.md
 
           # Extract title from release notes (first line starting with "# ")
           TITLE=$(head -n 1 release-notes.md | sed 's/^# //')
@@ -241,6 +202,15 @@ jobs:
           fi
           echo "title=$TITLE" >> $GITHUB_OUTPUT
 
+          # Append version warning if present
+          WARNING="${{ steps.version_check.outputs.warning }}"
+          if [ -n "$WARNING" ]; then
+            echo "" >> release-notes.md
+            echo "---" >> release-notes.md
+            echo "" >> release-notes.md
+            echo "$WARNING" >> release-notes.md
+          fi
+
           # Output for GitHub Actions
           {
             echo 'notes<<EOF'
@@ -248,20 +218,25 @@ jobs:
             echo EOF
           } >> $GITHUB_OUTPUT
 
+      - name: Version Check Summary
+        if: steps.version_check.outputs.warning != ''
+        run: |
+          echo "### ⚠️ Version Mismatch Detected" >> $GITHUB_STEP_SUMMARY
+          echo "" >> $GITHUB_STEP_SUMMARY
+          echo "${{ steps.version_check.outputs.warning }}" >> $GITHUB_STEP_SUMMARY
+
       - name: Create GitHub Release
-        if: steps.version_check.outputs.warning == ''
         uses: softprops/action-gh-release@v2
         with:
           name: ${{ steps.release_notes.outputs.title }}
           body: ${{ steps.release_notes.outputs.notes }}
           draft: false
-          prerelease: ${{ contains(github.ref, 'b') }}
+          prerelease: false
           generate_release_notes: false  # We provide our own
         env:
           GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
 
       - name: Summary
-        if: steps.version_check.outputs.warning == ''
         run: |
           echo "✅ Release notes generated and published!" >> $GITHUB_STEP_SUMMARY
           echo "" >> $GITHUB_STEP_SUMMARY

.github/workflows/validate.yml

@@ -7,15 +7,9 @@ on:
   push:
     branches:
       - main
-    paths-ignore:
-      - 'docs/**'
-      - '.github/workflows/docusaurus.yml'
   pull_request:
     branches:
       - main
-    paths-ignore:
-      - 'docs/**'
-      - '.github/workflows/docusaurus.yml'
 
 permissions: {}
 
@@ -29,10 +23,10 @@ jobs:
     runs-on: ubuntu-latest
     steps:
       - name: Checkout the repository
-        uses: actions/checkout@8e8c483db84b4bee98b60c0593521ed34d9990e8 # v6.0.1
+        uses: actions/checkout@93cb6efe18208431cddfb8368fd83d5badbf9bfd # v5.0.1
 
       - name: Run hassfest validation
-        uses: home-assistant/actions/hassfest@d56d093b9ab8d2105bc0cb6ee9bcc0ef4ec8b96d # master
+        uses: home-assistant/actions/hassfest@8ca6e134c077479b26138bd33520707e8d94ef59 # master
 
   hacs: # https://github.com/hacs/action
     name: HACS validation
@@ -42,3 +36,5 @@ jobs:
         uses: hacs/action@d556e736723344f83838d08488c983a15381059a # 22.5.0
         with:
           category: integration
+          # Remove this 'ignore' key when you have added brand images for your custom integration to https://github.com/home-assistant/brands
+          ignore: brands

AGENTS.md

@@ -4,8 +4,8 @@ This is a **Home Assistant custom component** for Tibber electricity price data,
 
 ## Documentation Metadata
 
--   **Last Major Update**: 2025-01-21
+-   **Last Major Update**: 2025-11-18
--   **Last Architecture Review**: 2025-01-21 (Phase 1: Added TypedDict documentation system, improved BaseCalculator with 8 helper methods. Phase 2: Documented Import Architecture - Hybrid Pattern (Trend/Volatility build own attributes), verified no circular dependencies, confirmed optimal TYPE_CHECKING usage across all 8 calculators.)
+-   **Last Architecture Review**: 2025-11-18 (Completed sensor/core.py refactoring: Calculator Pattern implementation with 8 specialized calculators and 8 attribute modules. Reduced core.py from 2,170 → 1,268 lines (42% reduction). Total 3,047 lines extracted to specialized packages.)
 -   **Last Code Example Cleanup**: 2025-11-18 (Removed redundant implementation details from AGENTS.md, added guidelines for when to include code examples)
 -   **Documentation Status**: ✅ Current (verified against codebase)
 
@@ -17,18 +17,14 @@ _Note: When proposing significant updates to this file, update the metadata abov
 
 When working with the codebase, Copilot MUST actively maintain consistency between this documentation and the actual code:
 
-**Scope:** "This documentation" and "this file" refer specifically to `AGENTS.md` in the repository root. This does NOT include user-facing documentation like `README.md`, Docusaurus sites, or comments in code. Those serve different purposes and are maintained separately.
+**Scope:** "This documentation" and "this file" refer specifically to `AGENTS.md` in the repository root. This does NOT include user-facing documentation like `README.md`, `/docs/user/`, or comments in code. Those serve different purposes and are maintained separately.
 
 **Documentation Organization:**
 
 -   **This file** (`AGENTS.md`): AI/Developer long-term memory, patterns, conventions
--   **`docs/user/`**: Docusaurus site for end-users (installation, configuration, usage examples)
+-   **`docs/user/`**: End-user guides (installation, configuration, usage examples)
-    -   Markdown files in `docs/user/docs/*.md`
+-   **`docs/development/`**: Contributor guides (setup, architecture, release management)
-    -   Navigation managed via `docs/user/sidebars.ts`
+-   **`README.md`**: Project overview with links to detailed documentation
--   **`docs/developer/`**: Docusaurus site for contributors (architecture, development guides)
-    -   Markdown files in `docs/developer/docs/*.md`
-    -   Navigation managed via `docs/developer/sidebars.ts`
--   **`README.md`**: Project overview with links to documentation sites
 
 **Automatic Inconsistency Detection:**
 
@@ -379,15 +375,15 @@ After successful refactoring:
 
 **Core Data Flow:**
 
-1. `TibberPricesApiClient` (`api.py`) queries Tibber's GraphQL API with `resolution:QUARTER_HOURLY` for user data and prices (day before yesterday/yesterday/today/tomorrow - 384 intervals total, ensuring trailing 24h averages are accurate for all intervals)
+1. `TibberPricesApiClient` (`api.py`) queries Tibber's GraphQL API with `resolution:QUARTER_HOURLY` for user data and prices (yesterday/today/tomorrow - 192 intervals total)
 2. `TibberPricesDataUpdateCoordinator` (`coordinator.py`) orchestrates updates every 15 minutes, manages persistent storage via `Store`, and schedules quarter-hour entity refreshes
 3. Price enrichment functions (`utils/price.py`, `utils/average.py`) calculate trailing/leading 24h averages, price differences, and rating levels for each 15-minute interval
 4. Entity platforms (`sensor/` package, `binary_sensor/` package) expose enriched data as Home Assistant entities
-5. Custom services (`services/` package) provide API endpoints for chart data export, ApexCharts YAML generation, and user data refresh
+5. Custom services (`services.py`) provide API endpoints for integrations like ApexCharts
 
 **Key Patterns:**
 
--   **Dual translation system**: Standard HA translations in `/translations/` (config flow, UI strings per HA schema), supplemental in `/custom_translations/` (entity descriptions not supported by HA schema). Both must stay in sync. Use `async_load_translations()` and `async_load_standard_translations()` from `const.py`. When to use which: `/translations/` is bound to official HA schema requirements; anything else goes in `/custom_translations/` (requires manual translation loading). **Schema reference**: `/schemas/json/translation_schema.json` provides the structure for `/translations/*.json` files based on [HA's translation documentation](https://developers.home-assistant.io/docs/internationalization/core).
+-   **Dual translation system**: Standard HA translations in `/translations/` (config flow, UI strings per HA schema), supplemental in `/custom_translations/` (entity descriptions not supported by HA schema). Both must stay in sync. Use `async_load_translations()` and `async_load_standard_translations()` from `const.py`. When to use which: `/translations/` is bound to official HA schema requirements; anything else goes in `/custom_translations/` (requires manual translation loading). **Schema reference**: `/scripts/json_schemas/translation_schema.json` provides the structure for `/translations/*.json` files based on [HA's translation documentation](https://developers.home-assistant.io/docs/internationalization/core).
 
     -   **Select selector translations**: Use `selector.{translation_key}.options.{value}` structure (NOT `selector.select.{translation_key}`). Translation keys map to JSON in `/translations/*.json` following the HA schema structure.
 
@@ -422,7 +418,7 @@ After successful refactoring:
     -   **Architecture Benefits**: 42% line reduction in core.py (2,170 → 1,268 lines), clear separation of concerns, improved testability, reusable components
     -   **See "Common Tasks" section** for detailed patterns and examples
 -   **Quarter-hour precision**: Entities update on 00/15/30/45-minute boundaries via `schedule_quarter_hour_refresh()` in `coordinator/listeners.py`, not just on data fetch intervals. Uses `async_track_utc_time_change(minute=[0, 15, 30, 45], second=0)` for absolute-time scheduling. Smart boundary tolerance (±2 seconds) in `sensor/helpers.py` → `round_to_nearest_quarter_hour()` handles HA scheduling jitter: if HA triggers at 14:59:58 → rounds to 15:00:00 (next interval), if HA restarts at 14:59:30 → stays at 14:45:00 (current interval). This ensures current price sensors update without waiting for the next API poll, while preventing premature data display during normal operation.
--   **Currency handling**: Multi-currency support with base/sub units (e.g., EUR/ct, NOK/øre) via `get_currency_info()` and `format_price_unit_*()` in `const.py`.
+-   **Currency handling**: Multi-currency support with major/minor units (e.g., EUR/ct, NOK/øre) via `get_currency_info()` and `format_price_unit_*()` in `const.py`.
 -   **Intelligent caching strategy**: Minimizes API calls while ensuring data freshness:
     -   User data cached for 24h (rarely changes)
     -   Price data validated against calendar day - cleared on midnight turnover to force fresh fetch
@@ -477,13 +473,7 @@ custom_components/tibber_prices/
 │   ├── __init__.py       #   Package exports
 │   ├── average.py        #   Trailing/leading average utilities
 │   └── price.py          #   Price enrichment, level/rating calculations
-├── services/             # Custom services package
+├── services.py           # Custom services (get_price, ApexCharts, etc.)
-│   ├── __init__.py       #   Service registration
-│   ├── chartdata.py      #   Chart data export service
-│   ├── apexcharts.py     #   ApexCharts YAML generator
-│   ├── refresh_user_data.py # User data refresh
-│   ├── formatters.py     #   Data transformation utilities
-│   └── helpers.py        #   Common service helpers
 ├── sensor/               # Sensor platform (package)
 │   ├── __init__.py       #   Platform setup (async_setup_entry)
 │   ├── core.py           #   TibberPricesSensor class (1,268 lines)
@@ -533,110 +523,6 @@ custom_components/tibber_prices/
 └── services.yaml         # Service definitions
 ```
 
-## Import Architecture and Dependency Management
-
-**CRITICAL: Import architecture follows strict layering to prevent circular dependencies.**
-
-### Dependency Flow (Calculator Pattern)
-
-**Clean Separation:**
-```
-sensor/calculators/  → sensor/attributes/  (Volatility only - Hybrid Pattern)
-sensor/calculators/  → sensor/helpers/     (DailyStat, RollingHour - Pure functions)
-sensor/calculators/  → entity_utils/       (Pure utility functions)
-sensor/calculators/  → const.py            (Constants only)
-
-sensor/attributes/   ✗  (NO imports from calculators/)
-sensor/helpers/      ✗  (NO imports from calculators/)
-```
-
-**Why this works:**
-- **One-way dependencies**: Calculators can import from attributes/helpers, but NOT vice versa
-- **No circular imports**: Reverse direction is empty (verified Jan 2025)
-- **Clean testing**: Each layer can be tested independently
-
-### Hybrid Pattern (Trend/Volatility Calculators)
-
-**Background:** During Nov 2025 refactoring, Trend and Volatility calculators retained attribute-building logic to avoid duplicating complex calculations. This creates a **backwards dependency** (calculator → attributes) but is INTENTIONAL.
-
-**Pattern:**
-1. **Calculator** computes value AND builds attribute dict
-2. **Core** stores attributes in `cached_data` dict
-3. **Attributes package** retrieves cached attributes via:
-   - `_add_cached_trend_attributes()` for trend sensors
-   - `_add_timing_or_volatility_attributes()` for volatility sensors
-
-**Example (Volatility):**
-```python
-# sensor/calculators/volatility.py
-from custom_components.tibber_prices.sensor.attributes import (
-    add_volatility_type_attributes,  # ← Backwards dependency (calculator → attributes)
-    get_prices_for_volatility,
-)
-
-def get_volatility_value(self, *, volatility_type: str) -> str | None:
-    # Calculate volatility level
-    volatility = calculate_volatility_level(prices, ...)
-
-    # Build attribute dict (stored for later)
-    self._last_volatility_attributes = {"volatility": volatility, ...}
-    add_volatility_type_attributes(self._last_volatility_attributes, ...)
-
-    return volatility
-
-def get_volatility_attributes(self) -> dict | None:
-    return self._last_volatility_attributes  # ← Retrieved by attributes package
-```
-
-**Trade-offs:**
-- ✅ **Pro**: Complex logic stays in ONE place (no duplication)
-- ✅ **Pro**: Calculator has full context for attribute decisions
-- ❌ **Con**: Violates strict separation (calculator builds attributes)
-- ❌ **Con**: Creates backwards dependency (testability impact)
-
-**Decision:** Pattern is **acceptable** for complex calculators (Trend, Volatility) where attribute logic is tightly coupled to calculation. Simple calculators (Interval, DailyStat, etc.) DO NOT follow this pattern.
-
-### TYPE_CHECKING Best Practices
-
-All calculator modules use `TYPE_CHECKING` correctly:
-
-**Pattern:**
-```python
-# Runtime imports (used in function bodies)
-from custom_components.tibber_prices.const import CONF_PRICE_RATING_THRESHOLD_HIGH
-from custom_components.tibber_prices.entity_utils import get_price_value
-
-from .base import TibberPricesBaseCalculator
-
-# Type-only imports (only for type hints)
-if TYPE_CHECKING:
-    from collections.abc import Callable
-    from typing import Any
-```
-
-**Rules:**
-- ✅ **Runtime imports**: Functions, classes, constants used in code → OUTSIDE TYPE_CHECKING
-- ✅ **Type-only imports**: Only used in type hints → INSIDE TYPE_CHECKING
-- ✅ **Coordinator import**: Always in base.py, inherited by all calculators
-
-**Verified Status (Jan 2025):**
-- All 8 calculators (base, interval, rolling_hour, daily_stat, window_24h, volatility, trend, timing, metadata) use TYPE_CHECKING correctly
-- No optimization needed - imports are already categorized optimally
-
-### Import Anti-Patterns to Avoid
-
-❌ **DON'T:**
-- Import from higher layers (attributes/helpers importing from calculators)
-- Use runtime imports for type-only dependencies
-- Create circular dependencies between packages
-- Import entire modules when only needing one function
-
-✅ **DO:**
-- Follow one-way dependency flow (calculators → attributes/helpers)
-- Use TYPE_CHECKING for type-only imports
-- Import specific items: `from .helpers import aggregate_price_data`
-- Document intentional backwards dependencies (Hybrid Pattern)
-
 ## Period Calculation System (Best/Peak Price Periods)
 
 **CRITICAL:** Period calculation uses multi-criteria filtering that can create **mathematical conflicts** at high flexibility values. Understanding these interactions is essential for reliable period detection.
@@ -741,9 +627,9 @@ When debugging period calculation issues:
 4. Check relaxation warnings: INFO at 25%, WARNING at 30% indicate suboptimal config
 
 **See:**
-- **Theory documentation**: `docs/developer/docs/period-calculation-theory.md` (comprehensive mathematical analysis, conflict conditions, configuration pitfalls)
+- **Theory documentation**: `docs/development/period-calculation-theory.md` (comprehensive mathematical analysis, conflict conditions, configuration pitfalls)
 - **Implementation**: `coordinator/period_handlers/` package (core.py, relaxation.py, level_filtering.py, period_building.py)
-- **User guide**: `docs/user/docs/period-calculation.md` (simplified user-facing explanations)
+- **User guide**: `docs/user/period-calculation.md` (simplified user-facing explanations)
 
 ## Development Environment Setup
 
@@ -773,12 +659,12 @@ When debugging period calculation issues:
 -   All scripts in `./scripts/` automatically use the correct `.venv`
 -   No need to manually activate venv or specify Python path
 -   Examples: `./scripts/lint`, `./scripts/develop`, `./scripts/lint-check`
--   Release management: `./scripts/release/prepare`, `./scripts/release/generate-notes`
+-   Release management: `./scripts/prepare-release`, `./scripts/generate-release-notes`
 
 **Release Note Backends (auto-installed in DevContainer):**
 
 -   **Rust toolchain**: Minimal Rust installation via DevContainer feature
--   **git-cliff**: Template-based release notes (fast, reliable, installed via cargo in `scripts/setup/setup`)
+-   **git-cliff**: Template-based release notes (fast, reliable, installed via cargo in `scripts/setup`)
 -   Manual grep/awk parsing as fallback (always available)
 
 **When generating shell commands:**
@@ -847,7 +733,7 @@ If you notice commands failing or missing dependencies:
 **Local validation:**
 
 ```bash
-./scripts/release/hassfest  # Lightweight local integration validation
+./scripts/hassfest  # Lightweight local integration validation
 ```
 
 Note: The local `hassfest` script performs basic validation checks (JSON syntax, required files, Python syntax). Full hassfest validation runs in GitHub Actions.
@@ -855,14 +741,9 @@ Note: The local `hassfest` script performs basic validation checks (JSON syntax,
 **Testing:**
 
 ```bash
-./scripts/test     # Run all tests (pytest with project configuration)
+pytest tests/      # Unit tests exist (test_*.py) but no framework enforced
-./scripts/test -v  # Verbose output
-./scripts/test -k test_midnight  # Run specific test by name
-./scripts/test tests/test_midnight_periods.py  # Run specific file
 ```
 
-Test framework: pytest with Home Assistant custom component support. Tests live in `/tests/` directory. Use `@pytest.mark.unit` for fast tests, `@pytest.mark.integration` for tests that use real coordinator/time services.
-
 ## Testing Changes
 
 **IMPORTANT: Never start `./scripts/develop` automatically.**
@@ -1027,81 +908,41 @@ Combine into single commit when:
 
 ### Conventional Commits Format
 
-**Reference:** Follow [Conventional Commits v1.0.0](https://www.conventionalcommits.org/en/v1.0.0/) specification.
-
 **Structure:**
 
 ```
-<type>[optional scope]: <description>
+<type>(<scope>): <short summary (max 50-72 chars)>
 
-[optional body]
+<detailed description, wrapped at 72 chars>
 
-[optional footer(s)]
+Impact: <user-visible effects or context for future release notes>
-```
-
-**Required Elements:**
-
--   **type**: Lowercase, communicates intent (feat, fix, docs, etc.)
--   **description**: Short summary (max 50-72 chars), imperative mood ("add" not "added"), lowercase start, no period
-
-**Optional Elements:**
-
--   **scope**: Parentheses after type, e.g., `feat(sensors):` - lowercase, specific area of change
--   **body**: Detailed explanation, wrap at 72 chars, explain WHAT and WHY (not HOW - code shows that)
--   **footer**: Breaking changes, issue references, or custom fields
-
-**Breaking Changes:**
-
-Use `BREAKING CHANGE:` footer or `!` after type/scope:
-```
-feat(api)!: drop support for legacy endpoint
-
-BREAKING CHANGE: The /v1/prices endpoint has been removed. Use /v2/prices instead.
-```
-
-**Types (Conventional Commits standard):**
-
--   `feat`: New feature (appears in release notes as "New Features")
--   `fix`: Bug fix (appears in release notes as "Bug Fixes")
--   `docs`: Documentation only (appears in release notes as "Documentation")
--   `style`: Code style/formatting (no behavior change, omitted from release notes)
--   `refactor`: Code restructure without behavior change (may or may not appear in release notes)
--   `perf`: Performance improvement (appears in release notes)
--   `test`: Test changes only (omitted from release notes)
--   `build`: Build system/dependencies (omitted from release notes)
--   `ci`: CI configuration (omitted from release notes)
--   `chore`: Maintenance tasks (usually omitted from release notes)
-
-**Scope (project-specific, optional but recommended):**
-
--   `translations`: Translation system changes
--   `config_flow`: Configuration flow changes
--   `sensors`: Sensor implementation
--   `binary_sensors`: Binary sensor implementation
--   `api`: API client changes
--   `coordinator`: Data coordinator changes
--   `services`: Service implementations
--   `docs`: Documentation files
-
-**Custom Footer - Impact Section:**
-
-Add `Impact:` footer for release note generation context (project-specific addition):
-
-```
-feat(services): add rolling window support
-
-Implement dynamic 48h window that adapts to data availability.
-
-Impact: Users can create auto-adapting price charts without manual
-day selection. Requires config-template-card for ApexCharts mode.
 ```
 
 **Best Practices:**
 
--   **Subject line**: Max 50 chars (hard limit 72), lowercase, imperative mood
+-   **Subject line**: Max 50 chars (hard limit 72), imperative mood ("Add" not "Added")
--   **Body**: Wrap at 72 chars, optional but useful for complex changes
+-   **Body**: Wrap at 72 chars, explain WHAT and WHY (not HOW - code shows that)
 -   **Blank line**: Required between subject and body
--   **Impact footer**: Optional but recommended for user-facing changes
+-   **Impact section**: Our addition for release note generation (optional but recommended)
+
+**Types:**
+
+-   `feat`: New feature (appears in release notes as "New Features")
+-   `fix`: Bug fix (appears in release notes as "Bug Fixes")
+-   `docs`: Documentation only (appears in release notes as "Documentation")
+-   `refactor`: Code restructure without behavior change (may or may not appear in release notes)
+-   `chore`: Maintenance tasks (usually omitted from release notes)
+-   `test`: Test changes only (omitted from release notes)
+-   `style`: Formatting changes (omitted from release notes)
+
+**Scope (optional but recommended):**
+
+-   `translations`: Translation system changes
+-   `config_flow`: Configuration flow changes
+-   `sensors`: Sensor implementation
+-   `api`: API client changes
+-   `coordinator`: Data coordinator changes
+-   `docs`: Documentation files
 
 ### Technical Commit Message Examples
 
@@ -1271,10 +1112,10 @@ The "Impact:" section bridges technical commits and future release notes:
 
 1. **Helper Script** (recommended, foolproof)
 
-    - Script: `./scripts/release/prepare VERSION`
+    - Script: `./scripts/prepare-release VERSION`
     - Bumps manifest.json version → commits → creates tag locally
     - You review and push when ready
-    - Example: `./scripts/release/prepare 0.3.0`
+    - Example: `./scripts/prepare-release 0.3.0`
 
 2. **Auto-Tag Workflow** (safety net)
 
@@ -1285,7 +1126,7 @@ The "Impact:" section bridges technical commits and future release notes:
 
 3. **Local Script** (testing, preview, and updating releases)
 
-    - Script: `./scripts/release/generate-notes [FROM_TAG] [TO_TAG]`
+    - Script: `./scripts/generate-release-notes [FROM_TAG] [TO_TAG]`
     - Parses Conventional Commits between tags
     - Supports multiple backends (auto-detected):
         - **AI-powered**: GitHub Copilot CLI (best, context-aware)
@@ -1297,7 +1138,7 @@ The "Impact:" section bridges technical commits and future release notes:
 
     ```bash
     # Generate and preview notes
-    ./scripts/release/generate-notes v0.2.0 v0.3.0
+    ./scripts/generate-release-notes v0.2.0 v0.3.0
 
     # If release exists, you'll see:
     # → Generated release notes
@@ -1306,7 +1147,7 @@ The "Impact:" section bridges technical commits and future release notes:
     # → Answer 'y' to auto-update, 'n' to skip
 
     # Force specific backend
-    RELEASE_NOTES_BACKEND=copilot ./scripts/release/generate-notes v0.2.0 v0.3.0
+    RELEASE_NOTES_BACKEND=copilot ./scripts/generate-release-notes v0.2.0 v0.3.0
     ```
 
 4. **GitHub UI Button** (manual, PR-based)
@@ -1327,7 +1168,7 @@ The "Impact:" section bridges technical commits and future release notes:
 
 ```bash
 # Step 1: Get version suggestion (analyzes commits since last release)
-./scripts/release/suggest-version
+./scripts/suggest-version
 
 # Output shows:
 # - Commit analysis (features, fixes, breaking changes)
@@ -1336,12 +1177,12 @@ The "Impact:" section bridges technical commits and future release notes:
 # - Preview and release commands
 
 # Step 2: Preview release notes (with AI if available)
-./scripts/release/generate-notes v0.2.0 HEAD
+./scripts/generate-release-notes v0.2.0 HEAD
 
 # Step 3: Prepare release (bumps manifest.json + creates tag)
-./scripts/release/prepare 0.3.0
+./scripts/prepare-release 0.3.0
 # Or without argument to show suggestion first:
-./scripts/release/prepare
+./scripts/prepare-release
 
 # Step 4: Review changes
 git log -1 --stat
@@ -1359,7 +1200,7 @@ If you want better release notes after the automated release:
 
 ```bash
 # Generate AI-powered notes and update existing release
-./scripts/release/generate-notes v0.2.0 v0.3.0
+./scripts/generate-release-notes v0.2.0 v0.3.0
 
 # Script will:
 # 1. Generate notes (uses AI if available locally)
@@ -1399,16 +1240,16 @@ git push
 
 ```bash
 # Generate from latest tag to HEAD
-./scripts/release/generate-notes
+./scripts/generate-release-notes
 
 # Generate between specific tags
-./scripts/release/generate-notes v1.0.0 v1.1.0
+./scripts/generate-release-notes v1.0.0 v1.1.0
 
 # Force specific backend
-RELEASE_NOTES_BACKEND=manual ./scripts/release/generate-notes
+RELEASE_NOTES_BACKEND=manual ./scripts/generate-release-notes
 
 # Disable AI (use in CI/CD)
-USE_AI=false ./scripts/release/generate-notes
+USE_AI=false ./scripts/generate-release-notes
 ```
 
 **Backend Selection Logic:**
@@ -1474,7 +1315,7 @@ All backends produce GitHub-flavored Markdown with consistent structure:
 
 ```bash
 # git-cliff (fast, reliable, used in CI/CD)
-# Auto-installed in DevContainer via scripts/setup/setup
+# Auto-installed in DevContainer via scripts/setup
 # See: https://git-cliff.org/docs/installation
 cargo install git-cliff  # or download binary from releases
 ````
@@ -1496,13 +1337,13 @@ cargo install git-cliff  # or download binary from releases
 
 ```bash
 # Run local validation (checks JSON syntax, Python syntax, required files)
-./scripts/release/hassfest
+./scripts/hassfest
 
 # Or validate JSON files manually if needed:
 python -m json.tool custom_components/tibber_prices/translations/de.json > /dev/null
 ```
 
-**Why:** The `./scripts/release/hassfest` script validates JSON syntax (translations, manifest), Python syntax, and required files. This catches common errors before pushing to GitHub Actions. For quick JSON-only checks, you can still use `python -m json.tool` directly.
+**Why:** The `./scripts/hassfest` script validates JSON syntax (translations, manifest), Python syntax, and required files. This catches common errors before pushing to GitHub Actions. For quick JSON-only checks, you can still use `python -m json.tool` directly.
 
 ## Linting Best Practices
 
@@ -1793,7 +1634,7 @@ Never use raw API price data directly. Always enrich via `enrich_price_info_with
 Always use `dt_util` from `homeassistant.util` instead of Python's `datetime` module for timezone-aware operations. **Critical:** Use `dt_util.as_local()` when comparing API timestamps to local time. Import datetime types only for type hints: `from datetime import date, datetime, timedelta`.
 
 **4. Coordinator Data Structure**
-Coordinator data follows structure: `coordinator.data = {"user_data": {...}, "priceInfo": [...], "currency": "EUR"}`. The `priceInfo` is a flat list containing all enriched interval dicts (yesterday + today + tomorrow). Currency is stored at top level for easy access. See `coordinator/core.py` for data management.
+Coordinator data follows structure: `coordinator.data = {"user_data": {...}, "priceInfo": {"yesterday": [...], "today": [...], "tomorrow": [...], "currency": "EUR"}}`. Each price list contains enriched interval dicts. See `coordinator/core.py` for data management.
 
 **5. Service Response Pattern**
 Services returning data must declare `supports_response=SupportsResponse.ONLY` in registration. See `services.py` for implementation patterns.
@@ -1812,17 +1653,6 @@ When using `DataUpdateCoordinator`, entities get updates automatically. Only imp
 **4. Service Response Declaration:**
 Services returning data MUST declare `supports_response` parameter. Use `SupportsResponse.ONLY` for data-only services, `OPTIONAL` for dual-purpose, `NONE` for action-only. See `services.py` for examples.
 
-**5. Entity Lifecycle & State Management:**
-All entities MUST implement these patterns for proper HA integration:
-
-- **`available` property**: Indicates if entity can be read/controlled. Return `False` when coordinator has no data yet or last update failed. See `entity.py` for base implementation. Special cases (e.g., `connection` binary_sensor) override to always return `True`.
-
-- **State Restore**: Inherit from `RestoreSensor` (sensors) or `RestoreEntity` (binary_sensors) to restore state after HA restart. Eliminates "unavailable" gaps in history. Restore logic in `async_added_to_hass()` using `async_get_last_state()` and `async_get_last_sensor_data()`. See `sensor/core.py` and `binary_sensor/core.py` for implementation.
-
-- **`force_update` property**: Set to `True` for entities where every state change should be recorded, even if value unchanged (e.g., `connection` sensor tracking connectivity issues). Default is `False`. See `binary_sensor/core.py` for example.
-
-**Why this matters**: Without `available`, entities show stale data during errors. Without state restore, history has gaps after HA restart. Without `force_update`, repeated state changes aren't visible in history.
-
 ## Code Quality Rules
 
 **CRITICAL: See "Linting Best Practices" section for comprehensive type checking (Pyright) and linting (Ruff) guidelines.**
@@ -1838,12 +1668,12 @@ This is a Home Assistant standard to avoid naming conflicts between integrations
 # ✅ CORRECT - Integration prefix + semantic purpose
 class TibberPricesApiClient:              # Integration + semantic role
 class TibberPricesDataUpdateCoordinator:  # Integration + semantic role
-class TibberPricesPriceDataManager:       # Integration + semantic role
+class TibberPricesDataFetcher:            # Integration + semantic role
 class TibberPricesSensor:                 # Integration + entity type
 class TibberPricesEntity:                 # Integration + entity type
 
 # ❌ INCORRECT - Missing integration prefix
-class PriceDataManager:    # Should be: TibberPricesPriceDataManager
+class DataFetcher:         # Should be: TibberPricesDataFetcher
 class TimeService:         # Should be: TibberPricesTimeService
 class PeriodCalculator:    # Should be: TibberPricesPeriodCalculator
 
@@ -1855,11 +1685,11 @@ class TibberPricesSensorCalculatorTrend:   # Too verbose, import path shows loca
 **IMPORTANT:** Do NOT include package hierarchy in class names. Python's import system provides the namespace:
 ```python
 # The import path IS the full namespace:
-from custom_components.tibber_prices.coordinator.price_data_manager import TibberPricesPriceDataManager
+from custom_components.tibber_prices.coordinator.data_fetching import TibberPricesDataFetcher
 from custom_components.tibber_prices.sensor.calculators.trend import TibberPricesTrendCalculator
 
 # Adding package names to class would be redundant:
-# TibberPricesCoordinatorPriceDataManager  ❌ NO - unnecessarily verbose
+# TibberPricesCoordinatorDataFetcher  ❌ NO - unnecessarily verbose
 # TibberPricesSensorCalculatorsTrendCalculator  ❌ NO - ridiculously long
 ```
 
@@ -1883,7 +1713,6 @@ Use semantic prefixes that describe the PURPOSE, not the package location.
 - 🟡 Type aliases and callbacks (e.g., `TimeServiceCallback` is acceptable)
 - 🟡 Small NamedTuples used only for internal function returns (e.g., within calculators)
 - 🟡 Enums that are clearly namespaced (e.g., `QueryType` in `api.queries`)
-- 🟡 **TypedDict classes**: Documentation-only constructs (never instantiated), used solely for IDE autocomplete - shorter names improve readability (e.g., `IntervalPriceAttributes`, `PeriodAttributes`)
 
 **Private Classes (Module-Internal):**
 
@@ -1898,6 +1727,8 @@ class _InternalHelper:
 result = _InternalHelper().process()
 ```
 
+**Important:** Currently (Nov 2025), this project has **NO private classes** - all classes are used across module boundaries and therefore need the `TibberPrices` prefix.
+
 **When to use private classes:**
 - ❌ **DON'T** use for code organization alone - if it deserves a class, it's usually public
 - ✅ **DO** use for internal implementation details (e.g., state machines, internal builders)
@@ -1905,19 +1736,75 @@ result = _InternalHelper().process()
 
 **Example of genuine private class use case:**
 ```python
-# In coordinator/price_data_manager.py
+# In coordinator/data_fetching.py
 class _ApiRetryStateMachine:
     """Internal state machine for retry logic. Never used outside this file."""
     def __init__(self, max_retries: int) -> None:
         self._attempts = 0
         self._max_retries = max_retries
 
-    # Only used by PriceDataManager methods in this file
+    # Only used by DataFetcher methods in this file
 ```
 
 In practice, most "helper" logic should be **functions**, not classes. Reserve classes for stateful components.
 
-### Ruff Code Style Guidelines
+**Current State (as of Nov 2025):**
+
+⚠️ **Known Issue**: Many classes in this project lack the `TibberPrices` prefix. This is a technical debt item that needs addressing.
+
+**Classes that need renaming:**
+```python
+# Coordinator module
+DataFetcher → TibberPricesDataFetcher
+DataTransformer → TibberPricesDataTransformer
+ListenerManager → TibberPricesListenerManager
+PeriodCalculator → TibberPricesPeriodCalculator
+TimeService → TibberPricesTimeService
+CacheData → TibberPricesCacheData
+
+# Config flow
+CannotConnectError → TibberPricesCannotConnectError
+InvalidAuthError → TibberPricesInvalidAuthError
+
+# Entity utils
+IconContext → TibberPricesIconContext
+
+# Sensor calculators (8 classes)
+BaseCalculator → TibberPricesBaseCalculator
+IntervalCalculator → TibberPricesIntervalCalculator
+RollingHourCalculator → TibberPricesRollingHourCalculator
+DailyStatCalculator → TibberPricesDailyStatCalculator
+Window24hCalculator → TibberPricesWindow24hCalculator
+VolatilityCalculator → TibberPricesVolatilityCalculator
+TrendCalculator → TibberPricesTrendCalculator
+TimingCalculator → TibberPricesTimingCalculator
+MetadataCalculator → TibberPricesMetadataCalculator
+
+# Period handlers (NamedTuples in types.py)
+IntervalCriteria → TibberPricesIntervalCriteria
+PeriodConfig → TibberPricesPeriodConfig
+PeriodData → TibberPricesPeriodData
+PeriodStatistics → TibberPricesPeriodStatistics
+ThresholdConfig → TibberPricesThresholdConfig
+SpikeCandidateContext → TibberPricesSpikeCandidateContext
+```
+
+**Action Required:**
+Before making changes to these classes, plan the refactoring:
+1. Create a plan in `/planning/class-naming-refactoring.md`
+2. Use multi_replace_string_in_file for bulk renames
+3. Run `./scripts/check` after each module
+4. Update imports across the codebase
+5. Test thoroughly with `./scripts/develop`
+
+**When adding NEW classes:**
+- ✅ ALWAYS use `TibberPrices` prefix for public classes
+- ✅ Document in docstring if prefix is intentionally omitted (with justification)
+- ✅ Check HA Core integrations for similar patterns when in doubt
+
+See `docs/development/coding-guidelines.md` for detailed naming conventions.
+
+### Ruff and Pyright Configuration
 
 **Ruff config (`pyproject.toml` under `[tool.ruff]`):**
 
@@ -2071,8 +1958,8 @@ Public entry points → direct helpers (call order) → pure utilities. Prefix p
 **Legacy/Backwards compatibility:**
 
 -   **Do NOT add legacy migration code** unless the change was already released in a version tag
--   **Check if released**: Use `./scripts/release/check-if-released <commit-hash>` to verify if code is in any `v*.*.*` tag
+-   **Check if released**: Use `./scripts/check-if-released <commit-hash>` to verify if code is in any `v*.*.*` tag
--   **Example**: If introducing breaking config change in commit `abc123`, run `./scripts/release/check-if-released abc123`:
+-   **Example**: If introducing breaking config change in commit `abc123`, run `./scripts/check-if-released abc123`:
     -   ✓ NOT RELEASED → No migration needed, just use new code
     -   ✗ ALREADY RELEASED → Migration may be needed for users upgrading from that version
 -   **Rule**: Only add backwards compatibility for changes that shipped to users via HACS/GitHub releases
@@ -2082,7 +1969,7 @@ Public entry points → direct helpers (call order) → pure utilities. Prefix p
 
 **Documentation language:**
 
--   **CRITICAL**: All user-facing documentation (`README.md`, `docs/user/docs/`, `docs/developer/docs/`) MUST be written in **English**
+-   **CRITICAL**: All user-facing documentation (`README.md`, `/docs/user/`, `/docs/development/`) MUST be written in **English**
 -   **Code comments**: Always use English for code comments and docstrings
 -   **UI translations**: Multi-language support exists in `/translations/` and `/custom_translations/` (de, en, nb, nl, sv) for UI strings only
 -   **Why English-only docs**: Ensures maintainability, accessibility to global community, and consistency with Home Assistant ecosystem
@@ -2120,7 +2007,7 @@ Public entry points → direct helpers (call order) → pure utilities. Prefix p
 
 **User Documentation Quality:**
 
-When writing or updating user-facing documentation (`docs/user/docs/` or `docs/developer/docs/`), follow these principles learned from real user feedback:
+When writing or updating user-facing documentation (`docs/user/`), follow these principles learned from real user feedback:
 
 -   **Clarity over completeness**: Users want to understand concepts, not read technical specifications
     -   ✅ Good: "Relaxation automatically loosens filters until enough periods are found"
@@ -2312,7 +2199,7 @@ df = (
 # ✅ Annotate function signatures (public functions)
 def get_current_interval_price(coordinator: DataUpdateCoordinator) -> float:
     """Get current price from coordinator."""
-    return coordinator.data["priceInfo"][0]["total"]
+    return coordinator.data["priceInfo"]["today"][0]["total"]
 
 # ✅ Use modern type syntax (Python 3.13)
 def process_prices(prices: list[dict[str, Any]]) -> dict[str, float]:
@@ -2408,8 +2295,7 @@ attributes = {
     "rating_level": ...,       # Price rating (LOW, NORMAL, HIGH)
 
     # 3. Price statistics (how much does it cost?)
-    "price_mean": ...,
+    "price_avg": ...,
-    "price_median": ...,
     "price_min": ...,
     "price_max": ...,
 
@@ -2425,8 +2311,8 @@ attributes = {
     "interval_count": ...,
 
     # 6. Meta information (technical details)
-    "pricePeriods": [...],          # Nested structures last
+    "periods": [...],          # Nested structures last
-    "priceInfo": [...],
+    "intervals": [...],
 
     # 7. Extended descriptions (always last)
     "description": "...",      # Short description from custom_translations (always shown)
@@ -2609,8 +2495,7 @@ This ensures timestamp is always the first key in the attribute dict, regardless
     "start": "2025-11-08T14:00:00+01:00",
     "end": "2025-11-08T15:00:00+01:00",
     "rating_level": "LOW",
-    "price_mean": 18.5,
+    "price_avg": 18.5,
-    "price_median": 18.3,
     "interval_count": 4,
     "intervals": [...]
 }
@@ -2621,7 +2506,7 @@ This ensures timestamp is always the first key in the attribute dict, regardless
     "interval_count": 4,
     "rating_level": "LOW",
     "start": "2025-11-08T14:00:00+01:00",
-    "price_mean": 18.5,
+    "price_avg": 18.5,
     "end": "2025-11-08T15:00:00+01:00"
 }
 ```
@@ -2666,8 +2551,8 @@ This ensures timestamp is always the first key in the attribute dict, regardless
 
 **Price-Related Attributes:**
 
--   Period statistics: `price_mean` (arithmetic mean), `price_median` (median value)
+-   Period averages: `period_price_avg` (average across the period)
--   Reference comparisons: `period_price_diff_from_daily_min` (period mean vs daily min)
+-   Reference comparisons: `period_price_diff_from_daily_min` (period avg vs daily min)
 -   Interval-specific: `interval_price_diff_from_daily_max` (current interval vs daily max)
 
 ### Before Adding New Attributes
@@ -2741,12 +2626,12 @@ The refactoring consolidated duplicate logic into unified methods in `sensor/cor
 
     -   Replaces: `_get_statistics_value()` (calendar day portion)
     -   Handles: Min/max/avg for calendar days (today/tomorrow)
-    -   Returns: Price in subunit currency units (cents/øre)
+    -   Returns: Price in minor currency units (cents/øre)
 
 -   **`_get_24h_window_value(stat_func)`**
     -   Replaces: `_get_average_value()`, `_get_minmax_value()`
     -   Handles: Trailing/leading 24h window statistics
-    -   Returns: Price in subunit currency units (cents/øre)
+    -   Returns: Price in minor currency units (cents/øre)
 
 Legacy wrapper methods still exist for backward compatibility but will be removed in a future cleanup phase.
 
@@ -2863,32 +2748,3 @@ Only after consulting the official HA docs did we discover the correct pattern:
 -   Translations: `sensor/definitions.py` (translation_key usage)
 -   Test fixtures: `tests/conftest.py`
 -   Time handling: Any file importing `dt_util`
-
-## Recorder History Optimization
-
-**CRITICAL: Always exclude non-essential attributes from Recorder to prevent database bloat.**
-
-**Implementation:**
-- Use `_unrecorded_attributes = frozenset({...})` as **class attribute** in entity classes
-- See `sensor/core.py` and `binary_sensor/core.py` for current implementation
-
-**What to exclude:**
-1. **Descriptions/help text** - `description`, `usage_tips` (static, large)
-2. **Large nested structures** - `periods`, `data`, `*_attributes` dicts (>1KB)
-3. **Frequently changing diagnostics** - `icon_color`, `cache_age`, status strings
-4. **Static/rarely changing config** - `currency`, `resolution`, `*_id` mappings
-5. **Temporary/time-bound data** - `next_api_poll`, `last_*` timestamps
-6. **Redundant/derived data** - `price_spread`, `diff_%` (calculable from other attrs)
-
-**What to keep:**
-- `timestamp` (always), all price values, `cache_age_minutes`, `updates_today`
-- Period timing (`start`, `end`, `duration_minutes`), price statistics
-- Boolean status flags, `relaxation_active`
-
-**When adding new attributes:**
-- Will this be useful in history 1 week from now? No → Exclude
-- Can this be calculated from other attributes? Yes → Exclude
-- Is this >100 bytes and not essential? Yes → Exclude
-
-**See:** `docs/developer/docs/recorder-optimization.md` for detailed categories and impact analysis
-

CONTRIBUTING.md

@@ -122,23 +122,13 @@ Always run before committing:
 - Enrich price data before exposing to entities
 - Follow Home Assistant entity naming conventions
 
-See [Coding Guidelines](docs/developer/docs/coding-guidelines.md) for complete details.
+See [Coding Guidelines](docs/development/coding-guidelines.md) for complete details.
 
 ## Documentation
 
-Documentation is organized in two Docusaurus sites:
+- **User guides**: Place in `docs/user/` (installation, configuration, usage)
-
+- **Developer guides**: Place in `docs/development/` (architecture, patterns)
-- **User docs** (`docs/user/`): Installation, configuration, usage guides
+- **Update translations**: When changing `translations/en.json`, update ALL language files
-  - Markdown files in `docs/user/docs/*.md`
-  - Navigation via `docs/user/sidebars.ts`
-- **Developer docs** (`docs/developer/`): Architecture, patterns, contribution guides
-  - Markdown files in `docs/developer/docs/*.md`
-  - Navigation via `docs/developer/sidebars.ts`
-
-**When adding new documentation:**
-1. Place file in appropriate `docs/*/docs/` directory
-2. Add to corresponding `sidebars.ts` for navigation
-3. Update translations when changing `translations/en.json` (update ALL language files)
 
 ## Reporting Bugs
 

README.md

@@ -1,8 +1,4 @@
-# Tibber Prices - Custom Home Assistant Integration
+# Tibber Price Information & Ratings
-
-<p align="center">
-  <img src="images/header.svg" alt="Tibber Prices Custom Integration for Tibber" width="600">
-</p>
 
 [![GitHub Release][releases-shield]][releases]
 [![GitHub Activity][commits-shield]][commits]
@@ -10,41 +6,30 @@
 
 [![hacs][hacsbadge]][hacs]
 [![Project Maintenance][maintenance-shield]][user_profile]
+[![BuyMeCoffee][buymecoffeebadge]][buymecoffee]
 
-<a href="https://www.buymeacoffee.com/jpawlowski" target="_blank"><img src="images/bmc-button.svg" alt="Buy Me A Coffee" height="41" width="174"></a>
+A Home Assistant integration that provides advanced price information and ratings from Tibber. This integration fetches **quarter-hourly** electricity prices, enriches them with statistical analysis, and provides smart indicators to help you optimize your energy consumption and save money.
 
-> **⚠️ Not affiliated with Tibber**
+![Tibber Price Information & Ratings](images/logo.png)
-> This is an independent, community-maintained custom integration for Home Assistant. It is **not** an official Tibber product and is **not** affiliated with or endorsed by Tibber AS.
-
-A custom Home Assistant integration that provides advanced electricity price information and ratings from Tibber. This integration fetches **quarter-hourly** electricity prices, enriches them with statistical analysis, and provides smart indicators to help you optimize your energy consumption and save money.
 
 ## 📖 Documentation
 
-**[📚 Complete Documentation](https://jpawlowski.github.io/hass.tibber_prices/)** - Two comprehensive documentation sites:
+-   **[User Guide](docs/user/)** - Installation, configuration, and usage guides
-
+    -   **[Period Calculation](docs/user/period-calculation.md)** - How Best/Peak Price periods are calculated
--   **[👤 User Documentation](https://jpawlowski.github.io/hass.tibber_prices/user/)** - Installation, configuration, usage guides, and examples
+-   **[Developer Guide](docs/development/)** - Contributing, architecture, and release process
--   **[🔧 Developer Documentation](https://jpawlowski.github.io/hass.tibber_prices/developer/)** - Architecture, contributing guidelines, and development setup
+-   **[Changelog](https://github.com/jpawlowski/hass.tibber_prices/releases)** - Release history and notes
-
-**Quick Links:**
--   [Installation Guide](https://jpawlowski.github.io/hass.tibber_prices/user/installation) - Step-by-step setup instructions
--   [Sensor Reference](https://jpawlowski.github.io/hass.tibber_prices/user/sensors) - Complete list of all sensors and attributes
--   [Chart Examples](https://jpawlowski.github.io/hass.tibber_prices/user/chart-examples) - ApexCharts visualizations
--   [Automation Examples](https://jpawlowski.github.io/hass.tibber_prices/user/automation-examples) - Real-world automation scenarios
--   [Changelog](https://github.com/jpawlowski/hass.tibber_prices/releases) - Release history and notes
 
 ## ✨ Features
 
--   **Quarter-Hourly Price Data**: Access detailed 15-minute interval pricing (384 data points across 4 days: day before yesterday/yesterday/today/tomorrow)
+-   **Quarter-Hourly Price Data**: Access detailed 15-minute interval pricing (192 data points across yesterday/today/tomorrow)
--   **Flexible Currency Display**: Choose between base currency (€, kr) or subunit (ct, øre) display - configurable per your preference with smart defaults
+-   **Current and Next Interval Prices**: Get real-time price data in both major currency (€, kr) and minor units (ct, øre)
 -   **Multi-Currency Support**: Automatic detection and formatting for EUR, NOK, SEK, DKK, USD, and GBP
 -   **Price Level Indicators**: Know when you're in a VERY_CHEAP, CHEAP, NORMAL, EXPENSIVE, or VERY_EXPENSIVE period
 -   **Statistical Sensors**: Track lowest, highest, and average prices for the day
 -   **Price Ratings**: Quarter-hourly ratings comparing current prices to 24-hour trailing averages
 -   **Smart Indicators**: Binary sensors to detect peak hours and best price hours for automations
--   **Beautiful ApexCharts**: Auto-generated chart configurations with dynamic Y-axis scaling ([see examples](https://jpawlowski.github.io/hass.tibber_prices/user/chart-examples))
--   **Chart Metadata Sensor**: Dynamic chart configuration for optimal visualization
 -   **Intelligent Caching**: Minimizes API calls while ensuring data freshness across Home Assistant restarts
--   **Custom Actions** (backend services): API endpoints for advanced integrations (ApexCharts support included)
+-   **Custom Services**: API endpoints for advanced integrations (ApexCharts support included)
 -   **Diagnostic Sensors**: Monitor data freshness and availability
 -   **Reliable API Usage**: Uses only official Tibber [`priceInfo`](https://developer.tibber.com/docs/reference#priceinfo) and [`priceInfoRange`](https://developer.tibber.com/docs/reference#subscription) endpoints - no legacy APIs. Price ratings and statistics are calculated locally for maximum reliability and future-proofing.
 
@@ -94,7 +79,7 @@ This will guide you through:
 -   Configure additional sensors in **Settings** → **Devices & Services** → **Tibber Price Information & Ratings** → **Entities**
 -   Use sensors in automations, dashboards, and scripts
 
-📖 **[Full Installation Guide →](https://jpawlowski.github.io/hass.tibber_prices/user/installation)**
+📖 **[Full Installation Guide →](docs/user/installation.md)**
 
 ## 📊 Available Entities
 
@@ -102,8 +87,6 @@ The integration provides **30+ sensors** across different categories. Key sensor
 
 > **Rich Sensor Attributes**: All sensors include extensive attributes with timestamps, context data, and detailed explanations. Enable **Extended Descriptions** in the integration options to add `long_description` and `usage_tips` attributes to every sensor, providing in-context documentation directly in Home Assistant's UI.
 
-**[📋 Complete Sensor Reference](https://jpawlowski.github.io/hass.tibber_prices/user/sensors)** - Full list with descriptions and attributes
-
 ### Core Price Sensors (Enabled by Default)
 
 | Entity                     | Description                                       |
@@ -146,8 +129,8 @@ The integration provides **30+ sensors** across different categories. Key sensor
 
 | Entity                    | Description                                                                               |
 | ------------------------- | ----------------------------------------------------------------------------------------- |
-| Peak Price Period         | ON when in a detected peak price period ([how it works](https://jpawlowski.github.io/hass.tibber_prices/user/period-calculation)) |
+| Peak Price Period         | ON when in a detected peak price period ([how it works](docs/user/period-calculation.md)) |
-| Best Price Period         | ON when in a detected best price period ([how it works](https://jpawlowski.github.io/hass.tibber_prices/user/period-calculation)) |
+| Best Price Period         | ON when in a detected best price period ([how it works](docs/user/period-calculation.md)) |
 | Tibber API Connection     | Connection status to Tibber API                                                           |
 | Tomorrow's Data Available | Whether tomorrow's price data is available                                                |
 
@@ -165,15 +148,13 @@ The following sensors are available but disabled by default. Enable them in `Set
 -   **Previous Interval Price** & **Previous Interval Price Level**: Historical data for the last 15-minute interval
 -   **Previous Interval Price Rating**: Rating for the previous interval
 -   **Trailing 24h Average Price**: Average of the past 24 hours from now
-    -   **Trailing 24h Minimum/Maximum Price**: Min/max in the past 24 hours
+-   **Trailing 24h Minimum/Maximum Price**: Min/max in the past 24 hours
 
-> **Note**: Currency display is configurable during setup. Choose between:
+> **Note**: All monetary sensors use minor currency units (ct/kWh, øre/kWh, ¢/kWh, p/kWh) automatically based on your Tibber account's currency. Supported: EUR, NOK, SEK, DKK, USD, GBP.
-> - **Base currency** (€/kWh, kr/kWh) - decimal values, differences visible from 3rd-4th decimal
-> - **Subunit** (ct/kWh, øre/kWh) - larger values, differences visible from 1st decimal
->
-> Smart defaults: EUR → subunit (German/Dutch preference), NOK/SEK/DKK → base (Scandinavian preference). Supported currencies: EUR, NOK, SEK, DKK, USD, GBP.
 
-## Automation Examples> **Note:** See the [full automation examples guide](https://jpawlowski.github.io/hass.tibber_prices/user/automation-examples) for more advanced recipes.
+## Automation Examples
+
+> **Note:** See the [full automation examples guide](docs/user/automation-examples.md) for more advanced recipes.
 
 ### Run Appliances During Cheap Hours
 
@@ -196,7 +177,7 @@ automation:
                 entity_id: switch.dishwasher
 ```
 
-> **Learn more:** The [period calculation guide](https://jpawlowski.github.io/hass.tibber_prices/user/period-calculation) explains how Best/Peak Price periods are identified and how you can configure filters (flexibility, minimum distance from average, price level filters with gap tolerance).
+> **Learn more:** The [period calculation guide](docs/user/period-calculation.md) explains how Best/Peak Price periods are identified and how you can configure filters (flexibility, minimum distance from average, price level filters with gap tolerance).
 
 ### Notify on Extremely High Prices
 
@@ -284,9 +265,8 @@ automation:
 ### Currency or units showing incorrectly
 
 -   Currency is automatically detected from your Tibber account
--   Display mode (base currency vs. subunit) can be configured in integration options: `Settings > Devices & Services > Tibber Price Information & Ratings > Configure`
+-   The integration supports EUR, NOK, SEK, DKK, USD, and GBP with appropriate minor units
--   Supported currencies: EUR, NOK, SEK, DKK, USD, and GBP
+-   Enable/disable major vs. minor unit sensors in `Settings > Devices & Services > Tibber Price Information & Ratings > Entities`
--   Smart defaults apply: EUR users get subunit (ct), Scandinavian users get base currency (kr)
 
 ## Advanced Features
 
@@ -326,47 +306,34 @@ template:
                 Price at {{ timestamp }}: {{ price }} ct/kWh
 ```
 
-📖 **[View all sensors and attributes →](https://jpawlowski.github.io/hass.tibber_prices/user/sensors)**
+📖 **[View all sensors and attributes →](docs/user/sensors.md)**
 
-### Dynamic Icons & Visual Indicators
+### Custom Services
 
-All sensors feature dynamic icons that change based on price levels, providing instant visual feedback in your dashboards.
+The integration provides custom services for advanced use cases:
 
-<img src="docs/user/static/img/entities-overview.jpg" width="400" alt="Entity list showing dynamic icons for different price states">
+-   `tibber_prices.get_price` - Fetch price data for specific days/times
-
+-   `tibber_prices.get_apexcharts_data` - Get formatted data for ApexCharts cards
-_Dynamic icons adapt to price levels, trends, and period states - showing CHEAP prices, FALLING trend, and active Best Price Period_
-
-📖 **[Dynamic Icons Guide →](https://jpawlowski.github.io/hass.tibber_prices/user/dynamic-icons)** | **[Icon Colors Guide →](https://jpawlowski.github.io/hass.tibber_prices/user/icon-colors)**
-
-### Custom Actions
-
-The integration provides custom actions (they still appear as services under the hood) for advanced use cases. These actions show up in Home Assistant under **Developer Tools → Actions**.
-
--   `tibber_prices.get_chartdata` - Get price data in chart-friendly formats for any visualization card
 -   `tibber_prices.get_apexcharts_yaml` - Generate complete ApexCharts configurations
 -   `tibber_prices.refresh_user_data` - Manually refresh account information
 
-📖 **[Action documentation and examples →](https://jpawlowski.github.io/hass.tibber_prices/user/actions)**
+📖 **[Service documentation and examples →](docs/user/services.md)**
 
-### Chart Visualizations (Optional)
+### ApexCharts Integration
 
-The integration includes built-in support for creating price visualization cards with automatic Y-axis scaling and color-coded series.
+The integration includes built-in support for creating beautiful price visualization cards. Use the `get_apexcharts_yaml` service to generate card configurations automatically.
 
-<img src="docs/user/static/img/charts/rolling-window.jpg" width="600" alt="Example: Dynamic 48h rolling window chart">
+📖 **[ApexCharts examples →](docs/user/automation-examples.md#apexcharts-cards)**
-
-_Optional: Dynamic 48h chart with automatic Y-axis scaling - generated via `get_apexcharts_yaml` action_
-
-📖 **[Chart examples and setup guide →](https://jpawlowski.github.io/hass.tibber_prices/user/chart-examples)**
 
 ## 🤝 Contributing
 
-Contributions are welcome! Please read the [Contributing Guidelines](CONTRIBUTING.md) and [Developer Documentation](https://jpawlowski.github.io/hass.tibber_prices/developer/) before submitting pull requests.
+Contributions are welcome! Please read the [Contributing Guidelines](CONTRIBUTING.md) and [Developer Guide](docs/development/) before submitting pull requests.
 
 ### For Contributors
 
--   **[Developer Setup](https://jpawlowski.github.io/hass.tibber_prices/developer/setup)** - Get started with DevContainer
+-   **[Developer Setup](docs/development/setup.md)** - Get started with DevContainer
--   **[Architecture Guide](https://jpawlowski.github.io/hass.tibber_prices/developer/architecture)** - Understand the codebase
+-   **[Architecture Guide](docs/development/architecture.md)** - Understand the codebase
--   **[Release Management](https://jpawlowski.github.io/hass.tibber_prices/developer/release-management)** - Release process and versioning
+-   **[Release Management](docs/development/release-management.md)** - Release process and versioning
 
 ## 🤖 Development Note
 

config/configuration.yaml

@@ -25,49 +25,8 @@ script:
 
 scene:
 
-energy:
-
 # https://www.home-assistant.io/integrations/logger/
 logger:
   default: info
   logs:
-    # Main integration logger - applies to ALL sub-loggers by default
     custom_components.tibber_prices: debug
-
-    # Reduce verbosity for details loggers (change to 'debug' for deep debugging)
-    # API client details (raw requests/responses - very verbose!)
-    custom_components.tibber_prices.api.client.details: info
-
-    # Period calculation details (all set to 'info' by default, change to 'debug' as needed):
-    # Relaxation strategy details (flex levels, per-day results)
-    custom_components.tibber_prices.coordinator.period_handlers.relaxation.details: info
-    # Filter statistics and criteria checks
-    custom_components.tibber_prices.coordinator.period_handlers.period_building.details: info
-    # Outlier/spike detection details
-    custom_components.tibber_prices.coordinator.period_handlers.outlier_filtering.details: info
-    # Period overlap resolution details
-    custom_components.tibber_prices.coordinator.period_handlers.period_overlap.details: info
-    # Outlier flex capping
-    custom_components.tibber_prices.coordinator.period_handlers.core.details: info
-    # Level filtering details (min_distance scaling)
-    custom_components.tibber_prices.coordinator.period_handlers.level_filtering.details: info
-
-    # Interval pool details (cache operations, GC):
-    # Cache lookup/miss, gap detection, fetch group additions
-    custom_components.tibber_prices.interval_pool.manager.details: info
-    # Garbage collection details (eviction, dead interval cleanup)
-    custom_components.tibber_prices.interval_pool.garbage_collector.details: info
-    # Gap detection and API fetching details
-    custom_components.tibber_prices.interval_pool.fetcher.details: info
-    # API endpoint routing decisions
-    custom_components.tibber_prices.interval_pool.routing.details: info
-    # Cache fetch group operations
-    custom_components.tibber_prices.interval_pool.cache.details: info
-    # Index rebuild operations
-    custom_components.tibber_prices.interval_pool.index.details: info
-    # Storage save operations
-    custom_components.tibber_prices.interval_pool.storage.details: info
-
-    # API helpers details (response validation):
-    # Data emptiness checks, structure validation
-    custom_components.tibber_prices.api.helpers.details: info

custom_components/tibber_prices/__init__.py

@@ -11,19 +11,15 @@ from typing import TYPE_CHECKING, Any
 
 import voluptuous as vol
 
-from homeassistant.config_entries import ConfigEntry, ConfigEntryState
+from homeassistant.config_entries import ConfigEntryState
 from homeassistant.const import CONF_ACCESS_TOKEN, Platform
-from homeassistant.exceptions import ConfigEntryAuthFailed
 from homeassistant.helpers.aiohttp_client import async_get_clientsession
 from homeassistant.helpers.storage import Store
 from homeassistant.loader import async_get_loaded_integration
 
 from .api import TibberPricesApiClient
 from .const import (
-    CONF_CURRENCY_DISPLAY_MODE,
     DATA_CHART_CONFIG,
-    DATA_CHART_METADATA_CONFIG,
-    DISPLAY_MODE_SUBUNIT,
     DOMAIN,
     LOGGER,
     async_load_standard_translations,
@@ -31,12 +27,6 @@ from .const import (
 )
 from .coordinator import STORAGE_VERSION, TibberPricesDataUpdateCoordinator
 from .data import TibberPricesData
-from .interval_pool import (
-    TibberPricesIntervalPool,
-    async_load_pool_state,
-    async_remove_pool_storage,
-    async_save_pool_state,
-)
 from .services import async_setup_services
 
 if TYPE_CHECKING:
@@ -47,8 +37,6 @@ if TYPE_CHECKING:
 PLATFORMS: list[Platform] = [
     Platform.SENSOR,
     Platform.BINARY_SENSOR,
-    Platform.NUMBER,
-    Platform.SWITCH,
 ]
 
 # Configuration schema for configuration.yaml
@@ -61,7 +49,7 @@ CONFIG_SCHEMA = vol.Schema(
                         vol.Optional("day"): vol.All(vol.Any(str, list), vol.Coerce(list)),
                         vol.Optional("resolution"): str,
                         vol.Optional("output_format"): str,
-                        vol.Optional("subunit_currency"): bool,
+                        vol.Optional("minor_currency"): bool,
                         vol.Optional("round_decimals"): vol.All(int, vol.Range(min=0, max=10)),
                         vol.Optional("include_level"): bool,
                         vol.Optional("include_rating_level"): bool,
@@ -105,85 +93,9 @@ async def async_setup(hass: HomeAssistant, config: dict[str, Any]) -> bool:
         LOGGER.debug("No chart_export configuration found in configuration.yaml")
         hass.data[DOMAIN][DATA_CHART_CONFIG] = {}
 
-    # Extract chart_metadata config if present
-    chart_metadata_config = domain_config.get("chart_metadata", {})
-
-    if chart_metadata_config:
-        LOGGER.debug("Loaded chart_metadata configuration from configuration.yaml")
-        hass.data[DOMAIN][DATA_CHART_METADATA_CONFIG] = chart_metadata_config
-    else:
-        LOGGER.debug("No chart_metadata configuration found in configuration.yaml")
-        hass.data[DOMAIN][DATA_CHART_METADATA_CONFIG] = {}
-
     return True
 
 
-async def _migrate_config_options(hass: HomeAssistant, entry: ConfigEntry) -> None:
-    """
-    Migrate config options for backward compatibility.
-
-    This ensures existing configs get sensible defaults when new options are added.
-    Runs automatically on integration startup.
-    """
-    migration_performed = False
-    migrated = dict(entry.options)
-
-    # Migration: Set currency_display_mode to subunit for legacy configs
-    # New configs (created after v1.1.0) get currency-appropriate defaults via get_default_options().
-    # This migration preserves legacy behavior where all prices were in subunit currency (cents/øre).
-    # Only runs for old config entries that don't have this option explicitly set.
-    if CONF_CURRENCY_DISPLAY_MODE not in migrated:
-        migrated[CONF_CURRENCY_DISPLAY_MODE] = DISPLAY_MODE_SUBUNIT
-        migration_performed = True
-        LOGGER.info(
-            "[%s] Migrated legacy config: Set currency_display_mode=%s (preserves pre-v1.1.0 behavior)",
-            entry.title,
-            DISPLAY_MODE_SUBUNIT,
-        )
-
-    # Save migrated options if any changes were made
-    if migration_performed:
-        hass.config_entries.async_update_entry(entry, options=migrated)
-        LOGGER.debug("[%s] Config migration completed", entry.title)
-
-
-def _get_access_token(hass: HomeAssistant, entry: ConfigEntry) -> str:
-    """
-    Get access token from entry or parent entry.
-
-    For parent entries, the token is stored in entry.data.
-    For subentries, we need to find the parent entry and get its token.
-
-    Args:
-        hass: HomeAssistant instance
-        entry: Config entry (parent or subentry)
-
-    Returns:
-        Access token string
-
-    Raises:
-        ConfigEntryAuthFailed: If no access token found
-
-    """
-    # Try to get token from this entry (works for parent)
-    if CONF_ACCESS_TOKEN in entry.data:
-        return entry.data[CONF_ACCESS_TOKEN]
-
-    # This is a subentry, find parent entry
-    # Parent entry is the one without subentries in its data structure
-    # and has the same domain
-    for potential_parent in hass.config_entries.async_entries(DOMAIN):
-        # Parent has ACCESS_TOKEN and is not the current entry
-        if potential_parent.entry_id != entry.entry_id and CONF_ACCESS_TOKEN in potential_parent.data:
-            # Check if this entry is actually a subentry of this parent
-            # (HA Core manages this relationship internally)
-            return potential_parent.data[CONF_ACCESS_TOKEN]
-
-    # No token found anywhere
-    msg = f"No access token found for entry {entry.entry_id}"
-    raise ConfigEntryAuthFailed(msg)
-
-
 # https://developers.home-assistant.io/docs/config_entries_index/#setting-up-an-entry
 async def async_setup_entry(
     hass: HomeAssistant,
@@ -191,10 +103,6 @@ async def async_setup_entry(
 ) -> bool:
     """Set up this integration using UI."""
     LOGGER.debug(f"[tibber_prices] async_setup_entry called for entry_id={entry.entry_id}")
-
-    # Migrate config options if needed (e.g., set default currency display mode for existing configs)
-    await _migrate_config_options(hass, entry)
-
     # Preload translations to populate the cache
     await async_load_translations(hass, "en")
     await async_load_standard_translations(hass, "en")
@@ -209,59 +117,10 @@ async def async_setup_entry(
 
     integration = async_get_loaded_integration(hass, entry.domain)
 
-    # Get access token (from this entry if parent, from parent if subentry)
-    access_token = _get_access_token(hass, entry)
-
-    # Create API client
-    api_client = TibberPricesApiClient(
-        access_token=access_token,
-        session=async_get_clientsession(hass),
-        version=str(integration.version) if integration.version else "unknown",
-    )
-
-    # Get home_id from config entry (required for single-home pool architecture)
-    home_id = entry.data.get("home_id")
-    if not home_id:
-        msg = f"[{entry.title}] Config entry missing home_id (required for interval pool)"
-        raise ConfigEntryAuthFailed(msg)
-
-    # Create or load interval pool for this config entry (single-home architecture)
-    pool_state = await async_load_pool_state(hass, entry.entry_id)
-    if pool_state:
-        interval_pool = TibberPricesIntervalPool.from_dict(
-            pool_state,
-            api=api_client,
-            hass=hass,
-            entry_id=entry.entry_id,
-        )
-        if interval_pool is None:
-            # Old multi-home format or corrupted → create new pool
-            LOGGER.info(
-                "[%s] Interval pool storage invalid/corrupted, creating new pool (will rebuild from API)",
-                entry.title,
-            )
-            interval_pool = TibberPricesIntervalPool(
-                home_id=home_id,
-                api=api_client,
-                hass=hass,
-                entry_id=entry.entry_id,
-            )
-        else:
-            LOGGER.debug("[%s] Interval pool restored from storage (auto-save enabled)", entry.title)
-    else:
-        interval_pool = TibberPricesIntervalPool(
-            home_id=home_id,
-            api=api_client,
-            hass=hass,
-            entry_id=entry.entry_id,
-        )
-        LOGGER.debug("[%s] Created new interval pool (auto-save enabled)", entry.title)
-
     coordinator = TibberPricesDataUpdateCoordinator(
         hass=hass,
         config_entry=entry,
-        api_client=api_client,
+        version=str(integration.version) if integration.version else "unknown",
-        interval_pool=interval_pool,
     )
 
     # CRITICAL: Load cache BEFORE first refresh to ensure user_data is available
@@ -270,17 +129,19 @@ async def async_setup_entry(
     await coordinator.load_cache()
 
     entry.runtime_data = TibberPricesData(
-        client=api_client,
+        client=TibberPricesApiClient(
+            access_token=entry.data[CONF_ACCESS_TOKEN],
+            session=async_get_clientsession(hass),
+            version=str(integration.version) if integration.version else "unknown",
+        ),
         integration=integration,
         coordinator=coordinator,
-        interval_pool=interval_pool,
     )
 
     # https://developers.home-assistant.io/docs/integration_fetching_data#coordinated-single-api-poll-for-data-for-all-entities
     if entry.state == ConfigEntryState.SETUP_IN_PROGRESS:
         await coordinator.async_config_entry_first_refresh()
-        # Note: Options update listener is registered in coordinator.__init__
+        entry.async_on_unload(entry.add_update_listener(async_reload_entry))
-        # (handles cache invalidation + refresh without full reload)
     else:
         await coordinator.async_refresh()
 
@@ -294,15 +155,6 @@ async def async_unload_entry(
     entry: TibberPricesConfigEntry,
 ) -> bool:
     """Unload a config entry."""
-    # Save interval pool state before unloading
-    if entry.runtime_data is not None and entry.runtime_data.interval_pool is not None:
-        pool_state = entry.runtime_data.interval_pool.to_dict()
-        await async_save_pool_state(hass, entry.entry_id, pool_state)
-        LOGGER.debug("[%s] Interval pool state saved on unload", entry.title)
-
-        # Shutdown interval pool (cancels background tasks)
-        await entry.runtime_data.interval_pool.async_shutdown()
-
     unload_ok = await hass.config_entries.async_unload_platforms(entry, PLATFORMS)
 
     if unload_ok and entry.runtime_data is not None:
@@ -311,7 +163,8 @@ async def async_unload_entry(
     # Unregister services if this was the last config entry
     if not hass.config_entries.async_entries(DOMAIN):
         for service in [
-            "get_chartdata",
+            "get_price",
+            "get_apexcharts_data",
             "get_apexcharts_yaml",
             "refresh_user_data",
         ]:
@@ -326,15 +179,10 @@ async def async_remove_entry(
     entry: TibberPricesConfigEntry,
 ) -> None:
     """Handle removal of an entry."""
-    # Remove coordinator cache storage
     if storage := Store(hass, STORAGE_VERSION, f"{DOMAIN}.{entry.entry_id}"):
         LOGGER.debug(f"[tibber_prices] async_remove_entry removing cache store for entry_id={entry.entry_id}")
         await storage.async_remove()
 
-    # Remove interval pool storage
-    await async_remove_pool_storage(hass, entry.entry_id)
-    LOGGER.debug(f"[tibber_prices] async_remove_entry removed interval pool storage for entry_id={entry.entry_id}")
-
 
 async def async_reload_entry(
     hass: HomeAssistant,

custom_components/tibber_prices/api/client.py

@@ -3,18 +3,14 @@
 from __future__ import annotations
 
 import asyncio
-import base64
 import logging
 import re
 import socket
-from datetime import datetime, timedelta
+from datetime import timedelta
 from typing import TYPE_CHECKING, Any
-from zoneinfo import ZoneInfo
 
 import aiohttp
 
-from homeassistant.util import dt as dt_utils
-
 from .exceptions import (
     TibberPricesApiClientAuthenticationError,
     TibberPricesApiClientCommunicationError,
@@ -23,6 +19,7 @@ from .exceptions import (
 )
 from .helpers import (
     flatten_price_info,
+    flatten_price_rating,
     prepare_headers,
     verify_graphql_response,
     verify_response_or_raise,
@@ -33,7 +30,6 @@ if TYPE_CHECKING:
     from custom_components.tibber_prices.coordinator.time_service import TibberPricesTimeService
 
 _LOGGER = logging.getLogger(__name__)
-_LOGGER_API_DETAILS = logging.getLogger(__name__ + ".details")
 
 
 class TibberPricesApiClient:
@@ -50,7 +46,7 @@ class TibberPricesApiClient:
         self._session = session
         self._version = version
         self._request_semaphore = asyncio.Semaphore(2)  # Max 2 concurrent requests
-        self.time: TibberPricesTimeService | None = None  # Set externally by coordinator (optional during config flow)
+        self.time: TibberPricesTimeService  # Set externally by coordinator (always initialized before use)
         self._last_request_time = None  # Set on first request
         self._min_request_interval = timedelta(seconds=1)  # Min 1 second between requests
         self._max_retries = 5
@@ -137,528 +133,190 @@ class TibberPricesApiClient:
             query_type=TibberPricesQueryType.USER,
         )
 
-    async def async_get_price_info_for_range(
+    async def async_get_price_info(self, home_ids: set[str]) -> dict:
-        self,
-        home_id: str,
-        user_data: dict[str, Any],
-        start_time: datetime,
-        end_time: datetime,
-    ) -> dict:
         """
-        Get price info for a specific time range with automatic routing.
+        Get price info data in flat format for specified homes.
-
-        This is a convenience wrapper around interval_pool.get_price_intervals_for_range().
 
         Args:
-            home_id: Home ID to fetch price data for.
+            home_ids: Set of home IDs to fetch data for.
-            user_data: User data dict containing home metadata (including timezone).
-            start_time: Start of the range (inclusive, timezone-aware).
-            end_time: End of the range (exclusive, timezone-aware).
 
         Returns:
-            Dict with "home_id" and "price_info" (list of intervals).
+            Dictionary with homes data keyed by home_id.
-
-        Raises:
-            TibberPricesApiClientError: If arguments invalid or requests fail.
 
         """
-        # Import here to avoid circular dependency (interval_pool imports TibberPricesApiClient)
+        return await self._get_price_info_for_specific_homes(home_ids)
-        from custom_components.tibber_prices.interval_pool import (  # noqa: PLC0415
-            get_price_intervals_for_range,
-        )
 
-        price_info = await get_price_intervals_for_range(
+    async def _get_price_info_for_specific_homes(self, home_ids: set[str]) -> dict:
-            api_client=self,
+        """Get price info for specific homes using GraphQL aliases."""
-            home_id=home_id,
+        if not home_ids:
-            user_data=user_data,
+            return {"homes": {}}
-            start_time=start_time,
-            end_time=end_time,
-        )
 
-        return {
+        # Build query with aliases for each home
-            "home_id": home_id,
+        # Example: home1: home(id: "abc") { ... }
-            "price_info": price_info,
+        home_queries = []
-        }
+        for idx, home_id in enumerate(sorted(home_ids)):
-
+            alias = f"home{idx}"
-    async def async_get_price_info(self, home_id: str, user_data: dict[str, Any]) -> dict:
+            home_query = f"""
-        """
+                {alias}: home(id: "{home_id}") {{
-        Get price info for a single home.
-
-        Uses timezone-aware cursor calculation based on the home's actual timezone
-        from Tibber API (not HA system timezone). This ensures correct "day before yesterday
-        midnight" calculation for homes in different timezones.
-
-        Args:
-            home_id: Home ID to fetch price data for.
-            user_data: User data dict containing home metadata (including timezone).
-                      REQUIRED - must be fetched before calling this method.
-
-        Returns:
-            Dict with "home_id", "price_info", and other home data.
-
-        Raises:
-            TibberPricesApiClientError: If TimeService not initialized or user_data missing.
-
-        """
-        if not self.time:
-            msg = "TimeService not initialized - required for price info processing"
-            raise TibberPricesApiClientError(msg)
-
-        if not user_data:
-            msg = "User data required for timezone-aware price fetching - fetch user data first"
-            raise TibberPricesApiClientError(msg)
-
-        if not home_id:
-            msg = "Home ID is required"
-            raise TibberPricesApiClientError(msg)
-
-        # Build home_id -> timezone mapping from user_data
-        home_timezones = self._extract_home_timezones(user_data)
-
-        # Get timezone for this home (fallback to HA system timezone)
-        home_tz = home_timezones.get(home_id)
-
-        # Calculate cursor: day before yesterday midnight in home's timezone
-        cursor = self._calculate_cursor_for_home(home_tz)
-
-        # Simple single-home query (no alias needed)
-        query = f"""
-            {{viewer{{
-                home(id: "{home_id}") {{
                     id
+                    consumption(resolution:DAILY,last:1) {{
+                        pageInfo{{currency}}
+                    }}
                     currentSubscription {{
-                        priceInfoRange(resolution:QUARTER_HOURLY, first:192, after: "{cursor}") {{
+                        priceInfoRange(resolution:QUARTER_HOURLY,last:192) {{
-                            pageInfo{{ count }}
                             edges{{node{{
-                                startsAt total level
+                                startsAt total energy tax level
                             }}}}
                         }}
                         priceInfo(resolution:QUARTER_HOURLY) {{
-                            today{{startsAt total level}}
+                            today{{startsAt total energy tax level}}
-                            tomorrow{{startsAt total level}}
+                            tomorrow{{startsAt total energy tax level}}
                         }}
                     }}
                 }}
-            }}}}
+            """
-        """
+            home_queries.append(home_query)
 
-        _LOGGER.debug("Fetching price info for home %s", home_id)
+        query = "{viewer{" + "".join(home_queries) + "}}"
+
+        _LOGGER.debug("Fetching price info for %d specific home(s)", len(home_ids))
 
         data = await self._api_wrapper(
             data={"query": query},
             query_type=TibberPricesQueryType.PRICE_INFO,
         )
 
-        # Parse response
+        # Parse aliased response
         viewer = data.get("viewer", {})
-        home = viewer.get("home")
+        homes_data = {}
 
-        if not home:
+        for idx, home_id in enumerate(sorted(home_ids)):
-            msg = f"Home {home_id} not found in API response"
+            alias = f"home{idx}"
-            _LOGGER.warning(msg)
+            home = viewer.get(alias)
-            return {"home_id": home_id, "price_info": []}
 
-        if "currentSubscription" in home and home["currentSubscription"] is not None:
+            if not home:
-            price_info = flatten_price_info(home["currentSubscription"])
+                _LOGGER.debug("Home %s not found in API response", home_id)
-        else:
+                homes_data[home_id] = {}
-            _LOGGER.warning(
+                continue
-                "Home %s has no active subscription - price data will be unavailable",
-                home_id,
-            )
-            price_info = []
 
-        return {
+            if "currentSubscription" in home and home["currentSubscription"] is not None:
-            "home_id": home_id,
+                # Extract currency from consumption data if available
-            "price_info": price_info,
+                currency = None
-        }
+                if home.get("consumption"):
+                    page_info = home["consumption"].get("pageInfo")
+                    if page_info:
+                        currency = page_info.get("currency")
 
-    async def async_get_price_info_range(
+                homes_data[home_id] = flatten_price_info(
-        self,
+                    home["currentSubscription"],
-        home_id: str,
+                    currency,
-        user_data: dict[str, Any],
+                    time=self.time,
-        start_time: datetime,
-        end_time: datetime,
-    ) -> dict:
-        """
-        Get historical price info for a specific time range using priceInfoRange endpoint.
-
-        Uses the priceInfoRange GraphQL endpoint for flexible historical data queries.
-        Intended for intervals BEFORE "day before yesterday midnight" (outside PRICE_INFO scope).
-
-        Automatically handles API pagination if Tibber limits batch size.
-
-        Args:
-            home_id: Home ID to fetch price data for.
-            user_data: User data dict containing home metadata (including timezone).
-            start_time: Start of the range (inclusive, timezone-aware).
-            end_time: End of the range (exclusive, timezone-aware).
-
-        Returns:
-            Dict with "home_id" and "price_info" (list of intervals).
-
-        Raises:
-            TibberPricesApiClientError: If arguments invalid or request fails.
-
-        """
-        if not user_data:
-            msg = "User data required for timezone-aware price fetching - fetch user data first"
-            raise TibberPricesApiClientError(msg)
-
-        if not home_id:
-            msg = "Home ID is required"
-            raise TibberPricesApiClientError(msg)
-
-        if start_time >= end_time:
-            msg = f"Invalid time range: start_time ({start_time}) must be before end_time ({end_time})"
-            raise TibberPricesApiClientError(msg)
-
-        _LOGGER_API_DETAILS.debug(
-            "fetch_price_info_range called with: start_time=%s (type=%s, tzinfo=%s), end_time=%s (type=%s, tzinfo=%s)",
-            start_time,
-            type(start_time),
-            start_time.tzinfo,
-            end_time,
-            type(end_time),
-            end_time.tzinfo,
-        )
-
-        # Calculate cursor and interval count
-        start_cursor = self._encode_cursor(start_time)
-        interval_count = self._calculate_interval_count(start_time, end_time)
-
-        _LOGGER_API_DETAILS.debug(
-            "Calculated cursor for range: start_time=%s, cursor_time=%s, encoded=%s",
-            start_time,
-            start_time,
-            start_cursor,
-        )
-
-        # Fetch all intervals with automatic paging
-        price_info = await self._fetch_price_info_with_paging(
-            home_id=home_id,
-            start_cursor=start_cursor,
-            interval_count=interval_count,
-        )
-
-        return {
-            "home_id": home_id,
-            "price_info": price_info,
-        }
-
-    def _calculate_interval_count(self, start_time: datetime, end_time: datetime) -> int:
-        """Calculate number of intervals needed based on date range."""
-        time_diff = end_time - start_time
-        resolution_change_date = datetime(2025, 10, 1, tzinfo=start_time.tzinfo)
-
-        if start_time < resolution_change_date:
-            # Pre-resolution-change: hourly intervals only
-            interval_count = int(time_diff.total_seconds() / 3600)  # 3600s = 1h
-            _LOGGER_API_DETAILS.debug(
-                "Time range is pre-2025-10-01: expecting hourly intervals (count: %d)",
-                interval_count,
-            )
-        else:
-            # Post-resolution-change: quarter-hourly intervals
-            interval_count = int(time_diff.total_seconds() / 900)  # 900s = 15min
-            _LOGGER_API_DETAILS.debug(
-                "Time range is post-2025-10-01: expecting quarter-hourly intervals (count: %d)",
-                interval_count,
-            )
-
-        return interval_count
-
-    async def _fetch_price_info_with_paging(
-        self,
-        home_id: str,
-        start_cursor: str,
-        interval_count: int,
-    ) -> list[dict[str, Any]]:
-        """
-        Fetch price info with automatic pagination if API limits batch size.
-
-        GraphQL Cursor Pagination:
-        - endCursor points to the last returned element (inclusive)
-        - Use 'after: endCursor' to get elements AFTER that cursor
-        - If count < requested, more pages available
-
-        Args:
-            home_id: Home ID to fetch price data for.
-            start_cursor: Base64-encoded start cursor.
-            interval_count: Total number of intervals to fetch.
-
-        Returns:
-            List of all price interval dicts across all pages.
-
-        """
-        price_info = []
-        remaining_intervals = interval_count
-        cursor = start_cursor
-        page = 0
-
-        while remaining_intervals > 0:
-            page += 1
-
-            # Fetch one page
-            page_data = await self._fetch_single_page(
-                home_id=home_id,
-                cursor=cursor,
-                requested_count=remaining_intervals,
-                page=page,
-            )
-
-            if not page_data:
-                break
-
-            # Extract intervals and pagination info
-            page_intervals = page_data["intervals"]
-            returned_count = page_data["count"]
-            end_cursor = page_data["end_cursor"]
-            has_next_page = page_data.get("has_next_page", False)
-
-            price_info.extend(page_intervals)
-
-            _LOGGER_API_DETAILS.debug(
-                "Page %d: Received %d intervals for home %s (total so far: %d/%d, endCursor=%s, hasNextPage=%s)",
-                page,
-                returned_count,
-                home_id,
-                len(price_info),
-                interval_count,
-                end_cursor,
-                has_next_page,
-            )
-
-            # Update remaining count
-            remaining_intervals -= returned_count
-
-            # Check if we need more pages
-            # Continue if: (1) we still need more intervals AND (2) API has more data
-            if remaining_intervals > 0 and end_cursor:
-                cursor = end_cursor
-                _LOGGER_API_DETAILS.debug(
-                    "Still need %d more intervals - fetching next page with cursor %s",
-                    remaining_intervals,
-                    cursor,
                 )
             else:
-                # Done: Either we have all intervals we need, or API has no more data
+                _LOGGER.debug(
-                if remaining_intervals > 0:
+                    "Home %s has no active subscription - price data will be unavailable",
-                    _LOGGER.warning(
+                    home_id,
-                        "API has no more data - received %d out of %d requested intervals (missing %d)",
+                )
-                        len(price_info),
+                homes_data[home_id] = {}
-                        interval_count,
-                        remaining_intervals,
-                    )
-                else:
-                    _LOGGER.debug(
-                        "Pagination complete - received all %d requested intervals",
-                        interval_count,
-                    )
-                break
 
-        _LOGGER_API_DETAILS.debug(
+        data["homes"] = homes_data
-            "Fetched %d total historical intervals for home %s across %d page(s)",
+        return data
-            len(price_info),
-            home_id,
-            page,
-        )
-
-        return price_info
-
-    async def _fetch_single_page(
-        self,
-        home_id: str,
-        cursor: str,
-        requested_count: int,
-        page: int,
-    ) -> dict[str, Any] | None:
-        """
-        Fetch a single page of price intervals.
-
-        Args:
-            home_id: Home ID to fetch price data for.
-            cursor: Base64-encoded cursor for this page.
-            requested_count: Number of intervals to request.
-            page: Page number (for logging).
-
-        Returns:
-            Dict with "intervals", "count", and "end_cursor" keys, or None if no data.
-
-        """
-        query = f"""
-            {{viewer{{
-                home(id: "{home_id}") {{
-                    id
-                    currentSubscription {{
-                        priceInfoRange(resolution:QUARTER_HOURLY, first:{requested_count}, after: "{cursor}") {{
-                            pageInfo{{
-                                count
-                                hasNextPage
-                                startCursor
-                                endCursor
-                            }}
-                            edges{{
-                                cursor
-                                node{{
-                                    startsAt total level
-                                }}
-                            }}
-                        }}
-                    }}
-                }}
-            }}}}
-        """
-
-        _LOGGER_API_DETAILS.debug(
-            "Fetching historical price info for home %s (page %d): %d intervals from cursor %s",
-            home_id,
-            page,
-            requested_count,
-            cursor,
-        )
 
+    async def async_get_daily_price_rating(self) -> dict:
+        """Get daily price rating data in flat format for all homes."""
         data = await self._api_wrapper(
-            data={"query": query},
+            data={
-            query_type=TibberPricesQueryType.PRICE_INFO_RANGE,
+                "query": """
+                    {viewer{homes{id,currentSubscription{priceRating{
+                        daily{
+                            currency
+                            entries{time total energy tax difference level}
+                        }
+                    }}}}}"""
+            },
+            query_type=TibberPricesQueryType.DAILY_RATING,
         )
+        homes = data.get("viewer", {}).get("homes", [])
 
-        # Parse response
+        homes_data = {}
-        viewer = data.get("viewer", {})
-        home = viewer.get("home")
-
-        if not home:
-            _LOGGER.warning("Home %s not found in API response", home_id)
-            return None
-
-        if "currentSubscription" not in home or home["currentSubscription"] is None:
-            _LOGGER.warning("Home %s has no active subscription - price data will be unavailable", home_id)
-            return None
-
-        # Extract priceInfoRange data
-        subscription = home["currentSubscription"]
-        price_info_range = subscription.get("priceInfoRange", {})
-        page_info = price_info_range.get("pageInfo", {})
-        edges = price_info_range.get("edges", [])
-
-        # Flatten edges to interval list
-        intervals = [edge["node"] for edge in edges if "node" in edge]
-
-        return {
-            "intervals": intervals,
-            "count": page_info.get("count", len(intervals)),
-            "end_cursor": page_info.get("endCursor"),
-            "has_next_page": page_info.get("hasNextPage", False),
-        }
-
-    def _extract_home_timezones(self, user_data: dict[str, Any]) -> dict[str, str]:
-        """
-        Extract home_id -> timezone mapping from user_data.
-
-        Args:
-            user_data: User data dict from async_get_viewer_details() (required).
-
-        Returns:
-            Dict mapping home_id to timezone string (e.g., "Europe/Oslo").
-
-        """
-        home_timezones = {}
-        viewer = user_data.get("viewer", {})
-        homes = viewer.get("homes", [])
-
         for home in homes:
             home_id = home.get("id")
-            timezone = home.get("timeZone")
+            if home_id:
+                if "currentSubscription" in home and home["currentSubscription"] is not None:
+                    homes_data[home_id] = flatten_price_rating(home["currentSubscription"])
+                else:
+                    _LOGGER.debug(
+                        "Home %s has no active subscription - daily rating data will be unavailable",
+                        home_id,
+                    )
+                    homes_data[home_id] = {}
 
-            if home_id and timezone:
+        data["homes"] = homes_data
-                home_timezones[home_id] = timezone
+        return data
-                _LOGGER_API_DETAILS.debug("Extracted timezone %s for home %s", timezone, home_id)
-            elif home_id:
-                _LOGGER.warning("Home %s has no timezone in user data, will use fallback", home_id)
 
-        return home_timezones
+    async def async_get_hourly_price_rating(self) -> dict:
+        """Get hourly price rating data in flat format for all homes."""
+        data = await self._api_wrapper(
+            data={
+                "query": """
+                    {viewer{homes{id,currentSubscription{priceRating{
+                        hourly{
+                            currency
+                            entries{time total energy tax difference level}
+                        }
+                    }}}}}"""
+            },
+            query_type=TibberPricesQueryType.HOURLY_RATING,
+        )
+        homes = data.get("viewer", {}).get("homes", [])
 
-    def _calculate_day_before_yesterday_midnight(self, home_timezone: str | None) -> datetime:
+        homes_data = {}
-        """
+        for home in homes:
-        Calculate day before yesterday midnight in home's timezone.
+            home_id = home.get("id")
+            if home_id:
+                if "currentSubscription" in home and home["currentSubscription"] is not None:
+                    homes_data[home_id] = flatten_price_rating(home["currentSubscription"])
+                else:
+                    _LOGGER.debug(
+                        "Home %s has no active subscription - hourly rating data will be unavailable",
+                        home_id,
+                    )
+                    homes_data[home_id] = {}
 
-        CRITICAL: Uses REAL TIME (dt_utils.now()), NOT TimeService.now().
+        data["homes"] = homes_data
-        This ensures API boundary calculations are based on actual current time,
+        return data
-        not simulated time from TimeService.
 
-        Args:
+    async def async_get_monthly_price_rating(self) -> dict:
-            home_timezone: Timezone string (e.g., "Europe/Oslo").
+        """Get monthly price rating data in flat format for all homes."""
-                          If None, falls back to HA system timezone.
+        data = await self._api_wrapper(
+            data={
+                "query": """
+                    {viewer{homes{id,currentSubscription{priceRating{
+                        monthly{
+                            currency
+                            entries{time total energy tax difference level}
+                        }
+                    }}}}}"""
+            },
+            query_type=TibberPricesQueryType.MONTHLY_RATING,
+        )
+        homes = data.get("viewer", {}).get("homes", [])
 
-        Returns:
+        homes_data = {}
-            Timezone-aware datetime for day before yesterday midnight.
+        for home in homes:
+            home_id = home.get("id")
+            if home_id:
+                if "currentSubscription" in home and home["currentSubscription"] is not None:
+                    homes_data[home_id] = flatten_price_rating(home["currentSubscription"])
+                else:
+                    _LOGGER.debug(
+                        "Home %s has no active subscription - monthly rating data will be unavailable",
+                        home_id,
+                    )
+                    homes_data[home_id] = {}
 
-        """
+        data["homes"] = homes_data
-        # Get current REAL time (not TimeService)
+        return data
-        now = dt_utils.now()
-
-        # Convert to home's timezone or fallback to HA system timezone
-        if home_timezone:
-            try:
-                tz = ZoneInfo(home_timezone)
-                now_in_home_tz = now.astimezone(tz)
-            except (KeyError, ValueError, OSError) as error:
-                _LOGGER.warning(
-                    "Invalid timezone %s (%s), falling back to HA system timezone",
-                    home_timezone,
-                    error,
-                )
-                now_in_home_tz = dt_utils.as_local(now)
-        else:
-            # Fallback to HA system timezone
-            now_in_home_tz = dt_utils.as_local(now)
-
-        # Calculate day before yesterday midnight
-        return (now_in_home_tz - timedelta(days=2)).replace(hour=0, minute=0, second=0, microsecond=0)
-
-    def _encode_cursor(self, timestamp: datetime) -> str:
-        """
-        Encode a timestamp as base64 cursor for GraphQL API.
-
-        Args:
-            timestamp: Timezone-aware datetime to encode.
-
-        Returns:
-            Base64-encoded ISO timestamp string.
-
-        """
-        iso_string = timestamp.isoformat()
-        return base64.b64encode(iso_string.encode()).decode()
-
-    def _parse_timestamp(self, timestamp_str: str) -> datetime:
-        """
-        Parse ISO timestamp string to timezone-aware datetime.
-
-        Args:
-            timestamp_str: ISO format timestamp string.
-
-        Returns:
-            Timezone-aware datetime object.
-
-        """
-        return dt_utils.parse_datetime(timestamp_str) or dt_utils.now()
-
-    def _calculate_cursor_for_home(self, home_timezone: str | None) -> str:
-        """
-        Calculate cursor (day before yesterday midnight) for a home's timezone.
-
-        Convenience wrapper around _calculate_day_before_yesterday_midnight()
-        and _encode_cursor() for backward compatibility with existing code.
-
-        Args:
-            home_timezone: Timezone string (e.g., "Europe/Oslo", "America/New_York").
-                          If None, falls back to HA system timezone.
-
-        Returns:
-            Base64-encoded ISO timestamp string for use as GraphQL cursor.
-
-        """
-        day_before_yesterday_midnight = self._calculate_day_before_yesterday_midnight(home_timezone)
-        return self._encode_cursor(day_before_yesterday_midnight)
 
     async def _make_request(
         self,
@@ -667,7 +325,7 @@ class TibberPricesApiClient:
         query_type: TibberPricesQueryType,
     ) -> dict[str, Any]:
         """Make an API request with comprehensive error handling for network issues."""
-        _LOGGER_API_DETAILS.debug("Making API request with data: %s", data)
+        _LOGGER.debug("Making API request with data: %s", data)
 
         try:
             # More granular timeout configuration for better network failure handling
@@ -687,7 +345,7 @@ class TibberPricesApiClient:
 
             verify_response_or_raise(response)
             response_json = await response.json()
-            _LOGGER_API_DETAILS.debug("Received API response: %s", response_json)
+            _LOGGER.debug("Received API response: %s", response_json)
 
             await verify_graphql_response(response_json, query_type)
 
@@ -795,7 +453,7 @@ class TibberPricesApiClient:
                 time_since_last_request = now - self._last_request_time
                 if time_since_last_request < self._min_request_interval:
                     sleep_time = (self._min_request_interval - time_since_last_request).total_seconds()
-                    _LOGGER_API_DETAILS.debug(
+                    _LOGGER.debug(
                         "Rate limiting: waiting %s seconds before next request",
                         sleep_time,
                     )
@@ -841,18 +499,23 @@ class TibberPricesApiClient:
         """Handle retry logic for API-specific errors."""
         error_msg = str(error)
 
-        # Non-retryable: Invalid queries, bad requests, empty data
+        # Non-retryable: Invalid queries
-        # Empty data means API has no data for the requested range - retrying won't help
+        if "Invalid GraphQL query" in error_msg or "Bad request" in error_msg:
-        if "Invalid GraphQL query" in error_msg or "Bad request" in error_msg or "Empty data received" in error_msg:
             return False, 0
 
-        # Rate limits - only retry if server explicitly says so
+        # Rate limits - special handling with extracted delay
         if "Rate limit exceeded" in error_msg or "rate limited" in error_msg.lower():
             delay = self._extract_retry_delay(error, retry)
             return True, delay
 
-        # Other API errors - not retryable (assume permanent issue)
+        # Empty data - retryable with capped exponential backoff
-        return False, 0
+        if "Empty data received" in error_msg:
+            delay = min(self._retry_delay * (2**retry), 60)  # Cap at 60 seconds
+            return True, delay
+
+        # Other API errors - retryable with capped exponential backoff
+        delay = min(self._retry_delay * (2**retry), 30)  # Cap at 30 seconds
+        return True, delay
 
     def _extract_retry_delay(self, error: Exception, retry: int) -> int:
         """Extract retry delay from rate limit error or use exponential backoff."""
@@ -886,24 +549,7 @@ class TibberPricesApiClient:
         headers: dict | None = None,
         query_type: TibberPricesQueryType = TibberPricesQueryType.USER,
     ) -> Any:
-        """
+        """Get information from the API with rate limiting and retry logic."""
-        Get information from the API with rate limiting and retry logic.
-
-        Exception Handling Strategy:
-        - AuthenticationError: Immediate raise, triggers reauth flow
-        - PermissionError: Immediate raise, non-retryable
-        - CommunicationError: Retry with exponential backoff
-        - ApiClientError (Rate Limit): Retry with Retry-After delay
-        - ApiClientError (Other): Retry only if explicitly retryable
-        - Network errors (aiohttp.ClientError, socket.gaierror, TimeoutError):
-          Converted to CommunicationError and retried
-
-        Retry Logic:
-        - Max retries: 5 (configurable via _max_retries)
-        - Base delay: 2 seconds (exponential backoff: 2s, 4s, 8s, 16s, 32s)
-        - Rate limit delay: Uses Retry-After header or falls back to exponential
-        - Caps: 30s for network errors, 120s for rate limits, 300s for Retry-After
-        """
         headers = headers or prepare_headers(self._access_token, self._version)
         last_error: Exception | None = None
 

custom_components/tibber_prices/api/helpers.py

@@ -3,6 +3,7 @@
 from __future__ import annotations
 
 import logging
+from datetime import timedelta
 from typing import TYPE_CHECKING
 
 from homeassistant.const import __version__ as ha_version
@@ -10,6 +11,8 @@ from homeassistant.const import __version__ as ha_version
 if TYPE_CHECKING:
     import aiohttp
 
+    from custom_components.tibber_prices.coordinator.time_service import TibberPricesTimeService
+
     from .queries import TibberPricesQueryType
 
 from .exceptions import (
@@ -19,85 +22,36 @@ from .exceptions import (
 )
 
 _LOGGER = logging.getLogger(__name__)
-_LOGGER_DETAILS = logging.getLogger(__name__ + ".details")
 
 HTTP_BAD_REQUEST = 400
 HTTP_UNAUTHORIZED = 401
 HTTP_FORBIDDEN = 403
 HTTP_TOO_MANY_REQUESTS = 429
-HTTP_INTERNAL_SERVER_ERROR = 500
-HTTP_BAD_GATEWAY = 502
-HTTP_SERVICE_UNAVAILABLE = 503
-HTTP_GATEWAY_TIMEOUT = 504
 
 
 def verify_response_or_raise(response: aiohttp.ClientResponse) -> None:
-    """
+    """Verify that the response is valid."""
-    Verify HTTP response and map to appropriate exceptions.
-
-    Error Mapping:
-    - 401 Unauthorized → AuthenticationError (non-retryable)
-    - 403 Forbidden → PermissionError (non-retryable)
-    - 429 Rate Limit → ApiClientError with retry support
-    - 400 Bad Request → ApiClientError (non-retryable, invalid query)
-    - 5xx Server Errors → CommunicationError (retryable)
-    - Other errors → Let aiohttp.raise_for_status() handle
-    """
-    # Authentication failures - non-retryable
     if response.status == HTTP_UNAUTHORIZED:
         _LOGGER.error("Tibber API authentication failed - check access token")
         raise TibberPricesApiClientAuthenticationError(TibberPricesApiClientAuthenticationError.INVALID_CREDENTIALS)
-
-    # Permission denied - non-retryable
     if response.status == HTTP_FORBIDDEN:
         _LOGGER.error("Tibber API access forbidden - insufficient permissions")
         raise TibberPricesApiClientPermissionError(TibberPricesApiClientPermissionError.INSUFFICIENT_PERMISSIONS)
-
-    # Rate limiting - retryable with explicit delay
     if response.status == HTTP_TOO_MANY_REQUESTS:
         # Check for Retry-After header that Tibber might send
         retry_after = response.headers.get("Retry-After", "unknown")
         _LOGGER.warning("Tibber API rate limit exceeded - retry after %s seconds", retry_after)
         raise TibberPricesApiClientError(TibberPricesApiClientError.RATE_LIMIT_ERROR.format(retry_after=retry_after))
-
-    # Bad request - non-retryable (invalid query)
     if response.status == HTTP_BAD_REQUEST:
         _LOGGER.error("Tibber API rejected request - likely invalid GraphQL query")
         raise TibberPricesApiClientError(
             TibberPricesApiClientError.INVALID_QUERY_ERROR.format(message="Bad request - likely invalid GraphQL query")
         )
-
-    # Server errors 5xx - retryable (temporary server issues)
-    if response.status in (
-        HTTP_INTERNAL_SERVER_ERROR,
-        HTTP_BAD_GATEWAY,
-        HTTP_SERVICE_UNAVAILABLE,
-        HTTP_GATEWAY_TIMEOUT,
-    ):
-        _LOGGER.warning(
-            "Tibber API server error %d - temporary issue, will retry",
-            response.status,
-        )
-        # Let this be caught as aiohttp.ClientResponseError in _api_wrapper
-        # where it's converted to CommunicationError with retry logic
-        response.raise_for_status()
-
-    # All other HTTP errors - let aiohttp handle
     response.raise_for_status()
 
 
 async def verify_graphql_response(response_json: dict, query_type: TibberPricesQueryType) -> None:
-    """
+    """Verify the GraphQL response for errors and data completeness, including empty data."""
-    Verify GraphQL response and map error codes to appropriate exceptions.
-
-    GraphQL Error Code Mapping:
-    - UNAUTHENTICATED → AuthenticationError (triggers reauth flow)
-    - FORBIDDEN → PermissionError (non-retryable)
-    - RATE_LIMITED/TOO_MANY_REQUESTS → ApiClientError (retryable)
-    - VALIDATION_ERROR/GRAPHQL_VALIDATION_FAILED → ApiClientError (non-retryable)
-    - Other codes → Generic ApiClientError (with code in message)
-    - Empty data → ApiClientError (non-retryable, API has no data)
-    """
     if "errors" in response_json:
         errors = response_json["errors"]
         if not errors:
@@ -144,137 +98,14 @@ async def verify_graphql_response(response_json: dict, query_type: TibberPricesQ
             TibberPricesApiClientError.GRAPHQL_ERROR.format(message="Response missing data object")
         )
 
-    # Empty data check - validate response completeness
+    # Empty data check (for retry logic) - always check, regardless of query_type
-    # This is NOT a retryable error - API simply has no data for the requested range
     if is_data_empty(response_json["data"], query_type.value):
-        _LOGGER_DETAILS.debug("Empty data detected for query_type: %s - API has no data available", query_type)
+        _LOGGER.debug("Empty data detected for query_type: %s", query_type)
         raise TibberPricesApiClientError(
             TibberPricesApiClientError.EMPTY_DATA_ERROR.format(query_type=query_type.value)
         )
 
 
-def _check_user_data_empty(data: dict) -> bool:
-    """Check if user data is empty or incomplete."""
-    has_user_id = (
-        "viewer" in data
-        and isinstance(data["viewer"], dict)
-        and "userId" in data["viewer"]
-        and data["viewer"]["userId"] is not None
-    )
-    has_homes = (
-        "viewer" in data
-        and isinstance(data["viewer"], dict)
-        and "homes" in data["viewer"]
-        and isinstance(data["viewer"]["homes"], list)
-        and len(data["viewer"]["homes"]) > 0
-    )
-    is_empty = not has_user_id or not has_homes
-    _LOGGER_DETAILS.debug(
-        "Viewer check - has_user_id: %s, has_homes: %s, is_empty: %s",
-        has_user_id,
-        has_homes,
-        is_empty,
-    )
-    return is_empty
-
-
-def _check_price_info_empty(data: dict) -> bool:
-    """
-    Check if price_info data is empty or incomplete.
-
-    Note: Missing currentSubscription is VALID (home without active contract).
-    Only check for structural issues, not missing data that legitimately might not exist.
-    """
-    viewer = data.get("viewer", {})
-    home_data = viewer.get("home")
-
-    if not home_data:
-        _LOGGER_DETAILS.debug("No home data found in price_info response")
-        return True
-
-    _LOGGER_DETAILS.debug("Checking price_info for single home")
-
-    # Missing currentSubscription is VALID - home has no active contract
-    # This is not an "empty data" error, it's a legitimate state
-    if "currentSubscription" not in home_data or home_data["currentSubscription"] is None:
-        _LOGGER_DETAILS.debug("No currentSubscription - home has no active contract (valid state)")
-        return False  # NOT empty - this is expected for homes without subscription
-
-    subscription = home_data["currentSubscription"]
-
-    # Check priceInfoRange (yesterday data - optional, may not be available)
-    has_yesterday = (
-        "priceInfoRange" in subscription
-        and subscription["priceInfoRange"] is not None
-        and "edges" in subscription["priceInfoRange"]
-        and subscription["priceInfoRange"]["edges"]
-    )
-
-    # Check priceInfo for today's data (required if subscription exists)
-    has_price_info = "priceInfo" in subscription and subscription["priceInfo"] is not None
-    has_today = (
-        has_price_info
-        and "today" in subscription["priceInfo"]
-        and subscription["priceInfo"]["today"] is not None
-        and len(subscription["priceInfo"]["today"]) > 0
-    )
-
-    # Only require today's data - yesterday is optional
-    # If subscription exists but no today data, that's a structural problem
-    is_empty = not has_today
-
-    _LOGGER_DETAILS.debug(
-        "Price info check - priceInfoRange: %s, today: %s, is_empty: %s",
-        bool(has_yesterday),
-        bool(has_today),
-        is_empty,
-    )
-    return is_empty
-
-
-def _check_price_info_range_empty(data: dict) -> bool:
-    """
-    Check if price_info_range data is empty or incomplete.
-
-    For historical range queries, empty edges array is VALID (no data available
-    for that time range, e.g., too old). Only structural problems are errors.
-    """
-    viewer = data.get("viewer", {})
-    home_data = viewer.get("home")
-
-    if not home_data:
-        _LOGGER_DETAILS.debug("No home data found in price_info_range response")
-        return True
-
-    subscription = home_data.get("currentSubscription")
-    if not subscription:
-        _LOGGER_DETAILS.debug("Missing currentSubscription in home")
-        return True
-
-    # For price_info_range, check if the structure exists
-    # Empty edges array is VALID (no data for that time range)
-    price_info_range = subscription.get("priceInfoRange")
-    if price_info_range is None:
-        _LOGGER_DETAILS.debug("Missing priceInfoRange in subscription")
-        return True
-
-    if "edges" not in price_info_range:
-        _LOGGER_DETAILS.debug("Missing edges key in priceInfoRange")
-        return True
-
-    edges = price_info_range["edges"]
-    if not isinstance(edges, list):
-        _LOGGER_DETAILS.debug("priceInfoRange edges is not a list")
-        return True
-
-    # Empty edges is VALID for historical queries (data not available)
-    _LOGGER_DETAILS.debug(
-        "Price info range check - structure valid, edge_count: %s (empty is OK for old data)",
-        len(edges),
-    )
-    return False  # Structure is valid, even if edges is empty
-
-
 def is_data_empty(data: dict, query_type: str) -> bool:
     """
     Check if the response data is empty or incomplete.
@@ -290,27 +121,126 @@ def is_data_empty(data: dict, query_type: str) -> bool:
     - Must have today data
     - tomorrow can be empty if we have valid historical and today data
 
-    For price info range:
+    For rating data:
-    - Must have priceInfoRange with edges
+    - Must have thresholdPercentages
-    Used by interval pool for historical data fetching
+    - Must have non-empty entries for the specific rating type
     """
-    _LOGGER_DETAILS.debug("Checking if data is empty for query_type %s", query_type)
+    _LOGGER.debug("Checking if data is empty for query_type %s", query_type)
 
+    is_empty = False
     try:
         if query_type == "user":
-            return _check_user_data_empty(data)
+            has_user_id = (
-        if query_type == "price_info":
+                "viewer" in data
-            return _check_price_info_empty(data)
+                and isinstance(data["viewer"], dict)
-        if query_type == "price_info_range":
+                and "userId" in data["viewer"]
-            return _check_price_info_range_empty(data)
+                and data["viewer"]["userId"] is not None
+            )
+            has_homes = (
+                "viewer" in data
+                and isinstance(data["viewer"], dict)
+                and "homes" in data["viewer"]
+                and isinstance(data["viewer"]["homes"], list)
+                and len(data["viewer"]["homes"]) > 0
+            )
+            is_empty = not has_user_id or not has_homes
+            _LOGGER.debug(
+                "Viewer check - has_user_id: %s, has_homes: %s, is_empty: %s",
+                has_user_id,
+                has_homes,
+                is_empty,
+            )
 
-        # Unknown query type
+        elif query_type == "price_info":
-        _LOGGER_DETAILS.debug("Unknown query type %s, treating as non-empty", query_type)
+            # Check for home aliases (home0, home1, etc.)
+            viewer = data.get("viewer", {})
+            home_aliases = [key for key in viewer if key.startswith("home") and key[4:].isdigit()]
+
+            if not home_aliases:
+                _LOGGER.debug("No home aliases found in price_info response")
+                is_empty = True
+            else:
+                # Check first home for valid data
+                _LOGGER.debug("Checking price_info with %d home(s)", len(home_aliases))
+                first_home = viewer.get(home_aliases[0])
+
+                if (
+                    not first_home
+                    or "currentSubscription" not in first_home
+                    or first_home["currentSubscription"] is None
+                ):
+                    _LOGGER.debug("Missing currentSubscription in first home")
+                    is_empty = True
+                else:
+                    subscription = first_home["currentSubscription"]
+
+                    # Check priceInfoRange (192 quarter-hourly intervals)
+                    has_historical = (
+                        "priceInfoRange" in subscription
+                        and subscription["priceInfoRange"] is not None
+                        and "edges" in subscription["priceInfoRange"]
+                        and subscription["priceInfoRange"]["edges"]
+                    )
+
+                    # Check priceInfo for today's data
+                    has_price_info = "priceInfo" in subscription and subscription["priceInfo"] is not None
+                    has_today = (
+                        has_price_info
+                        and "today" in subscription["priceInfo"]
+                        and subscription["priceInfo"]["today"] is not None
+                        and len(subscription["priceInfo"]["today"]) > 0
+                    )
+
+                    # Data is empty if we don't have historical data or today's data
+                    is_empty = not has_historical or not has_today
+
+                    _LOGGER.debug(
+                        "Price info check - priceInfoRange: %s, today: %s, is_empty: %s",
+                        bool(has_historical),
+                        bool(has_today),
+                        is_empty,
+                    )
+
+        elif query_type in ["daily", "hourly", "monthly"]:
+            # Check for homes existence and non-emptiness before accessing
+            if (
+                "viewer" not in data
+                or "homes" not in data["viewer"]
+                or not isinstance(data["viewer"]["homes"], list)
+                or len(data["viewer"]["homes"]) == 0
+                or "currentSubscription" not in data["viewer"]["homes"][0]
+                or data["viewer"]["homes"][0]["currentSubscription"] is None
+                or "priceRating" not in data["viewer"]["homes"][0]["currentSubscription"]
+            ):
+                _LOGGER.debug("Missing homes/currentSubscription/priceRating in rating check")
+                is_empty = True
+            else:
+                rating = data["viewer"]["homes"][0]["currentSubscription"]["priceRating"]
+
+                # Check rating entries
+                has_entries = (
+                    query_type in rating
+                    and rating[query_type] is not None
+                    and "entries" in rating[query_type]
+                    and rating[query_type]["entries"] is not None
+                    and len(rating[query_type]["entries"]) > 0
+                )
+
+                is_empty = not has_entries
+                _LOGGER.debug(
+                    "%s rating check - entries count: %d, is_empty: %s",
+                    query_type,
+                    len(rating[query_type]["entries"]) if has_entries else 0,
+                    is_empty,
+                )
+        else:
+            _LOGGER.debug("Unknown query type %s, treating as non-empty", query_type)
+            is_empty = False
     except (KeyError, IndexError, TypeError) as error:
-        _LOGGER_DETAILS.debug("Error checking data emptiness: %s", error)
+        _LOGGER.debug("Error checking data emptiness: %s", error)
-        return True
+        is_empty = True
-    else:
+
-        return False
+    return is_empty
 
 
 def prepare_headers(access_token: str, version: str) -> dict[str, str]:
@@ -322,30 +252,23 @@ def prepare_headers(access_token: str, version: str) -> dict[str, str]:
     }
 
 
-def flatten_price_info(subscription: dict) -> list[dict]:
+def flatten_price_info(subscription: dict, currency: str | None = None, *, time: TibberPricesTimeService) -> dict:
     """
     Transform and flatten priceInfo from full API data structure.
 
-    Returns a flat list of all price intervals ordered as:
+    Now handles priceInfoRange (192 quarter-hourly intervals) separately from
-    [day_before_yesterday_prices, yesterday_prices, today_prices, tomorrow_prices]
+    priceInfo (today and tomorrow data). Currency is stored as a separate attribute.
-
-    priceInfoRange fetches 192 quarter-hourly intervals starting from the day before
-    yesterday midnight (2 days of historical data), which provides sufficient data
-    for calculating trailing 24h averages for all intervals including yesterday.
-
-    Args:
-        subscription: The currentSubscription dictionary from API response.
-
-    Returns:
-        A flat list containing all price dictionaries (startsAt, total, level).
-
     """
-    # Use 'or {}' to handle None values (API may return None during maintenance)
+    price_info = subscription.get("priceInfo", {})
-    price_info_range = subscription.get("priceInfoRange") or {}
+    price_info_range = subscription.get("priceInfoRange", {})
 
-    # Transform priceInfoRange edges data (extract historical quarter-hourly prices)
+    # Get today and yesterday dates using TimeService
-    # This contains 192 intervals (2 days) starting from day before yesterday midnight
+    today_local = time.now().date()
-    historical_prices = []
+    yesterday_local = today_local - timedelta(days=1)
+    _LOGGER.debug("Processing data for yesterday's date: %s", yesterday_local)
+
+    # Transform priceInfoRange edges data (extract yesterday's quarter-hourly prices)
+    yesterday_prices = []
     if "edges" in price_info_range:
         edges = price_info_range["edges"]
 
@@ -353,9 +276,47 @@ def flatten_price_info(subscription: dict) -> list[dict]:
             if "node" not in edge:
                 _LOGGER.debug("Skipping edge without node: %s", edge)
                 continue
-            historical_prices.append(edge["node"])
 
-    # Return all intervals as a single flattened array
+            price_data = edge["node"]
-    # Use 'or {}' to handle None values (API may return None during maintenance)
+            # Parse timestamp using TimeService for proper timezone handling
-    price_info = subscription.get("priceInfo") or {}
+            starts_at = time.get_interval_time(price_data)
-    return historical_prices + (price_info.get("today") or []) + (price_info.get("tomorrow") or [])
+            if starts_at is None:
+                _LOGGER.debug("Could not parse timestamp: %s", price_data["startsAt"])
+                continue
+
+            price_date = starts_at.date()
+
+            # Only include prices from yesterday
+            if price_date == yesterday_local:
+                yesterday_prices.append(price_data)
+
+        _LOGGER.debug("Found %d price entries for yesterday", len(yesterday_prices))
+
+    return {
+        "yesterday": yesterday_prices,
+        "today": price_info.get("today", []),
+        "tomorrow": price_info.get("tomorrow", []),
+        "currency": currency,
+    }
+
+
+def flatten_price_rating(subscription: dict) -> dict:
+    """Extract and flatten priceRating from subscription, including currency."""
+    price_rating = subscription.get("priceRating", {})
+
+    def extract_entries_and_currency(rating: dict) -> tuple[list, str | None]:
+        if rating is None:
+            return [], None
+        return rating.get("entries", []), rating.get("currency")
+
+    hourly_entries, hourly_currency = extract_entries_and_currency(price_rating.get("hourly"))
+    daily_entries, daily_currency = extract_entries_and_currency(price_rating.get("daily"))
+    monthly_entries, monthly_currency = extract_entries_and_currency(price_rating.get("monthly"))
+    # Prefer hourly, then daily, then monthly for top-level currency
+    currency = hourly_currency or daily_currency or monthly_currency
+    return {
+        "hourly": hourly_entries,
+        "daily": daily_entries,
+        "monthly": monthly_entries,
+        "currency": currency,
+    }

custom_components/tibber_prices/api/queries.py

@@ -6,43 +6,10 @@ from enum import Enum
 
 
 class TibberPricesQueryType(Enum):
-    """
+    """Types of queries that can be made to the API."""
-    Types of queries that can be made to the API.
-
-    CRITICAL: Query type selection is dictated by Tibber's API design and caching strategy.
-
-    PRICE_INFO:
-        - Used for current day-relative data (day before yesterday/yesterday/today/tomorrow)
-        - API automatically determines "today" and "tomorrow" based on current time
-        - MUST be used when querying any data from these 4 days, even if you only need
-          specific intervals, because Tibber's API requires this endpoint for current data
-        - Provides the core dataset needed for live data, recent historical context
-          (important until tomorrow's data arrives), and tomorrow's forecast
-        - Tibber likely has optimized caching for this frequently-accessed data range
-        - Boundary: FROM "day before yesterday midnight" (real time) onwards
-
-    PRICE_INFO_RANGE:
-        - Used for historical data older than day before yesterday
-        - Allows flexible date range queries with cursor-based pagination
-        - Required for any intervals beyond the 4-day window of PRICE_INFO
-        - Use this for historical analysis, comparisons, or trend calculations
-        - Boundary: BEFORE "day before yesterday midnight" (real time)
-
-    ROUTING:
-        - Use async_get_price_info_for_range() wrapper for automatic routing
-        - Wrapper intelligently splits requests spanning the boundary:
-          * Fully historical range (end < boundary) → PRICE_INFO_RANGE only
-          * Fully recent range (start >= boundary) → PRICE_INFO only
-          * Spanning range → Both queries, merged results
-        - Boundary calculated using REAL TIME (dt_utils.now()), not TimeService
-          to ensure predictable API responses
-
-    USER:
-        - Fetches user account data and home metadata
-        - Separate from price data queries
-
-    """
 
     PRICE_INFO = "price_info"
-    PRICE_INFO_RANGE = "price_info_range"
+    DAILY_RATING = "daily"
+    HOURLY_RATING = "hourly"
+    MONTHLY_RATING = "monthly"
     USER = "user"

custom_components/tibber_prices/binary_sensor/attributes.py

@@ -4,17 +4,8 @@ from __future__ import annotations
 
 from typing import TYPE_CHECKING
 
-from custom_components.tibber_prices.const import get_display_unit_factor
-from custom_components.tibber_prices.coordinator.helpers import get_intervals_for_day_offsets
 from custom_components.tibber_prices.entity_utils import add_icon_color_attribute
 
-# Constants for price display conversion
-_SUBUNIT_FACTOR = 100  # Conversion factor for subunit currency (ct/øre)
-_SUBUNIT_PRECISION = 2  # Decimal places for subunit currency
-_BASE_PRECISION = 4  # Decimal places for base currency
-
-# Import TypedDict definitions for documentation (not used in signatures)
-
 if TYPE_CHECKING:
     from custom_components.tibber_prices.coordinator.time_service import TibberPricesTimeService
 
@@ -33,8 +24,6 @@ def get_tomorrow_data_available_attributes(
     """
     Build attributes for tomorrow_data_available sensor.
 
-    Returns TomorrowDataAvailableAttributes structure.
-
     Args:
         coordinator_data: Coordinator data dict
         time: TibberPricesTimeService instance
@@ -46,12 +35,12 @@ def get_tomorrow_data_available_attributes(
     if not coordinator_data:
         return None
 
-    # Use helper to get tomorrow's intervals
+    price_info = coordinator_data.get("priceInfo", {})
-    tomorrow_prices = get_intervals_for_day_offsets(coordinator_data, [1])
+    tomorrow_prices = price_info.get("tomorrow", [])
-    tomorrow_date = time.get_local_date(offset_days=1)
     interval_count = len(tomorrow_prices)
 
     # Get expected intervals for tomorrow (handles DST)
+    tomorrow_date = time.get_local_date(offset_days=1)
     expected_intervals = time.get_expected_intervals_for_day(tomorrow_date)
 
     if interval_count == 0:
@@ -72,24 +61,19 @@ def get_price_intervals_attributes(
     *,
     time: TibberPricesTimeService,
     reverse_sort: bool,
-    config_entry: TibberPricesConfigEntry,
 ) -> dict | None:
     """
     Build attributes for period-based sensors (best/peak price).
 
-    Returns PeriodAttributes structure.
-
     All data is already calculated in the coordinator - we just need to:
     1. Get period summaries from coordinator (already filtered and fully calculated)
     2. Add the current timestamp
     3. Find current or next period based on time
-    4. Convert prices to display units based on user configuration
 
     Args:
         coordinator_data: Coordinator data dict
         time: TibberPricesTimeService instance (required)
         reverse_sort: True for peak_price (highest first), False for best_price (lowest first)
-        config_entry: Config entry for display unit configuration
 
     Returns:
         Attributes dict with current/next period and all periods list
@@ -99,7 +83,7 @@ def get_price_intervals_attributes(
         return build_no_periods_result(time=time)
 
     # Get precomputed period summaries from coordinator
-    periods_data = coordinator_data.get("pricePeriods", {})
+    periods_data = coordinator_data.get("periods", {})
     period_type = "peak_price" if reverse_sort else "best_price"
     period_data = periods_data.get(period_type)
 
@@ -110,20 +94,11 @@ def get_price_intervals_attributes(
     if not period_summaries:
         return build_no_periods_result(time=time)
 
-    # Filter periods for today+tomorrow (sensors don't show yesterday's periods)
-    # Coordinator cache contains yesterday/today/tomorrow, but sensors only need today+tomorrow
-    now = time.now()
-    today_start = time.start_of_local_day(now)
-    filtered_periods = [period for period in period_summaries if period.get("end") and period["end"] >= today_start]
-
-    if not filtered_periods:
-        return build_no_periods_result(time=time)
-
     # Find current or next period based on current time
     current_period = None
 
     # First pass: find currently active period
-    for period in filtered_periods:
+    for period in period_summaries:
         start = period.get("start")
         end = period.get("end")
         if start and end and time.is_current_interval(start, end):
@@ -132,14 +107,14 @@ def get_price_intervals_attributes(
 
     # Second pass: find next future period if none is active
     if not current_period:
-        for period in filtered_periods:
+        for period in period_summaries:
             start = period.get("start")
             if start and time.is_in_future(start):
                 current_period = period
                 break
 
-    # Build final attributes (use filtered_periods for display)
+    # Build final attributes
-    return build_final_attributes_simple(current_period, filtered_periods, time=time, config_entry=config_entry)
+    return build_final_attributes_simple(current_period, period_summaries, time=time)
 
 
 def build_no_periods_result(*, time: TibberPricesTimeService) -> dict:
@@ -184,60 +159,26 @@ def add_decision_attributes(attributes: dict, current_period: dict) -> None:
         attributes["rating_difference_%"] = current_period["rating_difference_%"]
 
 
-def add_price_attributes(attributes: dict, current_period: dict, factor: int) -> None:
+def add_price_attributes(attributes: dict, current_period: dict) -> None:
-    """
+    """Add price statistics attributes (priority 3)."""
-    Add price statistics attributes (priority 3).
+    if "price_avg" in current_period:
-
+        attributes["price_avg"] = current_period["price_avg"]
-    Args:
-        attributes: Target dict to add attributes to
-        current_period: Period dict with price data (in base currency)
-        factor: Display unit conversion factor (100 for subunit, 1 for base)
-
-    """
-    # Convert prices from base currency to display units
-    precision = _SUBUNIT_PRECISION if factor == _SUBUNIT_FACTOR else _BASE_PRECISION
-
-    if "price_mean" in current_period:
-        attributes["price_mean"] = round(current_period["price_mean"] * factor, precision)
-    if "price_median" in current_period:
-        attributes["price_median"] = round(current_period["price_median"] * factor, precision)
     if "price_min" in current_period:
-        attributes["price_min"] = round(current_period["price_min"] * factor, precision)
+        attributes["price_min"] = current_period["price_min"]
     if "price_max" in current_period:
-        attributes["price_max"] = round(current_period["price_max"] * factor, precision)
+        attributes["price_max"] = current_period["price_max"]
     if "price_spread" in current_period:
-        attributes["price_spread"] = round(current_period["price_spread"] * factor, precision)
+        attributes["price_spread"] = current_period["price_spread"]
-    if "price_coefficient_variation_%" in current_period:
-        attributes["price_coefficient_variation_%"] = current_period["price_coefficient_variation_%"]
     if "volatility" in current_period:
-        attributes["volatility"] = current_period["volatility"]  # Volatility is not a price, keep as-is
+        attributes["volatility"] = current_period["volatility"]
 
 
-def add_comparison_attributes(attributes: dict, current_period: dict, factor: int) -> None:
+def add_comparison_attributes(attributes: dict, current_period: dict) -> None:
-    """
+    """Add price comparison attributes (priority 4)."""
-    Add price comparison attributes (priority 4).
-
-    Args:
-        attributes: Target dict to add attributes to
-        current_period: Period dict with price diff data (in base currency)
-        factor: Display unit conversion factor (100 for subunit, 1 for base)
-
-    """
-    # Convert price differences from base currency to display units
-    precision = _SUBUNIT_PRECISION if factor == _SUBUNIT_FACTOR else _BASE_PRECISION
-
     if "period_price_diff_from_daily_min" in current_period:
-        attributes["period_price_diff_from_daily_min"] = round(
+        attributes["period_price_diff_from_daily_min"] = current_period["period_price_diff_from_daily_min"]
-            current_period["period_price_diff_from_daily_min"] * factor, precision
-        )
     if "period_price_diff_from_daily_min_%" in current_period:
         attributes["period_price_diff_from_daily_min_%"] = current_period["period_price_diff_from_daily_min_%"]
-    if "period_price_diff_from_daily_max" in current_period:
-        attributes["period_price_diff_from_daily_max"] = round(
-            current_period["period_price_diff_from_daily_max"] * factor, precision
-        )
-    if "period_price_diff_from_daily_max_%" in current_period:
-        attributes["period_price_diff_from_daily_max_%"] = current_period["period_price_diff_from_daily_max_%"]
 
 
 def add_detail_attributes(attributes: dict, current_period: dict) -> None:
@@ -269,51 +210,11 @@ def add_relaxation_attributes(attributes: dict, current_period: dict) -> None:
             attributes["relaxation_threshold_applied_%"] = current_period["relaxation_threshold_applied_%"]
 
 
-def _convert_periods_to_display_units(period_summaries: list[dict], factor: int) -> list[dict]:
-    """
-    Convert price values in periods array to display units.
-
-    Args:
-        period_summaries: List of period dicts with price data (in base currency)
-        factor: Display unit conversion factor (100 for subunit, 1 for base)
-
-    Returns:
-        New list with converted period dicts
-
-    """
-    precision = _SUBUNIT_PRECISION if factor == _SUBUNIT_FACTOR else _BASE_PRECISION
-    converted_periods = []
-
-    for period in period_summaries:
-        converted = period.copy()
-
-        # Convert all price fields
-        price_fields = ["price_mean", "price_median", "price_min", "price_max", "price_spread"]
-        for field in price_fields:
-            if field in converted:
-                converted[field] = round(converted[field] * factor, precision)
-
-        # Convert price differences (not percentages)
-        if "period_price_diff_from_daily_min" in converted:
-            converted["period_price_diff_from_daily_min"] = round(
-                converted["period_price_diff_from_daily_min"] * factor, precision
-            )
-        if "period_price_diff_from_daily_max" in converted:
-            converted["period_price_diff_from_daily_max"] = round(
-                converted["period_price_diff_from_daily_max"] * factor, precision
-            )
-
-        converted_periods.append(converted)
-
-    return converted_periods
-
-
 def build_final_attributes_simple(
     current_period: dict | None,
     period_summaries: list[dict],
     *,
     time: TibberPricesTimeService,
-    config_entry: TibberPricesConfigEntry,
 ) -> dict:
     """
     Build the final attributes dictionary from coordinator's period summaries.
@@ -322,12 +223,11 @@ def build_final_attributes_simple(
     1. Adds the current timestamp (only thing calculated every 15min)
     2. Uses the current/next period from summaries
     3. Adds nested period summaries
-    4. Converts prices to display units based on user configuration
 
     Attributes are ordered following the documented priority:
     1. Time information (timestamp, start, end, duration)
     2. Core decision attributes (level, rating_level, rating_difference_%)
-    3. Price statistics (price_mean, price_median, price_min, price_max, price_spread, volatility)
+    3. Price statistics (price_avg, price_min, price_max, price_spread, volatility)
     4. Price differences (period_price_diff_from_daily_min, period_price_diff_from_daily_min_%)
     5. Detail information (period_interval_count, period_position, periods_total, periods_remaining)
     6. Relaxation information (relaxation_active, relaxation_level, relaxation_threshold_original_%,
@@ -338,7 +238,6 @@ def build_final_attributes_simple(
         current_period: The current or next period (already complete from coordinator)
         period_summaries: All period summaries from coordinator
         time: TibberPricesTimeService instance (required)
-        config_entry: Config entry for display unit configuration
 
     Returns:
         Complete attributes dict with all fields
@@ -348,9 +247,6 @@ def build_final_attributes_simple(
     current_minute = (now.minute // 15) * 15
     timestamp = now.replace(minute=current_minute, second=0, microsecond=0)
 
-    # Get display unit factor (100 for subunit, 1 for base currency)
-    factor = get_display_unit_factor(config_entry)
-
     if current_period:
         # Build attributes in priority order using helper methods
         attributes = {}
@@ -361,11 +257,11 @@ def build_final_attributes_simple(
         # 2. Core decision attributes
         add_decision_attributes(attributes, current_period)
 
-        # 3. Price statistics (converted to display units)
+        # 3. Price statistics
-        add_price_attributes(attributes, current_period, factor)
+        add_price_attributes(attributes, current_period)
 
-        # 4. Price differences (converted to display units)
+        # 4. Price differences
-        add_comparison_attributes(attributes, current_period, factor)
+        add_comparison_attributes(attributes, current_period)
 
         # 5. Detail information
         add_detail_attributes(attributes, current_period)
@@ -373,15 +269,15 @@ def build_final_attributes_simple(
         # 6. Relaxation information (only if period was relaxed)
         add_relaxation_attributes(attributes, current_period)
 
-        # 7. Meta information (periods array - prices converted to display units)
+        # 7. Meta information (periods array)
-        attributes["periods"] = _convert_periods_to_display_units(period_summaries, factor)
+        attributes["periods"] = period_summaries
 
         return attributes
 
-    # No current/next period found - return all periods with timestamp (prices converted)
+    # No current/next period found - return all periods with timestamp
     return {
         "timestamp": timestamp,
-        "periods": _convert_periods_to_display_units(period_summaries, factor),
+        "periods": period_summaries,
     }
 
 

custom_components/tibber_prices/binary_sensor/core.py

@@ -5,8 +5,6 @@ from __future__ import annotations
 from typing import TYPE_CHECKING
 
 from custom_components.tibber_prices.coordinator import TIME_SENSITIVE_ENTITY_KEYS
-from custom_components.tibber_prices.coordinator.core import get_connection_state
-from custom_components.tibber_prices.coordinator.helpers import get_intervals_for_day_offsets
 from custom_components.tibber_prices.entity import TibberPricesEntity
 from custom_components.tibber_prices.entity_utils import get_binary_sensor_icon
 from homeassistant.components.binary_sensor import (
@@ -14,8 +12,6 @@ from homeassistant.components.binary_sensor import (
     BinarySensorEntityDescription,
 )
 from homeassistant.core import callback
-from homeassistant.exceptions import ConfigEntryAuthFailed
-from homeassistant.helpers.restore_state import RestoreEntity
 
 from .attributes import (
     build_async_extra_state_attributes,
@@ -23,6 +19,7 @@ from .attributes import (
     get_price_intervals_attributes,
     get_tomorrow_data_available_attributes,
 )
+from .definitions import PERIOD_LOOKAHEAD_HOURS
 
 if TYPE_CHECKING:
     from collections.abc import Callable
@@ -33,41 +30,8 @@ if TYPE_CHECKING:
     from custom_components.tibber_prices.coordinator.time_service import TibberPricesTimeService
 
 
-class TibberPricesBinarySensor(TibberPricesEntity, BinarySensorEntity, RestoreEntity):
+class TibberPricesBinarySensor(TibberPricesEntity, BinarySensorEntity):
-    """tibber_prices binary_sensor class with state restoration."""
+    """tibber_prices binary_sensor class."""
-
-    # Attributes excluded from recorder history
-    # See: https://developers.home-assistant.io/docs/core/entity/#excluding-state-attributes-from-recorder-history
-    _unrecorded_attributes = frozenset(
-        {
-            "timestamp",
-            # Descriptions/Help Text (static, large)
-            "description",
-            "usage_tips",
-            # Large Nested Structures
-            "periods",  # Array of all period summaries
-            # Frequently Changing Diagnostics
-            "icon_color",
-            "data_status",
-            # Static/Rarely Changing
-            "level_value",
-            "rating_value",
-            "level_id",
-            "rating_id",
-            # Relaxation Details
-            "relaxation_level",
-            "relaxation_threshold_original_%",
-            "relaxation_threshold_applied_%",
-            # Redundant/Derived
-            "price_spread",
-            "volatility",
-            "rating_difference_%",
-            "period_price_diff_from_daily_min",
-            "period_price_diff_from_daily_min_%",
-            "periods_total",
-            "periods_remaining",
-        }
-    )
 
     def __init__(
         self,
@@ -85,11 +49,6 @@ class TibberPricesBinarySensor(TibberPricesEntity, BinarySensorEntity, RestoreEn
         """When entity is added to hass."""
         await super().async_added_to_hass()
 
-        # Restore last state if available
-        if (last_state := await self.async_get_last_state()) is not None and last_state.state in ("on", "off"):
-            # Restore binary state (on/off) - will be used until first coordinator update
-            self._attr_is_on = last_state.state == "on"
-
         # Register with coordinator for time-sensitive updates if applicable
         if self.entity_description.key in TIME_SENSITIVE_ENTITY_KEYS:
             self._time_sensitive_remove_listener = self.coordinator.async_add_time_sensitive_listener(
@@ -126,7 +85,7 @@ class TibberPricesBinarySensor(TibberPricesEntity, BinarySensorEntity, RestoreEn
         state_getters = {
             "peak_price_period": self._peak_price_state,
             "best_price_period": self._best_price_state,
-            "connection": lambda: get_connection_state(self.coordinator),
+            "connection": lambda: True if self.coordinator.data else None,
             "tomorrow_data_available": self._tomorrow_data_available_state,
             "has_ventilation_system": self._has_ventilation_system_state,
             "realtime_consumption_enabled": self._realtime_consumption_enabled_state,
@@ -138,12 +97,7 @@ class TibberPricesBinarySensor(TibberPricesEntity, BinarySensorEntity, RestoreEn
         """Return True if the current time is within a best price period."""
         if not self.coordinator.data:
             return None
-        attrs = get_price_intervals_attributes(
+        attrs = get_price_intervals_attributes(self.coordinator.data, reverse_sort=False, time=self.coordinator.time)
-            self.coordinator.data,
-            reverse_sort=False,
-            time=self.coordinator.time,
-            config_entry=self.coordinator.config_entry,
-        )
         if not attrs:
             return False  # Should not happen, but safety fallback
         start = attrs.get("start")
@@ -157,12 +111,7 @@ class TibberPricesBinarySensor(TibberPricesEntity, BinarySensorEntity, RestoreEn
         """Return True if the current time is within a peak price period."""
         if not self.coordinator.data:
             return None
-        attrs = get_price_intervals_attributes(
+        attrs = get_price_intervals_attributes(self.coordinator.data, reverse_sort=True, time=self.coordinator.time)
-            self.coordinator.data,
-            reverse_sort=True,
-            time=self.coordinator.time,
-            config_entry=self.coordinator.config_entry,
-        )
         if not attrs:
             return False  # Should not happen, but safety fallback
         start = attrs.get("start")
@@ -174,21 +123,14 @@ class TibberPricesBinarySensor(TibberPricesEntity, BinarySensorEntity, RestoreEn
 
     def _tomorrow_data_available_state(self) -> bool | None:
         """Return True if tomorrow's data is fully available, False if not, None if unknown."""
-        # Auth errors: Cannot reliably check - return unknown
-        # User must fix auth via reauth flow before we can determine tomorrow data availability
-        if isinstance(self.coordinator.last_exception, ConfigEntryAuthFailed):
-            return None
-
-        # No data: unknown state (initializing or error)
         if not self.coordinator.data:
             return None
-
+        price_info = self.coordinator.data.get("priceInfo", {})
-        # Check tomorrow data availability (normal operation)
+        tomorrow_prices = price_info.get("tomorrow", [])
-        tomorrow_prices = get_intervals_for_day_offsets(self.coordinator.data, [1])
-        tomorrow_date = self.coordinator.time.get_local_date(offset_days=1)
         interval_count = len(tomorrow_prices)
 
         # Get expected intervals for tomorrow (handles DST)
+        tomorrow_date = self.coordinator.time.get_local_date(offset_days=1)
         expected_intervals = self.coordinator.time.get_expected_intervals_for_day(tomorrow_date)
 
         if interval_count == expected_intervals:
@@ -197,31 +139,6 @@ class TibberPricesBinarySensor(TibberPricesEntity, BinarySensorEntity, RestoreEn
             return False
         return False
 
-    @property
-    def available(self) -> bool:
-        """
-        Return if entity is available.
-
-        Override base implementation for connection sensor which should
-        always be available to show connection state.
-        """
-        # Connection sensor is always available (shows connection state)
-        if self.entity_description.key == "connection":
-            return True
-
-        # All other binary sensors use base availability logic
-        return super().available
-
-    @property
-    def force_update(self) -> bool:
-        """
-        Force update for connection sensor to record all state changes.
-
-        Connection sensor should write every state change to history,
-        even if the state (on/off) is the same, to track connectivity issues.
-        """
-        return self.entity_description.key == "connection"
-
     def _has_ventilation_system_state(self) -> bool | None:
         """Return True if the home has a ventilation system."""
         if not self.coordinator.data:
@@ -280,19 +197,9 @@ class TibberPricesBinarySensor(TibberPricesEntity, BinarySensorEntity, RestoreEn
         key = self.entity_description.key
 
         if key == "peak_price_period":
-            return get_price_intervals_attributes(
+            return get_price_intervals_attributes(self.coordinator.data, reverse_sort=True, time=self.coordinator.time)
-                self.coordinator.data,
-                reverse_sort=True,
-                time=self.coordinator.time,
-                config_entry=self.coordinator.config_entry,
-            )
         if key == "best_price_period":
-            return get_price_intervals_attributes(
+            return get_price_intervals_attributes(self.coordinator.data, reverse_sort=False, time=self.coordinator.time)
-                self.coordinator.data,
-                reverse_sort=False,
-                time=self.coordinator.time,
-                config_entry=self.coordinator.config_entry,
-            )
         if key == "tomorrow_data_available":
             return self._get_tomorrow_data_available_attributes()
 
@@ -301,13 +208,11 @@ class TibberPricesBinarySensor(TibberPricesEntity, BinarySensorEntity, RestoreEn
     @callback
     def _handle_coordinator_update(self) -> None:
         """Handle updated data from the coordinator."""
-        # All binary sensors get push updates when coordinator has new data:
+        # Chart data export: No automatic refresh needed.
-        # - tomorrow_data_available: Reflects new data availability immediately after API fetch
+        # Data only refreshes on:
-        # - connection: Reflects connection state changes immediately
+        # 1. Initial sensor activation (async_added_to_hass)
-        # - chart_data_export: Updates chart data when price data changes
+        # 2. Config changes via Options Flow (triggers re-add)
-        # - peak_price_period, best_price_period: Update when periods change (also get Timer #2 updates)
+        # Hourly coordinator updates don't change the chart data content.
-        # - data_lifecycle_status: Gets both push and Timer #2 updates
-        self.async_write_ha_state()
 
     @property
     def is_on(self) -> bool | None:
@@ -345,10 +250,10 @@ class TibberPricesBinarySensor(TibberPricesEntity, BinarySensorEntity, RestoreEn
 
     def _has_future_periods(self) -> bool:
         """
-        Check if there are any future periods.
+        Check if there are periods starting within the next 6 hours.
 
-        Returns True if any period starts in the future (no time limit).
+        Returns True if any period starts between now and PERIOD_LOOKAHEAD_HOURS from now.
-        This ensures icons show "waiting" state whenever periods are scheduled.
+        This provides a practical planning horizon instead of hard midnight cutoff.
         """
         attrs = self._get_sensor_attributes()
         if not attrs or "periods" not in attrs:
@@ -357,15 +262,15 @@ class TibberPricesBinarySensor(TibberPricesEntity, BinarySensorEntity, RestoreEn
         time = self.coordinator.time
         periods = attrs.get("periods", [])
 
-        # Check if any period starts in the future (no time limit)
+        # Check if any period starts within the look-ahead window
         for period in periods:
             start_str = period.get("start")
             if start_str:
                 # Already datetime object (periods come from coordinator.data)
                 start_time = start_str if not isinstance(start_str, str) else time.parse_datetime(start_str)
 
-                # Period starts in the future
+                # Period starts in the future but within our horizon
-                if start_time and time.is_in_future(start_time):
+                if start_time and time.is_time_within_horizon(start_time, hours=PERIOD_LOOKAHEAD_HOURS):
                     return True
 
         return False

custom_components/tibber_prices/binary_sensor/definitions.py

@@ -8,8 +8,9 @@ from homeassistant.components.binary_sensor import (
 )
 from homeassistant.const import EntityCategory
 
-# Period lookahead removed - icons show "waiting" state if ANY future periods exist
+# Look-ahead window for future period detection (hours)
-# No artificial time limit - show all periods until midnight
+# Icons will show "waiting" state if a period starts within this window
+PERIOD_LOOKAHEAD_HOURS = 6
 
 ENTITY_DESCRIPTIONS = (
     BinarySensorEntityDescription(
@@ -38,7 +39,6 @@ ENTITY_DESCRIPTIONS = (
         icon="mdi:calendar-check",
         device_class=None,  # No specific device_class = shows generic "On/Off"
         entity_category=EntityCategory.DIAGNOSTIC,
-        entity_registry_enabled_default=True,  # Critical for automations
     ),
     BinarySensorEntityDescription(
         key="has_ventilation_system",

custom_components/tibber_prices/binary_sensor/types.py

@@ -1,173 +0,0 @@
-"""
-Type definitions for Tibber Prices binary sensor attributes.
-
-These TypedDict definitions serve as **documentation** of the attribute structure
-for each binary sensor type. They enable IDE autocomplete and type checking when
-working with attribute dictionaries.
-
-NOTE: In function signatures, we still use dict[str, Any] for flexibility,
-but these TypedDict definitions document what keys and types are expected.
-
-IMPORTANT: PriceLevel and PriceRating types are duplicated here to avoid
-cross-platform dependencies. Keep in sync with sensor/types.py.
-"""
-
-from __future__ import annotations
-
-from typing import Literal, TypedDict
-
-# ============================================================================
-# Literal Type Definitions (Duplicated from sensor/types.py)
-# ============================================================================
-# SYNC: Keep these in sync with:
-#   1. sensor/types.py (Literal type definitions)
-#   2. const.py (runtime string constants - single source of truth)
-#
-# const.py defines:
-#   - PRICE_LEVEL_VERY_CHEAP, PRICE_LEVEL_CHEAP, etc.
-#   - PRICE_RATING_LOW, PRICE_RATING_NORMAL, etc.
-#
-# These types are intentionally duplicated here to avoid cross-platform imports.
-# Binary sensor attributes need these types for type safety without importing
-# from sensor/ package (maintains platform separation).
-
-# Price level literals (shared with sensor platform - keep in sync!)
-PriceLevel = Literal[
-    "VERY_CHEAP",
-    "CHEAP",
-    "NORMAL",
-    "EXPENSIVE",
-    "VERY_EXPENSIVE",
-]
-
-# Price rating literals (shared with sensor platform - keep in sync!)
-PriceRating = Literal[
-    "LOW",
-    "NORMAL",
-    "HIGH",
-]
-
-
-class BaseAttributes(TypedDict, total=False):
-    """
-    Base attributes common to all binary sensors.
-
-    All binary sensor attributes include at minimum:
-    - timestamp: ISO 8601 string indicating when the state/attributes are valid
-    - error: Optional error message if something went wrong
-    """
-
-    timestamp: str
-    error: str
-
-
-class TomorrowDataAvailableAttributes(BaseAttributes, total=False):
-    """
-    Attributes for tomorrow_data_available binary sensor.
-
-    Indicates whether tomorrow's price data is available from Tibber API.
-    """
-
-    intervals_available: int  # Number of intervals available for tomorrow
-    data_status: Literal["none", "partial", "full"]  # Data completeness status
-
-
-class PeriodSummary(TypedDict, total=False):
-    """
-    Structure for period summary nested in period attributes.
-
-    Each period summary contains all calculated information about one period.
-    """
-
-    # Time information (priority 1)
-    start: str  # ISO 8601 timestamp of period start
-    end: str  # ISO 8601 timestamp of period end
-    duration_minutes: int  # Duration in minutes
-
-    # Core decision attributes (priority 2)
-    level: PriceLevel  # Price level classification
-    rating_level: PriceRating  # Price rating classification
-    rating_difference_pct: float  # Difference from daily average (%)
-
-    # Price statistics (priority 3)
-    price_mean: float  # Arithmetic mean price in period
-    price_median: float  # Median price in period
-    price_min: float  # Minimum price in period
-    price_max: float  # Maximum price in period
-    price_spread: float  # Price spread (max - min)
-    volatility: float  # Price volatility within period
-
-    # Price comparison (priority 4)
-    period_price_diff_from_daily_min: float  # Difference from daily min
-    period_price_diff_from_daily_min_pct: float  # Difference from daily min (%)
-
-    # Detail information (priority 5)
-    period_interval_count: int  # Number of intervals in period
-    period_position: int  # Period position (1-based)
-    periods_total: int  # Total number of periods
-    periods_remaining: int  # Remaining periods after this one
-
-    # Relaxation information (priority 6 - only if period was relaxed)
-    relaxation_active: bool  # Whether this period was found via relaxation
-    relaxation_level: int  # Relaxation level used (1-based)
-    relaxation_threshold_original_pct: float  # Original flex threshold (%)
-    relaxation_threshold_applied_pct: float  # Applied flex threshold after relaxation (%)
-
-
-class PeriodAttributes(BaseAttributes, total=False):
-    """
-    Attributes for period-based binary sensors (best_price_period, peak_price_period).
-
-    These sensors indicate whether the current/next cheap/expensive period is active.
-
-    Attributes follow priority ordering:
-    1. Time information (timestamp, start, end, duration_minutes)
-    2. Core decision attributes (level, rating_level, rating_difference_%)
-    3. Price statistics (price_mean, price_median, price_min, price_max, price_spread, volatility)
-    4. Price comparison (period_price_diff_from_daily_min, period_price_diff_from_daily_min_%)
-    5. Detail information (period_interval_count, period_position, periods_total, periods_remaining)
-    6. Relaxation information (only if period was relaxed)
-    7. Meta information (periods list)
-    """
-
-    # Time information (priority 1) - start/end refer to current/next period
-    start: str | None  # ISO 8601 timestamp of current/next period start
-    end: str | None  # ISO 8601 timestamp of current/next period end
-    duration_minutes: int  # Duration of current/next period in minutes
-
-    # Core decision attributes (priority 2)
-    level: PriceLevel  # Price level of current/next period
-    rating_level: PriceRating  # Price rating of current/next period
-    rating_difference_pct: float  # Difference from daily average (%)
-
-    # Price statistics (priority 3)
-    price_mean: float  # Arithmetic mean price in current/next period
-    price_median: float  # Median price in current/next period
-    price_min: float  # Minimum price in current/next period
-    price_max: float  # Maximum price in current/next period
-    price_spread: float  # Price spread (max - min) in current/next period
-    volatility: float  # Price volatility within current/next period
-
-    # Price comparison (priority 4)
-    period_price_diff_from_daily_min: float  # Difference from daily min
-    period_price_diff_from_daily_min_pct: float  # Difference from daily min (%)
-
-    # Detail information (priority 5)
-    period_interval_count: int  # Number of intervals in current/next period
-    period_position: int  # Period position (1-based)
-    periods_total: int  # Total number of periods found
-    periods_remaining: int  # Remaining periods after current/next one
-
-    # Relaxation information (priority 6 - only if period was relaxed)
-    relaxation_active: bool  # Whether current/next period was found via relaxation
-    relaxation_level: int  # Relaxation level used (1-based)
-    relaxation_threshold_original_pct: float  # Original flex threshold (%)
-    relaxation_threshold_applied_pct: float  # Applied flex threshold after relaxation (%)
-
-    # Meta information (priority 7)
-    periods: list[PeriodSummary]  # All periods found (sorted by start time)
-
-
-# Union type for all binary sensor attributes (for documentation purposes)
-# In actual code, use dict[str, Any] for flexibility
-BinarySensorAttributes = TomorrowDataAvailableAttributes | PeriodAttributes

custom_components/tibber_prices/config_flow.py

@@ -14,7 +14,6 @@ from .config_flow_handlers.schemas import (
     get_best_price_schema,
     get_options_init_schema,
     get_peak_price_schema,
-    get_price_level_schema,
     get_price_rating_schema,
     get_price_trend_schema,
     get_reauth_confirm_schema,
@@ -26,7 +25,7 @@ from .config_flow_handlers.schemas import (
 from .config_flow_handlers.subentry_flow import (
     TibberPricesSubentryFlowHandler as SubentryFlowHandler,
 )
-from .config_flow_handlers.user_flow import TibberPricesConfigFlowHandler as ConfigFlow
+from .config_flow_handlers.user_flow import TibberPricesFlowHandler as ConfigFlow
 from .config_flow_handlers.validators import (
     TibberPricesCannotConnectError,
     TibberPricesInvalidAuthError,
@@ -42,7 +41,6 @@ __all__ = [
     "get_best_price_schema",
     "get_options_init_schema",
     "get_peak_price_schema",
-    "get_price_level_schema",
     "get_price_rating_schema",
     "get_price_trend_schema",
     "get_reauth_confirm_schema",

custom_components/tibber_prices/config_flow_handlers/__init__.py

@@ -27,7 +27,6 @@ from custom_components.tibber_prices.config_flow_handlers.schemas import (
     get_best_price_schema,
     get_options_init_schema,
     get_peak_price_schema,
-    get_price_level_schema,
     get_price_rating_schema,
     get_price_trend_schema,
     get_reauth_confirm_schema,
@@ -40,7 +39,7 @@ from custom_components.tibber_prices.config_flow_handlers.subentry_flow import (
     TibberPricesSubentryFlowHandler,
 )
 from custom_components.tibber_prices.config_flow_handlers.user_flow import (
-    TibberPricesConfigFlowHandler,
+    TibberPricesFlowHandler,
 )
 from custom_components.tibber_prices.config_flow_handlers.validators import (
     TibberPricesCannotConnectError,
@@ -50,14 +49,13 @@ from custom_components.tibber_prices.config_flow_handlers.validators import (
 
 __all__ = [
     "TibberPricesCannotConnectError",
-    "TibberPricesConfigFlowHandler",
+    "TibberPricesFlowHandler",
     "TibberPricesInvalidAuthError",
     "TibberPricesOptionsFlowHandler",
     "TibberPricesSubentryFlowHandler",
     "get_best_price_schema",
     "get_options_init_schema",
     "get_peak_price_schema",
-    "get_price_level_schema",
     "get_price_rating_schema",
     "get_price_trend_schema",
     "get_reauth_confirm_schema",

custom_components/tibber_prices/config_flow_handlers/entity_check.py

@@ -1,243 +0,0 @@
-"""
-Entity check utilities for options flow.
-
-This module provides functions to check if relevant entities are enabled
-for specific options flow steps. If no relevant entities are enabled,
-a warning can be displayed to users.
-"""
-
-from __future__ import annotations
-
-import logging
-from typing import TYPE_CHECKING
-
-from custom_components.tibber_prices.const import DOMAIN
-from homeassistant.helpers.entity_registry import async_get as async_get_entity_registry
-
-if TYPE_CHECKING:
-    from homeassistant.config_entries import ConfigEntry
-    from homeassistant.core import HomeAssistant
-
-_LOGGER = logging.getLogger(__name__)
-
-# Maximum number of example sensors to show in warning message
-MAX_EXAMPLE_SENSORS = 3
-# Threshold for using "and" vs "," in formatted names
-NAMES_SIMPLE_JOIN_THRESHOLD = 2
-
-# Mapping of options flow steps to affected sensor keys
-# These are the entity keys (from sensor/definitions.py and binary_sensor/definitions.py)
-# that are affected by each settings page
-STEP_TO_SENSOR_KEYS: dict[str, list[str]] = {
-    # Price Rating settings affect all rating sensors
-    "current_interval_price_rating": [
-        # Interval rating sensors
-        "current_interval_price_rating",
-        "next_interval_price_rating",
-        "previous_interval_price_rating",
-        # Rolling hour rating sensors
-        "current_hour_price_rating",
-        "next_hour_price_rating",
-        # Daily rating sensors
-        "yesterday_price_rating",
-        "today_price_rating",
-        "tomorrow_price_rating",
-    ],
-    # Price Level settings affect level sensors and period binary sensors
-    "price_level": [
-        # Interval level sensors
-        "current_interval_price_level",
-        "next_interval_price_level",
-        "previous_interval_price_level",
-        # Rolling hour level sensors
-        "current_hour_price_level",
-        "next_hour_price_level",
-        # Daily level sensors
-        "yesterday_price_level",
-        "today_price_level",
-        "tomorrow_price_level",
-        # Binary sensors that use level filtering
-        "best_price_period",
-        "peak_price_period",
-    ],
-    # Volatility settings affect volatility sensors
-    "volatility": [
-        "today_volatility",
-        "tomorrow_volatility",
-        "next_24h_volatility",
-        "today_tomorrow_volatility",
-        # Also affects trend sensors (adaptive thresholds)
-        "current_price_trend",
-        "next_price_trend_change",
-        "price_trend_1h",
-        "price_trend_2h",
-        "price_trend_3h",
-        "price_trend_4h",
-        "price_trend_5h",
-        "price_trend_6h",
-        "price_trend_8h",
-        "price_trend_12h",
-    ],
-    # Best Price settings affect best price binary sensor and timing sensors
-    "best_price": [
-        # Binary sensor
-        "best_price_period",
-        # Timing sensors
-        "best_price_end_time",
-        "best_price_period_duration",
-        "best_price_remaining_minutes",
-        "best_price_progress",
-        "best_price_next_start_time",
-        "best_price_next_in_minutes",
-    ],
-    # Peak Price settings affect peak price binary sensor and timing sensors
-    "peak_price": [
-        # Binary sensor
-        "peak_price_period",
-        # Timing sensors
-        "peak_price_end_time",
-        "peak_price_period_duration",
-        "peak_price_remaining_minutes",
-        "peak_price_progress",
-        "peak_price_next_start_time",
-        "peak_price_next_in_minutes",
-    ],
-    # Price Trend settings affect trend sensors
-    "price_trend": [
-        "current_price_trend",
-        "next_price_trend_change",
-        "price_trend_1h",
-        "price_trend_2h",
-        "price_trend_3h",
-        "price_trend_4h",
-        "price_trend_5h",
-        "price_trend_6h",
-        "price_trend_8h",
-        "price_trend_12h",
-    ],
-}
-
-
-def check_relevant_entities_enabled(
-    hass: HomeAssistant,
-    config_entry: ConfigEntry,
-    step_id: str,
-) -> tuple[bool, list[str]]:
-    """
-    Check if any relevant entities for a settings step are enabled.
-
-    Args:
-        hass: Home Assistant instance
-        config_entry: Current config entry
-        step_id: The options flow step ID
-
-    Returns:
-        Tuple of (has_enabled_entities, list_of_example_sensor_names)
-        - has_enabled_entities: True if at least one relevant entity is enabled
-        - list_of_example_sensor_names: List of example sensor keys for the warning message
-
-    """
-    sensor_keys = STEP_TO_SENSOR_KEYS.get(step_id)
-    if not sensor_keys:
-        # No mapping for this step - no check needed
-        return True, []
-
-    entity_registry = async_get_entity_registry(hass)
-    entry_id = config_entry.entry_id
-
-    enabled_count = 0
-    example_sensors: list[str] = []
-
-    for entity in entity_registry.entities.values():
-        # Check if entity belongs to our integration and config entry
-        if entity.config_entry_id != entry_id:
-            continue
-        if entity.platform != DOMAIN:
-            continue
-
-        # Extract the sensor key from unique_id
-        # unique_id format: "{home_id}_{sensor_key}" or "{entry_id}_{sensor_key}"
-        unique_id = entity.unique_id or ""
-        # The sensor key is after the last underscore that separates the ID prefix
-        # We check if any of our target keys is contained in the unique_id
-        for sensor_key in sensor_keys:
-            if unique_id.endswith(f"_{sensor_key}") or unique_id == sensor_key:
-                # Found a matching entity
-                if entity.disabled_by is None:
-                    # Entity is enabled
-                    enabled_count += 1
-                    break
-                # Entity is disabled - add to examples (max MAX_EXAMPLE_SENSORS)
-                if len(example_sensors) < MAX_EXAMPLE_SENSORS and sensor_key not in example_sensors:
-                    example_sensors.append(sensor_key)
-                break
-
-    # If we found enabled entities, return success
-    if enabled_count > 0:
-        return True, []
-
-    # No enabled entities - return the example sensors for the warning
-    # If we haven't collected any examples yet, use the first from the mapping
-    if not example_sensors:
-        example_sensors = sensor_keys[:MAX_EXAMPLE_SENSORS]
-
-    return False, example_sensors
-
-
-def format_sensor_names_for_warning(sensor_keys: list[str]) -> str:
-    """
-    Format sensor keys into human-readable names for warning message.
-
-    Args:
-        sensor_keys: List of sensor keys
-
-    Returns:
-        Formatted string like "Best Price Period, Best Price End Time, ..."
-
-    """
-    # Convert snake_case keys to Title Case names
-    names = []
-    for key in sensor_keys:
-        # Replace underscores with spaces and title case
-        name = key.replace("_", " ").title()
-        names.append(name)
-
-    if len(names) <= NAMES_SIMPLE_JOIN_THRESHOLD:
-        return " and ".join(names)
-
-    return ", ".join(names[:-1]) + ", and " + names[-1]
-
-
-def check_chart_data_export_enabled(
-    hass: HomeAssistant,
-    config_entry: ConfigEntry,
-) -> bool:
-    """
-    Check if the Chart Data Export sensor is enabled.
-
-    Args:
-        hass: Home Assistant instance
-        config_entry: Current config entry
-
-    Returns:
-        True if the Chart Data Export sensor is enabled, False otherwise
-
-    """
-    entity_registry = async_get_entity_registry(hass)
-    entry_id = config_entry.entry_id
-
-    for entity in entity_registry.entities.values():
-        # Check if entity belongs to our integration and config entry
-        if entity.config_entry_id != entry_id:
-            continue
-        if entity.platform != DOMAIN:
-            continue
-
-        # Check for chart_data_export sensor
-        unique_id = entity.unique_id or ""
-        if unique_id.endswith("_chart_data_export") or unique_id == "chart_data_export":
-            # Found the entity - check if enabled
-            return entity.disabled_by is None
-
-    # Entity not found (shouldn't happen, but treat as disabled)
-    return False

custom_components/tibber_prices/config_flow_handlers/options_flow.py

@@ -3,81 +3,19 @@
 from __future__ import annotations
 
 import logging
-from copy import deepcopy
+from typing import Any, ClassVar
-from typing import TYPE_CHECKING, Any
 
-if TYPE_CHECKING:
-    from collections.abc import Mapping
-
-from custom_components.tibber_prices.config_flow_handlers.entity_check import (
-    check_chart_data_export_enabled,
-    check_relevant_entities_enabled,
-    format_sensor_names_for_warning,
-)
 from custom_components.tibber_prices.config_flow_handlers.schemas import (
-    ConfigOverrides,
     get_best_price_schema,
     get_chart_data_export_schema,
-    get_display_settings_schema,
     get_options_init_schema,
     get_peak_price_schema,
-    get_price_level_schema,
     get_price_rating_schema,
     get_price_trend_schema,
-    get_reset_to_defaults_schema,
     get_volatility_schema,
 )
-from custom_components.tibber_prices.config_flow_handlers.validators import (
+from custom_components.tibber_prices.const import DOMAIN
-    validate_best_price_distance_percentage,
-    validate_distance_percentage,
-    validate_flex_percentage,
-    validate_gap_count,
-    validate_min_periods,
-    validate_period_length,
-    validate_price_rating_threshold_high,
-    validate_price_rating_threshold_low,
-    validate_price_rating_thresholds,
-    validate_price_trend_falling,
-    validate_price_trend_rising,
-    validate_price_trend_strongly_falling,
-    validate_price_trend_strongly_rising,
-    validate_relaxation_attempts,
-    validate_volatility_threshold_high,
-    validate_volatility_threshold_moderate,
-    validate_volatility_threshold_very_high,
-    validate_volatility_thresholds,
-)
-from custom_components.tibber_prices.const import (
-    CONF_BEST_PRICE_FLEX,
-    CONF_BEST_PRICE_MAX_LEVEL_GAP_COUNT,
-    CONF_BEST_PRICE_MIN_DISTANCE_FROM_AVG,
-    CONF_BEST_PRICE_MIN_PERIOD_LENGTH,
-    CONF_MIN_PERIODS_BEST,
-    CONF_MIN_PERIODS_PEAK,
-    CONF_PEAK_PRICE_FLEX,
-    CONF_PEAK_PRICE_MAX_LEVEL_GAP_COUNT,
-    CONF_PEAK_PRICE_MIN_DISTANCE_FROM_AVG,
-    CONF_PEAK_PRICE_MIN_PERIOD_LENGTH,
-    CONF_PRICE_RATING_THRESHOLD_HIGH,
-    CONF_PRICE_RATING_THRESHOLD_LOW,
-    CONF_PRICE_TREND_THRESHOLD_FALLING,
-    CONF_PRICE_TREND_THRESHOLD_RISING,
-    CONF_PRICE_TREND_THRESHOLD_STRONGLY_FALLING,
-    CONF_PRICE_TREND_THRESHOLD_STRONGLY_RISING,
-    CONF_RELAXATION_ATTEMPTS_BEST,
-    CONF_RELAXATION_ATTEMPTS_PEAK,
-    CONF_VOLATILITY_THRESHOLD_HIGH,
-    CONF_VOLATILITY_THRESHOLD_MODERATE,
-    CONF_VOLATILITY_THRESHOLD_VERY_HIGH,
-    DEFAULT_VOLATILITY_THRESHOLD_HIGH,
-    DEFAULT_VOLATILITY_THRESHOLD_MODERATE,
-    DEFAULT_VOLATILITY_THRESHOLD_VERY_HIGH,
-    DOMAIN,
-    async_get_translation,
-    get_default_options,
-)
 from homeassistant.config_entries import ConfigFlowResult, OptionsFlow
-from homeassistant.helpers import entity_registry as er
 
 _LOGGER = logging.getLogger(__name__)
 
@@ -85,811 +23,131 @@ _LOGGER = logging.getLogger(__name__)
 class TibberPricesOptionsFlowHandler(OptionsFlow):
     """Handle options for tibber_prices entries."""
 
+    # Step progress tracking
+    _TOTAL_STEPS: ClassVar[int] = 7
+    _STEP_INFO: ClassVar[dict[str, int]] = {
+        "init": 1,
+        "current_interval_price_rating": 2,
+        "volatility": 3,
+        "best_price": 4,
+        "peak_price": 5,
+        "price_trend": 6,
+        "chart_data_export": 7,
+    }
+
     def __init__(self) -> None:
         """Initialize options flow."""
         self._options: dict[str, Any] = {}
 
-    def _merge_section_data(self, user_input: dict[str, Any]) -> None:
+    def _get_step_description_placeholders(self, step_id: str) -> dict[str, str]:
-        """
+        """Get description placeholders with step progress."""
-        Merge section data from form input into options.
+        if step_id not in self._STEP_INFO:
-
-        Home Assistant forms with section() return nested dicts like:
-        {"section_name": {"setting1": value1, "setting2": value2}}
-
-        We need to preserve this structure in config_entry.options.
-
-        Args:
-            user_input: Nested user input from form with sections
-
-        """
-        for section_key, section_data in user_input.items():
-            if isinstance(section_data, dict):
-                # This is a section - ensure the section exists in options
-                if section_key not in self._options:
-                    self._options[section_key] = {}
-                # Update the section with new values
-                self._options[section_key].update(section_data)
-            else:
-                # This is a direct value - keep it as is
-                self._options[section_key] = section_data
-
-    def _migrate_config_options(self, options: Mapping[str, Any]) -> dict[str, Any]:
-        """
-        Migrate deprecated config options to current format.
-
-        This removes obsolete keys and renames changed keys to maintain
-        compatibility with older config entries.
-
-        Args:
-            options: Original options dict from config_entry
-
-        Returns:
-            Migrated options dict with deprecated keys removed/renamed
-
-        """
-        # CRITICAL: Use deepcopy to avoid modifying the original config_entry.options
-        # If we use dict(options), nested dicts are still referenced, causing
-        # self._options modifications to leak into config_entry.options
-        migrated = deepcopy(dict(options))
-        migration_performed = False
-
-        # Migration 1: Rename relaxation_step_* to relaxation_attempts_*
-        # (Changed in v0.6.0 - commit 5a5c8ca)
-        if "relaxation_step_best" in migrated:
-            migrated["relaxation_attempts_best"] = migrated.pop("relaxation_step_best")
-            migration_performed = True
-            _LOGGER.info(
-                "Migrated config option: relaxation_step_best -> relaxation_attempts_best (value: %s)",
-                migrated["relaxation_attempts_best"],
-            )
-
-        if "relaxation_step_peak" in migrated:
-            migrated["relaxation_attempts_peak"] = migrated.pop("relaxation_step_peak")
-            migration_performed = True
-            _LOGGER.info(
-                "Migrated config option: relaxation_step_peak -> relaxation_attempts_peak (value: %s)",
-                migrated["relaxation_attempts_peak"],
-            )
-
-        # Migration 2: Remove obsolete volatility filter options
-        # (Removed in v0.9.0 - volatility filter feature removed)
-        obsolete_keys = [
-            "best_price_min_volatility",
-            "peak_price_min_volatility",
-            "min_volatility_for_periods",
-        ]
-
-        for key in obsolete_keys:
-            if key in migrated:
-                old_value = migrated.pop(key)
-                migration_performed = True
-                _LOGGER.info(
-                    "Removed obsolete config option: %s (was: %s)",
-                    key,
-                    old_value,
-                )
-
-        if migration_performed:
-            _LOGGER.info("Config migration completed - deprecated options cleaned up")
-
-        return migrated
-
-    def _save_options_if_changed(self) -> bool:
-        """
-        Save options only if they actually changed.
-
-        Returns:
-            True if options were updated, False if no changes detected
-
-        """
-        # Compare old and new options
-        if self.config_entry.options != self._options:
-            self.hass.config_entries.async_update_entry(
-                self.config_entry,
-                options=self._options,
-            )
-            return True
-        return False
-
-    def _get_entity_warning_placeholders(self, step_id: str) -> dict[str, str]:
-        """
-        Get description placeholders for entity availability warning.
-
-        Checks if any relevant entities for the step are enabled.
-        If not, adds a warning placeholder to display in the form description.
-
-        Args:
-            step_id: The options flow step ID
-
-        Returns:
-            Dictionary with placeholder keys for the form description
-
-        """
-        has_enabled, example_sensors = check_relevant_entities_enabled(self.hass, self.config_entry, step_id)
-
-        if has_enabled:
-            # No warning needed - return empty placeholder
-            return {"entity_warning": ""}
-
-        # Build warning message with example sensor names
-        sensor_names = format_sensor_names_for_warning(example_sensors)
-        return {
-            "entity_warning": f"\n\n⚠️ **Note:** No sensors affected by these settings are currently enabled. "
-            f"To use these settings, first enable relevant sensors like *{sensor_names}* "
-            f"in **Settings → Devices & Services → Tibber Prices → Entities**."
-        }
-
-    def _get_enabled_config_entities(self) -> set[str]:
-        """
-        Get config keys that have their config entity enabled.
-
-        Checks the entity registry for number/switch entities that override
-        config values. Returns the config_key for each enabled entity.
-
-        Returns:
-            Set of config keys (e.g., "best_price_flex", "enable_min_periods_best")
-
-        """
-        enabled_keys: set[str] = set()
-        ent_reg = er.async_get(self.hass)
-
-        _LOGGER.debug(
-            "Checking for enabled config override entities for entry %s",
-            self.config_entry.entry_id,
-        )
-
-        # Map entity keys to their config keys
-        # Entity keys are defined in number/definitions.py and switch/definitions.py
-        override_entities = {
-            # Number entities (best price)
-            "number.best_price_flex_override": "best_price_flex",
-            "number.best_price_min_distance_override": "best_price_min_distance_from_avg",
-            "number.best_price_min_period_length_override": "best_price_min_period_length",
-            "number.best_price_min_periods_override": "min_periods_best",
-            "number.best_price_relaxation_attempts_override": "relaxation_attempts_best",
-            "number.best_price_gap_count_override": "best_price_max_level_gap_count",
-            # Number entities (peak price)
-            "number.peak_price_flex_override": "peak_price_flex",
-            "number.peak_price_min_distance_override": "peak_price_min_distance_from_avg",
-            "number.peak_price_min_period_length_override": "peak_price_min_period_length",
-            "number.peak_price_min_periods_override": "min_periods_peak",
-            "number.peak_price_relaxation_attempts_override": "relaxation_attempts_peak",
-            "number.peak_price_gap_count_override": "peak_price_max_level_gap_count",
-            # Switch entities
-            "switch.best_price_enable_relaxation_override": "enable_min_periods_best",
-            "switch.peak_price_enable_relaxation_override": "enable_min_periods_peak",
-        }
-
-        # Check each possible override entity
-        for entity_id_suffix, config_key in override_entities.items():
-            # Entity IDs include device name, so we need to search by unique_id pattern
-            # The unique_id follows pattern: {config_entry_id}_{entity_key}
-            domain, entity_key = entity_id_suffix.split(".", 1)
-
-            # Find entity by iterating through registry
-            for entity_entry in ent_reg.entities.values():
-                if (
-                    entity_entry.domain == domain
-                    and entity_entry.config_entry_id == self.config_entry.entry_id
-                    and entity_entry.unique_id
-                    and entity_entry.unique_id.endswith(entity_key)
-                    and not entity_entry.disabled
-                ):
-                    _LOGGER.debug(
-                        "Found enabled config override entity: %s -> config_key=%s",
-                        entity_entry.entity_id,
-                        config_key,
-                    )
-                    enabled_keys.add(config_key)
-                    break
-
-        _LOGGER.debug("Enabled config override keys: %s", enabled_keys)
-        return enabled_keys
-
-    def _get_active_overrides(self) -> ConfigOverrides:
-        """
-        Build override dict from enabled config entities.
-
-        Returns a dict structure compatible with schema functions.
-        """
-        enabled_keys = self._get_enabled_config_entities()
-        if not enabled_keys:
-            _LOGGER.debug("No enabled config override entities found")
             return {}
 
-        # Build structure expected by schema: {section: {key: True}}
+        step_num = self._STEP_INFO[step_id]
-        # Section doesn't matter for read_only check, we just need the key present
-        overrides: ConfigOverrides = {"_enabled": {}}
-        for key in enabled_keys:
-            overrides["_enabled"][key] = True
 
-        _LOGGER.debug("Active overrides structure: %s", overrides)
+        # Get translations loaded by Home Assistant
-        return overrides
+        standard_translations_key = f"{DOMAIN}_standard_translations_{self.hass.config.language}"
+        translations = self.hass.data.get(standard_translations_key, {})
 
-    def _get_override_warning_placeholder(self, step_id: str, overrides: ConfigOverrides) -> dict[str, str]:
+        # Get step progress text from translations with placeholders
-        """
+        step_progress_template = translations.get("common", {}).get("step_progress", "Step {step_num} of {total_steps}")
-        Get description placeholder for config override warning.
+        step_progress = step_progress_template.format(step_num=step_num, total_steps=self._TOTAL_STEPS)
 
-        Args:
+        return {
-            step_id: The options flow step ID (e.g., "best_price", "peak_price")
+            "step_progress": step_progress,
-            overrides: Active overrides dictionary
-
-        Returns:
-            Dictionary with 'override_warning' placeholder
-
-        """
-        # Define which config keys belong to each step
-        step_keys: dict[str, set[str]] = {
-            "best_price": {
-                "best_price_flex",
-                "best_price_min_distance_from_avg",
-                "best_price_min_period_length",
-                "min_periods_best",
-                "relaxation_attempts_best",
-                "enable_min_periods_best",
-            },
-            "peak_price": {
-                "peak_price_flex",
-                "peak_price_min_distance_from_avg",
-                "peak_price_min_period_length",
-                "min_periods_peak",
-                "relaxation_attempts_peak",
-                "enable_min_periods_peak",
-            },
         }
 
-        keys_to_check = step_keys.get(step_id, set())
+    async def async_step_init(self, user_input: dict[str, Any] | None = None) -> ConfigFlowResult:
-        enabled_keys = overrides.get("_enabled", {})
+        """Manage the options - General Settings."""
-        override_count = sum(1 for k in enabled_keys if k in keys_to_check)
+        # Initialize options from config_entry on first call
+        if not self._options:
+            self._options = dict(self.config_entry.options)
 
-        if override_count > 0:
-            field_word = "field is" if override_count == 1 else "fields are"
-            return {
-                "override_warning": (
-                    f"\n\n🔒 **{override_count} {field_word} managed by configuration entities** "
-                    "(grayed out). Disable the config entity to edit here, "
-                    "or change the value directly via the entity."
-                )
-            }
-        return {"override_warning": ""}
-
-    async def _get_override_translations(self) -> dict[str, Any]:
-        """
-        Load override translations from common section.
-
-        Uses the system language setting from Home Assistant.
-        Note: HA Options Flow does not provide user_id in context,
-        so we cannot determine the individual user's language preference.
-
-        Returns:
-            Dictionary with override_warning_template, override_warning_and,
-            and override_field_label_* keys for each config field.
-
-        """
-        # Use system language - HA Options Flow context doesn't include user_id
-        language = self.hass.config.language or "en"
-        _LOGGER.debug("Loading override translations for language: %s", language)
-        translations: dict[str, Any] = {}
-
-        # Load template and connector from common section
-        template = await async_get_translation(self.hass, ["common", "override_warning_template"], language)
-        _LOGGER.debug("Loaded template: %s", template)
-        if template:
-            translations["override_warning_template"] = template
-
-        and_connector = await async_get_translation(self.hass, ["common", "override_warning_and"], language)
-        if and_connector:
-            translations["override_warning_and"] = and_connector
-
-        # Load flat field label translations
-        field_keys = [
-            "best_price_min_period_length",
-            "best_price_max_level_gap_count",
-            "best_price_flex",
-            "best_price_min_distance_from_avg",
-            "enable_min_periods_best",
-            "min_periods_best",
-            "relaxation_attempts_best",
-            "peak_price_min_period_length",
-            "peak_price_max_level_gap_count",
-            "peak_price_flex",
-            "peak_price_min_distance_from_avg",
-            "enable_min_periods_peak",
-            "min_periods_peak",
-            "relaxation_attempts_peak",
-        ]
-        for field_key in field_keys:
-            translation_key = f"override_field_label_{field_key}"
-            label = await async_get_translation(self.hass, ["common", translation_key], language)
-            if label:
-                translations[translation_key] = label
-
-        return translations
-
-    async def async_step_init(self, _user_input: dict[str, Any] | None = None) -> ConfigFlowResult:
-        """Manage the options - show menu."""
-        # Always reload options from config_entry to get latest saved state
-        # This ensures changes from previous steps are visible
-        self._options = self._migrate_config_options(self.config_entry.options)
-
-        # Show menu with all configuration categories
-        return self.async_show_menu(
-            step_id="init",
-            menu_options=[
-                "general_settings",
-                "display_settings",
-                "current_interval_price_rating",
-                "price_level",
-                "volatility",
-                "best_price",
-                "peak_price",
-                "price_trend",
-                "chart_data_export",
-                "reset_to_defaults",
-                "finish",
-            ],
-        )
-
-    async def async_step_reset_to_defaults(self, user_input: dict[str, Any] | None = None) -> ConfigFlowResult:
-        """Reset all settings to factory defaults."""
         if user_input is not None:
-            # Check if user confirmed the reset
-            if user_input.get("confirm_reset", False):
-                # Get currency from config_entry.data (this is immutable and safe)
-                currency_code = self.config_entry.data.get("currency", None)
-
-                # Completely replace options with fresh defaults (factory reset)
-                # This discards ALL old data including legacy structures
-                self._options = get_default_options(currency_code)
-
-                # Force save the new options
-                self._save_options_if_changed()
-
-                _LOGGER.info(
-                    "Factory reset performed for config entry '%s' - all settings restored to defaults",
-                    self.config_entry.title,
-                )
-
-                # Show success message and return to menu
-                return self.async_abort(reason="reset_successful")
-
-            # User didn't check the box - they want to cancel
-            # Show info message (not error) and return to menu
-            return self.async_abort(reason="reset_cancelled")
-
-        # Show confirmation form with checkbox
-        return self.async_show_form(
-            step_id="reset_to_defaults",
-            data_schema=get_reset_to_defaults_schema(),
-        )
-
-    async def async_step_finish(self, _user_input: dict[str, Any] | None = None) -> ConfigFlowResult:
-        """Close the options flow."""
-        # Use empty reason to close without any message
-        return self.async_abort(reason="finished")
-
-    async def async_step_general_settings(self, user_input: dict[str, Any] | None = None) -> ConfigFlowResult:
-        """Configure general settings."""
-        if user_input is not None:
-            # Update options with new values
             self._options.update(user_input)
-            # Save options only if changed (triggers listeners automatically)
+            return await self.async_step_current_interval_price_rating()
-            self._save_options_if_changed()
-            # Return to menu for more changes
-            return await self.async_step_init()
 
         return self.async_show_form(
-            step_id="general_settings",
+            step_id="init",
             data_schema=get_options_init_schema(self.config_entry.options),
             description_placeholders={
+                **self._get_step_description_placeholders("init"),
                 "user_login": self.config_entry.data.get("user_login", "N/A"),
             },
         )
 
-    async def async_step_display_settings(self, user_input: dict[str, Any] | None = None) -> ConfigFlowResult:
-        """Configure currency display settings."""
-        # Get currency from coordinator data (if available)
-        # During options flow setup, integration might not be fully loaded yet
-        currency_code = None
-        if DOMAIN in self.hass.data and self.config_entry.entry_id in self.hass.data[DOMAIN]:
-            tibber_data = self.hass.data[DOMAIN][self.config_entry.entry_id]
-            if tibber_data.coordinator.data:
-                currency_code = tibber_data.coordinator.data.get("currency")
-
-        if user_input is not None:
-            # Update options with new values
-            self._options.update(user_input)
-            # async_create_entry automatically handles change detection and listener triggering
-            self._save_options_if_changed()
-            # Return to menu for more changes
-            return await self.async_step_init()
-
-        return self.async_show_form(
-            step_id="display_settings",
-            data_schema=get_display_settings_schema(self.config_entry.options, currency_code),
-        )
-
     async def async_step_current_interval_price_rating(
         self, user_input: dict[str, Any] | None = None
     ) -> ConfigFlowResult:
         """Configure price rating thresholds."""
-        errors: dict[str, str] = {}
-
         if user_input is not None:
-            # Schema is now flattened - fields come directly in user_input
+            self._options.update(user_input)
-            # But we still need to store them in nested structure for coordinator
+            return await self.async_step_volatility()
-
-            # Validate low price rating threshold
-            if CONF_PRICE_RATING_THRESHOLD_LOW in user_input and not validate_price_rating_threshold_low(
-                user_input[CONF_PRICE_RATING_THRESHOLD_LOW]
-            ):
-                errors[CONF_PRICE_RATING_THRESHOLD_LOW] = "invalid_price_rating_low"
-
-            # Validate high price rating threshold
-            if CONF_PRICE_RATING_THRESHOLD_HIGH in user_input and not validate_price_rating_threshold_high(
-                user_input[CONF_PRICE_RATING_THRESHOLD_HIGH]
-            ):
-                errors[CONF_PRICE_RATING_THRESHOLD_HIGH] = "invalid_price_rating_high"
-
-            # Cross-validate both thresholds together (LOW must be < HIGH)
-            if not errors:
-                # Get current values directly from options (now flat)
-                low_val = user_input.get(
-                    CONF_PRICE_RATING_THRESHOLD_LOW, self._options.get(CONF_PRICE_RATING_THRESHOLD_LOW, -10)
-                )
-                high_val = user_input.get(
-                    CONF_PRICE_RATING_THRESHOLD_HIGH, self._options.get(CONF_PRICE_RATING_THRESHOLD_HIGH, 10)
-                )
-                if not validate_price_rating_thresholds(low_val, high_val):
-                    # This should never happen given the range constraints, but add error for safety
-                    errors["base"] = "invalid_price_rating_thresholds"
-
-            if not errors:
-                # Store flat data directly in options (no section wrapping)
-                self._options.update(user_input)
-                # async_create_entry automatically handles change detection and listener triggering
-                self._save_options_if_changed()
-            # Return to menu for more changes
-            return await self.async_step_init()
 
         return self.async_show_form(
             step_id="current_interval_price_rating",
             data_schema=get_price_rating_schema(self.config_entry.options),
-            errors=errors,
+            description_placeholders=self._get_step_description_placeholders("current_interval_price_rating"),
-            description_placeholders=self._get_entity_warning_placeholders("current_interval_price_rating"),
-        )
-
-    async def async_step_price_level(self, user_input: dict[str, Any] | None = None) -> ConfigFlowResult:
-        """Configure Tibber price level gap tolerance (smoothing for API 'level' field)."""
-        errors: dict[str, str] = {}
-
-        if user_input is not None:
-            # No validation needed - slider constraints ensure valid range
-            # Store flat data directly in options
-            self._options.update(user_input)
-            # async_create_entry automatically handles change detection and listener triggering
-            self._save_options_if_changed()
-            # Return to menu for more changes
-            return await self.async_step_init()
-
-        return self.async_show_form(
-            step_id="price_level",
-            data_schema=get_price_level_schema(self.config_entry.options),
-            errors=errors,
-            description_placeholders=self._get_entity_warning_placeholders("price_level"),
         )
 
     async def async_step_best_price(self, user_input: dict[str, Any] | None = None) -> ConfigFlowResult:
         """Configure best price period settings."""
-        errors: dict[str, str] = {}
-
         if user_input is not None:
-            # Extract settings from sections
+            self._options.update(user_input)
-            period_settings = user_input.get("period_settings", {})
+            return await self.async_step_peak_price()
-            flexibility_settings = user_input.get("flexibility_settings", {})
-            relaxation_settings = user_input.get("relaxation_and_target_periods", {})
-
-            # Validate period length
-            if CONF_BEST_PRICE_MIN_PERIOD_LENGTH in period_settings and not validate_period_length(
-                period_settings[CONF_BEST_PRICE_MIN_PERIOD_LENGTH]
-            ):
-                errors[CONF_BEST_PRICE_MIN_PERIOD_LENGTH] = "invalid_period_length"
-
-            # Validate flex percentage
-            if CONF_BEST_PRICE_FLEX in flexibility_settings and not validate_flex_percentage(
-                flexibility_settings[CONF_BEST_PRICE_FLEX]
-            ):
-                errors[CONF_BEST_PRICE_FLEX] = "invalid_flex"
-
-            # Validate distance from average (Best Price uses negative values)
-            if (
-                CONF_BEST_PRICE_MIN_DISTANCE_FROM_AVG in flexibility_settings
-                and not validate_best_price_distance_percentage(
-                    flexibility_settings[CONF_BEST_PRICE_MIN_DISTANCE_FROM_AVG]
-                )
-            ):
-                errors[CONF_BEST_PRICE_MIN_DISTANCE_FROM_AVG] = "invalid_best_price_distance"
-
-            # Validate minimum periods count
-            if CONF_MIN_PERIODS_BEST in relaxation_settings and not validate_min_periods(
-                relaxation_settings[CONF_MIN_PERIODS_BEST]
-            ):
-                errors[CONF_MIN_PERIODS_BEST] = "invalid_min_periods"
-
-            # Validate gap count
-            if CONF_BEST_PRICE_MAX_LEVEL_GAP_COUNT in period_settings and not validate_gap_count(
-                period_settings[CONF_BEST_PRICE_MAX_LEVEL_GAP_COUNT]
-            ):
-                errors[CONF_BEST_PRICE_MAX_LEVEL_GAP_COUNT] = "invalid_gap_count"
-
-            # Validate relaxation attempts
-            if CONF_RELAXATION_ATTEMPTS_BEST in relaxation_settings and not validate_relaxation_attempts(
-                relaxation_settings[CONF_RELAXATION_ATTEMPTS_BEST]
-            ):
-                errors[CONF_RELAXATION_ATTEMPTS_BEST] = "invalid_relaxation_attempts"
-
-            if not errors:
-                # Merge section data into options
-                self._merge_section_data(user_input)
-                # async_create_entry automatically handles change detection and listener triggering
-                self._save_options_if_changed()
-            # Return to menu for more changes
-            return await self.async_step_init()
-
-        overrides = self._get_active_overrides()
-        placeholders = self._get_entity_warning_placeholders("best_price")
-        placeholders.update(self._get_override_warning_placeholder("best_price", overrides))
-
-        # Load translations for override warnings
-        override_translations = await self._get_override_translations()
 
         return self.async_show_form(
             step_id="best_price",
-            data_schema=get_best_price_schema(
+            data_schema=get_best_price_schema(self.config_entry.options),
-                self.config_entry.options,
+            description_placeholders=self._get_step_description_placeholders("best_price"),
-                overrides=overrides,
-                translations=override_translations,
-            ),
-            errors=errors,
-            description_placeholders=placeholders,
         )
 
     async def async_step_peak_price(self, user_input: dict[str, Any] | None = None) -> ConfigFlowResult:
         """Configure peak price period settings."""
-        errors: dict[str, str] = {}
-
         if user_input is not None:
-            # Extract settings from sections
+            self._options.update(user_input)
-            period_settings = user_input.get("period_settings", {})
+            return await self.async_step_price_trend()
-            flexibility_settings = user_input.get("flexibility_settings", {})
-            relaxation_settings = user_input.get("relaxation_and_target_periods", {})
-
-            # Validate period length
-            if CONF_PEAK_PRICE_MIN_PERIOD_LENGTH in period_settings and not validate_period_length(
-                period_settings[CONF_PEAK_PRICE_MIN_PERIOD_LENGTH]
-            ):
-                errors[CONF_PEAK_PRICE_MIN_PERIOD_LENGTH] = "invalid_period_length"
-
-            # Validate flex percentage (peak uses negative values)
-            if CONF_PEAK_PRICE_FLEX in flexibility_settings and not validate_flex_percentage(
-                flexibility_settings[CONF_PEAK_PRICE_FLEX]
-            ):
-                errors[CONF_PEAK_PRICE_FLEX] = "invalid_flex"
-
-            # Validate distance from average (Peak Price uses positive values)
-            if CONF_PEAK_PRICE_MIN_DISTANCE_FROM_AVG in flexibility_settings and not validate_distance_percentage(
-                flexibility_settings[CONF_PEAK_PRICE_MIN_DISTANCE_FROM_AVG]
-            ):
-                errors[CONF_PEAK_PRICE_MIN_DISTANCE_FROM_AVG] = "invalid_peak_price_distance"
-
-            # Validate minimum periods count
-            if CONF_MIN_PERIODS_PEAK in relaxation_settings and not validate_min_periods(
-                relaxation_settings[CONF_MIN_PERIODS_PEAK]
-            ):
-                errors[CONF_MIN_PERIODS_PEAK] = "invalid_min_periods"
-
-            # Validate gap count
-            if CONF_PEAK_PRICE_MAX_LEVEL_GAP_COUNT in period_settings and not validate_gap_count(
-                period_settings[CONF_PEAK_PRICE_MAX_LEVEL_GAP_COUNT]
-            ):
-                errors[CONF_PEAK_PRICE_MAX_LEVEL_GAP_COUNT] = "invalid_gap_count"
-
-            # Validate relaxation attempts
-            if CONF_RELAXATION_ATTEMPTS_PEAK in relaxation_settings and not validate_relaxation_attempts(
-                relaxation_settings[CONF_RELAXATION_ATTEMPTS_PEAK]
-            ):
-                errors[CONF_RELAXATION_ATTEMPTS_PEAK] = "invalid_relaxation_attempts"
-
-            if not errors:
-                # Merge section data into options
-                self._merge_section_data(user_input)
-                # async_create_entry automatically handles change detection and listener triggering
-                self._save_options_if_changed()
-            # Return to menu for more changes
-            return await self.async_step_init()
-
-        overrides = self._get_active_overrides()
-        placeholders = self._get_entity_warning_placeholders("peak_price")
-        placeholders.update(self._get_override_warning_placeholder("peak_price", overrides))
-
-        # Load translations for override warnings
-        override_translations = await self._get_override_translations()
 
         return self.async_show_form(
             step_id="peak_price",
-            data_schema=get_peak_price_schema(
+            data_schema=get_peak_price_schema(self.config_entry.options),
-                self.config_entry.options,
+            description_placeholders=self._get_step_description_placeholders("peak_price"),
-                overrides=overrides,
-                translations=override_translations,
-            ),
-            errors=errors,
-            description_placeholders=placeholders,
         )
 
     async def async_step_price_trend(self, user_input: dict[str, Any] | None = None) -> ConfigFlowResult:
         """Configure price trend thresholds."""
-        errors: dict[str, str] = {}
-
         if user_input is not None:
-            # Schema is now flattened - fields come directly in user_input
+            self._options.update(user_input)
-            # Store them flat in options (no nested structure)
+            return await self.async_step_chart_data_export()
-
-            # Validate rising trend threshold
-            if CONF_PRICE_TREND_THRESHOLD_RISING in user_input and not validate_price_trend_rising(
-                user_input[CONF_PRICE_TREND_THRESHOLD_RISING]
-            ):
-                errors[CONF_PRICE_TREND_THRESHOLD_RISING] = "invalid_price_trend_rising"
-
-            # Validate falling trend threshold
-            if CONF_PRICE_TREND_THRESHOLD_FALLING in user_input and not validate_price_trend_falling(
-                user_input[CONF_PRICE_TREND_THRESHOLD_FALLING]
-            ):
-                errors[CONF_PRICE_TREND_THRESHOLD_FALLING] = "invalid_price_trend_falling"
-
-            # Validate strongly rising trend threshold
-            if CONF_PRICE_TREND_THRESHOLD_STRONGLY_RISING in user_input and not validate_price_trend_strongly_rising(
-                user_input[CONF_PRICE_TREND_THRESHOLD_STRONGLY_RISING]
-            ):
-                errors[CONF_PRICE_TREND_THRESHOLD_STRONGLY_RISING] = "invalid_price_trend_strongly_rising"
-
-            # Validate strongly falling trend threshold
-            if CONF_PRICE_TREND_THRESHOLD_STRONGLY_FALLING in user_input and not validate_price_trend_strongly_falling(
-                user_input[CONF_PRICE_TREND_THRESHOLD_STRONGLY_FALLING]
-            ):
-                errors[CONF_PRICE_TREND_THRESHOLD_STRONGLY_FALLING] = "invalid_price_trend_strongly_falling"
-
-            # Cross-validation: Ensure rising < strongly_rising and falling > strongly_falling
-            if not errors:
-                rising = user_input.get(CONF_PRICE_TREND_THRESHOLD_RISING)
-                strongly_rising = user_input.get(CONF_PRICE_TREND_THRESHOLD_STRONGLY_RISING)
-                falling = user_input.get(CONF_PRICE_TREND_THRESHOLD_FALLING)
-                strongly_falling = user_input.get(CONF_PRICE_TREND_THRESHOLD_STRONGLY_FALLING)
-
-                if rising is not None and strongly_rising is not None and rising >= strongly_rising:
-                    errors[CONF_PRICE_TREND_THRESHOLD_STRONGLY_RISING] = (
-                        "invalid_trend_strongly_rising_less_than_rising"
-                    )
-                if falling is not None and strongly_falling is not None and falling <= strongly_falling:
-                    errors[CONF_PRICE_TREND_THRESHOLD_STRONGLY_FALLING] = (
-                        "invalid_trend_strongly_falling_greater_than_falling"
-                    )
-
-            if not errors:
-                # Store flat data directly in options (no section wrapping)
-                self._options.update(user_input)
-                # async_create_entry automatically handles change detection and listener triggering
-                self._save_options_if_changed()
-            # Return to menu for more changes
-            return await self.async_step_init()
 
         return self.async_show_form(
             step_id="price_trend",
             data_schema=get_price_trend_schema(self.config_entry.options),
-            errors=errors,
+            description_placeholders=self._get_step_description_placeholders("price_trend"),
-            description_placeholders=self._get_entity_warning_placeholders("price_trend"),
         )
 
     async def async_step_chart_data_export(self, user_input: dict[str, Any] | None = None) -> ConfigFlowResult:
         """Info page for chart data export sensor."""
         if user_input is not None:
-            # No changes to save - just return to menu
+            # No validation needed - just an info page
-            return await self.async_step_init()
+            return self.async_create_entry(title="", data=self._options)
 
-        # Check if the chart data export sensor is enabled
+        # Show info-only form (no input fields)
-        is_enabled = check_chart_data_export_enabled(self.hass, self.config_entry)
-
-        # Show info-only form with status-dependent description
         return self.async_show_form(
             step_id="chart_data_export",
             data_schema=get_chart_data_export_schema(self.config_entry.options),
-            description_placeholders={
+            description_placeholders=self._get_step_description_placeholders("chart_data_export"),
-                "sensor_status_info": self._get_chart_export_status_info(is_enabled=is_enabled),
-            },
-        )
-
-    def _get_chart_export_status_info(self, *, is_enabled: bool) -> str:
-        """Get the status info block for chart data export sensor."""
-        if is_enabled:
-            return (
-                "✅ **Status: Sensor is enabled**\n\n"
-                "The Chart Data Export sensor is currently active and providing data as attributes.\n\n"
-                "**Configuration (optional):**\n\n"
-                "Default settings work out-of-the-box (today+tomorrow, 15-minute intervals, prices only).\n\n"
-                "For customization, add to **`configuration.yaml`**:\n\n"
-                "```yaml\n"
-                "tibber_prices:\n"
-                "  chart_export:\n"
-                "    day:\n"
-                "      - today\n"
-                "      - tomorrow\n"
-                "    include_level: true\n"
-                "    include_rating_level: true\n"
-                "```\n\n"
-                "**All parameters:** See `tibber_prices.get_chartdata` service documentation"
-            )
-        return (
-            "❌ **Status: Sensor is disabled**\n\n"
-            "**Enable the sensor:**\n\n"
-            "1. Open **Settings → Devices & Services → Tibber Prices**\n"
-            "2. Select your home → Find **'Chart Data Export'** (Diagnostic section)\n"
-            "3. **Enable the sensor** (disabled by default)"
         )
 
     async def async_step_volatility(self, user_input: dict[str, Any] | None = None) -> ConfigFlowResult:
         """Configure volatility thresholds and period filtering."""
-        errors: dict[str, str] = {}
-
         if user_input is not None:
-            # Schema is now flattened - fields come directly in user_input
+            self._options.update(user_input)
-
+            return await self.async_step_best_price()
-            # Validate moderate volatility threshold
-            if CONF_VOLATILITY_THRESHOLD_MODERATE in user_input and not validate_volatility_threshold_moderate(
-                user_input[CONF_VOLATILITY_THRESHOLD_MODERATE]
-            ):
-                errors[CONF_VOLATILITY_THRESHOLD_MODERATE] = "invalid_volatility_threshold_moderate"
-
-            # Validate high volatility threshold
-            if CONF_VOLATILITY_THRESHOLD_HIGH in user_input and not validate_volatility_threshold_high(
-                user_input[CONF_VOLATILITY_THRESHOLD_HIGH]
-            ):
-                errors[CONF_VOLATILITY_THRESHOLD_HIGH] = "invalid_volatility_threshold_high"
-
-            # Validate very high volatility threshold
-            if CONF_VOLATILITY_THRESHOLD_VERY_HIGH in user_input and not validate_volatility_threshold_very_high(
-                user_input[CONF_VOLATILITY_THRESHOLD_VERY_HIGH]
-            ):
-                errors[CONF_VOLATILITY_THRESHOLD_VERY_HIGH] = "invalid_volatility_threshold_very_high"
-
-            # Cross-validation: Ensure MODERATE < HIGH < VERY_HIGH
-            if not errors:
-                # Get current values directly from options (now flat)
-                moderate = user_input.get(
-                    CONF_VOLATILITY_THRESHOLD_MODERATE,
-                    self._options.get(CONF_VOLATILITY_THRESHOLD_MODERATE, DEFAULT_VOLATILITY_THRESHOLD_MODERATE),
-                )
-                high = user_input.get(
-                    CONF_VOLATILITY_THRESHOLD_HIGH,
-                    self._options.get(CONF_VOLATILITY_THRESHOLD_HIGH, DEFAULT_VOLATILITY_THRESHOLD_HIGH),
-                )
-                very_high = user_input.get(
-                    CONF_VOLATILITY_THRESHOLD_VERY_HIGH,
-                    self._options.get(CONF_VOLATILITY_THRESHOLD_VERY_HIGH, DEFAULT_VOLATILITY_THRESHOLD_VERY_HIGH),
-                )
-
-                if not validate_volatility_thresholds(moderate, high, very_high):
-                    errors["base"] = "invalid_volatility_thresholds"
-
-            if not errors:
-                # Store flat data directly in options (no section wrapping)
-                self._options.update(user_input)
-                # async_create_entry automatically handles change detection and listener triggering
-                self._save_options_if_changed()
-            # Return to menu for more changes
-            return await self.async_step_init()
 
         return self.async_show_form(
             step_id="volatility",
             data_schema=get_volatility_schema(self.config_entry.options),
-            errors=errors,
+            description_placeholders=self._get_step_description_placeholders("volatility"),
-            description_placeholders=self._get_entity_warning_placeholders("volatility"),
         )

custom_components/tibber_prices/config_flow_handlers/subentry_flow.py

@@ -1,309 +1,126 @@
-"""Subentry config flow for creating time-travel views."""
+"""Subentry config flow for adding additional Tibber homes."""
 
 from __future__ import annotations
 
 from typing import Any
 
-import voluptuous as vol
+from custom_components.tibber_prices.config_flow_handlers.schemas import (
-
+    get_select_home_schema,
+    get_subentry_init_schema,
+)
 from custom_components.tibber_prices.const import (
-    CONF_VIRTUAL_TIME_OFFSET_DAYS,
+    CONF_EXTENDED_DESCRIPTIONS,
-    CONF_VIRTUAL_TIME_OFFSET_HOURS,
+    DEFAULT_EXTENDED_DESCRIPTIONS,
-    CONF_VIRTUAL_TIME_OFFSET_MINUTES,
     DOMAIN,
 )
 from homeassistant.config_entries import ConfigSubentryFlow, SubentryFlowResult
-from homeassistant.helpers.selector import (
+from homeassistant.helpers.selector import SelectOptionDict
-    DurationSelector,
-    DurationSelectorConfig,
-    NumberSelector,
-    NumberSelectorConfig,
-    NumberSelectorMode,
-    SelectOptionDict,
-    SelectSelector,
-    SelectSelectorConfig,
-    SelectSelectorMode,
-)
 
 
 class TibberPricesSubentryFlowHandler(ConfigSubentryFlow):
-    """Handle subentry flows for tibber_prices (time-travel views)."""
+    """Handle subentry flows for tibber_prices."""
-
-    def __init__(self) -> None:
-        """Initialize the subentry flow handler."""
-        super().__init__()
-        self._selected_parent_entry_id: str | None = None
 
     async def async_step_user(self, user_input: dict[str, Any] | None = None) -> SubentryFlowResult:
-        """Step 1: Select which config entry should get a time-travel subentry."""
+        """User flow to add a new home."""
-        errors: dict[str, str] = {}
+        parent_entry = self._get_entry()
+        if not parent_entry or not hasattr(parent_entry, "runtime_data") or not parent_entry.runtime_data:
+            return self.async_abort(reason="no_parent_entry")
+
+        coordinator = parent_entry.runtime_data.coordinator
+
+        # Force refresh user data to get latest homes from Tibber API
+        await coordinator.refresh_user_data()
+
+        homes = coordinator.get_user_homes()
+        if not homes:
+            return self.async_abort(reason="no_available_homes")
 
         if user_input is not None:
-            self._selected_parent_entry_id = user_input["parent_entry_id"]
+            selected_home_id = user_input["home_id"]
-            return await self.async_step_time_offset()
+            selected_home = next((home for home in homes if home["id"] == selected_home_id), None)
 
-        # Get all main config entries (not subentries)
+            if not selected_home:
-        # Subentries have "_hist_" in their unique_id
+                return self.async_abort(reason="home_not_found")
-        main_entries = [
-            entry
-            for entry in self.hass.config_entries.async_entries(DOMAIN)
-            if entry.unique_id and "_hist_" not in entry.unique_id
-        ]
 
-        if not main_entries:
+            home_title = self._get_home_title(selected_home)
-            return self.async_abort(reason="no_main_entries")
+            home_id = selected_home["id"]
 
-        # Build options for entry selection
+            return self.async_create_entry(
-        entry_options = [
+                title=home_title,
-            SelectOptionDict(
+                data={
-                value=entry.entry_id,
+                    "home_id": home_id,
-                label=f"{entry.title} ({entry.data.get('user_login', 'N/A')})",
+                    "home_data": selected_home,
+                },
+                description=f"Subentry for {home_title}",
+                description_placeholders={"home_id": home_id},
+                unique_id=home_id,
             )
-            for entry in main_entries
+
+        # Get existing home IDs by checking all entries (parent + subentries)
+        existing_home_ids = {
+            entry.data["home_id"]
+            for entry in self.hass.config_entries.async_entries(DOMAIN)
+            if entry.data.get("home_id")
+        }
+
+        # Also include parent entry's home_id if it exists
+        if parent_entry.data.get("home_id"):
+            existing_home_ids.add(parent_entry.data["home_id"])
+
+        available_homes = [home for home in homes if home["id"] not in existing_home_ids]
+
+        if not available_homes:
+            return self.async_abort(reason="no_available_homes")
+
+        home_options = [
+            SelectOptionDict(
+                value=home["id"],
+                label=self._get_home_title(home),
+            )
+            for home in available_homes
         ]
 
         return self.async_show_form(
             step_id="user",
-            data_schema=vol.Schema(
+            data_schema=get_select_home_schema(home_options),
-                {
-                    vol.Required("parent_entry_id"): SelectSelector(
-                        SelectSelectorConfig(
-                            options=entry_options,
-                            mode=SelectSelectorMode.DROPDOWN,
-                        )
-                    ),
-                }
-            ),
             description_placeholders={},
-            errors=errors,
+            errors={},
         )
 
-    async def async_step_time_offset(self, user_input: dict[str, Any] | None = None) -> SubentryFlowResult:
+    def _get_home_title(self, home: dict) -> str:
-        """Step 2: Configure time offset for the time-travel view."""
+        """Generate a user-friendly title for a home."""
-        errors: dict[str, str] = {}
+        title = home.get("appNickname")
+        if title and title.strip():
+            return title.strip()
 
-        if user_input is not None:
+        address = home.get("address", {})
-            # Extract values (convert days to int to avoid float from slider)
+        if address:
-            offset_days = int(user_input.get(CONF_VIRTUAL_TIME_OFFSET_DAYS, 0))
+            parts = []
+            if address.get("address1"):
+                parts.append(address["address1"])
+            if address.get("city"):
+                parts.append(address["city"])
+            if parts:
+                return ", ".join(parts)
 
-            # DurationSelector returns dict with 'hours', 'minutes', and 'seconds' keys
+        return home.get("id", "Unknown Home")
-            # We normalize to minute precision (ignore seconds)
-            time_offset = user_input.get("time_offset", {})
-            offset_hours = -abs(int(time_offset.get("hours", 0)))  # Always negative for historical data
-            offset_minutes = -abs(int(time_offset.get("minutes", 0)))  # Always negative for historical data
-            # Note: Seconds are ignored - we only support minute-level precision
-
-            # Validate that at least one offset is negative (historical data only)
-            if offset_days >= 0 and offset_hours >= 0 and offset_minutes >= 0:
-                errors["base"] = "no_time_offset"
-
-            if not errors:
-                # Get parent entry
-                if not self._selected_parent_entry_id:
-                    return self.async_abort(reason="parent_entry_not_found")
-
-                parent_entry = self.hass.config_entries.async_get_entry(self._selected_parent_entry_id)
-                if not parent_entry:
-                    return self.async_abort(reason="parent_entry_not_found")
-
-                # Get home data from parent entry
-                home_id = parent_entry.data.get("home_id")
-                home_data = parent_entry.data.get("home_data", {})
-                user_login = parent_entry.data.get("user_login", "N/A")
-
-                # Build unique_id with time offset signature
-                offset_str = f"d{offset_days}h{offset_hours}m{offset_minutes}"
-                user_id = parent_entry.unique_id.split("_")[0] if parent_entry.unique_id else home_id
-                unique_id = f"{user_id}_{home_id}_hist_{offset_str}"
-
-                # Check if this exact time offset already exists
-                for entry in self.hass.config_entries.async_entries(DOMAIN):
-                    if entry.unique_id == unique_id:
-                        return self.async_abort(reason="already_configured")
-
-                # No duplicate found - create the entry
-                offset_desc = self._format_offset_description(offset_days, offset_hours, offset_minutes)
-                subentry_title = f"{parent_entry.title} ({offset_desc})"
-
-                # Note: Subentries inherit options from parent entry automatically
-                # Options parameter is not supported by ConfigSubentryFlow.async_create_entry()
-
-                return self.async_create_entry(
-                    title=subentry_title,
-                    data={
-                        "home_id": home_id,
-                        "home_data": home_data,
-                        "user_login": user_login,
-                        CONF_VIRTUAL_TIME_OFFSET_DAYS: offset_days,
-                        CONF_VIRTUAL_TIME_OFFSET_HOURS: offset_hours,
-                        CONF_VIRTUAL_TIME_OFFSET_MINUTES: offset_minutes,
-                    },
-                    description=f"Time-travel view: {offset_desc}",
-                    description_placeholders={"offset": offset_desc},
-                    unique_id=unique_id,
-                )
-
-        return self.async_show_form(
-            step_id="time_offset",
-            data_schema=vol.Schema(
-                {
-                    vol.Required(CONF_VIRTUAL_TIME_OFFSET_DAYS, default=0): NumberSelector(
-                        NumberSelectorConfig(
-                            mode=NumberSelectorMode.SLIDER,
-                            min=-374,
-                            max=0,
-                            step=1,
-                        )
-                    ),
-                    vol.Optional("time_offset", default={"hours": 0, "minutes": 0}): DurationSelector(
-                        DurationSelectorConfig(
-                            allow_negative=False,  # We handle sign automatically
-                            enable_day=False,  # Days are handled by the slider above
-                        )
-                    ),
-                }
-            ),
-            description_placeholders={},
-            errors=errors,
-        )
-
-    def _format_offset_description(self, days: int, hours: int, minutes: int) -> str:
-        """
-        Format time offset into human-readable description.
-
-        Examples:
-            -7, 0, 0 -> "7 days ago" (English) / "vor 7 Tagen" (German)
-            0, -2, 0 -> "2 hours ago" (English) / "vor 2 Stunden" (German)
-            -7, -2, -30 -> "7 days - 02:30" (compact format when time is added)
-
-        """
-        # Get translations from custom_translations (loaded via async_load_translations)
-        translations_key = f"{DOMAIN}_translations_{self.hass.config.language}"
-        translations = self.hass.data.get(translations_key, {})
-        time_units = translations.get("time_units", {})
-
-        # Fallback to English if translations not available
-        if not time_units:
-            time_units = {
-                "day": "{count} day",
-                "days": "{count} days",
-                "hour": "{count} hour",
-                "hours": "{count} hours",
-                "minute": "{count} minute",
-                "minutes": "{count} minutes",
-                "ago": "{parts} ago",
-                "now": "now",
-            }
-
-        # Check if we have hours or minutes (need compact format)
-        has_time = hours != 0 or minutes != 0
-
-        if days != 0 and has_time:
-            # Compact format: "7 days - 02:30"
-            count = abs(days)
-            unit_key = "days" if count != 1 else "day"
-            day_part = time_units[unit_key].format(count=count)
-            time_part = f"{abs(hours):02d}:{abs(minutes):02d}"
-            return f"{day_part} - {time_part}"
-
-        # Standard format: separate parts with spaces
-        parts = []
-
-        if days != 0:
-            count = abs(days)
-            unit_key = "days" if count != 1 else "day"
-            parts.append(time_units[unit_key].format(count=count))
-
-        if hours != 0:
-            count = abs(hours)
-            unit_key = "hours" if count != 1 else "hour"
-            parts.append(time_units[unit_key].format(count=count))
-
-        if minutes != 0:
-            count = abs(minutes)
-            unit_key = "minutes" if count != 1 else "minute"
-            parts.append(time_units[unit_key].format(count=count))
-
-        if not parts:
-            return time_units.get("now", "now")
-
-        # All offsets should be negative (historical data only)
-        # Join parts with space and apply "ago" template
-        return time_units["ago"].format(parts=" ".join(parts))
 
     async def async_step_init(self, user_input: dict | None = None) -> SubentryFlowResult:
-        """Manage the options for an existing subentry (time-travel settings)."""
+        """Manage the options for a subentry."""
         subentry = self._get_reconfigure_subentry()
         errors: dict[str, str] = {}
 
         if user_input is not None:
-            # Extract values (convert days to int to avoid float from slider)
+            return self.async_update_and_abort(
-            offset_days = int(user_input.get(CONF_VIRTUAL_TIME_OFFSET_DAYS, 0))
+                self._get_entry(),
+                subentry,
+                data_updates=user_input,
+            )
 
-            # DurationSelector returns dict with 'hours', 'minutes', and 'seconds' keys
+        extended_descriptions = subentry.data.get(CONF_EXTENDED_DESCRIPTIONS, DEFAULT_EXTENDED_DESCRIPTIONS)
-            # We normalize to minute precision (ignore seconds)
-            time_offset = user_input.get("time_offset", {})
-            offset_hours = -abs(int(time_offset.get("hours", 0)))  # Always negative for historical data
-            offset_minutes = -abs(int(time_offset.get("minutes", 0)))  # Always negative for historical data
-            # Note: Seconds are ignored - we only support minute-level precision
-
-            # Validate that at least one offset is negative (historical data only)
-            if offset_days >= 0 and offset_hours >= 0 and offset_minutes >= 0:
-                errors["base"] = "no_time_offset"
-            else:
-                # Get parent entry to extract home_id and user_id
-                parent_entry = self._get_entry()
-                home_id = parent_entry.data.get("home_id")
-
-                # Build new unique_id with updated offset signature
-                offset_str = f"d{offset_days}h{offset_hours}m{offset_minutes}"
-                user_id = parent_entry.unique_id.split("_")[0] if parent_entry.unique_id else home_id
-                new_unique_id = f"{user_id}_{home_id}_hist_{offset_str}"
-
-                # Generate new title with updated offset description
-                offset_desc = self._format_offset_description(offset_days, offset_hours, offset_minutes)
-                # Extract parent title (remove old offset description in parentheses)
-                parent_title = parent_entry.title.split(" (")[0] if " (" in parent_entry.title else parent_entry.title
-                new_title = f"{parent_title} ({offset_desc})"
-
-                return self.async_update_and_abort(
-                    parent_entry,
-                    subentry,
-                    unique_id=new_unique_id,
-                    title=new_title,
-                    data_updates=user_input,
-                )
-
-        offset_days = subentry.data.get(CONF_VIRTUAL_TIME_OFFSET_DAYS, 0)
-        offset_hours = subentry.data.get(CONF_VIRTUAL_TIME_OFFSET_HOURS, 0)
-        offset_minutes = subentry.data.get(CONF_VIRTUAL_TIME_OFFSET_MINUTES, 0)
-
-        # Prepare time offset dict for DurationSelector (always positive, we negate on save)
-        time_offset_dict = {"hours": 0, "minutes": 0}  # Default to zeros
-        if offset_hours != 0:
-            time_offset_dict["hours"] = abs(offset_hours)
-        if offset_minutes != 0:
-            time_offset_dict["minutes"] = abs(offset_minutes)
 
         return self.async_show_form(
             step_id="init",
-            data_schema=vol.Schema(
+            data_schema=get_subentry_init_schema(extended_descriptions=extended_descriptions),
-                {
-                    vol.Required(CONF_VIRTUAL_TIME_OFFSET_DAYS, default=offset_days): NumberSelector(
-                        NumberSelectorConfig(
-                            mode=NumberSelectorMode.SLIDER,
-                            min=-374,
-                            max=0,
-                            step=1,
-                        )
-                    ),
-                    vol.Optional("time_offset", default=time_offset_dict): DurationSelector(
-                        DurationSelectorConfig(
-                            allow_negative=False,  # We handle sign automatically
-                            enable_day=False,  # Days are handled by the slider above
-                        )
-                    ),
-                }
-            ),
             errors=errors,
         )

custom_components/tibber_prices/config_flow_handlers/user_flow.py

@@ -2,11 +2,8 @@
 
 from __future__ import annotations
 
-from datetime import datetime
 from typing import TYPE_CHECKING, Any
 
-import voluptuous as vol
-
 from custom_components.tibber_prices.config_flow_handlers.options_flow import (
     TibberPricesOptionsFlowHandler,
 )
@@ -15,17 +12,15 @@ from custom_components.tibber_prices.config_flow_handlers.schemas import (
     get_select_home_schema,
     get_user_schema,
 )
+from custom_components.tibber_prices.config_flow_handlers.subentry_flow import (
+    TibberPricesSubentryFlowHandler,
+)
 from custom_components.tibber_prices.config_flow_handlers.validators import (
     TibberPricesCannotConnectError,
     TibberPricesInvalidAuthError,
     validate_api_token,
 )
-from custom_components.tibber_prices.const import (
+from custom_components.tibber_prices.const import DOMAIN, LOGGER
-    DOMAIN,
-    LOGGER,
-    get_default_options,
-    get_translation,
-)
 from homeassistant.config_entries import (
     ConfigEntry,
     ConfigFlow,
@@ -34,18 +29,13 @@ from homeassistant.config_entries import (
 )
 from homeassistant.const import CONF_ACCESS_TOKEN
 from homeassistant.core import callback
-from homeassistant.helpers.selector import (
+from homeassistant.helpers.selector import SelectOptionDict
-    SelectOptionDict,
-    SelectSelector,
-    SelectSelectorConfig,
-    SelectSelectorMode,
-)
 
 if TYPE_CHECKING:
     from homeassistant.config_entries import ConfigSubentryFlow
 
 
-class TibberPricesConfigFlowHandler(ConfigFlow, domain=DOMAIN):
+class TibberPricesFlowHandler(ConfigFlow, domain=DOMAIN):
     """Config flow for tibber_prices."""
 
     VERSION = 1
@@ -68,12 +58,7 @@ class TibberPricesConfigFlowHandler(ConfigFlow, domain=DOMAIN):
         config_entry: ConfigEntry,  # noqa: ARG003
     ) -> dict[str, type[ConfigSubentryFlow]]:
         """Return subentries supported by this integration."""
-        # Temporarily disabled: Time-travel feature not yet fully implemented
+        return {"home": TibberPricesSubentryFlowHandler}
-        # When enabled, this causes "Devices that don't belong to a sub-entry" warning
-        # because subentries don't have their own entities yet.
-        # See: https://github.com/home-assistant/core/issues/147570
-        # Will be re-enabled when time-travel functionality is implemented
-        return {}
 
     @staticmethod
     @callback
@@ -141,117 +126,13 @@ class TibberPricesConfigFlowHandler(ConfigFlow, domain=DOMAIN):
             step_id="reauth_confirm",
             data_schema=get_reauth_confirm_schema(),
             errors=_errors,
-            description_placeholders={"tibber_url": "https://developer.tibber.com"},
         )
 
     async def async_step_user(
         self,
         user_input: dict | None = None,
     ) -> ConfigFlowResult:
-        """Handle a flow initialized by the user. Choose account or enter new token."""
+        """Handle a flow initialized by the user. Only ask for access token."""
-        # Get existing accounts
-        existing_entries = self.hass.config_entries.async_entries(DOMAIN)
-
-        # If there are existing accounts, offer choice
-        if existing_entries and user_input is None:
-            return await self.async_step_account_choice()
-
-        # Otherwise, go directly to token input
-        return await self.async_step_new_token(user_input)
-
-    async def async_step_account_choice(
-        self,
-        user_input: dict | None = None,
-    ) -> ConfigFlowResult:
-        """Let user choose between existing account or new token."""
-        if user_input is not None:
-            choice = user_input["account_choice"]
-
-            if choice == "new_token":
-                return await self.async_step_new_token()
-
-            # User selected an existing account - copy its token
-            selected_entry_id = choice
-            selected_entry = next(
-                (
-                    entry
-                    for entry in self.hass.config_entries.async_entries(DOMAIN)
-                    if entry.entry_id == selected_entry_id
-                ),
-                None,
-            )
-
-            if not selected_entry:
-                return self.async_abort(reason="unknown")
-
-            # Copy token from selected entry and proceed
-            access_token = selected_entry.data.get(CONF_ACCESS_TOKEN)
-            if not access_token:
-                return self.async_abort(reason="unknown")
-
-            return await self.async_step_new_token({CONF_ACCESS_TOKEN: access_token})
-
-        # Build options: unique user accounts (grouped by user_id) + "New Token" option
-        existing_entries = self.hass.config_entries.async_entries(DOMAIN)
-
-        # Group entries by user_id to show unique accounts
-        # Minimum parts in unique_id format: user_id_home_id
-        min_unique_id_parts = 2
-
-        seen_users = {}
-        for entry in existing_entries:
-            # Extract user_id from unique_id (format: user_id_home_id or user_id_home_id_sub/hist_...)
-            unique_id = entry.unique_id
-            if unique_id:
-                # Split by underscore and take first part as user_id
-                parts = unique_id.split("_")
-                if len(parts) >= min_unique_id_parts:
-                    user_id = parts[0]
-                    if user_id not in seen_users:
-                        seen_users[user_id] = entry
-
-        # Build dropdown options from unique user accounts
-        account_options = [
-            SelectOptionDict(
-                value=entry.entry_id,
-                label=f"{entry.title} ({entry.data.get('user_login', 'N/A')})",
-            )
-            for entry in seen_users.values()
-        ]
-        # Add "new_token" option with translated label
-        new_token_label = (
-            get_translation(
-                ["selector", "account_choice", "options", "new_token"],
-                self.hass.config.language,
-            )
-            or "Add new Tibber account API token"
-        )
-        account_options.append(
-            SelectOptionDict(
-                value="new_token",
-                label=new_token_label,
-            )
-        )
-
-        return self.async_show_form(
-            step_id="account_choice",
-            data_schema=vol.Schema(
-                {
-                    vol.Required("account_choice"): SelectSelector(
-                        SelectSelectorConfig(
-                            options=account_options,
-                            mode=SelectSelectorMode.DROPDOWN,
-                        )
-                    ),
-                }
-            ),
-        )
-
-    async def async_step_new_token(
-        self,
-        user_input: dict | None = None,
-    ) -> ConfigFlowResult:
-        """Handle token input (new or copied from existing account)."""
         _errors = {}
         if user_input is not None:
             try:
@@ -278,6 +159,9 @@ class TibberPricesConfigFlowHandler(ConfigFlow, domain=DOMAIN):
 
                 LOGGER.debug("Viewer data received: %s", viewer)
 
+                await self.async_set_unique_id(unique_id=str(user_id))
+                self._abort_if_unique_id_configured()
+
                 # Store viewer data in the flow for use in the next step
                 self._viewer = viewer
                 self._access_token = user_input[CONF_ACCESS_TOKEN]
@@ -289,95 +173,25 @@ class TibberPricesConfigFlowHandler(ConfigFlow, domain=DOMAIN):
                 return await self.async_step_select_home()
 
         return self.async_show_form(
-            step_id="new_token",
+            step_id="user",
             data_schema=get_user_schema((user_input or {}).get(CONF_ACCESS_TOKEN)),
             errors=_errors,
-            description_placeholders={"tibber_url": "https://developer.tibber.com"},
         )
 
-    async def async_step_select_home(self, user_input: dict | None = None) -> ConfigFlowResult:  # noqa: PLR0911
+    async def async_step_select_home(self, user_input: dict | None = None) -> ConfigFlowResult:
         """Handle home selection during initial setup."""
         homes = self._viewer.get("homes", []) if self._viewer else []
 
         if not homes:
             return self.async_abort(reason="unknown")
 
-        # Filter out already configured homes
-        configured_home_ids = {
-            entry.data.get("home_id")
-            for entry in self.hass.config_entries.async_entries(DOMAIN)
-            if entry.data.get("home_id")
-        }
-        available_homes = [home for home in homes if home["id"] not in configured_home_ids]
-
-        # If no homes available, abort
-        if not available_homes:
-            return self.async_abort(reason="already_configured")
-
         if user_input is not None:
             selected_home_id = user_input["home_id"]
-            selected_home = next((home for home in available_homes if home["id"] == selected_home_id), None)
+            selected_home = next((home for home in homes if home["id"] == selected_home_id), None)
 
             if not selected_home:
                 return self.async_abort(reason="unknown")
 
-            # Validate that home has an active or future subscription
-            subscription_status = self._get_subscription_status(selected_home)
-
-            if subscription_status == "none":
-                return self.async_show_form(
-                    step_id="select_home",
-                    data_schema=get_select_home_schema(
-                        [
-                            SelectOptionDict(
-                                value=home["id"],
-                                label=self._get_home_title_with_status(home),
-                            )
-                            for home in available_homes
-                        ]
-                    ),
-                    errors={"home_id": "no_active_subscription"},
-                )
-
-            if subscription_status == "expired":
-                return self.async_show_form(
-                    step_id="select_home",
-                    data_schema=get_select_home_schema(
-                        [
-                            SelectOptionDict(
-                                value=home["id"],
-                                label=self._get_home_title_with_status(home),
-                            )
-                            for home in available_homes
-                        ]
-                    ),
-                    errors={"home_id": "subscription_expired"},
-                )
-
-            # Set unique_id to user_id + home_id combination
-            # This allows multiple homes per user account (single-home architecture)
-            unique_id = f"{self._user_id}_{selected_home_id}"
-            await self.async_set_unique_id(unique_id)
-            self._abort_if_unique_id_configured()
-
-            # Note: This check is now redundant since we filter available_homes upfront,
-            # but kept as defensive programming in case of race conditions
-            for entry in self.hass.config_entries.async_entries(DOMAIN):
-                if entry.data.get("home_id") == selected_home_id:
-                    return self.async_show_form(
-                        step_id="select_home",
-                        data_schema=get_select_home_schema(
-                            [
-                                SelectOptionDict(
-                                    value=home["id"],
-                                    label=self._get_home_title(home),
-                                )
-                                for home in available_homes
-                            ]
-                        ),
-                        errors={"home_id": "home_already_configured"},
-                    )
-
             data = {
                 CONF_ACCESS_TOKEN: self._access_token or "",
                 "home_id": selected_home_id,
@@ -386,32 +200,18 @@ class TibberPricesConfigFlowHandler(ConfigFlow, domain=DOMAIN):
                 "user_login": self._user_login or "N/A",
             }
 
-            # Extract currency from home data for intelligent defaults
-            currency_code = None
-            if (
-                selected_home
-                and (subscription := selected_home.get("currentSubscription"))
-                and (price_info := subscription.get("priceInfo"))
-                and (current_price := price_info.get("current"))
-            ):
-                currency_code = current_price.get("currency")
-
-            # Generate entry title from home address (not appNickname)
-            entry_title = self._get_entry_title(selected_home)
-
             return self.async_create_entry(
-                title=entry_title,
+                title=self._user_name or "Unknown User",
                 data=data,
                 description=f"{self._user_login} ({self._user_id})",
-                options=get_default_options(currency_code),
             )
 
         home_options = [
             SelectOptionDict(
                 value=home["id"],
-                label=self._get_home_title_with_status(home),
+                label=self._get_home_title(home),
             )
-            for home in available_homes
+            for home in homes
         ]
 
         return self.async_show_form(
@@ -434,138 +234,9 @@ class TibberPricesConfigFlowHandler(ConfigFlow, domain=DOMAIN):
 
         return home_ids
 
-    @staticmethod
-    def _get_subscription_status(home: dict) -> str:
-        """
-        Check subscription status of home.
-
-        Returns:
-            - "active": Subscription is currently active
-            - "future": Subscription exists but starts in the future (validFrom > now)
-            - "expired": Subscription exists but has ended (validTo < now)
-            - "none": No subscription exists
-
-        """
-        subscription = home.get("currentSubscription")
-
-        if subscription is None or subscription.get("status") is None:
-            return "none"
-
-        # Check validTo (contract end date)
-        valid_to = subscription.get("validTo")
-        if valid_to:
-            try:
-                valid_to_dt = datetime.fromisoformat(valid_to)
-                if valid_to_dt < datetime.now(valid_to_dt.tzinfo):
-                    return "expired"
-            except (ValueError, AttributeError):
-                pass  # If parsing fails, continue with other checks
-
-        # Check validFrom (contract start date)
-        valid_from = subscription.get("validFrom")
-        if valid_from:
-            try:
-                valid_from_dt = datetime.fromisoformat(valid_from)
-                if valid_from_dt > datetime.now(valid_from_dt.tzinfo):
-                    return "future"
-            except (ValueError, AttributeError):
-                pass  # If parsing fails, assume active
-
-        return "active"
-
-    def _get_home_title_with_status(self, home: dict) -> str:
-        """Generate a user-friendly title for a home with subscription status."""
-        base_title = self._get_home_title(home)
-        status = self._get_subscription_status(home)
-
-        if status == "none":
-            return f"{base_title} ⚠️ (No active contract)"
-        if status == "expired":
-            return f"{base_title} ⚠️ (Contract expired)"
-        if status == "future":
-            return f"{base_title} ⚠️ (Contract starts soon)"
-
-        return base_title
-
-    @staticmethod
-    def _format_city_name(city: str) -> str:
-        """
-        Format city name to title case.
-
-        Converts 'MÜNCHEN' to 'München', handles multi-word cities like
-        'BAD TÖLZ' -> 'Bad Tölz', and hyphenated cities like
-        'GARMISCH-PARTENKIRCHEN' -> 'Garmisch-Partenkirchen'.
-        """
-        if not city:
-            return city
-
-        # Split by space and hyphen while preserving delimiters
-        words = []
-        current_word = ""
-
-        for char in city:
-            if char in (" ", "-"):
-                if current_word:
-                    words.append(current_word)
-                words.append(char)  # Preserve delimiter
-                current_word = ""
-            else:
-                current_word += char
-
-        if current_word:  # Add last word
-            words.append(current_word)
-
-        # Capitalize first letter of each word (not delimiters)
-        formatted_words = []
-        for word in words:
-            if word in (" ", "-"):
-                formatted_words.append(word)
-            else:
-                # Capitalize first letter, lowercase rest
-                formatted_words.append(word.capitalize())
-
-        return "".join(formatted_words)
-
-    @staticmethod
-    def _get_entry_title(home: dict) -> str:
-        """
-        Generate entry title from address (for config entry title).
-
-        Uses 'address1, City' format, e.g. 'Pählstraße 6B, München'.
-        Does NOT use appNickname (that's for _get_home_title).
-        """
-        address = home.get("address", {})
-
-        if not address:
-            # Fallback to home ID if no address
-            return home.get("id", "Unknown Home")
-
-        parts = []
-
-        # Always prefer address1
-        address1 = address.get("address1")
-        if address1 and address1.strip():
-            parts.append(address1.strip())
-
-        # Format city name (convert MÜNCHEN -> München)
-        city = address.get("city")
-        if city and city.strip():
-            formatted_city = TibberPricesConfigFlowHandler._format_city_name(city.strip())
-            parts.append(formatted_city)
-
-        if parts:
-            return ", ".join(parts)
-
-        # Final fallback
-        return home.get("id", "Unknown Home")
-
     @staticmethod
     def _get_home_title(home: dict) -> str:
-        """
+        """Generate a user-friendly title for a home."""
-        Generate a user-friendly title for a home (for dropdown display).
-
-        Prefers appNickname, falls back to address.
-        """
         title = home.get("appNickname")
         if title and title.strip():
             return title.strip()
@@ -576,10 +247,7 @@ class TibberPricesConfigFlowHandler(ConfigFlow, domain=DOMAIN):
             if address.get("address1"):
                 parts.append(address["address1"])
             if address.get("city"):
-                # Format city for display too
+                parts.append(address["city"])
-                city = address["city"]
-                formatted_city = TibberPricesConfigFlowHandler._format_city_name(city)
-                parts.append(formatted_city)
             if parts:
                 return ", ".join(parts)
 

custom_components/tibber_prices/config_flow_handlers/validators.py

@@ -10,35 +10,7 @@ from custom_components.tibber_prices.api import (
     TibberPricesApiClientCommunicationError,
     TibberPricesApiClientError,
 )
-from custom_components.tibber_prices.const import (
+from custom_components.tibber_prices.const import DOMAIN
-    DOMAIN,
-    MAX_DISTANCE_PERCENTAGE,
-    MAX_FLEX_PERCENTAGE,
-    MAX_GAP_COUNT,
-    MAX_MIN_PERIODS,
-    MAX_PRICE_RATING_THRESHOLD_HIGH,
-    MAX_PRICE_RATING_THRESHOLD_LOW,
-    MAX_PRICE_TREND_FALLING,
-    MAX_PRICE_TREND_RISING,
-    MAX_PRICE_TREND_STRONGLY_FALLING,
-    MAX_PRICE_TREND_STRONGLY_RISING,
-    MAX_RELAXATION_ATTEMPTS,
-    MAX_VOLATILITY_THRESHOLD_HIGH,
-    MAX_VOLATILITY_THRESHOLD_MODERATE,
-    MAX_VOLATILITY_THRESHOLD_VERY_HIGH,
-    MIN_GAP_COUNT,
-    MIN_PERIOD_LENGTH,
-    MIN_PRICE_RATING_THRESHOLD_HIGH,
-    MIN_PRICE_RATING_THRESHOLD_LOW,
-    MIN_PRICE_TREND_FALLING,
-    MIN_PRICE_TREND_RISING,
-    MIN_PRICE_TREND_STRONGLY_FALLING,
-    MIN_PRICE_TREND_STRONGLY_RISING,
-    MIN_RELAXATION_ATTEMPTS,
-    MIN_VOLATILITY_THRESHOLD_HIGH,
-    MIN_VOLATILITY_THRESHOLD_MODERATE,
-    MIN_VOLATILITY_THRESHOLD_VERY_HIGH,
-)
 from homeassistant.exceptions import HomeAssistantError
 from homeassistant.helpers.aiohttp_client import async_create_clientsession
 from homeassistant.loader import async_get_integration
@@ -46,6 +18,10 @@ from homeassistant.loader import async_get_integration
 if TYPE_CHECKING:
     from homeassistant.core import HomeAssistant
 
+# Constants for validation
+MAX_FLEX_PERCENTAGE = 100.0
+MAX_MIN_PERIODS = 10  # Arbitrary upper limit for sanity
+
 
 class TibberPricesInvalidAuthError(HomeAssistantError):
     """Error to indicate invalid authentication."""
@@ -88,18 +64,34 @@ async def validate_api_token(hass: HomeAssistant, token: str) -> dict:
         raise TibberPricesCannotConnectError from exception
 
 
+def validate_threshold_range(value: float, min_val: float, max_val: float) -> bool:
+    """
+    Validate threshold is within allowed range.
+
+    Args:
+        value: Value to validate
+        min_val: Minimum allowed value
+        max_val: Maximum allowed value
+
+    Returns:
+        True if value is within range
+
+    """
+    return min_val <= value <= max_val
+
+
 def validate_period_length(minutes: int) -> bool:
     """
-    Validate period length is a positive multiple of 15 minutes.
+    Validate period length is multiple of 15 minutes.
 
     Args:
         minutes: Period length in minutes
 
     Returns:
-        True if length is valid (multiple of 15, at least MIN_PERIOD_LENGTH)
+        True if length is valid
 
     """
-    return minutes % 15 == 0 and minutes >= MIN_PERIOD_LENGTH
+    return minutes > 0 and minutes % 15 == 0
 
 
 def validate_flex_percentage(flex: float) -> bool:
@@ -107,13 +99,13 @@ def validate_flex_percentage(flex: float) -> bool:
     Validate flexibility percentage is within bounds.
 
     Args:
-        flex: Flexibility percentage (can be negative for peak price)
+        flex: Flexibility percentage
 
     Returns:
-        True if percentage is valid (-MAX_FLEX to +MAX_FLEX)
+        True if percentage is valid
 
     """
-    return -MAX_FLEX_PERCENTAGE <= flex <= MAX_FLEX_PERCENTAGE
+    return 0.0 <= flex <= MAX_FLEX_PERCENTAGE
 
 
 def validate_min_periods(count: int) -> bool:
@@ -121,251 +113,10 @@ def validate_min_periods(count: int) -> bool:
     Validate minimum periods count is reasonable.
 
     Args:
-        count: Number of minimum periods per day
+        count: Number of minimum periods
 
     Returns:
-        True if count is valid (1 to MAX_MIN_PERIODS)
+        True if count is valid
 
     """
-    return 1 <= count <= MAX_MIN_PERIODS
+    return count > 0 and count <= MAX_MIN_PERIODS
-
-
-def validate_distance_percentage(distance: float) -> bool:
-    """
-    Validate distance from average percentage (for Peak Price - positive values).
-
-    Args:
-        distance: Distance percentage (0-50% is typical range)
-
-    Returns:
-        True if distance is valid (0-MAX_DISTANCE_PERCENTAGE)
-
-    """
-    return 0.0 <= distance <= MAX_DISTANCE_PERCENTAGE
-
-
-def validate_best_price_distance_percentage(distance: float) -> bool:
-    """
-    Validate distance from average percentage (for Best Price - negative values).
-
-    Args:
-        distance: Distance percentage (-50% to 0% range, negative = below average)
-
-    Returns:
-        True if distance is valid (-MAX_DISTANCE_PERCENTAGE to 0)
-
-    """
-    return -MAX_DISTANCE_PERCENTAGE <= distance <= 0.0
-
-
-def validate_gap_count(count: int) -> bool:
-    """
-    Validate gap count is within bounds.
-
-    Args:
-        count: Gap count (0-8)
-
-    Returns:
-        True if count is valid (MIN_GAP_COUNT to MAX_GAP_COUNT)
-
-    """
-    return MIN_GAP_COUNT <= count <= MAX_GAP_COUNT
-
-
-def validate_relaxation_attempts(attempts: int) -> bool:
-    """
-    Validate relaxation attempts count is within bounds.
-
-    Args:
-        attempts: Number of relaxation attempts (1-12)
-
-    Returns:
-        True if attempts is valid (MIN_RELAXATION_ATTEMPTS to MAX_RELAXATION_ATTEMPTS)
-
-    """
-    return MIN_RELAXATION_ATTEMPTS <= attempts <= MAX_RELAXATION_ATTEMPTS
-
-
-def validate_price_rating_threshold_low(threshold: int) -> bool:
-    """
-    Validate low price rating threshold.
-
-    Args:
-        threshold: Low rating threshold percentage (-50 to -5)
-
-    Returns:
-        True if threshold is valid (MIN_PRICE_RATING_THRESHOLD_LOW to MAX_PRICE_RATING_THRESHOLD_LOW)
-
-    """
-    return MIN_PRICE_RATING_THRESHOLD_LOW <= threshold <= MAX_PRICE_RATING_THRESHOLD_LOW
-
-
-def validate_price_rating_threshold_high(threshold: int) -> bool:
-    """
-    Validate high price rating threshold.
-
-    Args:
-        threshold: High rating threshold percentage (5 to 50)
-
-    Returns:
-        True if threshold is valid (MIN_PRICE_RATING_THRESHOLD_HIGH to MAX_PRICE_RATING_THRESHOLD_HIGH)
-
-    """
-    return MIN_PRICE_RATING_THRESHOLD_HIGH <= threshold <= MAX_PRICE_RATING_THRESHOLD_HIGH
-
-
-def validate_price_rating_thresholds(threshold_low: int, threshold_high: int) -> bool:
-    """
-    Cross-validate both price rating thresholds together.
-
-    Ensures that LOW threshold < HIGH threshold with proper gap to avoid
-    overlap at 0%. LOW should be negative (below average), HIGH should be
-    positive (above average).
-
-    Args:
-        threshold_low: Low rating threshold percentage (-50 to -5)
-        threshold_high: High rating threshold percentage (5 to 50)
-
-    Returns:
-        True if both thresholds are valid individually AND threshold_low < threshold_high
-
-    """
-    # Validate individual ranges first
-    if not validate_price_rating_threshold_low(threshold_low):
-        return False
-    if not validate_price_rating_threshold_high(threshold_high):
-        return False
-
-    # Ensure LOW is always less than HIGH (should always be true given the ranges,
-    # but explicit check for safety)
-    return threshold_low < threshold_high
-
-
-def validate_volatility_threshold_moderate(threshold: float) -> bool:
-    """
-    Validate moderate volatility threshold.
-
-    Args:
-        threshold: Moderate volatility threshold percentage (5.0 to 25.0)
-
-    Returns:
-        True if threshold is valid (MIN_VOLATILITY_THRESHOLD_MODERATE to MAX_VOLATILITY_THRESHOLD_MODERATE)
-
-    """
-    return MIN_VOLATILITY_THRESHOLD_MODERATE <= threshold <= MAX_VOLATILITY_THRESHOLD_MODERATE
-
-
-def validate_volatility_threshold_high(threshold: float) -> bool:
-    """
-    Validate high volatility threshold.
-
-    Args:
-        threshold: High volatility threshold percentage (20.0 to 40.0)
-
-    Returns:
-        True if threshold is valid (MIN_VOLATILITY_THRESHOLD_HIGH to MAX_VOLATILITY_THRESHOLD_HIGH)
-
-    """
-    return MIN_VOLATILITY_THRESHOLD_HIGH <= threshold <= MAX_VOLATILITY_THRESHOLD_HIGH
-
-
-def validate_volatility_threshold_very_high(threshold: float) -> bool:
-    """
-    Validate very high volatility threshold.
-
-    Args:
-        threshold: Very high volatility threshold percentage (35.0 to 80.0)
-
-    Returns:
-        True if threshold is valid (MIN_VOLATILITY_THRESHOLD_VERY_HIGH to MAX_VOLATILITY_THRESHOLD_VERY_HIGH)
-
-    """
-    return MIN_VOLATILITY_THRESHOLD_VERY_HIGH <= threshold <= MAX_VOLATILITY_THRESHOLD_VERY_HIGH
-
-
-def validate_volatility_thresholds(
-    threshold_moderate: float,
-    threshold_high: float,
-    threshold_very_high: float,
-) -> bool:
-    """
-    Cross-validate all three volatility thresholds together.
-
-    Ensures that MODERATE < HIGH < VERY_HIGH to maintain logical classification
-    boundaries. Each threshold represents an escalating level of price volatility.
-
-    Args:
-        threshold_moderate: Moderate volatility threshold (5.0 to 25.0)
-        threshold_high: High volatility threshold (20.0 to 40.0)
-        threshold_very_high: Very high volatility threshold (35.0 to 80.0)
-
-    Returns:
-        True if all thresholds are valid individually AND maintain proper ordering
-
-    """
-    # Validate individual ranges first
-    if not validate_volatility_threshold_moderate(threshold_moderate):
-        return False
-    if not validate_volatility_threshold_high(threshold_high):
-        return False
-    if not validate_volatility_threshold_very_high(threshold_very_high):
-        return False
-
-    # Ensure cascading order: MODERATE < HIGH < VERY_HIGH
-    return threshold_moderate < threshold_high < threshold_very_high
-
-
-def validate_price_trend_rising(threshold: int) -> bool:
-    """
-    Validate rising price trend threshold.
-
-    Args:
-        threshold: Rising trend threshold percentage (1 to 50)
-
-    Returns:
-        True if threshold is valid (MIN_PRICE_TREND_RISING to MAX_PRICE_TREND_RISING)
-
-    """
-    return MIN_PRICE_TREND_RISING <= threshold <= MAX_PRICE_TREND_RISING
-
-
-def validate_price_trend_falling(threshold: int) -> bool:
-    """
-    Validate falling price trend threshold.
-
-    Args:
-        threshold: Falling trend threshold percentage (-50 to -1)
-
-    Returns:
-        True if threshold is valid (MIN_PRICE_TREND_FALLING to MAX_PRICE_TREND_FALLING)
-
-    """
-    return MIN_PRICE_TREND_FALLING <= threshold <= MAX_PRICE_TREND_FALLING
-
-
-def validate_price_trend_strongly_rising(threshold: int) -> bool:
-    """
-    Validate strongly rising price trend threshold.
-
-    Args:
-        threshold: Strongly rising trend threshold percentage (2 to 100)
-
-    Returns:
-        True if threshold is valid (MIN_PRICE_TREND_STRONGLY_RISING to MAX_PRICE_TREND_STRONGLY_RISING)
-
-    """
-    return MIN_PRICE_TREND_STRONGLY_RISING <= threshold <= MAX_PRICE_TREND_STRONGLY_RISING
-
-
-def validate_price_trend_strongly_falling(threshold: int) -> bool:
-    """
-    Validate strongly falling price trend threshold.
-
-    Args:
-        threshold: Strongly falling trend threshold percentage (-100 to -2)
-
-    Returns:
-        True if threshold is valid (MIN_PRICE_TREND_STRONGLY_FALLING to MAX_PRICE_TREND_STRONGLY_FALLING)
-
-    """
-    return MIN_PRICE_TREND_STRONGLY_FALLING <= threshold <= MAX_PRICE_TREND_STRONGLY_FALLING

custom_components/tibber_prices/const.py

@@ -1,11 +1,10 @@
 """Constants for the Tibber Price Analytics integration."""
 
-from __future__ import annotations
-
 import json
 import logging
+from collections.abc import Sequence
 from pathlib import Path
-from typing import TYPE_CHECKING, Any
+from typing import Any
 
 import aiofiles
 
@@ -15,27 +14,16 @@ from homeassistant.const import (
     UnitOfPower,
     UnitOfTime,
 )
-
+from homeassistant.core import HomeAssistant
-if TYPE_CHECKING:
-    from collections.abc import Sequence
-
-    from homeassistant.config_entries import ConfigEntry
-    from homeassistant.core import HomeAssistant
 
 DOMAIN = "tibber_prices"
 LOGGER = logging.getLogger(__package__)
 
 # Data storage keys
 DATA_CHART_CONFIG = "chart_config"  # Key for chart export config in hass.data
-DATA_CHART_METADATA_CONFIG = "chart_metadata_config"  # Key for chart metadata config in hass.data
 
 # Configuration keys
 CONF_EXTENDED_DESCRIPTIONS = "extended_descriptions"
-CONF_VIRTUAL_TIME_OFFSET_DAYS = (
-    "virtual_time_offset_days"  # Time-travel: days offset (negative only, e.g., -7 = 7 days ago)
-)
-CONF_VIRTUAL_TIME_OFFSET_HOURS = "virtual_time_offset_hours"  # Time-travel: hours offset (-23 to +23)
-CONF_VIRTUAL_TIME_OFFSET_MINUTES = "virtual_time_offset_minutes"  # Time-travel: minutes offset (-59 to +59)
 CONF_BEST_PRICE_FLEX = "best_price_flex"
 CONF_PEAK_PRICE_FLEX = "peak_price_flex"
 CONF_BEST_PRICE_MIN_DISTANCE_FROM_AVG = "best_price_min_distance_from_avg"
@@ -44,14 +32,8 @@ CONF_BEST_PRICE_MIN_PERIOD_LENGTH = "best_price_min_period_length"
 CONF_PEAK_PRICE_MIN_PERIOD_LENGTH = "peak_price_min_period_length"
 CONF_PRICE_RATING_THRESHOLD_LOW = "price_rating_threshold_low"
 CONF_PRICE_RATING_THRESHOLD_HIGH = "price_rating_threshold_high"
-CONF_PRICE_RATING_HYSTERESIS = "price_rating_hysteresis"
-CONF_PRICE_RATING_GAP_TOLERANCE = "price_rating_gap_tolerance"
-CONF_PRICE_LEVEL_GAP_TOLERANCE = "price_level_gap_tolerance"
-CONF_AVERAGE_SENSOR_DISPLAY = "average_sensor_display"  # "median" or "mean"
 CONF_PRICE_TREND_THRESHOLD_RISING = "price_trend_threshold_rising"
 CONF_PRICE_TREND_THRESHOLD_FALLING = "price_trend_threshold_falling"
-CONF_PRICE_TREND_THRESHOLD_STRONGLY_RISING = "price_trend_threshold_strongly_rising"
-CONF_PRICE_TREND_THRESHOLD_STRONGLY_FALLING = "price_trend_threshold_strongly_falling"
 CONF_VOLATILITY_THRESHOLD_MODERATE = "volatility_threshold_moderate"
 CONF_VOLATILITY_THRESHOLD_HIGH = "volatility_threshold_high"
 CONF_VOLATILITY_THRESHOLD_VERY_HIGH = "volatility_threshold_very_high"
@@ -72,9 +54,6 @@ ATTRIBUTION = "Data provided by Tibber"
 # Integration name should match manifest.json
 DEFAULT_NAME = "Tibber Price Information & Ratings"
 DEFAULT_EXTENDED_DESCRIPTIONS = False
-DEFAULT_VIRTUAL_TIME_OFFSET_DAYS = 0  # No time offset (live mode)
-DEFAULT_VIRTUAL_TIME_OFFSET_HOURS = 0
-DEFAULT_VIRTUAL_TIME_OFFSET_MINUTES = 0
 DEFAULT_BEST_PRICE_FLEX = 15  # 15% base flexibility - optimal for relaxation mode (default enabled)
 # Peak price flexibility is set to -20% (20% base flexibility - optimal for relaxation mode).
 # This is intentionally more flexible than best price (15%) because peak price periods can be more variable,
@@ -83,12 +62,8 @@ DEFAULT_BEST_PRICE_FLEX = 15  # 15% base flexibility - optimal for relaxation mo
 # (e.g., -20% means MAX * 0.8), not above the average price.
 # A higher percentage allows for more conservative detection, reducing false negatives for peak price warnings.
 DEFAULT_PEAK_PRICE_FLEX = -20  # 20% base flexibility (user-facing, percent)
-DEFAULT_BEST_PRICE_MIN_DISTANCE_FROM_AVG = (
+DEFAULT_BEST_PRICE_MIN_DISTANCE_FROM_AVG = 5  # 5% minimum distance from daily average (ensures significance)
-    -5
+DEFAULT_PEAK_PRICE_MIN_DISTANCE_FROM_AVG = 5  # 5% minimum distance from daily average (ensures significance)
-)  # -5% minimum distance from daily average (below average, ensures significance)
-DEFAULT_PEAK_PRICE_MIN_DISTANCE_FROM_AVG = (
-    5  # 5% minimum distance from daily average (above average, ensures significance)
-)
 DEFAULT_BEST_PRICE_MIN_PERIOD_LENGTH = 60  # 60 minutes minimum period length for best price (user-facing, minutes)
 # Note: Peak price warnings are allowed for shorter periods (30 min) than best price periods (60 min).
 # This asymmetry is intentional: shorter peak periods are acceptable for alerting users to brief expensive spikes,
@@ -97,16 +72,8 @@ DEFAULT_BEST_PRICE_MIN_PERIOD_LENGTH = 60  # 60 minutes minimum period length fo
 DEFAULT_PEAK_PRICE_MIN_PERIOD_LENGTH = 30  # 30 minutes minimum period length for peak price (user-facing, minutes)
 DEFAULT_PRICE_RATING_THRESHOLD_LOW = -10  # Default rating threshold low percentage
 DEFAULT_PRICE_RATING_THRESHOLD_HIGH = 10  # Default rating threshold high percentage
-DEFAULT_PRICE_RATING_HYSTERESIS = 2.0  # Hysteresis percentage to prevent flickering at threshold boundaries
-DEFAULT_PRICE_RATING_GAP_TOLERANCE = 1  # Max consecutive intervals to smooth out (0 = disabled)
-DEFAULT_PRICE_LEVEL_GAP_TOLERANCE = 1  # Max consecutive intervals to smooth out for price level (0 = disabled)
-DEFAULT_AVERAGE_SENSOR_DISPLAY = "median"  # Default: show median in state, mean in attributes
 DEFAULT_PRICE_TREND_THRESHOLD_RISING = 3  # Default trend threshold for rising prices (%)
 DEFAULT_PRICE_TREND_THRESHOLD_FALLING = -3  # Default trend threshold for falling prices (%, negative value)
-# Strong trend thresholds default to 2x the base threshold.
-# These are independently configurable to allow fine-tuning of "strongly" detection.
-DEFAULT_PRICE_TREND_THRESHOLD_STRONGLY_RISING = 6  # Default strong rising threshold (%)
-DEFAULT_PRICE_TREND_THRESHOLD_STRONGLY_FALLING = -6  # Default strong falling threshold (%, negative value)
 # Default volatility thresholds (relative values using coefficient of variation)
 # Coefficient of variation = (standard_deviation / mean) * 100%
 # These thresholds are unitless and work across different price levels
@@ -125,58 +92,6 @@ DEFAULT_ENABLE_MIN_PERIODS_PEAK = True  # Default: minimum periods feature enabl
 DEFAULT_MIN_PERIODS_PEAK = 2  # Default: require at least 2 peak price periods (when enabled)
 DEFAULT_RELAXATION_ATTEMPTS_PEAK = 11  # Default: 11 steps allows escalation from 20% to 50% (3% increment per step)
 
-# Validation limits (used in GUI schemas and server-side validation)
-# These ensure consistency between frontend and backend validation
-MAX_FLEX_PERCENTAGE = 50  # Maximum flexibility percentage (aligned with GUI slider and MAX_SAFE_FLEX)
-MAX_DISTANCE_PERCENTAGE = 50  # Maximum distance from average percentage (GUI slider limit)
-MAX_GAP_COUNT = 8  # Maximum gap count for level filtering (GUI slider limit)
-MAX_MIN_PERIODS = 10  # Maximum number of minimum periods per day (GUI slider limit)
-MAX_RELAXATION_ATTEMPTS = 12  # Maximum relaxation attempts (GUI slider limit)
-MIN_PERIOD_LENGTH = 15  # Minimum period length in minutes (1 quarter hour)
-MAX_MIN_PERIOD_LENGTH = 180  # Maximum for minimum period length setting (3 hours - realistic for required minimum)
-
-# Price rating threshold limits
-# LOW threshold: negative values (prices below average) - practical range -50% to -5%
-# HIGH threshold: positive values (prices above average) - practical range +5% to +50%
-# Ensure minimum 5% gap between thresholds to avoid overlap at 0%
-MIN_PRICE_RATING_THRESHOLD_LOW = -50  # Minimum value for low rating threshold
-MAX_PRICE_RATING_THRESHOLD_LOW = -5  # Maximum value for low rating threshold (must be < HIGH)
-MIN_PRICE_RATING_THRESHOLD_HIGH = 5  # Minimum value for high rating threshold (must be > LOW)
-MAX_PRICE_RATING_THRESHOLD_HIGH = 50  # Maximum value for high rating threshold
-MIN_PRICE_RATING_HYSTERESIS = 0.0  # Minimum hysteresis (0 = disabled)
-MAX_PRICE_RATING_HYSTERESIS = 5.0  # Maximum hysteresis (5% band)
-MIN_PRICE_RATING_GAP_TOLERANCE = 0  # Minimum gap tolerance (0 = disabled)
-MAX_PRICE_RATING_GAP_TOLERANCE = 4  # Maximum gap tolerance (4 intervals = 1 hour)
-MIN_PRICE_LEVEL_GAP_TOLERANCE = 0  # Minimum gap tolerance for price level (0 = disabled)
-MAX_PRICE_LEVEL_GAP_TOLERANCE = 4  # Maximum gap tolerance for price level (4 intervals = 1 hour)
-
-# Volatility threshold limits
-# MODERATE threshold: practical range 5% to 25% (entry point for noticeable fluctuation)
-# HIGH threshold: practical range 20% to 40% (significant price swings)
-# VERY_HIGH threshold: practical range 35% to 80% (extreme volatility)
-# Ensure cascading: MODERATE < HIGH < VERY_HIGH with ~5% minimum gaps
-MIN_VOLATILITY_THRESHOLD_MODERATE = 5.0  # Minimum for moderate volatility threshold
-MAX_VOLATILITY_THRESHOLD_MODERATE = 25.0  # Maximum for moderate volatility threshold (must be < HIGH)
-MIN_VOLATILITY_THRESHOLD_HIGH = 20.0  # Minimum for high volatility threshold (must be > MODERATE)
-MAX_VOLATILITY_THRESHOLD_HIGH = 40.0  # Maximum for high volatility threshold (must be < VERY_HIGH)
-MIN_VOLATILITY_THRESHOLD_VERY_HIGH = 35.0  # Minimum for very high volatility threshold (must be > HIGH)
-MAX_VOLATILITY_THRESHOLD_VERY_HIGH = 80.0  # Maximum for very high volatility threshold
-
-# Price trend threshold limits
-MIN_PRICE_TREND_RISING = 1  # Minimum rising trend threshold
-MAX_PRICE_TREND_RISING = 50  # Maximum rising trend threshold
-MIN_PRICE_TREND_FALLING = -50  # Minimum falling trend threshold (negative)
-MAX_PRICE_TREND_FALLING = -1  # Maximum falling trend threshold (negative)
-# Strong trend thresholds have higher ranges to allow detection of significant moves
-MIN_PRICE_TREND_STRONGLY_RISING = 2  # Minimum strongly rising threshold (must be > rising)
-MAX_PRICE_TREND_STRONGLY_RISING = 100  # Maximum strongly rising threshold
-MIN_PRICE_TREND_STRONGLY_FALLING = -100  # Minimum strongly falling threshold (negative)
-MAX_PRICE_TREND_STRONGLY_FALLING = -2  # Maximum strongly falling threshold (must be < falling)
-
-# Gap count and relaxation limits
-MIN_GAP_COUNT = 0  # Minimum gap count
-MIN_RELAXATION_ATTEMPTS = 1  # Minimum relaxation attempts
-
 # Home types
 HOME_TYPE_APARTMENT = "APARTMENT"
 HOME_TYPE_ROWHOUSE = "ROWHOUSE"
@@ -194,22 +109,12 @@ HOME_TYPES = {
 # Currency mapping: ISO code -> (major_symbol, minor_symbol, minor_name)
 # For currencies with Home Assistant constants, use those; otherwise define custom ones
 CURRENCY_INFO = {
-    "EUR": (CURRENCY_EURO, "ct", "Cents"),
+    "EUR": (CURRENCY_EURO, "ct", "cents"),
-    "NOK": ("kr", "øre", "Øre"),
+    "NOK": ("kr", "øre", "øre"),
-    "SEK": ("kr", "öre", "Öre"),
+    "SEK": ("kr", "öre", "öre"),
-    "DKK": ("kr", "øre", "Øre"),
+    "DKK": ("kr", "øre", "øre"),
-    "USD": (CURRENCY_DOLLAR, "¢", "Cents"),
+    "USD": (CURRENCY_DOLLAR, "¢", "cents"),
-    "GBP": ("£", "p", "Pence"),
+    "GBP": ("£", "p", "pence"),
-}
-
-# Base currency names: ISO code -> full currency name (in local language)
-CURRENCY_NAMES = {
-    "EUR": "Euro",
-    "NOK": "Norske kroner",
-    "SEK": "Svenska kronor",
-    "DKK": "Danske kroner",
-    "USD": "US Dollar",
-    "GBP": "British Pound",
 }
 
 
@@ -231,9 +136,9 @@ def get_currency_info(currency_code: str | None) -> tuple[str, str, str]:
     return CURRENCY_INFO.get(currency_code.upper(), CURRENCY_INFO["EUR"])
 
 
-def format_price_unit_base(currency_code: str | None) -> str:
+def format_price_unit_major(currency_code: str | None) -> str:
     """
-    Format the price unit string with base currency unit (e.g., '€/kWh').
+    Format the price unit string with major currency unit (e.g., '€/kWh').
 
     Args:
         currency_code: ISO 4217 currency code (e.g., 'EUR', 'NOK', 'SEK')
@@ -242,13 +147,13 @@ def format_price_unit_base(currency_code: str | None) -> str:
         Formatted unit string like '€/kWh' or 'kr/kWh'
 
     """
-    base_symbol, _, _ = get_currency_info(currency_code)
+    major_symbol, _, _ = get_currency_info(currency_code)
-    return f"{base_symbol}/{UnitOfPower.KILO_WATT}{UnitOfTime.HOURS}"
+    return f"{major_symbol}/{UnitOfPower.KILO_WATT}{UnitOfTime.HOURS}"
 
 
-def format_price_unit_subunit(currency_code: str | None) -> str:
+def format_price_unit_minor(currency_code: str | None) -> str:
     """
-    Format the price unit string with subunit currency unit (e.g., 'ct/kWh').
+    Format the price unit string with minor currency unit (e.g., 'ct/kWh').
 
     Args:
         currency_code: ISO 4217 currency code (e.g., 'EUR', 'NOK', 'SEK')
@@ -257,190 +162,11 @@ def format_price_unit_subunit(currency_code: str | None) -> str:
         Formatted unit string like 'ct/kWh' or 'øre/kWh'
 
     """
-    _, subunit_symbol, _ = get_currency_info(currency_code)
+    _, minor_symbol, _ = get_currency_info(currency_code)
-    return f"{subunit_symbol}/{UnitOfPower.KILO_WATT}{UnitOfTime.HOURS}"
+    return f"{minor_symbol}/{UnitOfPower.KILO_WATT}{UnitOfTime.HOURS}"
 
 
-def get_currency_name(currency_code: str | None) -> str:
+# Price level constants from Tibber API
-    """
-    Get the full name of the base currency.
-
-    Args:
-        currency_code: ISO 4217 currency code (e.g., 'EUR', 'NOK', 'SEK')
-
-    Returns:
-        Full currency name like 'Euro' or 'Norwegian Krone'
-        Defaults to 'Euro' if currency is not recognized
-
-    """
-    if not currency_code:
-        currency_code = "EUR"
-
-    return CURRENCY_NAMES.get(currency_code.upper(), CURRENCY_NAMES["EUR"])
-
-
-# ============================================================================
-# Currency Display Mode Configuration
-# ============================================================================
-
-# Configuration key for currency display mode
-CONF_CURRENCY_DISPLAY_MODE = "currency_display_mode"
-
-# Display mode values
-DISPLAY_MODE_BASE = "base"  # Display in base currency units (€, kr)
-DISPLAY_MODE_SUBUNIT = "subunit"  # Display in subunit currency units (ct, øre)
-
-# Intelligent per-currency defaults based on market analysis
-# EUR: Subunit (cents) - established convention in Germany/Netherlands
-# NOK/SEK/DKK: Base (kroner) - Scandinavian preference for whole units
-# USD/GBP: Base - international standard
-DEFAULT_CURRENCY_DISPLAY = {
-    "EUR": DISPLAY_MODE_SUBUNIT,
-    "NOK": DISPLAY_MODE_BASE,
-    "SEK": DISPLAY_MODE_BASE,
-    "DKK": DISPLAY_MODE_BASE,
-    "USD": DISPLAY_MODE_BASE,
-    "GBP": DISPLAY_MODE_BASE,
-}
-
-
-def get_default_currency_display(currency_code: str | None) -> str:
-    """
-    Get intelligent default display mode for a currency.
-
-    Args:
-        currency_code: ISO 4217 currency code (e.g., 'EUR', 'NOK')
-
-    Returns:
-        Default display mode ('base' or 'subunit')
-
-    """
-    if not currency_code:
-        return DISPLAY_MODE_SUBUNIT  # Fallback default
-
-    return DEFAULT_CURRENCY_DISPLAY.get(currency_code.upper(), DISPLAY_MODE_SUBUNIT)
-
-
-def get_default_options(currency_code: str | None) -> dict[str, Any]:
-    """
-    Get complete default options for a new config entry.
-
-    This ensures new config entries have explicitly set defaults based on their currency,
-    distinguishing them from legacy config entries that need migration.
-
-    Options structure has been flattened for single-section steps:
-    - Flat values: extended_descriptions, average_sensor_display, currency_display_mode,
-      price_rating_thresholds, volatility_thresholds, price_trend_thresholds, time offsets
-    - Nested sections (multi-section steps only): period_settings, flexibility_settings,
-      relaxation_and_target_periods
-
-    Args:
-        currency_code: ISO 4217 currency code (e.g., 'EUR', 'NOK')
-
-    Returns:
-        Dictionary with all default option values in nested section structure
-
-    """
-    return {
-        # Flat configuration values
-        CONF_EXTENDED_DESCRIPTIONS: DEFAULT_EXTENDED_DESCRIPTIONS,
-        CONF_AVERAGE_SENSOR_DISPLAY: DEFAULT_AVERAGE_SENSOR_DISPLAY,
-        CONF_CURRENCY_DISPLAY_MODE: get_default_currency_display(currency_code),
-        CONF_VIRTUAL_TIME_OFFSET_DAYS: DEFAULT_VIRTUAL_TIME_OFFSET_DAYS,
-        CONF_VIRTUAL_TIME_OFFSET_HOURS: DEFAULT_VIRTUAL_TIME_OFFSET_HOURS,
-        CONF_VIRTUAL_TIME_OFFSET_MINUTES: DEFAULT_VIRTUAL_TIME_OFFSET_MINUTES,
-        # Price rating settings (flat - single-section step)
-        CONF_PRICE_RATING_THRESHOLD_LOW: DEFAULT_PRICE_RATING_THRESHOLD_LOW,
-        CONF_PRICE_RATING_THRESHOLD_HIGH: DEFAULT_PRICE_RATING_THRESHOLD_HIGH,
-        CONF_PRICE_RATING_HYSTERESIS: DEFAULT_PRICE_RATING_HYSTERESIS,
-        CONF_PRICE_RATING_GAP_TOLERANCE: DEFAULT_PRICE_RATING_GAP_TOLERANCE,
-        CONF_PRICE_LEVEL_GAP_TOLERANCE: DEFAULT_PRICE_LEVEL_GAP_TOLERANCE,
-        # Volatility thresholds (flat - single-section step)
-        CONF_VOLATILITY_THRESHOLD_MODERATE: DEFAULT_VOLATILITY_THRESHOLD_MODERATE,
-        CONF_VOLATILITY_THRESHOLD_HIGH: DEFAULT_VOLATILITY_THRESHOLD_HIGH,
-        CONF_VOLATILITY_THRESHOLD_VERY_HIGH: DEFAULT_VOLATILITY_THRESHOLD_VERY_HIGH,
-        # Price trend thresholds (flat - single-section step)
-        CONF_PRICE_TREND_THRESHOLD_RISING: DEFAULT_PRICE_TREND_THRESHOLD_RISING,
-        CONF_PRICE_TREND_THRESHOLD_FALLING: DEFAULT_PRICE_TREND_THRESHOLD_FALLING,
-        # Nested section: Period settings (shared by best/peak price)
-        "period_settings": {
-            CONF_BEST_PRICE_MIN_PERIOD_LENGTH: DEFAULT_BEST_PRICE_MIN_PERIOD_LENGTH,
-            CONF_PEAK_PRICE_MIN_PERIOD_LENGTH: DEFAULT_PEAK_PRICE_MIN_PERIOD_LENGTH,
-            CONF_BEST_PRICE_MAX_LEVEL_GAP_COUNT: DEFAULT_BEST_PRICE_MAX_LEVEL_GAP_COUNT,
-            CONF_PEAK_PRICE_MAX_LEVEL_GAP_COUNT: DEFAULT_PEAK_PRICE_MAX_LEVEL_GAP_COUNT,
-            CONF_BEST_PRICE_MAX_LEVEL: DEFAULT_BEST_PRICE_MAX_LEVEL,
-            CONF_PEAK_PRICE_MIN_LEVEL: DEFAULT_PEAK_PRICE_MIN_LEVEL,
-        },
-        # Nested section: Flexibility settings (shared by best/peak price)
-        "flexibility_settings": {
-            CONF_BEST_PRICE_FLEX: DEFAULT_BEST_PRICE_FLEX,
-            CONF_PEAK_PRICE_FLEX: DEFAULT_PEAK_PRICE_FLEX,
-            CONF_BEST_PRICE_MIN_DISTANCE_FROM_AVG: DEFAULT_BEST_PRICE_MIN_DISTANCE_FROM_AVG,
-            CONF_PEAK_PRICE_MIN_DISTANCE_FROM_AVG: DEFAULT_PEAK_PRICE_MIN_DISTANCE_FROM_AVG,
-        },
-        # Nested section: Relaxation and target periods (shared by best/peak price)
-        "relaxation_and_target_periods": {
-            CONF_ENABLE_MIN_PERIODS_BEST: DEFAULT_ENABLE_MIN_PERIODS_BEST,
-            CONF_MIN_PERIODS_BEST: DEFAULT_MIN_PERIODS_BEST,
-            CONF_RELAXATION_ATTEMPTS_BEST: DEFAULT_RELAXATION_ATTEMPTS_BEST,
-            CONF_ENABLE_MIN_PERIODS_PEAK: DEFAULT_ENABLE_MIN_PERIODS_PEAK,
-            CONF_MIN_PERIODS_PEAK: DEFAULT_MIN_PERIODS_PEAK,
-            CONF_RELAXATION_ATTEMPTS_PEAK: DEFAULT_RELAXATION_ATTEMPTS_PEAK,
-        },
-    }
-
-
-def get_display_unit_factor(config_entry: ConfigEntry) -> int:
-    """
-    Get multiplication factor for converting base to display currency.
-
-    Internal storage is ALWAYS in base currency (4 decimals precision).
-    This function returns the conversion factor based on user configuration.
-
-    Args:
-        config_entry: ConfigEntry with currency_display_mode option
-
-    Returns:
-        100 for subunit currency display, 1 for base currency display
-
-    Example:
-        price_base = 0.2534  # Internal: 0.2534 €/kWh
-        factor = get_display_unit_factor(config_entry)
-        display_value = round(price_base * factor, 2)
-        # → 25.34 ct/kWh (subunit) or 0.25 €/kWh (base)
-
-    """
-    display_mode = config_entry.options.get(CONF_CURRENCY_DISPLAY_MODE, DISPLAY_MODE_SUBUNIT)
-    return 100 if display_mode == DISPLAY_MODE_SUBUNIT else 1
-
-
-def get_display_unit_string(config_entry: ConfigEntry, currency_code: str | None) -> str:
-    """
-    Get unit string for display based on configuration.
-
-    Args:
-        config_entry: ConfigEntry with currency_display_mode option
-        currency_code: ISO 4217 currency code
-
-    Returns:
-        Formatted unit string (e.g., 'ct/kWh' or '€/kWh')
-
-    """
-    display_mode = config_entry.options.get(CONF_CURRENCY_DISPLAY_MODE, DISPLAY_MODE_SUBUNIT)
-
-    if display_mode == DISPLAY_MODE_SUBUNIT:
-        return format_price_unit_subunit(currency_code)
-    return format_price_unit_base(currency_code)
-
-
-# ============================================================================
-# Price Level, Rating, and Volatility Constants
-# ============================================================================
-# IMPORTANT: These string constants are the single source of truth for
-# valid enum values. The Literal types in sensor/types.py and binary_sensor/types.py
-# should be kept in sync with these values manually.
-
-# Price level constants (from Tibber API)
 PRICE_LEVEL_VERY_CHEAP = "VERY_CHEAP"
 PRICE_LEVEL_CHEAP = "CHEAP"
 PRICE_LEVEL_NORMAL = "NORMAL"
@@ -452,20 +178,12 @@ PRICE_RATING_LOW = "LOW"
 PRICE_RATING_NORMAL = "NORMAL"
 PRICE_RATING_HIGH = "HIGH"
 
-# Price volatility level constants
+# Price volatility levels (based on coefficient of variation: std_dev / mean * 100%)
 VOLATILITY_LOW = "LOW"
 VOLATILITY_MODERATE = "MODERATE"
 VOLATILITY_HIGH = "HIGH"
 VOLATILITY_VERY_HIGH = "VERY_HIGH"
 
-# Price trend constants (calculated values with 5-level scale)
-# Used by trend sensors: momentary, short-term, mid-term, long-term
-PRICE_TREND_STRONGLY_FALLING = "strongly_falling"
-PRICE_TREND_FALLING = "falling"
-PRICE_TREND_STABLE = "stable"
-PRICE_TREND_RISING = "rising"
-PRICE_TREND_STRONGLY_RISING = "strongly_rising"
-
 # Sensor options (lowercase versions for ENUM device class)
 # NOTE: These constants define the valid enum options, but they are not used directly
 # in sensor/definitions.py due to import timing issues. Instead, the options are defined inline
@@ -491,15 +209,6 @@ VOLATILITY_OPTIONS = [
     VOLATILITY_VERY_HIGH.lower(),
 ]
 
-# Trend options for enum sensors (lowercase versions for ENUM device class)
-PRICE_TREND_OPTIONS = [
-    PRICE_TREND_STRONGLY_FALLING,
-    PRICE_TREND_FALLING,
-    PRICE_TREND_STABLE,
-    PRICE_TREND_RISING,
-    PRICE_TREND_STRONGLY_RISING,
-]
-
 # Valid options for best price maximum level filter
 # Sorted from cheap to expensive: user selects "up to how expensive"
 BEST_PRICE_MAX_LEVEL_OPTIONS = [
@@ -542,16 +251,6 @@ PRICE_RATING_MAPPING = {
     PRICE_RATING_HIGH: 1,
 }
 
-# Mapping for comparing price trends (used for sorting and automation comparisons)
-# Values range from -2 (strongly falling) to +2 (strongly rising), with 0 = stable
-PRICE_TREND_MAPPING = {
-    PRICE_TREND_STRONGLY_FALLING: -2,
-    PRICE_TREND_FALLING: -1,
-    PRICE_TREND_STABLE: 0,
-    PRICE_TREND_RISING: 1,
-    PRICE_TREND_STRONGLY_RISING: 2,
-}
-
 # Icon mapping for price levels (dynamic icons based on level)
 PRICE_LEVEL_ICON_MAPPING = {
     PRICE_LEVEL_VERY_CHEAP: "mdi:gauge-empty",

custom_components/tibber_prices/coordinator/cache.py

@@ -1,28 +1,4 @@
-"""
+"""Cache management for coordinator module."""
-Cache management for coordinator persistent storage.
-
-This module handles persistent storage for the coordinator, storing:
-- user_data: Account/home metadata (required, refreshed daily)
-- Timestamps for cache validation and lifecycle tracking
-
-**Storage Architecture (as of v0.25.0):**
-
-There are TWO persistent storage files per config entry:
-
-1. `tibber_prices.{entry_id}` (this module)
-   - user_data: Account info, home metadata, timezone, currency
-   - Timestamps: last_user_update, last_midnight_check
-
-2. `tibber_prices.interval_pool.{entry_id}` (interval_pool/storage.py)
-   - Intervals: Deduplicated quarter-hourly price data (source of truth)
-   - Fetch metadata: When each interval was fetched
-   - Protected range: Which intervals to keep during cleanup
-
-**Single Source of Truth:**
-Price intervals are ONLY stored in IntervalPool. This cache stores only
-user metadata and timestamps. The IntervalPool handles all price data
-fetching, caching, and persistence independently.
-"""
 
 from __future__ import annotations
 
@@ -40,9 +16,11 @@ _LOGGER = logging.getLogger(__name__)
 
 
 class TibberPricesCacheData(NamedTuple):
-    """Cache data structure for user metadata (price data is in IntervalPool)."""
+    """Cache data structure."""
 
+    price_data: dict[str, Any] | None
     user_data: dict[str, Any] | None
+    last_price_update: datetime | None
     last_user_update: datetime | None
     last_midnight_check: datetime | None
 
@@ -53,16 +31,20 @@ async def load_cache(
     *,
     time: TibberPricesTimeService,
 ) -> TibberPricesCacheData:
-    """Load cached user data from storage (price data is in IntervalPool)."""
+    """Load cached data from storage."""
     try:
         stored = await store.async_load()
         if stored:
+            cached_price_data = stored.get("price_data")
             cached_user_data = stored.get("user_data")
 
             # Restore timestamps
+            last_price_update = None
             last_user_update = None
             last_midnight_check = None
 
+            if last_price_update_str := stored.get("last_price_update"):
+                last_price_update = time.parse_datetime(last_price_update_str)
             if last_user_update_str := stored.get("last_user_update"):
                 last_user_update = time.parse_datetime(last_user_update_str)
             if last_midnight_check_str := stored.get("last_midnight_check"):
@@ -70,7 +52,9 @@ async def load_cache(
 
             _LOGGER.debug("%s Cache loaded successfully", log_prefix)
             return TibberPricesCacheData(
+                price_data=cached_price_data,
                 user_data=cached_user_data,
+                last_price_update=last_price_update,
                 last_user_update=last_user_update,
                 last_midnight_check=last_midnight_check,
             )
@@ -80,7 +64,9 @@ async def load_cache(
         _LOGGER.warning("%s Failed to load cache: %s", log_prefix, ex)
 
     return TibberPricesCacheData(
+        price_data=None,
         user_data=None,
+        last_price_update=None,
         last_user_update=None,
         last_midnight_check=None,
     )
@@ -91,9 +77,11 @@ async def save_cache(
     cache_data: TibberPricesCacheData,
     log_prefix: str,
 ) -> None:
-    """Store cache data (user metadata only, price data is in IntervalPool)."""
+    """Store cache data."""
     data = {
+        "price_data": cache_data.price_data,
         "user_data": cache_data.user_data,
+        "last_price_update": (cache_data.last_price_update.isoformat() if cache_data.last_price_update else None),
         "last_user_update": (cache_data.last_user_update.isoformat() if cache_data.last_user_update else None),
         "last_midnight_check": (cache_data.last_midnight_check.isoformat() if cache_data.last_midnight_check else None),
     }
@@ -103,3 +91,36 @@ async def save_cache(
         _LOGGER.debug("%s Cache stored successfully", log_prefix)
     except OSError:
         _LOGGER.exception("%s Failed to store cache", log_prefix)
+
+
+def is_cache_valid(
+    cache_data: TibberPricesCacheData,
+    log_prefix: str,
+    *,
+    time: TibberPricesTimeService,
+) -> bool:
+    """
+    Validate if cached price data is still current.
+
+    Returns False if:
+    - No cached data exists
+    - Cached data is from a different calendar day (in local timezone)
+    - Midnight turnover has occurred since cache was saved
+
+    """
+    if cache_data.price_data is None or cache_data.last_price_update is None:
+        return False
+
+    current_local_date = time.as_local(time.now()).date()
+    last_update_local_date = time.as_local(cache_data.last_price_update).date()
+
+    if current_local_date != last_update_local_date:
+        _LOGGER.debug(
+            "%s Cache date mismatch: cached=%s, current=%s",
+            log_prefix,
+            last_update_local_date,
+            current_local_date,
+        )
+        return False
+
+    return True

custom_components/tibber_prices/coordinator/constants.py

@@ -31,7 +31,6 @@ TIME_SENSITIVE_ENTITY_KEYS = frozenset(
     {
         # Current/next/previous price sensors
         "current_interval_price",
-        "current_interval_price_base",
         "next_interval_price",
         "previous_interval_price",
         # Current/next/previous price levels
@@ -85,11 +84,7 @@ TIME_SENSITIVE_ENTITY_KEYS = frozenset(
         "best_price_next_start_time",
         "peak_price_end_time",
         "peak_price_next_start_time",
-        # Lifecycle sensor needs quarter-hour precision for state transitions:
+        # Lifecycle sensor (needs quarter-hour updates for turnover_pending detection at 23:45)
-        # - 23:45: turnover_pending (last interval before midnight)
-        # - 00:00: turnover complete (after midnight API update)
-        # - 13:00: searching_tomorrow (when tomorrow data search begins)
-        # Uses state-change filter in _handle_time_sensitive_update() to prevent recorder spam
         "data_lifecycle_status",
     }
 )

custom_components/tibber_prices/coordinator/core.py

@@ -6,17 +6,21 @@ import logging
 from datetime import timedelta
 from typing import TYPE_CHECKING, Any
 
+from homeassistant.const import CONF_ACCESS_TOKEN
 from homeassistant.core import CALLBACK_TYPE, HomeAssistant, callback
+from homeassistant.helpers import aiohttp_client
 from homeassistant.helpers.storage import Store
-from homeassistant.helpers.update_coordinator import DataUpdateCoordinator
+from homeassistant.helpers.update_coordinator import DataUpdateCoordinator, UpdateFailed
 
 if TYPE_CHECKING:
+    from collections.abc import Callable
     from datetime import date, datetime
 
     from homeassistant.config_entries import ConfigEntry
 
     from .listeners import TimeServiceCallback
 
+from custom_components.tibber_prices import const as _const
 from custom_components.tibber_prices.api import (
     TibberPricesApiClient,
     TibberPricesApiClientAuthenticationError,
@@ -27,19 +31,16 @@ from custom_components.tibber_prices.const import DOMAIN
 from custom_components.tibber_prices.utils.price import (
     find_price_data_for_interval,
 )
-from homeassistant.exceptions import ConfigEntryAuthFailed
 
 from . import helpers
 from .constants import (
     STORAGE_VERSION,
     UPDATE_INTERVAL,
 )
+from .data_fetching import TibberPricesDataFetcher
 from .data_transformation import TibberPricesDataTransformer
 from .listeners import TibberPricesListenerManager
-from .midnight_handler import TibberPricesMidnightHandler
 from .periods import TibberPricesPeriodCalculator
-from .price_data_manager import TibberPricesPriceDataManager
-from .repairs import TibberPricesRepairManager
 from .time_service import TibberPricesTimeService
 
 _LOGGER = logging.getLogger(__name__)
@@ -47,44 +48,6 @@ _LOGGER = logging.getLogger(__name__)
 # Lifecycle state transition thresholds
 FRESH_TO_CACHED_SECONDS = 300  # 5 minutes
 
-
-def get_connection_state(coordinator: TibberPricesDataUpdateCoordinator) -> bool | None:
-    """
-    Determine API connection state based on lifecycle and exceptions.
-
-    This is the source of truth for the connection binary sensor.
-    It ensures consistency between lifecycle_status and connection state.
-
-    Returns:
-        True: Connected and working (cached or fresh data)
-        False: Connection failed or auth failed
-        None: Unknown state (no data yet, initializing)
-
-    Logic:
-        - Auth failures → definitively disconnected (False)
-        - Other errors with cached data → considered connected (True, using cache)
-        - No errors with data → connected (True)
-        - No data and no error → initializing (None)
-
-    """
-    # Auth failures = definitively disconnected
-    # User must provide new token via reauth flow
-    if isinstance(coordinator.last_exception, ConfigEntryAuthFailed):
-        return False
-
-    # Other errors but cache available = considered connected (using cached data as fallback)
-    # This shows "on" but lifecycle_status will show "error" to indicate degraded operation
-    if coordinator.last_exception and coordinator.data:
-        return True
-
-    # No error and data available = connected
-    if coordinator.data:
-        return True
-
-    # No data and no error = initializing (unknown state)
-    return None
-
-
 # =============================================================================
 # TIMER SYSTEM - Three independent update mechanisms:
 # =============================================================================
@@ -141,17 +104,6 @@ def get_connection_state(coordinator: TibberPricesDataUpdateCoordinator) -> bool
 #   - No race condition possible - Python datetime.date() comparison is thread-safe
 #   - _last_transformation_time is separate and tracks when data was last transformed (for cache)
 #
-#   CRITICAL - Dual Listener System:
-#   After midnight turnover, BOTH listener groups must be notified:
-#   1. Normal listeners (async_update_listeners) - standard HA entities
-#   2. Time-sensitive listeners (_async_update_time_sensitive_listeners) - quarter-hour entities
-#
-#   Why? Entities like best_price_period and peak_price_period register as time-sensitive
-#   listeners and won't update if only async_update_listeners() is called. This caused
-#   the bug where period binary sensors showed stale data until the next quarter-hour
-#   refresh at 00:15 (they were updated then because Timer #2 explicitly calls
-#   _async_update_time_sensitive_listeners in its normal flow).
-#
 # =============================================================================
 
 
@@ -162,8 +114,7 @@ class TibberPricesDataUpdateCoordinator(DataUpdateCoordinator[dict[str, Any]]):
         self,
         hass: HomeAssistant,
         config_entry: ConfigEntry,
-        api_client: TibberPricesApiClient,
+        version: str,
-        interval_pool: Any,  # TibberPricesIntervalPool - Any to avoid circular import
     ) -> None:
         """Initialize the coordinator."""
         super().__init__(
@@ -174,17 +125,11 @@ class TibberPricesDataUpdateCoordinator(DataUpdateCoordinator[dict[str, Any]]):
         )
 
         self.config_entry = config_entry
-
+        self.api = TibberPricesApiClient(
-        # Get home_id from config entry
+            access_token=config_entry.data[CONF_ACCESS_TOKEN],
-        self._home_id = config_entry.data.get("home_id", "")
+            session=aiohttp_client.async_get_clientsession(hass),
-        if not self._home_id:
+            version=version,
-            _LOGGER.error("No home_id found in config entry %s", config_entry.entry_id)
+        )
-
-        # Use the API client from runtime_data (created in __init__.py with proper TOKEN handling)
-        self.api = api_client
-
-        # Use the shared interval pool (one per config entry/Tibber account)
-        self.interval_pool = interval_pool
 
         # Storage for persistence
         storage_key = f"{DOMAIN}.{config_entry.entry_id}"
@@ -193,8 +138,8 @@ class TibberPricesDataUpdateCoordinator(DataUpdateCoordinator[dict[str, Any]]):
         # Log prefix for identifying this coordinator instance
         self._log_prefix = f"[{config_entry.title}]"
 
-        # Note: In the new architecture, all coordinators (parent + subentries) fetch their own data
+        # Track if this is the main entry (first one created)
-        # No distinction between "main" and "sub" coordinators anymore
+        self._is_main_entry = not self._has_existing_main_coordinator()
 
         # Initialize time service (single source of truth for all time operations)
         self.time = TibberPricesTimeService()
@@ -204,62 +149,48 @@ class TibberPricesDataUpdateCoordinator(DataUpdateCoordinator[dict[str, Any]]):
 
         # Initialize helper modules
         self._listener_manager = TibberPricesListenerManager(hass, self._log_prefix)
-        self._midnight_handler = TibberPricesMidnightHandler()
+        self._data_fetcher = TibberPricesDataFetcher(
-        self._price_data_manager = TibberPricesPriceDataManager(
             api=self.api,
             store=self._store,
             log_prefix=self._log_prefix,
             user_update_interval=timedelta(days=1),
             time=self.time,
-            home_id=self._home_id,
-            interval_pool=self.interval_pool,
-        )
-        # Create period calculator BEFORE data transformer (transformer needs it in lambda)
-        self._period_calculator = TibberPricesPeriodCalculator(
-            config_entry=config_entry,
-            log_prefix=self._log_prefix,
-            get_config_override_fn=self.get_config_override,
         )
         self._data_transformer = TibberPricesDataTransformer(
             config_entry=config_entry,
             log_prefix=self._log_prefix,
-            calculate_periods_fn=lambda price_info: self._period_calculator.calculate_periods_for_price_info(
+            perform_turnover_fn=self._perform_midnight_turnover,
-                price_info
-            ),
             time=self.time,
         )
-        self._repair_manager = TibberPricesRepairManager(
+        self._period_calculator = TibberPricesPeriodCalculator(
-            hass=hass,
+            config_entry=config_entry,
-            entry_id=config_entry.entry_id,
+            log_prefix=self._log_prefix,
-            home_name=config_entry.title,
         )
 
         # Register options update listener to invalidate config caches
         config_entry.async_on_unload(config_entry.add_update_listener(self._handle_options_update))
 
-        # User data cache (price data is in IntervalPool)
+        # Legacy compatibility - keep references for methods that access directly
         self._cached_user_data: dict[str, Any] | None = None
         self._last_user_update: datetime | None = None
         self._user_update_interval = timedelta(days=1)
+        self._cached_price_data: dict[str, Any] | None = None
+        self._last_price_update: datetime | None = None
+        self._cached_transformed_data: dict[str, Any] | None = None
+        self._last_transformation_config: dict[str, Any] | None = None
+        self._last_transformation_time: datetime | None = None  # When data was last transformed (for cache)
+        self._last_midnight_turnover_check: datetime | None = None  # Last midnight turnover detection check
+        self._last_actual_turnover: datetime | None = None  # When midnight turnover actually happened
 
-        # Data lifecycle tracking
+        # Data lifecycle tracking for diagnostic sensor
-        # Note: _lifecycle_state is used for DIAGNOSTICS only (diagnostics.py export).
-        # The lifecycle SENSOR calculates its state dynamically in get_lifecycle_state(),
-        # using: _is_fetching, last_exception, time calculations, _needs_tomorrow_data(),
-        # and _last_price_update. It does NOT read _lifecycle_state!
         self._lifecycle_state: str = (
-            "cached"  # For diagnostics: cached, fresh, refreshing, searching_tomorrow, turnover_pending, error
+            "cached"  # Current state: cached, fresh, refreshing, searching_tomorrow, turnover_pending, error
         )
-        self._last_price_update: datetime | None = None  # When price data was last fetched from API
         self._api_calls_today: int = 0  # Counter for API calls today
         self._last_api_call_date: date | None = None  # Date of last API call (for daily reset)
-        self._is_fetching: bool = False  # Flag to track active API fetch (read by lifecycle sensor)
+        self._is_fetching: bool = False  # Flag to track active API fetch
         self._last_coordinator_update: datetime | None = None  # When Timer #1 last ran (_async_update_data)
-
+        self._lifecycle_callbacks: list[Callable[[], None]] = []  # Push-update callbacks for lifecycle sensor
-        # Runtime config overrides from config entities (number/switch)
-        # Structure: {"section_name": {"config_key": value, ...}, ...}
-        # When set, these override the corresponding options from config_entry.options
-        self._config_overrides: dict[str, dict[str, Any]] = {}
 
         # Start timers
         self._listener_manager.schedule_quarter_hour_refresh(self._handle_quarter_hour_refresh)
@@ -271,129 +202,12 @@ class TibberPricesDataUpdateCoordinator(DataUpdateCoordinator[dict[str, Any]]):
         getattr(_LOGGER, level)(prefixed_message, *args, **kwargs)
 
     async def _handle_options_update(self, _hass: HomeAssistant, _config_entry: ConfigEntry) -> None:
-        """Handle options update by invalidating config caches and re-transforming data."""
+        """Handle options update by invalidating config caches."""
-        self._log("debug", "Options update triggered, re-transforming data")
+        self._log("debug", "Options updated, invalidating config caches")
         self._data_transformer.invalidate_config_cache()
         self._period_calculator.invalidate_config_cache()
-
+        # Trigger a refresh to apply new configuration
-        # Re-transform existing data with new configuration
+        await self.async_request_refresh()
-        # This updates rating_levels, volatility, and period calculations
-        # without needing to fetch new data from the API
-        if self.data and "priceInfo" in self.data:
-            # Extract raw price_info and re-transform
-            raw_data = {"price_info": self.data["priceInfo"]}
-            self.data = self._transform_data(raw_data)
-            self.async_update_listeners()
-        else:
-            self._log("debug", "No data to re-transform")
-
-    # =========================================================================
-    # Runtime Config Override Methods (for number/switch entities)
-    # =========================================================================
-
-    def set_config_override(self, config_key: str, config_section: str, value: Any) -> None:
-        """
-        Set a runtime config override value.
-
-        These overrides take precedence over options from config_entry.options
-        and are used by number/switch entities for runtime configuration.
-
-        Args:
-            config_key: The configuration key (e.g., CONF_BEST_PRICE_FLEX)
-            config_section: The section in options (e.g., "flexibility_settings")
-            value: The override value
-
-        """
-        if config_section not in self._config_overrides:
-            self._config_overrides[config_section] = {}
-        self._config_overrides[config_section][config_key] = value
-        self._log(
-            "debug",
-            "Config override set: %s.%s = %s",
-            config_section,
-            config_key,
-            value,
-        )
-
-    def remove_config_override(self, config_key: str, config_section: str) -> None:
-        """
-        Remove a runtime config override value.
-
-        After removal, the value from config_entry.options will be used again.
-
-        Args:
-            config_key: The configuration key to remove
-            config_section: The section the key belongs to
-
-        """
-        if config_section in self._config_overrides:
-            self._config_overrides[config_section].pop(config_key, None)
-            # Clean up empty sections
-            if not self._config_overrides[config_section]:
-                del self._config_overrides[config_section]
-        self._log(
-            "debug",
-            "Config override removed: %s.%s",
-            config_section,
-            config_key,
-        )
-
-    def get_config_override(self, config_key: str, config_section: str) -> Any | None:
-        """
-        Get a runtime config override value if set.
-
-        Args:
-            config_key: The configuration key to check
-            config_section: The section the key belongs to
-
-        Returns:
-            The override value if set, None otherwise
-
-        """
-        return self._config_overrides.get(config_section, {}).get(config_key)
-
-    def has_config_override(self, config_key: str, config_section: str) -> bool:
-        """
-        Check if a runtime config override is set.
-
-        Args:
-            config_key: The configuration key to check
-            config_section: The section the key belongs to
-
-        Returns:
-            True if an override is set, False otherwise
-
-        """
-        return config_key in self._config_overrides.get(config_section, {})
-
-    def get_active_overrides(self) -> dict[str, dict[str, Any]]:
-        """
-        Get all active config overrides.
-
-        Returns:
-            Dictionary of all active overrides by section
-
-        """
-        return self._config_overrides.copy()
-
-    async def async_handle_config_override_update(self) -> None:
-        """
-        Handle config override change by invalidating caches and re-transforming data.
-
-        This is called by number/switch entities when their values change.
-        Uses the same logic as options update to ensure consistent behavior.
-        """
-        self._log("debug", "Config override update triggered, re-transforming data")
-        self._data_transformer.invalidate_config_cache()
-        self._period_calculator.invalidate_config_cache()
-
-        # Re-transform existing data with new configuration
-        if self.data and "priceInfo" in self.data:
-            raw_data = {"price_info": self.data["priceInfo"]}
-            self.data = self._transform_data(raw_data)
-            self.async_update_listeners()
-        else:
-            self._log("debug", "No data to re-transform")
 
     @callback
     def async_add_time_sensitive_listener(self, update_callback: TimeServiceCallback) -> CALLBACK_TYPE:
@@ -473,7 +287,7 @@ class TibberPricesDataUpdateCoordinator(DataUpdateCoordinator[dict[str, Any]]):
 
         # Update helper modules with fresh TimeService instance
         self.api.time = time_service
-        self._price_data_manager.time = time_service
+        self._data_fetcher.time = time_service
         self._data_transformer.time = time_service
         self._period_calculator.time = time_service
 
@@ -539,13 +353,16 @@ class TibberPricesDataUpdateCoordinator(DataUpdateCoordinator[dict[str, Any]]):
             True if midnight turnover is needed, False if already done
 
         """
-        # Initialize handler on first use
+        current_date = now.date()
-        if self._midnight_handler.last_check_time is None:
+
-            self._midnight_handler.update_check_time(now)
+        # First time check - initialize (no turnover needed)
+        if self._last_midnight_turnover_check is None:
             return False
 
-        # Delegate to midnight handler
+        last_check_date = self._last_midnight_turnover_check.date()
-        return self._midnight_handler.is_turnover_needed(now)
+
+        # Turnover needed if we've crossed into a new day
+        return current_date > last_check_date
 
     def _perform_midnight_data_rotation(self, now: datetime) -> None:
         """
@@ -563,7 +380,7 @@ class TibberPricesDataUpdateCoordinator(DataUpdateCoordinator[dict[str, Any]]):
         """
         current_date = now.date()
         last_check_date = (
-            self._midnight_handler.last_check_time.date() if self._midnight_handler.last_check_time else current_date
+            self._last_midnight_turnover_check.date() if self._last_midnight_turnover_check else current_date
         )
 
         self._log(
@@ -573,16 +390,28 @@ class TibberPricesDataUpdateCoordinator(DataUpdateCoordinator[dict[str, Any]]):
             current_date,
         )
 
-        # With flat interval list architecture and IntervalPool as source of truth,
+        # Perform rotation on cached data if available
-        # no data rotation needed! get_intervals_for_day_offsets() automatically
+        if self._cached_price_data and "homes" in self._cached_price_data:
-        # filters by date. Just re-transform to refresh enrichment.
+            for home_id, home_data in self._cached_price_data["homes"].items():
-        if self.data and "priceInfo" in self.data:
+                if "price_info" in home_data:
-            # Re-transform data to ensure enrichment is refreshed for new day
+                    price_info = home_data["price_info"]
-            raw_data = {"price_info": self.data["priceInfo"]}
+                    rotated = self._perform_midnight_turnover(price_info)
-            self.data = self._transform_data(raw_data)
+                    home_data["price_info"] = rotated
+                    self._log("debug", "Rotated price data for home %s", home_id)
+
+            # Update coordinator's data with enriched rotated data
+            if self.data:
+                # Re-transform data to ensure enrichment is applied to rotated data
+                if self.is_main_entry():
+                    self.data = self._transform_data_for_main_entry(self._cached_price_data)
+                else:
+                    # For subentry, get fresh data from main coordinator after rotation
+                    # Main coordinator will have performed rotation already
+                    self.data["timestamp"] = now
 
         # Mark turnover as done for today (atomic update)
-        self._midnight_handler.mark_turnover_done(now)
+        self._last_midnight_turnover_check = now
+        self._last_actual_turnover = now  # Record when actual turnover happened
 
     @callback
     def _check_and_handle_midnight_turnover(self, now: datetime) -> bool:
@@ -610,44 +439,46 @@ class TibberPricesDataUpdateCoordinator(DataUpdateCoordinator[dict[str, Any]]):
         self._log("info", "[Timer #2] Midnight turnover detected, performing data rotation")
         self._perform_midnight_data_rotation(now)
 
-        # CRITICAL: Notify BOTH listener groups after midnight turnover
+        # Notify listeners about updated data
-        # - async_update_listeners(): Notifies normal entities (via HA's DataUpdateCoordinator)
-        # - async_update_time_sensitive_listeners(): Notifies time-sensitive entities (custom system)
-        # Without both calls, period binary sensors (best_price_period, peak_price_period)
-        # won't update because they're time-sensitive listeners, not normal listeners.
         self.async_update_listeners()
 
-        # Create TimeService with fresh reference time for time-sensitive entity updates
-        time_service = TibberPricesTimeService()
-        self._async_update_time_sensitive_listeners(time_service)
-
         return True
 
+    def register_lifecycle_callback(self, callback: Callable[[], None]) -> None:
+        """
+        Register callback for lifecycle state changes (push updates).
+
+        This allows the lifecycle sensor to receive immediate updates when
+        the coordinator's lifecycle state changes, instead of waiting for
+        the next polling cycle.
+
+        Args:
+            callback: Function to call when lifecycle state changes (typically async_write_ha_state)
+
+        """
+        if callback not in self._lifecycle_callbacks:
+            self._lifecycle_callbacks.append(callback)
+
+    def _notify_lifecycle_change(self) -> None:
+        """Notify registered callbacks about lifecycle state change (push update)."""
+        for lifecycle_callback in self._lifecycle_callbacks:
+            lifecycle_callback()
+
     async def async_shutdown(self) -> None:
-        """
+        """Shut down the coordinator and clean up timers."""
-        Shut down the coordinator and clean up timers.
-
-        Cancels all three timer types:
-        - Timer #1: API polling (coordinator update timer)
-        - Timer #2: Quarter-hour entity updates
-        - Timer #3: Minute timing sensor updates
-
-        Also saves cache to persist any unsaved changes and clears all repairs.
-        """
-        # Cancel all timers first
         self._listener_manager.cancel_timers()
 
-        # Clear all repairs when integration is removed or disabled
+    def _has_existing_main_coordinator(self) -> bool:
-        await self._repair_manager.clear_all_repairs()
+        """Check if there's already a main coordinator in hass.data."""
+        domain_data = self.hass.data.get(DOMAIN, {})
+        return any(
+            isinstance(coordinator, TibberPricesDataUpdateCoordinator) and coordinator.is_main_entry()
+            for coordinator in domain_data.values()
+        )
 
-        # Save cache to persist any unsaved data
+    def is_main_entry(self) -> bool:
-        # This ensures we don't lose data if HA is shutting down
+        """Return True if this is the main entry that fetches data for all homes."""
-        try:
+        return self._is_main_entry
-            await self._store_cache()
-            self._log("debug", "Cache saved during shutdown")
-        except OSError as err:
-            # Log but don't raise - shutdown should complete even if cache save fails
-            self._log("error", "Failed to save cache during shutdown: %s", err)
 
     async def _async_update_data(self) -> dict[str, Any]:
         """
@@ -666,26 +497,24 @@ class TibberPricesDataUpdateCoordinator(DataUpdateCoordinator[dict[str, Any]]):
 
         # Transition lifecycle state from "fresh" to "cached" if enough time passed
         # (5 minutes threshold defined in lifecycle calculator)
-        # Note: This updates _lifecycle_state for diagnostics only.
+        if self._lifecycle_state == "fresh" and self._last_price_update:
-        # The lifecycle sensor calculates its state dynamically in get_lifecycle_state(),
+            age = current_time - self._last_price_update
-        # checking _last_price_update timestamp directly.
+            if age.total_seconds() > FRESH_TO_CACHED_SECONDS:
-        if self._lifecycle_state == "fresh":
+                self._lifecycle_state = "cached"
-            # After 5 minutes, data is considered "cached" (no longer "just fetched")
-            self._lifecycle_state = "cached"
 
         # Update helper modules with fresh TimeService instance
         self.api.time = self.time
-        self._price_data_manager.time = self.time
+        self._data_fetcher.time = self.time
         self._data_transformer.time = self.time
         self._period_calculator.time = self.time
 
-        # Load cache if not already loaded (user data only, price data is in Pool)
+        # Load cache if not already loaded
-        if self._cached_user_data is None:
+        if self._cached_price_data is None and self._cached_user_data is None:
             await self.load_cache()
 
-        # Initialize midnight handler on first run
+        # Initialize midnight turnover check on first run
-        if self._midnight_handler.last_check_time is None:
+        if self._last_midnight_turnover_check is None:
-            self._midnight_handler.update_check_time(current_time)
+            self._last_midnight_turnover_check = current_time
 
         # CRITICAL: Check for midnight turnover FIRST (before any data operations)
         # This prevents race condition with Timer #2 (quarter-hour refresh)
@@ -696,65 +525,46 @@ class TibberPricesDataUpdateCoordinator(DataUpdateCoordinator[dict[str, Any]]):
             self._perform_midnight_data_rotation(current_time)
             # After rotation, save cache and notify entities
             await self._store_cache()
-
-            # CRITICAL: Notify time-sensitive listeners explicitly
-            # When Timer #1 performs turnover, returning self.data will trigger
-            # async_update_listeners() (normal listeners) automatically via DataUpdateCoordinator.
-            # But time-sensitive listeners (like best_price_period, peak_price_period)
-            # won't be notified unless we explicitly call their update method.
-            # This ensures ALL entities see the updated periods after midnight turnover.
-            time_service = TibberPricesTimeService()
-            self._async_update_time_sensitive_listeners(time_service)
-
             # Return current data (enriched after rotation) to trigger entity updates
             if self.data:
                 return self.data
 
         try:
-            # Reset API call counter if day changed
+            if self.is_main_entry():
-            current_date = current_time.date()
+                # Set lifecycle state to refreshing before API call
-            if self._last_api_call_date != current_date:
+                self._lifecycle_state = "refreshing"
-                self._api_calls_today = 0
+                self._is_fetching = True
-                self._last_api_call_date = current_date
+                self._notify_lifecycle_change()  # Push update: now refreshing
 
-            # Set _is_fetching flag - lifecycle sensor shows "refreshing" during fetch
+                # Reset API call counter if day changed
-            # Note: Lifecycle sensor reads this flag directly in get_lifecycle_state()
+                current_date = current_time.date()
-            self._is_fetching = True
+                if self._last_api_call_date != current_date:
+                    self._api_calls_today = 0
+                    self._last_api_call_date = current_date
 
-            # Get current price info to check if tomorrow data already exists
+                # Main entry fetches data for all homes
-            current_price_info = self.data.get("priceInfo", []) if self.data else []
+                configured_home_ids = self._get_configured_home_ids()
+                result = await self._data_fetcher.handle_main_entry_update(
+                    current_time,
+                    configured_home_ids,
+                    self._transform_data_for_main_entry,
+                )
 
-            result, api_called = await self._price_data_manager.handle_main_entry_update(
+                # Update lifecycle tracking after successful fetch
-                current_time,
+                self._is_fetching = False
-                self._home_id,
-                self._transform_data,
-                current_price_info=current_price_info,
-            )
-
-            # CRITICAL: Reset fetching flag AFTER data fetch completes
-            self._is_fetching = False
-
-            # Sync user_data cache (price data is in IntervalPool)
-            self._cached_user_data = self._price_data_manager.cached_user_data
-
-            # Update lifecycle tracking - ONLY if API was actually called
-            # (not when returning cached data)
-            if api_called and result and "priceInfo" in result and len(result["priceInfo"]) > 0:
-                self._last_price_update = current_time  # Track when data was fetched from API
                 self._api_calls_today += 1
                 self._lifecycle_state = "fresh"  # Data just fetched
-                _LOGGER.debug(
+                self._notify_lifecycle_change()  # Push update: fresh data available
-                    "API call completed: Fetched %d intervals, updating lifecycle to 'fresh'",
+
-                    len(result["priceInfo"]),
+                # CRITICAL: Sync cached_user_data after API call (for new integrations without cache)
-                )
+                # handle_main_entry_update() may have fetched user_data via update_user_data_if_needed()
-                # Note: _lifecycle_state is for diagnostics only.
+                self._cached_user_data = self._data_fetcher.cached_user_data
-                # Lifecycle sensor calculates state dynamically from _last_price_update.
+                # Sync _last_price_update for lifecycle tracking
-            elif not api_called:
+                self._last_price_update = self._data_fetcher._last_price_update  # noqa: SLF001 - Sync for lifecycle tracking
-                # Using cached data - lifecycle stays as is (cached/searching_tomorrow/etc.)
+                return result
-                _LOGGER.debug(
+            # Subentries get data from main coordinator (no lifecycle tracking - they don't fetch)
-                    "Using cached data: %d intervals from pool, no API call made",
+            return await self._handle_subentry_update()
-                    len(result.get("priceInfo", [])),
+
-                )
         except (
             TibberPricesApiClientAuthenticationError,
             TibberPricesApiClientCommunicationError,
@@ -762,93 +572,115 @@ class TibberPricesDataUpdateCoordinator(DataUpdateCoordinator[dict[str, Any]]):
         ) as err:
             # Reset lifecycle state on error
             self._is_fetching = False
-            self._lifecycle_state = "error"  # For diagnostics
+            self._lifecycle_state = "error"
-            # Note: Lifecycle sensor detects errors via coordinator.last_exception
+            self._notify_lifecycle_change()  # Push update: error occurred
-
+            return await self._data_fetcher.handle_api_error(
-            # Track rate limit errors for repair system
+                err,
-            await self._track_rate_limit_error(err)
+                self._transform_data_for_main_entry,
-
-            # Handle API error - will re-raise as ConfigEntryAuthFailed or UpdateFailed
-            # Note: With IntervalPool, there's no local cache fallback here.
-            # The Pool has its own persistence for offline recovery.
-            await self._price_data_manager.handle_api_error(err)
-            # Note: handle_api_error always raises, this is never reached
-            return {}  # Satisfy type checker
-        else:
-            # Check for repair conditions after successful update
-            await self._check_repair_conditions(result, current_time)
-            return result
-
-    async def _track_rate_limit_error(self, error: Exception) -> None:
-        """Track rate limit errors for repair notification system."""
-        error_str = str(error).lower()
-        is_rate_limit = "429" in error_str or "rate limit" in error_str or "too many requests" in error_str
-        if is_rate_limit:
-            await self._repair_manager.track_rate_limit_error()
-
-    async def _check_repair_conditions(
-        self,
-        result: dict[str, Any],
-        current_time: datetime,
-    ) -> None:
-        """Check and manage repair conditions after successful data update."""
-        # 1. Home not found detection (home was removed from Tibber account)
-        if result and result.get("_home_not_found"):
-            await self._repair_manager.create_home_not_found_repair()
-            # Remove the marker before returning to entities
-            result.pop("_home_not_found", None)
-        else:
-            # Home exists - clear any existing repair
-            await self._repair_manager.clear_home_not_found_repair()
-
-        # 2. Tomorrow data availability (after 18:00)
-        if result and "priceInfo" in result:
-            has_tomorrow_data = self._price_data_manager.has_tomorrow_data(result["priceInfo"])
-            await self._repair_manager.check_tomorrow_data_availability(
-                has_tomorrow_data=has_tomorrow_data,
-                current_time=current_time,
             )
 
-        # 3. Clear rate limit tracking on successful API call
+    async def _handle_subentry_update(self) -> dict[str, Any]:
-        await self._repair_manager.clear_rate_limit_tracking()
+        """Handle update for subentry - get data from main coordinator."""
+        main_data = await self._get_data_from_main_coordinator()
+        return self._transform_data_for_subentry(main_data)
+
+    async def _get_data_from_main_coordinator(self) -> dict[str, Any]:
+        """Get data from the main coordinator (subentries only)."""
+        # Find the main coordinator
+        main_coordinator = self._find_main_coordinator()
+        if not main_coordinator:
+            msg = "Main coordinator not found"
+            raise UpdateFailed(msg)
+
+        # Wait for main coordinator to have data
+        if main_coordinator.data is None:
+            main_coordinator.async_set_updated_data({})
+
+        # Return the main coordinator's data
+        return main_coordinator.data or {}
+
+    def _find_main_coordinator(self) -> TibberPricesDataUpdateCoordinator | None:
+        """Find the main coordinator that fetches data for all homes."""
+        domain_data = self.hass.data.get(DOMAIN, {})
+        for coordinator in domain_data.values():
+            if (
+                isinstance(coordinator, TibberPricesDataUpdateCoordinator)
+                and coordinator.is_main_entry()
+                and coordinator != self
+            ):
+                return coordinator
+        return None
+
+    def _get_configured_home_ids(self) -> set[str]:
+        """Get all home_ids that have active config entries (main + subentries)."""
+        home_ids = helpers.get_configured_home_ids(self.hass)
+
+        self._log(
+            "debug",
+            "Found %d configured home(s): %s",
+            len(home_ids),
+            ", ".join(sorted(home_ids)),
+        )
+
+        return home_ids
 
     async def load_cache(self) -> None:
-        """Load cached user data from storage (price data is in IntervalPool)."""
+        """Load cached data from storage."""
-        await self._price_data_manager.load_cache()
+        await self._data_fetcher.load_cache()
-        # Sync user data reference
+        # Sync legacy references
-        self._cached_user_data = self._price_data_manager.cached_user_data
+        self._cached_price_data = self._data_fetcher.cached_price_data
-        self._last_user_update = self._price_data_manager._last_user_update  # noqa: SLF001 - Sync for lifecycle tracking
+        self._cached_user_data = self._data_fetcher.cached_user_data
+        self._last_price_update = self._data_fetcher._last_price_update  # noqa: SLF001 - Sync for lifecycle tracking
+        self._last_user_update = self._data_fetcher._last_user_update  # noqa: SLF001 - Sync for lifecycle tracking
 
-        # Note: Midnight handler state is now based on current date
+        # Initialize _last_actual_turnover: If cache is from today, assume turnover happened at midnight
-        # Since price data is in IntervalPool (persistent), we just need to
+        if self._last_price_update:
-        # ensure turnover doesn't happen twice if HA restarts after midnight
+            cache_date = self.time.as_local(self._last_price_update).date()
-        today_midnight = self.time.as_local(self.time.now()).replace(hour=0, minute=0, second=0, microsecond=0)
+            today_date = self.time.as_local(self.time.now()).date()
-        # Mark today's midnight as done to prevent double turnover on HA restart
+            if cache_date == today_date:
-        self._midnight_handler.mark_turnover_done(today_midnight)
+                # Cache is from today, so midnight turnover already happened
+                today_midnight = self.time.as_local(self.time.now()).replace(hour=0, minute=0, second=0, microsecond=0)
+                self._last_actual_turnover = today_midnight
+
+    def _perform_midnight_turnover(self, price_info: dict[str, Any]) -> dict[str, Any]:
+        """
+        Perform midnight turnover on price data.
+
+        Moves: today → yesterday, tomorrow → today, clears tomorrow.
+
+        This handles cases where:
+        - Server was running through midnight
+        - Cache is being refreshed and needs proper day rotation
+
+        Args:
+            price_info: The price info dict with 'today', 'tomorrow', 'yesterday' keys
+
+        Returns:
+            Updated price_info with rotated day data
+
+        """
+        return helpers.perform_midnight_turnover(price_info, time=self.time)
 
     async def _store_cache(self) -> None:
-        """Store cache data (user metadata only, price data is in IntervalPool)."""
+        """Store cache data."""
-        await self._price_data_manager.store_cache(self._midnight_handler.last_check_time)
+        await self._data_fetcher.store_cache(self._last_midnight_turnover_check)
 
-    def _needs_tomorrow_data(self) -> bool:
+    def _needs_tomorrow_data(self, tomorrow_date: date) -> bool:
         """Check if tomorrow data is missing or invalid."""
-        # Check self.data (from Pool) instead of _cached_price_data
+        return helpers.needs_tomorrow_data(self._cached_price_data, tomorrow_date)
-        if not self.data or "priceInfo" not in self.data:
-            return True
-        return helpers.needs_tomorrow_data({"price_info": self.data["priceInfo"]})
 
-    def _has_valid_tomorrow_data(self) -> bool:
+    def _has_valid_tomorrow_data(self, tomorrow_date: date) -> bool:
         """Check if we have valid tomorrow data (inverse of _needs_tomorrow_data)."""
-        return not self._needs_tomorrow_data()
+        return not self._needs_tomorrow_data(tomorrow_date)
 
     @callback
     def _merge_cached_data(self) -> dict[str, Any]:
-        """Return current data (from Pool)."""
+        """Merge cached data into the expected format for main entry."""
-        if not self.data:
+        if not self._cached_price_data:
             return {}
-        return self.data
+        return self._transform_data_for_main_entry(self._cached_price_data)
 
-    def _get_threshold_percentages(self) -> dict[str, int | float]:
+    def _get_threshold_percentages(self) -> dict[str, int]:
         """Get threshold percentages from config options."""
         return self._data_transformer.get_threshold_percentages()
 
@@ -856,36 +688,154 @@ class TibberPricesDataUpdateCoordinator(DataUpdateCoordinator[dict[str, Any]]):
         """Calculate periods (best price and peak price) for the given price info."""
         return self._period_calculator.calculate_periods_for_price_info(price_info)
 
-    def _transform_data(self, raw_data: dict[str, Any]) -> dict[str, Any]:
+    def _get_current_transformation_config(self) -> dict[str, Any]:
+        """Get current configuration that affects data transformation."""
+        return {
+            "thresholds": self._get_threshold_percentages(),
+            "volatility_thresholds": {
+                "moderate": self.config_entry.options.get(_const.CONF_VOLATILITY_THRESHOLD_MODERATE, 15.0),
+                "high": self.config_entry.options.get(_const.CONF_VOLATILITY_THRESHOLD_HIGH, 25.0),
+                "very_high": self.config_entry.options.get(_const.CONF_VOLATILITY_THRESHOLD_VERY_HIGH, 40.0),
+            },
+            "best_price_config": {
+                "flex": self.config_entry.options.get(_const.CONF_BEST_PRICE_FLEX, 15.0),
+                "max_level": self.config_entry.options.get(_const.CONF_BEST_PRICE_MAX_LEVEL, "NORMAL"),
+                "min_period_length": self.config_entry.options.get(_const.CONF_BEST_PRICE_MIN_PERIOD_LENGTH, 4),
+                "min_distance_from_avg": self.config_entry.options.get(
+                    _const.CONF_BEST_PRICE_MIN_DISTANCE_FROM_AVG, -5.0
+                ),
+                "max_level_gap_count": self.config_entry.options.get(_const.CONF_BEST_PRICE_MAX_LEVEL_GAP_COUNT, 0),
+                "enable_min_periods": self.config_entry.options.get(_const.CONF_ENABLE_MIN_PERIODS_BEST, False),
+                "min_periods": self.config_entry.options.get(_const.CONF_MIN_PERIODS_BEST, 2),
+                "relaxation_attempts": self.config_entry.options.get(_const.CONF_RELAXATION_ATTEMPTS_BEST, 4),
+            },
+            "peak_price_config": {
+                "flex": self.config_entry.options.get(_const.CONF_PEAK_PRICE_FLEX, 15.0),
+                "min_level": self.config_entry.options.get(_const.CONF_PEAK_PRICE_MIN_LEVEL, "HIGH"),
+                "min_period_length": self.config_entry.options.get(_const.CONF_PEAK_PRICE_MIN_PERIOD_LENGTH, 4),
+                "min_distance_from_avg": self.config_entry.options.get(
+                    _const.CONF_PEAK_PRICE_MIN_DISTANCE_FROM_AVG, 5.0
+                ),
+                "max_level_gap_count": self.config_entry.options.get(_const.CONF_PEAK_PRICE_MAX_LEVEL_GAP_COUNT, 0),
+                "enable_min_periods": self.config_entry.options.get(_const.CONF_ENABLE_MIN_PERIODS_PEAK, False),
+                "min_periods": self.config_entry.options.get(_const.CONF_MIN_PERIODS_PEAK, 2),
+                "relaxation_attempts": self.config_entry.options.get(_const.CONF_RELAXATION_ATTEMPTS_PEAK, 4),
+            },
+        }
+
+    def _should_retransform_data(self, current_time: datetime) -> bool:
+        """Check if data transformation should be performed."""
+        # No cached transformed data - must transform
+        if self._cached_transformed_data is None:
+            return True
+
+        # Configuration changed - must retransform
+        current_config = self._get_current_transformation_config()
+        if current_config != self._last_transformation_config:
+            self._log("debug", "Configuration changed, retransforming data")
+            return True
+
+        # Check for midnight turnover
+        now_local = self.time.as_local(current_time)
+        current_date = now_local.date()
+
+        if self._last_transformation_time is None:
+            return True
+
+        last_transform_local = self.time.as_local(self._last_transformation_time)
+        last_transform_date = last_transform_local.date()
+
+        if current_date != last_transform_date:
+            self._log("debug", "Midnight turnover detected, retransforming data")
+            return True
+
+        return False
+
+    def _transform_data_for_main_entry(self, raw_data: dict[str, Any]) -> dict[str, Any]:
         """Transform raw data for main entry (aggregated view of all homes)."""
-        # Delegate complete transformation to DataTransformer (enrichment + periods)
+        current_time = self.time.now()
-        # DataTransformer handles its own caching internally
+
-        return self._data_transformer.transform_data(raw_data)
+        # Return cached transformed data if no retransformation needed
+        if not self._should_retransform_data(current_time) and self._cached_transformed_data is not None:
+            self._log("debug", "Using cached transformed data (no transformation needed)")
+            return self._cached_transformed_data
+
+        self._log("debug", "Transforming price data (enrichment only, periods calculated separately)")
+
+        # Delegate actual transformation to DataTransformer (enrichment only)
+        transformed_data = self._data_transformer.transform_data_for_main_entry(raw_data)
+
+        # Add periods (calculated and cached separately by PeriodCalculator)
+        if "priceInfo" in transformed_data:
+            transformed_data["periods"] = self._calculate_periods_for_price_info(transformed_data["priceInfo"])
+
+        # Cache the transformed data
+        self._cached_transformed_data = transformed_data
+        self._last_transformation_config = self._get_current_transformation_config()
+        self._last_transformation_time = current_time
+
+        return transformed_data
+
+    def _transform_data_for_subentry(self, main_data: dict[str, Any]) -> dict[str, Any]:
+        """Transform main coordinator data for subentry (home-specific view)."""
+        current_time = self.time.now()
+
+        # Return cached transformed data if no retransformation needed
+        if not self._should_retransform_data(current_time) and self._cached_transformed_data is not None:
+            self._log("debug", "Using cached transformed data (no transformation needed)")
+            return self._cached_transformed_data
+
+        self._log("debug", "Transforming price data for home (enrichment only, periods calculated separately)")
+
+        home_id = self.config_entry.data.get("home_id")
+        if not home_id:
+            return main_data
+
+        # Delegate actual transformation to DataTransformer (enrichment only)
+        transformed_data = self._data_transformer.transform_data_for_subentry(main_data, home_id)
+
+        # Add periods (calculated and cached separately by PeriodCalculator)
+        if "priceInfo" in transformed_data:
+            transformed_data["periods"] = self._calculate_periods_for_price_info(transformed_data["priceInfo"])
+
+        # Cache the transformed data
+        self._cached_transformed_data = transformed_data
+        self._last_transformation_config = self._get_current_transformation_config()
+        self._last_transformation_time = current_time
+
+        return transformed_data
 
     # --- Methods expected by sensors and services ---
 
     def get_home_data(self, home_id: str) -> dict[str, Any] | None:
-        """Get data for a specific home (returns this coordinator's data if home_id matches)."""
+        """Get data for a specific home."""
         if not self.data:
             return None
 
-        # In new architecture, each coordinator manages one home only
+        homes_data = self.data.get("homes", {})
-        # Return data only if requesting this coordinator's home
+        return homes_data.get(home_id)
-        if home_id == self._home_id:
-            return self.data
-
-        return None
 
     def get_current_interval(self) -> dict[str, Any] | None:
         """Get the price data for the current interval."""
         if not self.data:
             return None
 
-        if not self.data:
+        price_info = self.data.get("priceInfo", {})
+        if not price_info:
             return None
 
         now = self.time.now()
-        return find_price_data_for_interval(self.data, now, time=self.time)
+        return find_price_data_for_interval(price_info, now, time=self.time)
+
+    def get_all_intervals(self) -> list[dict[str, Any]]:
+        """Get all price intervals (today + tomorrow)."""
+        if not self.data:
+            return []
+
+        price_info = self.data.get("priceInfo", {})
+        today_prices = price_info.get("today", [])
+        tomorrow_prices = price_info.get("tomorrow", [])
+        return today_prices + tomorrow_prices
 
     async def refresh_user_data(self) -> bool:
         """Force refresh of user data and return True if data was updated."""

custom_components/tibber_prices/coordinator/data_fetching.py

@@ -0,0 +1,314 @@
+"""Data fetching logic for the coordinator."""
+
+from __future__ import annotations
+
+import asyncio
+import logging
+import secrets
+from typing import TYPE_CHECKING, Any
+
+if TYPE_CHECKING:
+    from datetime import timedelta
+
+from custom_components.tibber_prices.api import (
+    TibberPricesApiClientAuthenticationError,
+    TibberPricesApiClientCommunicationError,
+    TibberPricesApiClientError,
+)
+from homeassistant.core import callback
+from homeassistant.exceptions import ConfigEntryAuthFailed
+from homeassistant.helpers.update_coordinator import UpdateFailed
+
+from . import cache, helpers
+from .constants import TOMORROW_DATA_CHECK_HOUR, TOMORROW_DATA_RANDOM_DELAY_MAX
+
+if TYPE_CHECKING:
+    from collections.abc import Callable
+    from datetime import date, datetime
+
+    from custom_components.tibber_prices.api import TibberPricesApiClient
+
+    from .time_service import TibberPricesTimeService
+
+_LOGGER = logging.getLogger(__name__)
+
+
+class TibberPricesDataFetcher:
+    """Handles data fetching, caching, and main/subentry coordination."""
+
+    def __init__(
+        self,
+        api: TibberPricesApiClient,
+        store: Any,
+        log_prefix: str,
+        user_update_interval: timedelta,
+        time: TibberPricesTimeService,
+    ) -> None:
+        """Initialize the data fetcher."""
+        self.api = api
+        self._store = store
+        self._log_prefix = log_prefix
+        self._user_update_interval = user_update_interval
+        self.time: TibberPricesTimeService = time
+
+        # Cached data
+        self._cached_price_data: dict[str, Any] | None = None
+        self._cached_user_data: dict[str, Any] | None = None
+        self._last_price_update: datetime | None = None
+        self._last_user_update: datetime | None = None
+
+    def _log(self, level: str, message: str, *args: object, **kwargs: object) -> None:
+        """Log with coordinator-specific prefix."""
+        prefixed_message = f"{self._log_prefix} {message}"
+        getattr(_LOGGER, level)(prefixed_message, *args, **kwargs)
+
+    async def load_cache(self) -> None:
+        """Load cached data from storage."""
+        cache_data = await cache.load_cache(self._store, self._log_prefix, time=self.time)
+
+        self._cached_price_data = cache_data.price_data
+        self._cached_user_data = cache_data.user_data
+        self._last_price_update = cache_data.last_price_update
+        self._last_user_update = cache_data.last_user_update
+
+        # Parse timestamps if we loaded price data from cache
+        if self._cached_price_data:
+            self._cached_price_data = helpers.parse_all_timestamps(self._cached_price_data, time=self.time)
+
+        # Validate cache: check if price data is from a previous day
+        if not cache.is_cache_valid(cache_data, self._log_prefix, time=self.time):
+            self._log("info", "Cached price data is from a previous day, clearing cache to fetch fresh data")
+            self._cached_price_data = None
+            self._last_price_update = None
+            await self.store_cache()
+
+    async def store_cache(self, last_midnight_check: datetime | None = None) -> None:
+        """Store cache data."""
+        cache_data = cache.TibberPricesCacheData(
+            price_data=self._cached_price_data,
+            user_data=self._cached_user_data,
+            last_price_update=self._last_price_update,
+            last_user_update=self._last_user_update,
+            last_midnight_check=last_midnight_check,
+        )
+        await cache.save_cache(self._store, cache_data, self._log_prefix)
+
+    async def update_user_data_if_needed(self, current_time: datetime) -> bool:
+        """
+        Update user data if needed (daily check).
+
+        Returns:
+            True if user data was updated, False otherwise
+
+        """
+        if self._last_user_update is None or current_time - self._last_user_update >= self._user_update_interval:
+            try:
+                self._log("debug", "Updating user data")
+                user_data = await self.api.async_get_viewer_details()
+                self._cached_user_data = user_data
+                self._last_user_update = current_time
+                self._log("debug", "User data updated successfully")
+            except (
+                TibberPricesApiClientError,
+                TibberPricesApiClientCommunicationError,
+            ) as ex:
+                self._log("warning", "Failed to update user data: %s", ex)
+                return False  # Update failed
+            else:
+                return True  # User data was updated
+        return False  # No update needed
+
+    @callback
+    def should_update_price_data(self, current_time: datetime) -> bool | str:
+        """
+        Check if price data should be updated from the API.
+
+        API calls only happen when truly needed:
+        1. No cached data exists
+        2. Cache is invalid (from previous day - detected by _is_cache_valid)
+        3. After 13:00 local time and tomorrow's data is missing or invalid
+
+        Cache validity is ensured by:
+        - _is_cache_valid() checks date mismatch on load
+        - Midnight turnover clears cache (Timer #2)
+        - Tomorrow data validation after 13:00
+
+        No periodic "safety" updates - trust the cache validation!
+
+        Returns:
+            bool or str: True for immediate update, "tomorrow_check" for tomorrow
+                        data check (needs random delay), False for no update
+
+        """
+        if self._cached_price_data is None:
+            self._log("debug", "API update needed: No cached price data")
+            return True
+        if self._last_price_update is None:
+            self._log("debug", "API update needed: No last price update timestamp")
+            return True
+
+        # Get tomorrow's date using TimeService
+        _, tomorrow_midnight = self.time.get_day_boundaries("today")
+        tomorrow_date = tomorrow_midnight.date()
+        now_local = self.time.as_local(current_time)
+
+        # Check if after 13:00 and tomorrow data is missing or invalid
+        if (
+            now_local.hour >= TOMORROW_DATA_CHECK_HOUR
+            and self._cached_price_data
+            and "homes" in self._cached_price_data
+            and self.needs_tomorrow_data(tomorrow_date)
+        ):
+            self._log(
+                "debug",
+                "API update needed: After %s:00 and tomorrow's data missing/invalid",
+                TOMORROW_DATA_CHECK_HOUR,
+            )
+            # Return special marker to indicate this is a tomorrow data check
+            # Caller should add random delay to spread load
+            return "tomorrow_check"
+
+        # No update needed - cache is valid and complete
+        return False
+
+    def needs_tomorrow_data(self, tomorrow_date: date) -> bool:
+        """Check if tomorrow data is missing or invalid."""
+        return helpers.needs_tomorrow_data(self._cached_price_data, tomorrow_date)
+
+    async def fetch_all_homes_data(self, configured_home_ids: set[str], current_time: datetime) -> dict[str, Any]:
+        """Fetch data for all homes (main coordinator only)."""
+        if not configured_home_ids:
+            self._log("warning", "No configured homes found - cannot fetch price data")
+            return {
+                "timestamp": current_time,
+                "homes": {},
+            }
+
+        # Get price data for configured homes only (API call with specific home_ids)
+        self._log("debug", "Fetching price data for %d configured home(s)", len(configured_home_ids))
+        price_data = await self.api.async_get_price_info(home_ids=configured_home_ids)
+
+        all_homes_data = {}
+        homes_list = price_data.get("homes", {})
+
+        # Process returned data
+        for home_id, home_price_data in homes_list.items():
+            # Store raw price data without enrichment
+            # Enrichment will be done dynamically when data is transformed
+            home_data = {
+                "price_info": home_price_data,
+            }
+            all_homes_data[home_id] = home_data
+
+        self._log(
+            "debug",
+            "Successfully fetched data for %d home(s)",
+            len(all_homes_data),
+        )
+
+        return {
+            "timestamp": current_time,
+            "homes": all_homes_data,
+        }
+
+    async def handle_main_entry_update(
+        self,
+        current_time: datetime,
+        configured_home_ids: set[str],
+        transform_fn: Callable[[dict[str, Any]], dict[str, Any]],
+    ) -> dict[str, Any]:
+        """Handle update for main entry - fetch data for all homes."""
+        # Update user data if needed (daily check)
+        user_data_updated = await self.update_user_data_if_needed(current_time)
+
+        # Check if we need to update price data
+        should_update = self.should_update_price_data(current_time)
+
+        if should_update:
+            # If this is a tomorrow data check, add random delay to spread API load
+            if should_update == "tomorrow_check":
+                # Use secrets for better randomness distribution
+                delay = secrets.randbelow(TOMORROW_DATA_RANDOM_DELAY_MAX + 1)
+                self._log(
+                    "debug",
+                    "Tomorrow data check - adding random delay of %d seconds to spread load",
+                    delay,
+                )
+                await asyncio.sleep(delay)
+
+            self._log("debug", "Fetching fresh price data from API")
+            raw_data = await self.fetch_all_homes_data(configured_home_ids, current_time)
+            # Parse timestamps immediately after API fetch
+            raw_data = helpers.parse_all_timestamps(raw_data, time=self.time)
+            # Cache the data (now with datetime objects)
+            self._cached_price_data = raw_data
+            self._last_price_update = current_time
+            await self.store_cache()
+            # Transform for main entry: provide aggregated view
+            return transform_fn(raw_data)
+
+        # Use cached data if available
+        if self._cached_price_data is not None:
+            # If user data was updated, we need to return transformed data to trigger entity updates
+            # This ensures diagnostic sensors (home_type, grid_company, etc.) get refreshed
+            if user_data_updated:
+                self._log("debug", "User data updated - returning transformed data to update diagnostic sensors")
+            else:
+                self._log("debug", "Using cached price data (no API call needed)")
+            return transform_fn(self._cached_price_data)
+
+        # Fallback: no cache and no update needed (shouldn't happen)
+        self._log("warning", "No cached data available and update not triggered - returning empty data")
+        return {
+            "timestamp": current_time,
+            "homes": {},
+            "priceInfo": {},
+        }
+
+    async def handle_api_error(
+        self,
+        error: Exception,
+        transform_fn: Callable[[dict[str, Any]], dict[str, Any]],
+    ) -> dict[str, Any]:
+        """Handle API errors with fallback to cached data."""
+        if isinstance(error, TibberPricesApiClientAuthenticationError):
+            msg = "Invalid access token"
+            raise ConfigEntryAuthFailed(msg) from error
+
+        # Use cached data as fallback if available
+        if self._cached_price_data is not None:
+            self._log("warning", "API error, using cached data: %s", error)
+            return transform_fn(self._cached_price_data)
+
+        msg = f"Error communicating with API: {error}"
+        raise UpdateFailed(msg) from error
+
+    def perform_midnight_turnover(self, price_info: dict[str, Any]) -> dict[str, Any]:
+        """
+        Perform midnight turnover on price data.
+
+        Moves: today → yesterday, tomorrow → today, clears tomorrow.
+
+        Args:
+            price_info: The price info dict with 'today', 'tomorrow', 'yesterday' keys
+
+        Returns:
+            Updated price_info with rotated day data
+
+        """
+        return helpers.perform_midnight_turnover(price_info, time=self.time)
+
+    @property
+    def cached_price_data(self) -> dict[str, Any] | None:
+        """Get cached price data."""
+        return self._cached_price_data
+
+    @cached_price_data.setter
+    def cached_price_data(self, value: dict[str, Any] | None) -> None:
+        """Set cached price data."""
+        self._cached_price_data = value
+
+    @property
+    def cached_user_data(self) -> dict[str, Any] | None:
+        """Get cached user data."""
+        return self._cached_user_data

custom_components/tibber_prices/coordinator/data_transformation.py

@@ -2,7 +2,6 @@
 
 from __future__ import annotations
 
-import copy
 import logging
 from typing import TYPE_CHECKING, Any
 
@@ -27,20 +26,19 @@ class TibberPricesDataTransformer:
         self,
         config_entry: ConfigEntry,
         log_prefix: str,
-        calculate_periods_fn: Callable[[dict[str, Any]], dict[str, Any]],
+        perform_turnover_fn: Callable[[dict[str, Any]], dict[str, Any]],
         time: TibberPricesTimeService,
     ) -> None:
         """Initialize the data transformer."""
         self.config_entry = config_entry
         self._log_prefix = log_prefix
-        self._calculate_periods_fn = calculate_periods_fn
+        self._perform_turnover_fn = perform_turnover_fn
         self.time: TibberPricesTimeService = time
 
         # Transformation cache
         self._cached_transformed_data: dict[str, Any] | None = None
         self._last_transformation_config: dict[str, Any] | None = None
         self._last_midnight_check: datetime | None = None
-        self._last_source_data_timestamp: datetime | None = None  # Track when source data changed
         self._config_cache: dict[str, Any] | None = None
         self._config_cache_valid = False
 
@@ -49,50 +47,19 @@ class TibberPricesDataTransformer:
         prefixed_message = f"{self._log_prefix} {message}"
         getattr(_LOGGER, level)(prefixed_message, *args, **kwargs)
 
-    def get_threshold_percentages(self) -> dict[str, int | float]:
+    def get_threshold_percentages(self) -> dict[str, int]:
-        """
+        """Get threshold percentages from config options."""
-        Get threshold percentages, hysteresis and gap tolerance for RATING_LEVEL from config options.
-
-        CRITICAL: This function is ONLY for rating_level (internal calculation: LOW/NORMAL/HIGH).
-        Do NOT use for price level (Tibber API: VERY_CHEAP/CHEAP/NORMAL/EXPENSIVE/VERY_EXPENSIVE).
-        """
         options = self.config_entry.options or {}
         return {
             "low": options.get(_const.CONF_PRICE_RATING_THRESHOLD_LOW, _const.DEFAULT_PRICE_RATING_THRESHOLD_LOW),
             "high": options.get(_const.CONF_PRICE_RATING_THRESHOLD_HIGH, _const.DEFAULT_PRICE_RATING_THRESHOLD_HIGH),
-            "hysteresis": options.get(_const.CONF_PRICE_RATING_HYSTERESIS, _const.DEFAULT_PRICE_RATING_HYSTERESIS),
-            "gap_tolerance": options.get(
-                _const.CONF_PRICE_RATING_GAP_TOLERANCE, _const.DEFAULT_PRICE_RATING_GAP_TOLERANCE
-            ),
         }
 
-    def get_level_gap_tolerance(self) -> int:
-        """
-        Get gap tolerance for PRICE LEVEL (Tibber API) from config options.
-
-        CRITICAL: This is separate from rating_level gap tolerance.
-        Price level comes from Tibber API (VERY_CHEAP/CHEAP/NORMAL/EXPENSIVE/VERY_EXPENSIVE).
-        Rating level is calculated internally (LOW/NORMAL/HIGH).
-        """
-        options = self.config_entry.options or {}
-        return options.get(_const.CONF_PRICE_LEVEL_GAP_TOLERANCE, _const.DEFAULT_PRICE_LEVEL_GAP_TOLERANCE)
-
     def invalidate_config_cache(self) -> None:
-        """
+        """Invalidate config cache when options change."""
-        Invalidate config cache AND transformation cache when options change.
-
-        CRITICAL: When options like gap_tolerance, hysteresis, or price_level_gap_tolerance
-        change, we must clear BOTH caches:
-        1. Config cache (_config_cache) - forces config rebuild on next check
-        2. Transformation cache (_cached_transformed_data) - forces data re-enrichment
-
-        This ensures that the next call to transform_data() will re-calculate
-        rating_levels and apply new gap tolerance settings to existing price data.
-        """
         self._config_cache_valid = False
         self._config_cache = None
-        self._cached_transformed_data = None  # Force re-transformation with new config
+        self._log("debug", "Config cache invalidated")
-        self._last_transformation_config = None  # Force config comparison to trigger
 
     def _get_current_transformation_config(self) -> dict[str, Any]:
         """
@@ -105,53 +72,36 @@ class TibberPricesDataTransformer:
             return self._config_cache
 
         # Build config dictionary (expensive operation)
-        options = self.config_entry.options
-
-        # Best/peak price remain nested (multi-section steps)
-        best_period_section = options.get("period_settings", {})
-        best_flex_section = options.get("flexibility_settings", {})
-        best_relax_section = options.get("relaxation_and_target_periods", {})
-        peak_period_section = options.get("period_settings", {})
-        peak_flex_section = options.get("flexibility_settings", {})
-        peak_relax_section = options.get("relaxation_and_target_periods", {})
-
         config = {
             "thresholds": self.get_threshold_percentages(),
-            "level_gap_tolerance": self.get_level_gap_tolerance(),  # Separate: Tibber's price level smoothing
-            # Volatility thresholds now flat (single-section step)
             "volatility_thresholds": {
-                "moderate": options.get(_const.CONF_VOLATILITY_THRESHOLD_MODERATE, 15.0),
+                "moderate": self.config_entry.options.get(_const.CONF_VOLATILITY_THRESHOLD_MODERATE, 15.0),
-                "high": options.get(_const.CONF_VOLATILITY_THRESHOLD_HIGH, 25.0),
+                "high": self.config_entry.options.get(_const.CONF_VOLATILITY_THRESHOLD_HIGH, 25.0),
-                "very_high": options.get(_const.CONF_VOLATILITY_THRESHOLD_VERY_HIGH, 40.0),
+                "very_high": self.config_entry.options.get(_const.CONF_VOLATILITY_THRESHOLD_VERY_HIGH, 40.0),
-            },
-            # Price trend thresholds now flat (single-section step)
-            "price_trend_thresholds": {
-                "rising": options.get(
-                    _const.CONF_PRICE_TREND_THRESHOLD_RISING, _const.DEFAULT_PRICE_TREND_THRESHOLD_RISING
-                ),
-                "falling": options.get(
-                    _const.CONF_PRICE_TREND_THRESHOLD_FALLING, _const.DEFAULT_PRICE_TREND_THRESHOLD_FALLING
-                ),
             },
             "best_price_config": {
-                "flex": best_flex_section.get(_const.CONF_BEST_PRICE_FLEX, 15.0),
+                "flex": self.config_entry.options.get(_const.CONF_BEST_PRICE_FLEX, 15.0),
-                "max_level": best_period_section.get(_const.CONF_BEST_PRICE_MAX_LEVEL, "NORMAL"),
+                "max_level": self.config_entry.options.get(_const.CONF_BEST_PRICE_MAX_LEVEL, "NORMAL"),
-                "min_period_length": best_period_section.get(_const.CONF_BEST_PRICE_MIN_PERIOD_LENGTH, 4),
+                "min_period_length": self.config_entry.options.get(_const.CONF_BEST_PRICE_MIN_PERIOD_LENGTH, 4),
-                "min_distance_from_avg": best_flex_section.get(_const.CONF_BEST_PRICE_MIN_DISTANCE_FROM_AVG, -5.0),
+                "min_distance_from_avg": self.config_entry.options.get(
-                "max_level_gap_count": best_period_section.get(_const.CONF_BEST_PRICE_MAX_LEVEL_GAP_COUNT, 0),
+                    _const.CONF_BEST_PRICE_MIN_DISTANCE_FROM_AVG, -5.0
-                "enable_min_periods": best_relax_section.get(_const.CONF_ENABLE_MIN_PERIODS_BEST, False),
+                ),
-                "min_periods": best_relax_section.get(_const.CONF_MIN_PERIODS_BEST, 2),
+                "max_level_gap_count": self.config_entry.options.get(_const.CONF_BEST_PRICE_MAX_LEVEL_GAP_COUNT, 0),
-                "relaxation_attempts": best_relax_section.get(_const.CONF_RELAXATION_ATTEMPTS_BEST, 4),
+                "enable_min_periods": self.config_entry.options.get(_const.CONF_ENABLE_MIN_PERIODS_BEST, False),
+                "min_periods": self.config_entry.options.get(_const.CONF_MIN_PERIODS_BEST, 2),
+                "relaxation_attempts": self.config_entry.options.get(_const.CONF_RELAXATION_ATTEMPTS_BEST, 4),
             },
             "peak_price_config": {
-                "flex": peak_flex_section.get(_const.CONF_PEAK_PRICE_FLEX, 15.0),
+                "flex": self.config_entry.options.get(_const.CONF_PEAK_PRICE_FLEX, 15.0),
-                "min_level": peak_period_section.get(_const.CONF_PEAK_PRICE_MIN_LEVEL, "HIGH"),
+                "min_level": self.config_entry.options.get(_const.CONF_PEAK_PRICE_MIN_LEVEL, "HIGH"),
-                "min_period_length": peak_period_section.get(_const.CONF_PEAK_PRICE_MIN_PERIOD_LENGTH, 4),
+                "min_period_length": self.config_entry.options.get(_const.CONF_PEAK_PRICE_MIN_PERIOD_LENGTH, 4),
-                "min_distance_from_avg": peak_flex_section.get(_const.CONF_PEAK_PRICE_MIN_DISTANCE_FROM_AVG, 5.0),
+                "min_distance_from_avg": self.config_entry.options.get(
-                "max_level_gap_count": peak_period_section.get(_const.CONF_PEAK_PRICE_MAX_LEVEL_GAP_COUNT, 0),
+                    _const.CONF_PEAK_PRICE_MIN_DISTANCE_FROM_AVG, 5.0
-                "enable_min_periods": peak_relax_section.get(_const.CONF_ENABLE_MIN_PERIODS_PEAK, False),
+                ),
-                "min_periods": peak_relax_section.get(_const.CONF_MIN_PERIODS_PEAK, 2),
+                "max_level_gap_count": self.config_entry.options.get(_const.CONF_PEAK_PRICE_MAX_LEVEL_GAP_COUNT, 0),
-                "relaxation_attempts": peak_relax_section.get(_const.CONF_RELAXATION_ATTEMPTS_PEAK, 4),
+                "enable_min_periods": self.config_entry.options.get(_const.CONF_ENABLE_MIN_PERIODS_PEAK, False),
+                "min_periods": self.config_entry.options.get(_const.CONF_MIN_PERIODS_PEAK, 2),
+                "relaxation_attempts": self.config_entry.options.get(_const.CONF_RELAXATION_ATTEMPTS_PEAK, 4),
             },
         }
 
@@ -160,33 +110,16 @@ class TibberPricesDataTransformer:
         self._config_cache_valid = True
         return config
 
-    def _should_retransform_data(self, current_time: datetime, source_data_timestamp: datetime | None = None) -> bool:
+    def _should_retransform_data(self, current_time: datetime) -> bool:
-        """
+        """Check if data transformation should be performed."""
-        Check if data transformation should be performed.
-
-        Args:
-            current_time: Current time for midnight check
-            source_data_timestamp: Timestamp of source data (if available)
-
-        Returns:
-            True if retransformation needed, False if cached data can be used
-
-        """
         # No cached transformed data - must transform
         if self._cached_transformed_data is None:
             return True
 
-        # Source data changed - must retransform
-        # This detects when new API data was fetched (e.g., tomorrow data arrival)
-        if source_data_timestamp is not None and source_data_timestamp != self._last_source_data_timestamp:
-            self._log("debug", "Source data changed, retransforming data")
-            return True
-
         # Configuration changed - must retransform
         current_config = self._get_current_transformation_config()
-        config_changed = current_config != self._last_transformation_config
+        if current_config != self._last_transformation_config:
-
+            self._log("debug", "Configuration changed, retransforming data")
-        if config_changed:
             return True
 
         # Check for midnight turnover
@@ -205,80 +138,120 @@ class TibberPricesDataTransformer:
 
         return False
 
-    def transform_data(self, raw_data: dict[str, Any]) -> dict[str, Any]:
+    def transform_data_for_main_entry(self, raw_data: dict[str, Any]) -> dict[str, Any]:
-        """Transform raw data for main entry (single home view)."""
+        """Transform raw data for main entry (aggregated view of all homes)."""
         current_time = self.time.now()
-        source_data_timestamp = raw_data.get("timestamp")
 
         # Return cached transformed data if no retransformation needed
-        should_retransform = self._should_retransform_data(current_time, source_data_timestamp)
+        if not self._should_retransform_data(current_time) and self._cached_transformed_data is not None:
-        has_cache = self._cached_transformed_data is not None
-
-        self._log(
-            "info",
-            "transform_data: should_retransform=%s, has_cache=%s",
-            should_retransform,
-            has_cache,
-        )
-
-        if not should_retransform and has_cache:
             self._log("debug", "Using cached transformed data (no transformation needed)")
-            # has_cache ensures _cached_transformed_data is not None
+            return self._cached_transformed_data
-            return self._cached_transformed_data  # type: ignore[return-value]
 
-        self._log("debug", "Transforming price data (enrichment + period calculation)")
+        self._log("debug", "Transforming price data (enrichment only, periods cached separately)")
 
-        # Extract data from single-home structure
+        # For main entry, we can show data from the first home as default
-        home_id = raw_data.get("home_id", "")
+        # or provide an aggregated view
-        # CRITICAL: Make a deep copy of intervals to avoid modifying cached raw data
+        homes_data = raw_data.get("homes", {})
-        # The enrichment function modifies intervals in-place, which would corrupt
+        if not homes_data:
-        # the original API data and make re-enrichment with different settings impossible
-        all_intervals = copy.deepcopy(raw_data.get("price_info", []))
-        currency = raw_data.get("currency", "EUR")
-
-        if not all_intervals:
             return {
                 "timestamp": raw_data.get("timestamp"),
-                "home_id": home_id,
+                "homes": {},
-                "priceInfo": [],
+                "priceInfo": {},
-                "pricePeriods": {
-                    "best_price": [],
-                    "peak_price": [],
-                },
-                "currency": currency,
             }
 
-        # Enrich price info dynamically with calculated differences and rating levels
+        # Use the first home's data as the main entry's data
-        # (Modifies all_intervals in-place, returns same list)
+        first_home_data = next(iter(homes_data.values()))
-        thresholds = self.get_threshold_percentages()  # Only for rating_level
+        price_info = first_home_data.get("price_info", {})
-        level_gap_tolerance = self.get_level_gap_tolerance()  # Separate: for Tibber's price level
 
-        enriched_intervals = enrich_price_info_with_differences(
+        # Perform midnight turnover if needed (handles day transitions)
-            all_intervals,
+        price_info = self._perform_turnover_fn(price_info)
+
+        # Ensure all required keys exist (API might not return tomorrow data yet)
+        price_info.setdefault("yesterday", [])
+        price_info.setdefault("today", [])
+        price_info.setdefault("tomorrow", [])
+        price_info.setdefault("currency", "EUR")
+
+        # Enrich price info dynamically with calculated differences and rating levels
+        # This ensures enrichment is always up-to-date, especially after midnight turnover
+        thresholds = self.get_threshold_percentages()
+        price_info = enrich_price_info_with_differences(
+            price_info,
             threshold_low=thresholds["low"],
             threshold_high=thresholds["high"],
-            hysteresis=float(thresholds["hysteresis"]),
-            gap_tolerance=int(thresholds["gap_tolerance"]),
-            level_gap_tolerance=level_gap_tolerance,
-            time=self.time,
         )
 
-        # Store enriched intervals directly as priceInfo (flat list)
+        # Note: Periods are calculated and cached separately by PeriodCalculator
-        transformed_data = {
+        # to avoid redundant caching (periods were cached twice before)
-            "home_id": home_id,
-            "priceInfo": enriched_intervals,
-            "currency": currency,
-        }
 
-        # Calculate periods (best price and peak price)
+        transformed_data = {
-        if "priceInfo" in transformed_data:
+            "timestamp": raw_data.get("timestamp"),
-            transformed_data["pricePeriods"] = self._calculate_periods_fn(transformed_data["priceInfo"])
+            "homes": homes_data,
+            "priceInfo": price_info,
+        }
+
+        # Cache the transformed data
+        self._cached_transformed_data = transformed_data
+        self._last_transformation_config = self._get_current_transformation_config()
+        self._last_midnight_check = current_time
+
+        return transformed_data
+
+    def transform_data_for_subentry(self, main_data: dict[str, Any], home_id: str) -> dict[str, Any]:
+        """Transform main coordinator data for subentry (home-specific view)."""
+        current_time = self.time.now()
+
+        # Return cached transformed data if no retransformation needed
+        if not self._should_retransform_data(current_time) and self._cached_transformed_data is not None:
+            self._log("debug", "Using cached transformed data (no transformation needed)")
+            return self._cached_transformed_data
+
+        self._log("debug", "Transforming price data for home (enrichment only, periods cached separately)")
+
+        if not home_id:
+            return main_data
+
+        homes_data = main_data.get("homes", {})
+        home_data = homes_data.get(home_id, {})
+
+        if not home_data:
+            return {
+                "timestamp": main_data.get("timestamp"),
+                "priceInfo": {},
+            }
+
+        price_info = home_data.get("price_info", {})
+
+        # Perform midnight turnover if needed (handles day transitions)
+        price_info = self._perform_turnover_fn(price_info)
+
+        # Ensure all required keys exist (API might not return tomorrow data yet)
+        price_info.setdefault("yesterday", [])
+        price_info.setdefault("today", [])
+        price_info.setdefault("tomorrow", [])
+        price_info.setdefault("currency", "EUR")
+
+        # Enrich price info dynamically with calculated differences and rating levels
+        # This ensures enrichment is always up-to-date, especially after midnight turnover
+        thresholds = self.get_threshold_percentages()
+        price_info = enrich_price_info_with_differences(
+            price_info,
+            threshold_low=thresholds["low"],
+            threshold_high=thresholds["high"],
+        )
+
+        # Note: Periods are calculated and cached separately by PeriodCalculator
+        # to avoid redundant caching (periods were cached twice before)
+
+        transformed_data = {
+            "timestamp": main_data.get("timestamp"),
+            "priceInfo": price_info,
+        }
 
         # Cache the transformed data
         self._cached_transformed_data = transformed_data
         self._last_transformation_config = self._get_current_transformation_config()
         self._last_midnight_check = current_time
-        self._last_source_data_timestamp = source_data_timestamp
 
         return transformed_data
 

custom_components/tibber_prices/coordinator/helpers.py

@@ -3,139 +3,104 @@
 from __future__ import annotations
 
 import logging
-from datetime import timedelta
 from typing import TYPE_CHECKING, Any
 
-from homeassistant.util import dt as dt_util
-
 if TYPE_CHECKING:
+    from datetime import date
+
+    from homeassistant.core import HomeAssistant
+
     from .time_service import TibberPricesTimeService
 
+from custom_components.tibber_prices.const import DOMAIN
+
 _LOGGER = logging.getLogger(__name__)
 
 
-def get_intervals_for_day_offsets(
+def get_configured_home_ids(hass: HomeAssistant) -> set[str]:
-    coordinator_data: dict[str, Any] | None,
+    """Get all home_ids that have active config entries (main + subentries)."""
-    offsets: list[int],
+    home_ids = set()
-) -> list[dict[str, Any]]:
-    """
-    Get intervals for specific day offsets from coordinator data.
 
-    This is the core function for filtering intervals by date offset.
+    # Collect home_ids from all config entries for this domain
-    Abstracts the data structure - callers don't need to know where intervals are stored.
+    for entry in hass.config_entries.async_entries(DOMAIN):
+        if home_id := entry.data.get("home_id"):
+            home_ids.add(home_id)
 
-    Performance optimized:
+    return home_ids
-    - Date comparison using .date() on datetime objects (fast)
-    - Single pass through intervals with date caching
-    - Only processes requested offsets
-
-    Args:
-        coordinator_data: Coordinator data dict (typically coordinator.data).
-        offsets: List of day offsets relative to today (e.g., [0, 1] for today and tomorrow).
-                 Range: -374 to +1 (allows historical comparisons up to one year + one week).
-                 0 = today, -1 = yesterday, +1 = tomorrow, -7 = one week ago, etc.
-
-    Returns:
-        List of intervals matching the requested day offsets, in chronological order.
-
-    Example:
-        # Get only today's intervals
-        today_intervals = get_intervals_for_day_offsets(coordinator.data, [0])
-
-        # Get today and tomorrow
-        future_intervals = get_intervals_for_day_offsets(coordinator.data, [0, 1])
-
-        # Get all available intervals
-        all = get_intervals_for_day_offsets(coordinator.data, [-1, 0, 1])
-
-        # Compare last week with same week one year ago
-        comparison = get_intervals_for_day_offsets(coordinator.data, [-7, -371])
-
-    """
-    if not coordinator_data:
-        return []
-
-    # Validate offsets are within acceptable range
-    min_offset = -374  # One year + one week for comparisons
-    max_offset = 1  # Tomorrow (we don't have data further in the future)
-
-    # Extract intervals from coordinator data structure (priceInfo is now a list)
-    all_intervals = coordinator_data.get("priceInfo", [])
-
-    if not all_intervals:
-        return []
-
-    # Get current local date for comparison (no TimeService needed - use dt_util directly)
-    now_local = dt_util.now()
-    today_date = now_local.date()
-
-    # Build set of target dates based on requested offsets
-    target_dates = set()
-    for offset in offsets:
-        # Silently clamp offsets to valid range (don't fail on invalid input)
-        if offset < min_offset or offset > max_offset:
-            continue
-        target_date = today_date + timedelta(days=offset)
-        target_dates.add(target_date)
-
-    if not target_dates:
-        return []
-
-    # Filter intervals matching target dates
-    # Optimized: single pass, date() called once per interval
-    result = []
-    for interval in all_intervals:
-        starts_at = interval.get("startsAt")
-        if not starts_at:
-            continue
-
-        # Handle both datetime objects and strings (for flexibility)
-        if isinstance(starts_at, str):
-            # Parse if string (should be rare after parse_all_timestamps)
-            starts_at = dt_util.parse_datetime(starts_at)
-            if not starts_at:
-                continue
-            starts_at = dt_util.as_local(starts_at)
-
-        # Fast date comparison using datetime.date()
-        interval_date = starts_at.date()
-        if interval_date in target_dates:
-            result.append(interval)
-
-    return result
 
 
 def needs_tomorrow_data(
     cached_price_data: dict[str, Any] | None,
+    tomorrow_date: date,
 ) -> bool:
-    """
+    """Check if tomorrow data is missing or invalid."""
-    Check if tomorrow data is missing or invalid in cached price data.
+    if not cached_price_data or "homes" not in cached_price_data:
-
-    Expects single-home cache format: {"price_info": [...], "home_id": "xxx"}
-
-    Old multi-home format (v0.14.0) is automatically invalidated by is_cache_valid()
-    in cache.py, so we only need to handle the current format here.
-
-    Uses get_intervals_for_day_offsets() to automatically determine tomorrow
-    based on current date. No explicit date parameter needed.
-
-    Args:
-        cached_price_data: Cached price data in single-home structure
-
-    Returns:
-        True if tomorrow's data is missing, False otherwise
-
-    """
-    if not cached_price_data or "price_info" not in cached_price_data:
         return False
 
-    # Single-home format: {"price_info": [...], "home_id": "xxx"}
+    # Use provided TimeService or create new one
-    # Use helper to get tomorrow's intervals (offset +1 from current date)
-    coordinator_data = {"priceInfo": cached_price_data.get("price_info", [])}
-    tomorrow_intervals = get_intervals_for_day_offsets(coordinator_data, [1])
 
-    # If no intervals for tomorrow found, we need tomorrow data
+    for home_data in cached_price_data["homes"].values():
-    return len(tomorrow_intervals) == 0
+        price_info = home_data.get("price_info", {})
+        tomorrow_prices = price_info.get("tomorrow", [])
+
+        # Check if tomorrow data is missing
+        if not tomorrow_prices:
+            return True
+
+        # Check if tomorrow data is actually for tomorrow (validate date)
+        first_price = tomorrow_prices[0]
+        if starts_at := first_price.get("startsAt"):  # Already datetime in local timezone
+            price_date = starts_at.date()
+            if price_date != tomorrow_date:
+                return True
+
+    return False
+
+
+def perform_midnight_turnover(price_info: dict[str, Any], *, time: TibberPricesTimeService) -> dict[str, Any]:
+    """
+    Perform midnight turnover on price data.
+
+    Moves: today → yesterday, tomorrow → today, clears tomorrow.
+
+    This handles cases where:
+    - Server was running through midnight
+    - Cache is being refreshed and needs proper day rotation
+
+    Args:
+        price_info: The price info dict with 'today', 'tomorrow', 'yesterday' keys
+        time: TibberPricesTimeService instance (required)
+
+    Returns:
+        Updated price_info with rotated day data
+
+    """
+    # Use provided TimeService or create new one
+
+    current_local_date = time.now().date()
+
+    # Extract current data
+    today_prices = price_info.get("today", [])
+    tomorrow_prices = price_info.get("tomorrow", [])
+
+    # Check if any of today's prices are from the previous day
+    prices_need_rotation = False
+    if today_prices:
+        first_today_price = today_prices[0].get("startsAt")  # Already datetime in local timezone
+        if first_today_price:
+            first_today_price_date = first_today_price.date()
+            prices_need_rotation = first_today_price_date < current_local_date
+
+    if prices_need_rotation:
+        return {
+            "yesterday": today_prices,
+            "today": tomorrow_prices,
+            "tomorrow": [],
+            "currency": price_info.get("currency", "EUR"),
+        }
+
+    # No rotation needed, return original
+    return price_info
 
 
 def parse_all_timestamps(price_data: dict[str, Any], *, time: TibberPricesTimeService) -> dict[str, Any]:
@@ -148,28 +113,29 @@ def parse_all_timestamps(price_data: dict[str, Any], *, time: TibberPricesTimeSe
     Performance: ~200 timestamps parsed ONCE instead of multiple times per update cycle.
 
     Args:
-        price_data: Raw API data with string timestamps (single-home structure)
+        price_data: Raw API data with string timestamps
         time: TibberPricesTimeService for parsing
 
     Returns:
         Same structure but with datetime objects instead of strings
 
     """
-    if not price_data:
+    if not price_data or "homes" not in price_data:
         return price_data
 
-    # Single-home structure: price_info is a flat list of intervals
+    # Process each home
-    price_info = price_data.get("price_info", [])
+    for home_data in price_data["homes"].values():
+        price_info = home_data.get("price_info", {})
 
-    # Skip if price_info is not a list (empty or invalid)
+        # Process each day's intervals
-    if not isinstance(price_info, list):
+        for day_key in ["yesterday", "today", "tomorrow"]:
-        return price_data
+            intervals = price_info.get(day_key, [])
-
+            for interval in intervals:
-    # Parse timestamps in flat interval list
+                if (starts_at_str := interval.get("startsAt")) and isinstance(starts_at_str, str):
-    for interval in price_info:
+                    # Parse once, convert to local timezone, store as datetime object
-        if (starts_at_str := interval.get("startsAt")) and isinstance(starts_at_str, str):
+                    interval["startsAt"] = time.parse_and_localize(starts_at_str)
-            # Parse once, convert to local timezone, store as datetime object
+                    # If already datetime (e.g., from cache), skip parsing
-            interval["startsAt"] = time.parse_and_localize(starts_at_str)
-            # If already datetime (e.g., from cache), skip parsing
 
     return price_data
+
+    return price_info

custom_components/tibber_prices/coordinator/listeners.py

@@ -157,18 +157,7 @@ class TibberPricesListenerManager:
         self,
         handler_callback: Callable[[datetime], None],
     ) -> None:
-        """
+        """Schedule 30-second entity refresh for timing sensors."""
-        Schedule 30-second entity refresh for timing sensors (Timer #3).
-
-        This is Timer #3 in the integration's timer architecture. It MUST trigger
-        at exact 30-second boundaries (0, 30 seconds) to keep timing sensors
-        (countdown, time-to) accurate.
-
-        Home Assistant may introduce small scheduling delays (jitter), which are
-        corrected using _BOUNDARY_TOLERANCE_SECONDS in time_service.py.
-
-        Runs independently of Timer #1 (API polling), which operates at random offsets.
-        """
         # Cancel any existing timer
         if self._minute_timer_cancel:
             self._minute_timer_cancel()

custom_components/tibber_prices/coordinator/midnight_handler.py

@@ -1,121 +0,0 @@
-"""
-Midnight turnover detection and coordination handler.
-
-This module provides atomic coordination logic for midnight turnover between
-multiple timers (DataUpdateCoordinator and quarter-hour refresh timer).
-
-The handler ensures that midnight turnover happens exactly once per day,
-regardless of which timer detects it first.
-"""
-
-from __future__ import annotations
-
-from typing import TYPE_CHECKING
-
-if TYPE_CHECKING:
-    from datetime import datetime
-
-
-class TibberPricesMidnightHandler:
-    """
-    Handles midnight turnover detection and atomic coordination.
-
-    This class encapsulates the logic for detecting when midnight has passed
-    and ensuring that data rotation happens exactly once per day.
-
-    The atomic coordination works without locks by comparing date values:
-    - Timer #1 and Timer #2 both check if current_date > last_checked_date
-    - First timer to succeed marks the date as checked
-    - Second timer sees dates are equal and skips turnover
-    - Timer #3 doesn't participate in midnight logic (only 30-second timing updates)
-
-    HA Restart Handling:
-    - If HA restarts after midnight, _last_midnight_check is None (fresh handler)
-    - But _last_actual_turnover is restored from cache with yesterday's date
-    - is_turnover_needed() detects the date mismatch and returns True
-    - Missed midnight turnover is caught up on first timer run after restart
-
-    Attributes:
-        _last_midnight_check: Last datetime when midnight turnover was checked
-        _last_actual_turnover: Last datetime when turnover actually happened
-
-    """
-
-    def __init__(self) -> None:
-        """Initialize the midnight handler."""
-        self._last_midnight_check: datetime | None = None
-        self._last_actual_turnover: datetime | None = None
-
-    def is_turnover_needed(self, now: datetime) -> bool:
-        """
-        Check if midnight turnover is needed without side effects.
-
-        This is a pure check function - it doesn't modify state. Call
-        mark_turnover_done() after successfully performing the turnover.
-
-        IMPORTANT: If handler is uninitialized (HA restart), this checks if we
-        need to catch up on midnight turnover that happened while HA was down.
-
-        Args:
-            now: Current datetime to check
-
-        Returns:
-            True if midnight has passed since last check, False otherwise
-
-        """
-        # First time initialization after HA restart
-        if self._last_midnight_check is None:
-            # Check if we need to catch up on missed midnight turnover
-            # If last_actual_turnover exists, we can determine if midnight was missed
-            if self._last_actual_turnover is not None:
-                last_turnover_date = self._last_actual_turnover.date()
-                current_date = now.date()
-                # Turnover needed if we're on a different day than last turnover
-                return current_date > last_turnover_date
-            # Both None = fresh start, no turnover needed yet
-            return False
-
-        # Extract date components
-        last_checked_date = self._last_midnight_check.date()
-        current_date = now.date()
-
-        # Midnight crossed if current date is after last checked date
-        return current_date > last_checked_date
-
-    def mark_turnover_done(self, now: datetime) -> None:
-        """
-        Mark that midnight turnover has been completed.
-
-        Updates both check timestamp and actual turnover timestamp to prevent
-        duplicate turnover by another timer.
-
-        Args:
-            now: Current datetime when turnover was completed
-
-        """
-        self._last_midnight_check = now
-        self._last_actual_turnover = now
-
-    def update_check_time(self, now: datetime) -> None:
-        """
-        Update the last check time without marking turnover as done.
-
-        Used for initializing the handler or updating the check timestamp
-        without triggering turnover logic.
-
-        Args:
-            now: Current datetime to set as last check time
-
-        """
-        if self._last_midnight_check is None:
-            self._last_midnight_check = now
-
-    @property
-    def last_turnover_time(self) -> datetime | None:
-        """Get the timestamp of the last actual turnover."""
-        return self._last_actual_turnover
-
-    @property
-    def last_check_time(self) -> datetime | None:
-        """Get the timestamp of the last midnight check."""
-        return self._last_midnight_check

custom_components/tibber_prices/coordinator/period_handlers/core.py

@@ -16,10 +16,8 @@ from .period_building import (
     add_interval_ends,
     build_periods,
     calculate_reference_prices,
-    extend_periods_across_midnight,
     filter_periods_by_end_date,
     filter_periods_by_min_length,
-    filter_superseded_periods,
     split_intervals_by_day,
 )
 from .period_statistics import (
@@ -54,10 +52,10 @@ def calculate_periods(
     7. Extract period summaries (start/end times, not full price data)
 
     Args:
-        all_prices: All price data points from yesterday/today/tomorrow.
+        all_prices: All price data points from yesterday/today/tomorrow
         config: Period configuration containing reverse_sort, flex, min_distance_from_avg,
-                min_period_length, threshold_low, and threshold_high.
+                min_period_length, threshold_low, and threshold_high
-        time: TibberPricesTimeService instance (required).
+        time: TibberPricesTimeService instance (required)
 
     Returns:
         Dict with:
@@ -75,7 +73,7 @@ def calculate_periods(
 
     # Extract config values
     reverse_sort = config.reverse_sort
-    flex_raw = config.flex  # Already normalized to positive by get_period_config()
+    flex_raw = config.flex
     min_distance_from_avg = config.min_distance_from_avg
     min_period_length = config.min_period_length
     threshold_low = config.threshold_low
@@ -83,14 +81,13 @@ def calculate_periods(
 
     # CRITICAL: Hard cap flex at 50% to prevent degenerate behavior
     # Above 50%, period detection becomes unreliable (too many intervals qualify)
-    # NOTE: flex_raw is already positive from normalization in get_period_config()
     flex = flex_raw
-    if flex_raw > MAX_SAFE_FLEX:
+    if abs(flex_raw) > MAX_SAFE_FLEX:
-        flex = MAX_SAFE_FLEX
+        flex = MAX_SAFE_FLEX if flex_raw > 0 else -MAX_SAFE_FLEX
         _LOGGER.warning(
             "Flex %.1f%% exceeds maximum safe value! Capping at %.0f%%. "
             "Recommendation: Use 15-20%% with relaxation enabled, or 25-35%% without relaxation.",
-            flex_raw * 100,
+            abs(flex_raw) * 100,
             MAX_SAFE_FLEX * 100,
         )
 
@@ -129,13 +126,9 @@ def calculate_periods(
     # High flex (>25%) makes outlier detection too permissive, accepting
     # unstable price contexts as "normal". This breaks period formation.
     # User's flex setting still applies to period criteria (in_flex check).
-
-    # Import details logger locally (core.py imports logger locally in function)
-    _LOGGER_DETAILS = logging.getLogger(__name__ + ".details")  # noqa: N806
-
     outlier_flex = min(abs(flex) * 100, MAX_OUTLIER_FLEX * 100)
     if abs(flex) * 100 > MAX_OUTLIER_FLEX * 100:
-        _LOGGER_DETAILS.debug(
+        _LOGGER.debug(
             "%sOutlier filtering: Using capped flex %.1f%% (user setting: %.1f%%)",
             INDENT_L0,
             outlier_flex,
@@ -152,7 +145,6 @@ def calculate_periods(
     price_context = {
         "ref_prices": ref_prices,
         "avg_prices": avg_price_by_day,
-        "intervals_by_day": intervals_by_day,  # Needed for day volatility calculation
         "flex": flex,
         "min_distance_from_avg": min_distance_from_avg,
     }
@@ -185,14 +177,12 @@ def calculate_periods(
     # Step 5: Add interval ends
     add_interval_ends(raw_periods, time=time)
 
-    # Step 6: Filter periods by end date (keep periods ending yesterday or later)
+    # Step 6: Filter periods by end date (keep periods ending today or later)
-    # This ensures coordinator cache contains yesterday/today/tomorrow periods
-    # Sensors filter further for today+tomorrow, services can access all cached periods
     raw_periods = filter_periods_by_end_date(raw_periods, time=time)
 
-    # Step 7: Extract lightweight period summaries (no full price data)
+    # Step 8: Extract lightweight period summaries (no full price data)
-    # Note: Periods are filtered by end date to keep yesterday/today/tomorrow.
+    # Note: Filtering for current/future is done here based on end date,
-    # This preserves periods that started day-before-yesterday but end yesterday.
+    # not start date. This preserves periods that started yesterday but end today.
     thresholds = TibberPricesThresholdConfig(
         threshold_low=threshold_low,
         threshold_high=threshold_high,
@@ -209,26 +199,6 @@ def calculate_periods(
         time=time,
     )
 
-    # Step 8: Cross-day extension for late-night periods
-    # If a best-price period ends near midnight and tomorrow has continued low prices,
-    # extend the period across midnight to give users the full cheap window
-    period_summaries = extend_periods_across_midnight(
-        period_summaries,
-        all_prices_sorted,
-        price_context,
-        time=time,
-        reverse_sort=reverse_sort,
-    )
-
-    # Step 9: Filter superseded periods
-    # When tomorrow data is available, late-night today periods that were found via
-    # relaxation may be obsolete if tomorrow has significantly better alternatives
-    period_summaries = filter_superseded_periods(
-        period_summaries,
-        time=time,
-        reverse_sort=reverse_sort,
-    )
-
     return {
         "periods": period_summaries,  # Lightweight summaries only
         "metadata": {

custom_components/tibber_prices/coordinator/period_handlers/level_filtering.py

@@ -109,11 +109,6 @@ def check_interval_criteria(
     """
     Check if interval meets flex and minimum distance criteria.
 
-    CRITICAL: This function works with NORMALIZED values (always positive):
-    - criteria.flex: Always positive (e.g., 0.20 for 20%)
-    - criteria.min_distance_from_avg: Always positive (e.g., 5.0 for 5%)
-    - criteria.reverse_sort: Determines direction (True=Peak, False=Best)
-
     Args:
         price: Interval price
         criteria: Interval criteria (ref_price, avg_price, flex, etc.)
@@ -122,54 +117,22 @@ def check_interval_criteria(
         Tuple of (in_flex, meets_min_distance)
 
     """
-    # Normalize inputs to absolute values for consistent calculation
+    # Calculate percentage difference from reference
-    flex_abs = abs(criteria.flex)
+    percent_diff = ((price - criteria.ref_price) / criteria.ref_price) * 100 if criteria.ref_price != 0 else 0.0
-    min_distance_abs = abs(criteria.min_distance_from_avg)
 
-    # ============================================================
+    # Check if interval qualifies for the period
-    # FLEX FILTER: Check if price is within flex threshold of reference
+    in_flex = percent_diff >= criteria.flex * 100 if criteria.reverse_sort else percent_diff <= criteria.flex * 100
-    # ============================================================
-    # Reference price is:
-    # - Peak price (reverse_sort=True): daily MAXIMUM
-    # - Best price (reverse_sort=False): daily MINIMUM
-    #
-    # Flex band calculation (using absolute values):
-    # - Peak price: [max - max*flex, max] → accept prices near the maximum
-    # - Best price: [min, min + min*flex] → accept prices near the minimum
-    #
-    # Examples with flex=20%:
-    # - Peak: max=30 ct → accept [24, 30] ct (prices ≥ 24 ct)
-    # - Best: min=10 ct → accept [10, 12] ct (prices ≤ 12 ct)
 
-    if criteria.ref_price == 0:
-        # Zero reference: flex has no effect, use strict equality
-        in_flex = price == 0
-    else:
-        # Calculate flex amount using absolute value
-        flex_amount = abs(criteria.ref_price) * flex_abs
-
-        if criteria.reverse_sort:
-            # Peak price: accept prices >= (ref_price - flex_amount)
-            # Prices must be CLOSE TO or AT the maximum
-            flex_threshold = criteria.ref_price - flex_amount
-            in_flex = price >= flex_threshold
-        else:
-            # Best price: accept prices <= (ref_price + flex_amount)
-            # Accept ALL low prices up to the flex threshold, not just those >= minimum
-            # This ensures that if there are multiple low-price intervals, all that meet
-            # the threshold are included, regardless of whether they're before or after
-            # the daily minimum in the chronological sequence.
-            flex_threshold = criteria.ref_price + flex_amount
-            in_flex = price <= flex_threshold
-
-    # ============================================================
-    # MIN_DISTANCE FILTER: Check if price is far enough from average
-    # ============================================================
     # CRITICAL: Adjust min_distance dynamically based on flex to prevent conflicts
     # Problem: High flex (e.g., 50%) can conflict with fixed min_distance (e.g., 5%)
     # Solution: When flex is high, reduce min_distance requirement proportionally
+    #
+    # At low flex (≤20%), use full min_distance (e.g., 5%)
+    # At high flex (≥40%), reduce min_distance to avoid over-filtering
+    # Linear interpolation between 20-40% flex range
 
-    adjusted_min_distance = min_distance_abs
+    adjusted_min_distance = criteria.min_distance_from_avg
+    flex_abs = abs(criteria.flex)
 
     if flex_abs > FLEX_SCALING_THRESHOLD:
         # Scale down min_distance as flex increases
@@ -178,30 +141,28 @@ def check_interval_criteria(
         # At 50% flex: multiplier = 0.25 (quarter min_distance)
         flex_excess = flex_abs - 0.20  # How much above 20%
         scale_factor = max(0.25, 1.0 - (flex_excess * 2.5))  # Linear reduction, min 25%
-        adjusted_min_distance = min_distance_abs * scale_factor
+        adjusted_min_distance = criteria.min_distance_from_avg * scale_factor
 
         # Log adjustment at DEBUG level (only when significant reduction)
         if scale_factor < SCALE_FACTOR_WARNING_THRESHOLD:
             import logging  # noqa: PLC0415
 
-            _LOGGER = logging.getLogger(f"{__name__}.details")  # noqa: N806
+            _LOGGER = logging.getLogger(__name__)  # noqa: N806
             _LOGGER.debug(
                 "High flex %.1f%% detected: Reducing min_distance %.1f%% → %.1f%% (scale %.2f)",
                 flex_abs * 100,
-                min_distance_abs,
+                criteria.min_distance_from_avg,
                 adjusted_min_distance,
                 scale_factor,
             )
 
-    # Calculate threshold from average (using normalized positive distance)
+    # Minimum distance from average (using adjusted value)
-    # - Peak price: threshold = avg * (1 + distance/100) → prices must be ABOVE avg+distance
-    # - Best price: threshold = avg * (1 - distance/100) → prices must be BELOW avg-distance
     if criteria.reverse_sort:
-        # Peak: price must be >= avg * (1 + distance%)
+        # Peak price: must be at least adjusted_min_distance% above average
         min_distance_threshold = criteria.avg_price * (1 + adjusted_min_distance / 100)
         meets_min_distance = price >= min_distance_threshold
     else:
-        # Best: price must be <= avg * (1 - distance%)
+        # Best price: must be at least adjusted_min_distance% below average
         min_distance_threshold = criteria.avg_price * (1 - adjusted_min_distance / 100)
         meets_min_distance = price <= min_distance_threshold
 

custom_components/tibber_prices/coordinator/period_handlers/outlier_filtering.py

@@ -15,34 +15,18 @@ Uses statistical methods:
 from __future__ import annotations
 
 import logging
-from datetime import datetime
 from typing import NamedTuple
 
-from custom_components.tibber_prices.utils.price import calculate_coefficient_of_variation
-
 _LOGGER = logging.getLogger(__name__)
-_LOGGER_DETAILS = logging.getLogger(__name__ + ".details")
 
 # Outlier filtering constants
 MIN_CONTEXT_SIZE = 3  # Minimum intervals needed before/after for analysis
+CONFIDENCE_LEVEL = 2.0  # Standard deviations for 95% confidence interval
 VOLATILITY_THRESHOLD = 0.05  # 5% max relative std dev for zigzag detection
 SYMMETRY_THRESHOLD = 1.5  # Max std dev difference for symmetric spike
 RELATIVE_VOLATILITY_THRESHOLD = 2.0  # Window volatility vs context (cluster detection)
 ASYMMETRY_TAIL_WINDOW = 6  # Skip asymmetry check for last ~1.5h (6 intervals) of available data
 ZIGZAG_TAIL_WINDOW = 6  # Skip zigzag/cluster detection for last ~1.5h (6 intervals)
-EXTREMES_PROTECTION_TOLERANCE = 0.001  # Protect prices within 0.1% of daily min/max from smoothing
-
-# Adaptive confidence level constants
-# Uses coefficient of variation (CV) from utils/price.py for consistency with volatility sensors
-# On flat days (low CV), we're more conservative (higher confidence = fewer smoothed)
-# On volatile days (high CV), we're more aggressive (lower confidence = more smoothed)
-CONFIDENCE_LEVEL_MIN = 1.5  # Minimum confidence (volatile days: smooth more aggressively)
-CONFIDENCE_LEVEL_MAX = 2.5  # Maximum confidence (flat days: smooth more conservatively)
-CONFIDENCE_LEVEL_DEFAULT = 2.0  # Default: 95% confidence interval (2 std devs)
-# CV thresholds for adaptive confidence (align with volatility sensor defaults)
-# These are in percentage points (e.g., 10.0 = 10% CV)
-DAILY_CV_LOW = 10.0  # ≤10% CV = flat day (use max confidence)
-DAILY_CV_HIGH = 30.0  # ≥30% CV = volatile day (use min confidence)
 
 # Module-local log indentation (each module starts at level 0)
 INDENT_L0 = ""  # All logs in this module (no indentation needed)
@@ -68,7 +52,7 @@ def _should_skip_tail_check(
 ) -> bool:
     """Return True when remaining intervals fall inside tail window and log why."""
     if remaining_intervals < tail_window:
-        _LOGGER_DETAILS.debug(
+        _LOGGER.debug(
             "%sSpike at %s: Skipping %s check (only %d intervals remaining)",
             INDENT_L0,
             interval_label,
@@ -206,7 +190,7 @@ def _validate_spike_candidate(
 
     context_diff_pct = abs(avg_after - avg_before) / avg_before if avg_before > 0 else 0
     if context_diff_pct > candidate.flexibility_ratio:
-        _LOGGER_DETAILS.debug(
+        _LOGGER.debug(
             "%sInterval %s: Context unstable (%.1f%% change) - not a spike",
             INDENT_L0,
             candidate.current.get("startsAt", "unknown interval"),
@@ -220,7 +204,7 @@ def _validate_spike_candidate(
         "asymmetry",
         candidate.current.get("startsAt", "unknown interval"),
     ) and not _check_symmetry(avg_before, avg_after, candidate.stats["std_dev"]):
-        _LOGGER_DETAILS.debug(
+        _LOGGER.debug(
             "%sSpike at %s rejected: Asymmetric (before=%.2f, after=%.2f ct/kWh)",
             INDENT_L0,
             candidate.current.get("startsAt", "unknown interval"),
@@ -238,7 +222,7 @@ def _validate_spike_candidate(
         return True
 
     if _detect_zigzag_pattern(candidate.analysis_window, candidate.stats["std_dev"]):
-        _LOGGER_DETAILS.debug(
+        _LOGGER.debug(
             "%sSpike at %s rejected: Zigzag/cluster pattern detected",
             INDENT_L0,
             candidate.current.get("startsAt", "unknown interval"),
@@ -248,166 +232,6 @@ def _validate_spike_candidate(
     return True
 
 
-def _calculate_daily_extremes(intervals: list[dict]) -> dict[str, tuple[float, float]]:
-    """
-    Calculate daily min/max prices for each day in the interval list.
-
-    These extremes are used to protect reference prices from being smoothed.
-    The daily minimum is the reference for best_price periods, and the daily
-    maximum is the reference for peak_price periods - smoothing these would
-    break period detection.
-
-    Args:
-        intervals: List of price intervals with 'startsAt' and 'total' keys
-
-    Returns:
-        Dict mapping date strings to (min_price, max_price) tuples
-
-    """
-    daily_prices: dict[str, list[float]] = {}
-
-    for interval in intervals:
-        starts_at = interval.get("startsAt")
-        if starts_at is None:
-            continue
-
-        # Handle both datetime objects and ISO strings
-        dt = datetime.fromisoformat(starts_at) if isinstance(starts_at, str) else starts_at
-
-        date_key = dt.strftime("%Y-%m-%d")
-        price = float(interval["total"])
-        daily_prices.setdefault(date_key, []).append(price)
-
-    # Calculate min/max for each day
-    return {date_key: (min(prices), max(prices)) for date_key, prices in daily_prices.items()}
-
-
-def _calculate_daily_cv(intervals: list[dict]) -> dict[str, float]:
-    """
-    Calculate daily coefficient of variation (CV) for each day.
-
-    Uses the same CV calculation as volatility sensors for consistency.
-    CV = (std_dev / mean) * 100, expressed as percentage.
-
-    Used to adapt the confidence level for outlier detection:
-    - Flat days (low CV): Higher confidence → fewer false positives
-    - Volatile days (high CV): Lower confidence → catch more real outliers
-
-    Args:
-        intervals: List of price intervals with 'startsAt' and 'total' keys
-
-    Returns:
-        Dict mapping date strings to CV percentage (e.g., 15.0 for 15% CV)
-
-    """
-    daily_prices: dict[str, list[float]] = {}
-
-    for interval in intervals:
-        starts_at = interval.get("startsAt")
-        if starts_at is None:
-            continue
-
-        dt = datetime.fromisoformat(starts_at) if isinstance(starts_at, str) else starts_at
-        date_key = dt.strftime("%Y-%m-%d")
-        price = float(interval["total"])
-        daily_prices.setdefault(date_key, []).append(price)
-
-    # Calculate CV using the shared function from utils/price.py
-    result = {}
-    for date_key, prices in daily_prices.items():
-        cv = calculate_coefficient_of_variation(prices)
-        result[date_key] = cv if cv is not None else 0.0
-    return result
-
-
-def _get_adaptive_confidence_level(
-    interval: dict,
-    daily_cv: dict[str, float],
-) -> float:
-    """
-    Get adaptive confidence level based on daily coefficient of variation (CV).
-
-    Maps daily CV to confidence level:
-    - Low CV (≤10%): High confidence (2.5) → conservative, fewer smoothed
-    - High CV (≥30%): Low confidence (1.5) → aggressive, more smoothed
-    - Between: Linear interpolation
-
-    Uses the same CV calculation as volatility sensors for consistency.
-
-    Args:
-        interval: Price interval dict with 'startsAt' key
-        daily_cv: Dict from _calculate_daily_cv()
-
-    Returns:
-        Confidence level multiplier for std_dev threshold
-
-    """
-    starts_at = interval.get("startsAt")
-    if starts_at is None:
-        return CONFIDENCE_LEVEL_DEFAULT
-
-    dt = datetime.fromisoformat(starts_at) if isinstance(starts_at, str) else starts_at
-    date_key = dt.strftime("%Y-%m-%d")
-
-    cv = daily_cv.get(date_key, 0.0)
-
-    # Linear interpolation between LOW and HIGH CV
-    # Low CV → high confidence (conservative)
-    # High CV → low confidence (aggressive)
-    if cv <= DAILY_CV_LOW:
-        return CONFIDENCE_LEVEL_MAX
-    if cv >= DAILY_CV_HIGH:
-        return CONFIDENCE_LEVEL_MIN
-
-    # Linear interpolation: as CV increases, confidence decreases
-    ratio = (cv - DAILY_CV_LOW) / (DAILY_CV_HIGH - DAILY_CV_LOW)
-    return CONFIDENCE_LEVEL_MAX - (ratio * (CONFIDENCE_LEVEL_MAX - CONFIDENCE_LEVEL_MIN))
-
-
-def _is_daily_extreme(
-    interval: dict,
-    daily_extremes: dict[str, tuple[float, float]],
-    tolerance: float = EXTREMES_PROTECTION_TOLERANCE,
-) -> bool:
-    """
-    Check if an interval's price is at or very near a daily extreme.
-
-    Prices at daily extremes should never be smoothed because:
-    - Daily minimum is the reference for best_price period detection
-    - Daily maximum is the reference for peak_price period detection
-    - Smoothing these would cause periods to miss their most important intervals
-
-    Args:
-        interval: Price interval dict with 'startsAt' and 'total' keys
-        daily_extremes: Dict from _calculate_daily_extremes()
-        tolerance: Relative tolerance for matching (default 0.1%)
-
-    Returns:
-        True if the price is at or very near a daily min or max
-
-    """
-    starts_at = interval.get("startsAt")
-    if starts_at is None:
-        return False
-
-    # Handle both datetime objects and ISO strings
-    dt = datetime.fromisoformat(starts_at) if isinstance(starts_at, str) else starts_at
-
-    date_key = dt.strftime("%Y-%m-%d")
-    if date_key not in daily_extremes:
-        return False
-
-    price = float(interval["total"])
-    daily_min, daily_max = daily_extremes[date_key]
-
-    # Check if price is within tolerance of daily min or max
-    # Using relative tolerance: |price - extreme| <= extreme * tolerance
-    min_threshold = daily_min * (1 + tolerance)
-    max_threshold = daily_max * (1 - tolerance)
-
-    return price <= min_threshold or price >= max_threshold
-
-
 def filter_price_outliers(
     intervals: list[dict],
     flexibility_pct: float,
@@ -435,29 +259,15 @@ def filter_price_outliers(
         Intervals with smoothed prices (marked with _smoothed flag)
 
     """
-    # Convert percentage to ratio once for all comparisons (e.g., 15.0 → 0.15)
-    flexibility_ratio = flexibility_pct / 100
-
-    # Calculate daily extremes to protect reference prices from smoothing
-    # Daily min is the reference for best_price, daily max for peak_price
-    daily_extremes = _calculate_daily_extremes(intervals)
-
-    # Calculate daily coefficient of variation (CV) for adaptive confidence levels
-    # Uses same CV calculation as volatility sensors for consistency
-    # Flat days → conservative smoothing, volatile days → aggressive smoothing
-    daily_cv = _calculate_daily_cv(intervals)
-
-    # Log CV info for debugging (CV is in percentage points, e.g., 15.0 = 15%)
-    cv_info = ", ".join(f"{date}: {cv:.1f}%" for date, cv in sorted(daily_cv.items()))
     _LOGGER.info(
-        "%sSmoothing price outliers: %d intervals, flex=%.1f%%, daily CV: %s",
+        "%sSmoothing price outliers: %d intervals, flex=%.1f%%",
         INDENT_L0,
         len(intervals),
         flexibility_pct,
-        cv_info,
     )
 
-    protected_count = 0
+    # Convert percentage to ratio once for all comparisons (e.g., 15.0 → 0.15)
+    flexibility_ratio = flexibility_pct / 100
 
     result = []
     smoothed_count = 0
@@ -465,20 +275,6 @@ def filter_price_outliers(
     for i, current in enumerate(intervals):
         current_price = current["total"]
 
-        # CRITICAL: Never smooth daily extremes - they are the reference prices!
-        # Smoothing the daily min would break best_price period detection,
-        # smoothing the daily max would break peak_price period detection.
-        if _is_daily_extreme(current, daily_extremes):
-            result.append(current)
-            protected_count += 1
-            _LOGGER_DETAILS.debug(
-                "%sProtected daily extreme at %s: %.2f ct/kWh (not smoothed)",
-                INDENT_L0,
-                current.get("startsAt", f"index {i}"),
-                current_price * 100,
-            )
-            continue
-
         # Get context windows (3 intervals before and after)
         context_before = intervals[max(0, i - MIN_CONTEXT_SIZE) : i]
         context_after = intervals[i + 1 : min(len(intervals), i + 1 + MIN_CONTEXT_SIZE)]
@@ -500,11 +296,8 @@ def filter_price_outliers(
         # Calculate how far current price deviates from expected
         residual = abs(current_price - expected_price)
 
-        # Adaptive confidence level based on daily CV:
+        # Tolerance based on statistical confidence (2 std dev = 95% confidence)
-        # - Flat days (low CV): higher confidence (2.5) → fewer false positives
+        tolerance = stats["std_dev"] * CONFIDENCE_LEVEL
-        # - Volatile days (high CV): lower confidence (1.5) → catch more real spikes
-        confidence_level = _get_adaptive_confidence_level(current, daily_cv)
-        tolerance = stats["std_dev"] * confidence_level
 
         # Not a spike if within tolerance
         if residual <= tolerance:
@@ -537,23 +330,24 @@ def filter_price_outliers(
         result.append(smoothed)
         smoothed_count += 1
 
-        _LOGGER_DETAILS.debug(
+        _LOGGER.debug(
-            "%sSmoothed spike at %s: %.2f → %.2f ct/kWh (residual: %.2f, tolerance: %.2f, confidence: %.2f)",
+            "%sSmoothed spike at %s: %.2f → %.2f ct/kWh (residual: %.2f, tolerance: %.2f, trend_slope: %.4f)",
             INDENT_L0,
             current.get("startsAt", f"index {i}"),
             current_price * 100,
             expected_price * 100,
             residual * 100,
             tolerance * 100,
-            confidence_level,
+            stats["trend_slope"] * 100,
         )
 
-    if smoothed_count > 0 or protected_count > 0:
+    if smoothed_count > 0:
         _LOGGER.info(
-            "%sPrice outlier smoothing complete: %d smoothed, %d protected (daily extremes)",
+            "%sPrice outlier smoothing complete: %d/%d intervals smoothed (%.1f%%)",
             INDENT_L0,
             smoothed_count,
-            protected_count,
+            len(intervals),
+            (smoothed_count / len(intervals)) * 100,
         )
 
     return result

custom_components/tibber_prices/coordinator/period_handlers/period_building.py

@@ -3,12 +3,13 @@
 from __future__ import annotations
 
 import logging
-from datetime import date, datetime, timedelta
 from typing import TYPE_CHECKING, Any
 
 from custom_components.tibber_prices.const import PRICE_LEVEL_MAPPING
 
 if TYPE_CHECKING:
+    from datetime import date
+
     from custom_components.tibber_prices.coordinator.time_service import TibberPricesTimeService
 
 from .level_filtering import (
@@ -18,7 +19,6 @@ from .level_filtering import (
 from .types import TibberPricesIntervalCriteria
 
 _LOGGER = logging.getLogger(__name__)
-_LOGGER_DETAILS = logging.getLogger(__name__ + ".details")
 
 # Module-local log indentation (each module starts at level 0)
 INDENT_L0 = ""  # Entry point / main function
@@ -65,10 +65,9 @@ def build_periods(  # noqa: PLR0913, PLR0915, PLR0912 - Complex period building
     """
     Build periods, allowing periods to cross midnight (day boundary).
 
-    Periods can span multiple days. Each interval is evaluated against the reference
+    Periods can span multiple days. Each period uses the reference price (min/max) from
-    price (min/max) and average price of its own day. This ensures fair filtering
+    the day when the period started, ensuring consistent filtering criteria throughout
-    criteria even when periods cross midnight, where prices can jump significantly
+    the period even when crossing midnight.
-    due to different forecasting uncertainty (prices at day end vs. day start).
 
     Args:
         all_prices: All price data points
@@ -92,7 +91,7 @@ def build_periods(  # noqa: PLR0913, PLR0915, PLR0912 - Complex period building
         level_filter_active = True
         filter_direction = "≥" if reverse_sort else "≤"
         gap_info = f", gap_tolerance={gap_count}" if gap_count > 0 else ""
-        _LOGGER_DETAILS.debug(
+        _LOGGER.debug(
             "%sLevel filter active: %s (order %s, require interval level %s filter level%s)",
             INDENT_L0,
             level_filter.upper(),
@@ -102,10 +101,11 @@ def build_periods(  # noqa: PLR0913, PLR0915, PLR0912 - Complex period building
         )
     else:
         status = "RELAXED to ANY" if (level_filter and level_filter.lower() == "any") else "DISABLED (not configured)"
-        _LOGGER_DETAILS.debug("%sLevel filter: %s (accepting all levels)", INDENT_L0, status)
+        _LOGGER.debug("%sLevel filter: %s (accepting all levels)", INDENT_L0, status)
 
     periods: list[list[dict]] = []
     current_period: list[dict] = []
+    period_start_date: date | None = None  # Track start day of current period
     consecutive_gaps = 0  # Track consecutive intervals that deviate by 1 level step
     intervals_checked = 0
     intervals_filtered_by_level = 0
@@ -125,14 +125,11 @@ def build_periods(  # noqa: PLR0913, PLR0915, PLR0912 - Complex period building
 
         intervals_checked += 1
 
-        # CRITICAL: Always use reference price from the interval's own day
+        # Use reference price from period start day (for consistency across midnight)
-        # Each interval must meet the criteria of its own day, not the period start day.
+        # If no period active, use current interval's day
-        # This ensures fair filtering even when periods cross midnight, where prices
+        ref_date = period_start_date if period_start_date is not None else date_key
-        # can jump significantly (last intervals of a day have more risk buffer than
-        # first intervals of next day, as they're set with different uncertainty levels).
-        ref_date = date_key
 
-        # Check flex and minimum distance criteria (using smoothed price and interval's own day reference)
+        # Check flex and minimum distance criteria (using smoothed price and period start date reference)
         criteria = TibberPricesIntervalCriteria(
             ref_price=ref_prices[ref_date],
             avg_price=avg_prices[ref_date],
@@ -167,6 +164,10 @@ def build_periods(  # noqa: PLR0913, PLR0915, PLR0912 - Complex period building
 
         # Add to period if all criteria are met
         if in_flex and meets_min_distance and meets_level:
+            # Start new period if none active
+            if not current_period:
+                period_start_date = date_key  # Lock reference to start day
+
             current_period.append(
                 {
                     "interval_hour": starts_at.hour,
@@ -183,6 +184,7 @@ def build_periods(  # noqa: PLR0913, PLR0915, PLR0912 - Complex period building
             # Criteria no longer met, end current period
             periods.append(current_period)
             current_period = []
+            period_start_date = None  # Reset period start date
             consecutive_gaps = 0  # Reset gap counter
 
     # Add final period if exists
@@ -191,14 +193,14 @@ def build_periods(  # noqa: PLR0913, PLR0915, PLR0912 - Complex period building
 
     # Log detailed filter statistics
     if intervals_checked > 0:
-        _LOGGER_DETAILS.debug(
+        _LOGGER.debug(
             "%sFilter statistics: %d intervals checked",
             INDENT_L0,
             intervals_checked,
         )
         if intervals_filtered_by_flex > 0:
             flex_pct = (intervals_filtered_by_flex / intervals_checked) * 100
-            _LOGGER_DETAILS.debug(
+            _LOGGER.debug(
                 "%s  Filtered by FLEX (price too far from ref): %d/%d (%.1f%%)",
                 INDENT_L0,
                 intervals_filtered_by_flex,
@@ -207,7 +209,7 @@ def build_periods(  # noqa: PLR0913, PLR0915, PLR0912 - Complex period building
             )
         if intervals_filtered_by_min_distance > 0:
             distance_pct = (intervals_filtered_by_min_distance / intervals_checked) * 100
-            _LOGGER_DETAILS.debug(
+            _LOGGER.debug(
                 "%s  Filtered by MIN_DISTANCE (price too close to avg): %d/%d (%.1f%%)",
                 INDENT_L0,
                 intervals_filtered_by_min_distance,
@@ -216,7 +218,7 @@ def build_periods(  # noqa: PLR0913, PLR0915, PLR0912 - Complex period building
             )
         if level_filter_active and intervals_filtered_by_level > 0:
             level_pct = (intervals_filtered_by_level / intervals_checked) * 100
-            _LOGGER_DETAILS.debug(
+            _LOGGER.debug(
                 "%s  Filtered by LEVEL (wrong price level): %d/%d (%.1f%%)",
                 INDENT_L0,
                 intervals_filtered_by_level,
@@ -247,21 +249,19 @@ def add_interval_ends(periods: list[list[dict]], *, time: TibberPricesTimeServic
 
 def filter_periods_by_end_date(periods: list[list[dict]], *, time: TibberPricesTimeService) -> list[list[dict]]:
     """
-    Filter periods to keep only relevant ones for yesterday, today, and tomorrow.
+    Filter periods to keep only relevant ones for today and tomorrow.
 
     Keep periods that:
-    - End yesterday or later (>= start of yesterday)
+    - End in the future (> now)
+    - End today but after the start of the day (not exactly at midnight)
 
     This removes:
-    - Periods that ended before yesterday (day-before-yesterday or earlier)
+    - Periods that ended yesterday
-
+    - Periods that ended exactly at midnight today (they're completely in the past)
-    Rationale: Coordinator caches periods for yesterday/today/tomorrow so that:
-    - Binary sensors can filter for today+tomorrow (current/next periods)
-    - Services can access yesterday's periods when user requests "yesterday" data
     """
     now = time.now()
-    # Calculate start of yesterday (midnight yesterday)
+    today = now.date()
-    yesterday_start = time.start_of_local_day(now) - time.get_interval_duration() * 96  # 96 intervals = 24 hours
+    midnight_today = time.start_of_local_day(now)
 
     filtered = []
     for period in periods:
@@ -275,433 +275,13 @@ def filter_periods_by_end_date(periods: list[list[dict]], *, time: TibberPricesT
         if not period_end:
             continue
 
-        # Keep if period ends yesterday or later
+        # Keep if period ends in the future
-        if period_end >= yesterday_start:
+        if time.is_in_future(period_end):
+            filtered.append(period)
+            continue
+
+        # Keep if period ends today but AFTER midnight (not exactly at midnight)
+        if period_end.date() == today and period_end > midnight_today:
             filtered.append(period)
 
     return filtered
-
-
-def _categorize_periods_for_supersession(
-    period_summaries: list[dict],
-    today: date,
-    tomorrow: date,
-    late_hour_threshold: int,
-    early_hour_limit: int,
-) -> tuple[list[dict], list[dict], list[dict]]:
-    """Categorize periods into today-late, tomorrow-early, and other."""
-    today_late: list[dict] = []
-    tomorrow_early: list[dict] = []
-    other: list[dict] = []
-
-    for period in period_summaries:
-        period_start = period.get("start")
-        period_end = period.get("end")
-
-        if not period_start or not period_end:
-            other.append(period)
-        # Today late-night periods: START today at or after late_hour_threshold (e.g., 20:00)
-        # Note: period_end could be tomorrow (e.g., 23:30-00:00 spans midnight)
-        elif period_start.date() == today and period_start.hour >= late_hour_threshold:
-            today_late.append(period)
-        # Tomorrow early-morning periods: START tomorrow before early_hour_limit (e.g., 08:00)
-        elif period_start.date() == tomorrow and period_start.hour < early_hour_limit:
-            tomorrow_early.append(period)
-        else:
-            other.append(period)
-
-    return today_late, tomorrow_early, other
-
-
-def _filter_superseded_today_periods(
-    today_late_periods: list[dict],
-    best_tomorrow: dict,
-    best_tomorrow_price: float,
-    improvement_threshold: float,
-) -> list[dict]:
-    """Filter today periods that are superseded by a better tomorrow period."""
-    kept: list[dict] = []
-
-    for today_period in today_late_periods:
-        today_price = today_period.get("price_mean")
-
-        if today_price is None:
-            kept.append(today_period)
-            continue
-
-        # Calculate how much better tomorrow is (as percentage)
-        improvement_pct = ((today_price - best_tomorrow_price) / today_price * 100) if today_price > 0 else 0
-
-        _LOGGER.debug(
-            "Supersession check: Today %s-%s (%.4f) vs Tomorrow %s-%s (%.4f) = %.1f%% improvement (threshold: %.1f%%)",
-            today_period["start"].strftime("%H:%M"),
-            today_period["end"].strftime("%H:%M"),
-            today_price,
-            best_tomorrow["start"].strftime("%H:%M"),
-            best_tomorrow["end"].strftime("%H:%M"),
-            best_tomorrow_price,
-            improvement_pct,
-            improvement_threshold,
-        )
-
-        if improvement_pct >= improvement_threshold:
-            _LOGGER.info(
-                "Period superseded: Today %s-%s (%.2f) replaced by Tomorrow %s-%s (%.2f, %.1f%% better)",
-                today_period["start"].strftime("%H:%M"),
-                today_period["end"].strftime("%H:%M"),
-                today_price,
-                best_tomorrow["start"].strftime("%H:%M"),
-                best_tomorrow["end"].strftime("%H:%M"),
-                best_tomorrow_price,
-                improvement_pct,
-            )
-        else:
-            kept.append(today_period)
-
-    return kept
-
-
-def filter_superseded_periods(
-    period_summaries: list[dict],
-    *,
-    time: TibberPricesTimeService,
-    reverse_sort: bool,
-) -> list[dict]:
-    """
-    Filter out late-night today periods that are superseded by better tomorrow periods.
-
-    When tomorrow's data becomes available, some late-night periods that were found
-    through relaxation may no longer make sense. If tomorrow has a significantly
-    better period in the early morning, the late-night today period is obsolete.
-
-    Example:
-    - Today 23:30-00:00 at 0.70 kr (found via relaxation, was best available)
-    - Tomorrow 04:00-05:30 at 0.50 kr (much better alternative)
-    → The today period is superseded and should be filtered out
-
-    This only applies to best-price periods (reverse_sort=False).
-    Peak-price periods are not filtered this way.
-
-    """
-    from .types import (  # noqa: PLC0415
-        CROSS_DAY_LATE_PERIOD_START_HOUR,
-        CROSS_DAY_MAX_EXTENSION_HOUR,
-        SUPERSESSION_PRICE_IMPROVEMENT_PCT,
-    )
-
-    _LOGGER.debug(
-        "filter_superseded_periods called: %d periods, reverse_sort=%s",
-        len(period_summaries) if period_summaries else 0,
-        reverse_sort,
-    )
-
-    # Only filter for best-price periods
-    if reverse_sort or not period_summaries:
-        return period_summaries
-
-    now = time.now()
-    today = now.date()
-    tomorrow = today + timedelta(days=1)
-
-    # Categorize periods
-    today_late, tomorrow_early, other = _categorize_periods_for_supersession(
-        period_summaries,
-        today,
-        tomorrow,
-        CROSS_DAY_LATE_PERIOD_START_HOUR,
-        CROSS_DAY_MAX_EXTENSION_HOUR,
-    )
-
-    _LOGGER.debug(
-        "Supersession categorization: today_late=%d, tomorrow_early=%d, other=%d",
-        len(today_late),
-        len(tomorrow_early),
-        len(other),
-    )
-
-    # If no tomorrow early periods, nothing to compare against
-    if not tomorrow_early:
-        _LOGGER.debug("No tomorrow early periods - skipping supersession check")
-        return period_summaries
-
-    # Find the best tomorrow early period (lowest mean price)
-    best_tomorrow = min(tomorrow_early, key=lambda p: p.get("price_mean", float("inf")))
-    best_tomorrow_price = best_tomorrow.get("price_mean")
-
-    if best_tomorrow_price is None:
-        return period_summaries
-
-    # Filter superseded today periods
-    kept_today = _filter_superseded_today_periods(
-        today_late,
-        best_tomorrow,
-        best_tomorrow_price,
-        SUPERSESSION_PRICE_IMPROVEMENT_PCT,
-    )
-
-    # Reconstruct and sort by start time
-    result = other + kept_today + tomorrow_early
-    result.sort(key=lambda p: p.get("start") or time.now())
-
-    return result
-
-
-def _is_period_eligible_for_extension(
-    period: dict,
-    today: date,
-    late_hour_threshold: int,
-) -> bool:
-    """
-    Check if a period is eligible for cross-day extension.
-
-    Eligibility criteria:
-    - Period has valid start and end times
-    - Period ends on today (not yesterday or tomorrow)
-    - Period ends late (after late_hour_threshold, e.g. 20:00)
-
-    """
-    period_end = period.get("end")
-    period_start = period.get("start")
-
-    if not period_end or not period_start:
-        return False
-
-    if period_end.date() != today:
-        return False
-
-    return period_end.hour >= late_hour_threshold
-
-
-def _find_extension_intervals(
-    period_end: datetime,
-    price_lookup: dict[str, dict],
-    criteria: Any,
-    max_extension_time: datetime,
-    interval_duration: timedelta,
-) -> list[dict]:
-    """
-    Find consecutive intervals after period_end that meet criteria.
-
-    Iterates forward from period_end, adding intervals while they
-    meet the flex and min_distance criteria. Stops at first failure
-    or when reaching max_extension_time.
-
-    """
-    from .level_filtering import check_interval_criteria  # noqa: PLC0415
-
-    extension_intervals: list[dict] = []
-    check_time = period_end
-
-    while check_time < max_extension_time:
-        price_data = price_lookup.get(check_time.isoformat())
-        if not price_data:
-            break  # No more data
-
-        price = float(price_data["total"])
-        in_flex, meets_min_distance = check_interval_criteria(price, criteria)
-
-        if not (in_flex and meets_min_distance):
-            break  # Criteria no longer met
-
-        extension_intervals.append(price_data)
-        check_time = check_time + interval_duration
-
-    return extension_intervals
-
-
-def _collect_original_period_prices(
-    period_start: datetime,
-    period_end: datetime,
-    price_lookup: dict[str, dict],
-    interval_duration: timedelta,
-) -> list[float]:
-    """Collect prices from original period for CV calculation."""
-    prices: list[float] = []
-    current = period_start
-    while current < period_end:
-        price_data = price_lookup.get(current.isoformat())
-        if price_data:
-            prices.append(float(price_data["total"]))
-        current = current + interval_duration
-    return prices
-
-
-def _build_extended_period(
-    period: dict,
-    extension_intervals: list[dict],
-    combined_prices: list[float],
-    combined_cv: float,
-    interval_duration: timedelta,
-) -> dict:
-    """Create extended period dict with updated statistics."""
-    period_start = period["start"]
-    period_end = period["end"]
-    new_end = period_end + (interval_duration * len(extension_intervals))
-
-    extended = period.copy()
-    extended["end"] = new_end
-    extended["duration_minutes"] = int((new_end - period_start).total_seconds() / 60)
-    extended["period_interval_count"] = len(combined_prices)
-    extended["cross_day_extended"] = True
-    extended["cross_day_extension_intervals"] = len(extension_intervals)
-
-    # Recalculate price statistics
-    extended["price_min"] = min(combined_prices)
-    extended["price_max"] = max(combined_prices)
-    extended["price_mean"] = sum(combined_prices) / len(combined_prices)
-    extended["price_spread"] = extended["price_max"] - extended["price_min"]
-    extended["price_coefficient_variation_%"] = round(combined_cv, 1)
-
-    return extended
-
-
-def extend_periods_across_midnight(
-    period_summaries: list[dict],
-    all_prices: list[dict],
-    price_context: dict[str, Any],
-    *,
-    time: TibberPricesTimeService,
-    reverse_sort: bool,
-) -> list[dict]:
-    """
-    Extend late-night periods across midnight if favorable prices continue.
-
-    When a period ends close to midnight and tomorrow's data shows continued
-    favorable prices, extend the period into the next day. This prevents
-    artificial period breaks at midnight when it's actually better to continue.
-
-    Example: Best price period 22:00-23:45 today could extend to 04:00 tomorrow
-    if prices remain low overnight.
-
-    Rules:
-    - Only extends periods ending after CROSS_DAY_LATE_PERIOD_START_HOUR (20:00)
-    - Won't extend beyond CROSS_DAY_MAX_EXTENSION_HOUR (08:00) next day
-    - Extension must pass same flex criteria as original period
-    - Quality Gate (CV check) applies to extended period
-
-    Args:
-        period_summaries: List of period summary dicts (already processed)
-        all_prices: All price intervals including tomorrow
-        price_context: Dict with ref_prices, avg_prices, flex, min_distance_from_avg
-        time: Time service instance
-        reverse_sort: True for peak price, False for best price
-
-    Returns:
-        Updated list of period summaries with extensions applied
-
-    """
-    from custom_components.tibber_prices.utils.price import calculate_coefficient_of_variation  # noqa: PLC0415
-
-    from .types import (  # noqa: PLC0415
-        CROSS_DAY_LATE_PERIOD_START_HOUR,
-        CROSS_DAY_MAX_EXTENSION_HOUR,
-        PERIOD_MAX_CV,
-        TibberPricesIntervalCriteria,
-    )
-
-    if not period_summaries or not all_prices:
-        return period_summaries
-
-    # Build price lookup by timestamp
-    price_lookup: dict[str, dict] = {}
-    for price_data in all_prices:
-        interval_time = time.get_interval_time(price_data)
-        if interval_time:
-            price_lookup[interval_time.isoformat()] = price_data
-
-    ref_prices = price_context.get("ref_prices", {})
-    avg_prices = price_context.get("avg_prices", {})
-    flex = price_context.get("flex", 0.15)
-    min_distance = price_context.get("min_distance_from_avg", 0)
-
-    now = time.now()
-    today = now.date()
-    tomorrow = today + timedelta(days=1)
-    interval_duration = time.get_interval_duration()
-
-    # Max extension time (e.g., 08:00 tomorrow)
-    max_extension_time = time.start_of_local_day(now) + timedelta(days=1, hours=CROSS_DAY_MAX_EXTENSION_HOUR)
-
-    extended_summaries = []
-
-    for period in period_summaries:
-        # Check eligibility for extension
-        if not _is_period_eligible_for_extension(period, today, CROSS_DAY_LATE_PERIOD_START_HOUR):
-            extended_summaries.append(period)
-            continue
-
-        # Get tomorrow's reference prices
-        tomorrow_ref = ref_prices.get(tomorrow) or ref_prices.get(str(tomorrow))
-        tomorrow_avg = avg_prices.get(tomorrow) or avg_prices.get(str(tomorrow))
-
-        if tomorrow_ref is None or tomorrow_avg is None:
-            extended_summaries.append(period)
-            continue
-
-        # Set up criteria for extension check
-        criteria = TibberPricesIntervalCriteria(
-            ref_price=tomorrow_ref,
-            avg_price=tomorrow_avg,
-            flex=flex,
-            min_distance_from_avg=min_distance,
-            reverse_sort=reverse_sort,
-        )
-
-        # Find extension intervals
-        extension_intervals = _find_extension_intervals(
-            period["end"],
-            price_lookup,
-            criteria,
-            max_extension_time,
-            interval_duration,
-        )
-
-        if not extension_intervals:
-            extended_summaries.append(period)
-            continue
-
-        # Collect all prices for CV check
-        original_prices = _collect_original_period_prices(
-            period["start"],
-            period["end"],
-            price_lookup,
-            interval_duration,
-        )
-        extension_prices = [float(p["total"]) for p in extension_intervals]
-        combined_prices = original_prices + extension_prices
-
-        # Quality Gate: Check CV of extended period
-        combined_cv = calculate_coefficient_of_variation(combined_prices)
-
-        if combined_cv is not None and combined_cv <= PERIOD_MAX_CV:
-            # Extension passes quality gate
-            extended_period = _build_extended_period(
-                period,
-                extension_intervals,
-                combined_prices,
-                combined_cv,
-                interval_duration,
-            )
-
-            _LOGGER.info(
-                "Cross-day extension: Period %s-%s extended to %s (+%d intervals, CV=%.1f%%)",
-                period["start"].strftime("%H:%M"),
-                period["end"].strftime("%H:%M"),
-                extended_period["end"].strftime("%H:%M"),
-                len(extension_intervals),
-                combined_cv,
-            )
-            extended_summaries.append(extended_period)
-        else:
-            # Extension would exceed quality gate
-            _LOGGER_DETAILS.debug(
-                "%sCross-day extension rejected for period %s-%s: CV=%.1f%% > %.1f%%",
-                INDENT_L0,
-                period["start"].strftime("%H:%M"),
-                period["end"].strftime("%H:%M"),
-                combined_cv or 0,
-                PERIOD_MAX_CV,
-            )
-            extended_summaries.append(period)
-
-    return extended_summaries

custom_components/tibber_prices/coordinator/period_handlers/period_overlap.py

@@ -9,7 +9,6 @@ if TYPE_CHECKING:
     from custom_components.tibber_prices.coordinator.time_service import TibberPricesTimeService
 
 _LOGGER = logging.getLogger(__name__)
-_LOGGER_DETAILS = logging.getLogger(__name__ + ".details")
 
 # Module-local log indentation (each module starts at level 0)
 INDENT_L0 = ""  # Entry point / main function
@@ -17,41 +16,6 @@ INDENT_L1 = "  "  # Nested logic / loop iterations
 INDENT_L2 = "    "  # Deeper nesting
 
 
-def _estimate_merged_cv(period1: dict, period2: dict) -> float | None:
-    """
-    Estimate the CV of a merged period from two period summaries.
-
-    Since we don't have the raw prices, we estimate using the combined min/max range.
-    This is a conservative estimate - the actual CV could be higher or lower.
-
-    Formula: CV ≈ (range / 2) / mean * 100
-    Where range = max - min, mean = (min + max) / 2
-
-    This approximation assumes roughly uniform distribution within the range.
-    """
-    p1_min = period1.get("price_min")
-    p1_max = period1.get("price_max")
-    p2_min = period2.get("price_min")
-    p2_max = period2.get("price_max")
-
-    if None in (p1_min, p1_max, p2_min, p2_max):
-        return None
-
-    # Cast to float - None case handled above
-    combined_min = min(float(p1_min), float(p2_min))  # type: ignore[arg-type]
-    combined_max = max(float(p1_max), float(p2_max))  # type: ignore[arg-type]
-
-    if combined_min <= 0:
-        return None
-
-    combined_mean = (combined_min + combined_max) / 2
-    price_range = combined_max - combined_min
-
-    # CV estimate based on range (assuming uniform distribution)
-    # For uniform distribution: std_dev ≈ range / sqrt(12) ≈ range / 3.46
-    return (price_range / 3.46) / combined_mean * 100
-
-
 def recalculate_period_metadata(periods: list[dict], *, time: TibberPricesTimeService) -> None:
     """
     Recalculate period metadata after merging periods.
@@ -140,7 +104,7 @@ def merge_adjacent_periods(period1: dict, period2: dict) -> dict:
         "period2_end": period2["end"].isoformat(),
     }
 
-    _LOGGER_DETAILS.debug(
+    _LOGGER.debug(
         "%sMerged periods: %s-%s + %s-%s → %s-%s (duration: %d min)",
         INDENT_L2,
         period1["start"].strftime("%H:%M"),
@@ -155,119 +119,6 @@ def merge_adjacent_periods(period1: dict, period2: dict) -> dict:
     return merged
 
 
-def _check_merge_quality_gate(periods_to_merge: list[tuple[int, dict]], relaxed: dict) -> bool:
-    """
-    Check if merging would create a period that's too heterogeneous.
-
-    Returns True if merge is allowed, False if blocked by Quality Gate.
-    """
-    from .types import PERIOD_MAX_CV  # noqa: PLC0415
-
-    relaxed_start = relaxed["start"]
-    relaxed_end = relaxed["end"]
-
-    for _idx, existing in periods_to_merge:
-        estimated_cv = _estimate_merged_cv(existing, relaxed)
-        if estimated_cv is not None and estimated_cv > PERIOD_MAX_CV:
-            _LOGGER.debug(
-                "Merge blocked by Quality Gate: %s-%s + %s-%s would have CV≈%.1f%% (max: %.1f%%)",
-                existing["start"].strftime("%H:%M"),
-                existing["end"].strftime("%H:%M"),
-                relaxed_start.strftime("%H:%M"),
-                relaxed_end.strftime("%H:%M"),
-                estimated_cv,
-                PERIOD_MAX_CV,
-            )
-            return False
-    return True
-
-
-def _would_swallow_existing(relaxed: dict, existing_periods: list[dict]) -> bool:
-    """
-    Check if the relaxed period would "swallow" any existing period.
-
-    A period is "swallowed" if the new relaxed period completely contains it.
-    In this case, we should NOT merge - the existing smaller period is more
-    homogeneous and should be preserved.
-
-    This prevents relaxation from replacing good small periods with larger,
-    more heterogeneous ones.
-
-    Returns:
-        True if any existing period would be swallowed (merge should be blocked)
-        False if safe to proceed with merge evaluation
-
-    """
-    relaxed_start = relaxed["start"]
-    relaxed_end = relaxed["end"]
-
-    for existing in existing_periods:
-        existing_start = existing["start"]
-        existing_end = existing["end"]
-
-        # Check if relaxed completely contains existing
-        if relaxed_start <= existing_start and relaxed_end >= existing_end:
-            _LOGGER.debug(
-                "Blocking merge: %s-%s would swallow %s-%s (keeping smaller period)",
-                relaxed_start.strftime("%H:%M"),
-                relaxed_end.strftime("%H:%M"),
-                existing_start.strftime("%H:%M"),
-                existing_end.strftime("%H:%M"),
-            )
-            return True
-
-    return False
-
-
-def _is_duplicate_period(relaxed: dict, existing_periods: list[dict], tolerance_seconds: int = 60) -> bool:
-    """Check if relaxed period is a duplicate of any existing period."""
-    relaxed_start = relaxed["start"]
-    relaxed_end = relaxed["end"]
-
-    for existing in existing_periods:
-        if (
-            abs((relaxed_start - existing["start"]).total_seconds()) < tolerance_seconds
-            and abs((relaxed_end - existing["end"]).total_seconds()) < tolerance_seconds
-        ):
-            _LOGGER_DETAILS.debug(
-                "%sSkipping duplicate period %s-%s (already exists)",
-                INDENT_L1,
-                relaxed_start.strftime("%H:%M"),
-                relaxed_end.strftime("%H:%M"),
-            )
-            return True
-    return False
-
-
-def _find_adjacent_or_overlapping(relaxed: dict, existing_periods: list[dict]) -> list[tuple[int, dict]]:
-    """Find all periods that are adjacent to or overlapping with the relaxed period."""
-    relaxed_start = relaxed["start"]
-    relaxed_end = relaxed["end"]
-    periods_to_merge = []
-
-    for idx, existing in enumerate(existing_periods):
-        existing_start = existing["start"]
-        existing_end = existing["end"]
-
-        # Check if adjacent (no gap) or overlapping
-        is_adjacent = relaxed_end == existing_start or relaxed_start == existing_end
-        is_overlapping = relaxed_start < existing_end and relaxed_end > existing_start
-
-        if is_adjacent or is_overlapping:
-            periods_to_merge.append((idx, existing))
-            _LOGGER_DETAILS.debug(
-                "%sPeriod %s-%s %s with existing period %s-%s",
-                INDENT_L1,
-                relaxed_start.strftime("%H:%M"),
-                relaxed_end.strftime("%H:%M"),
-                "overlaps" if is_overlapping else "is adjacent to",
-                existing_start.strftime("%H:%M"),
-                existing_end.strftime("%H:%M"),
-            )
-
-    return periods_to_merge
-
-
 def resolve_period_overlaps(
     existing_periods: list[dict],
     new_relaxed_periods: list[dict],
@@ -278,10 +129,6 @@ def resolve_period_overlaps(
     Adjacent or overlapping periods are merged into single continuous periods.
     The newer period's relaxation attributes override the older period's.
 
-    Quality Gate: Merging is blocked if the combined period would have
-    an estimated CV above PERIOD_MAX_CV (25%), to prevent creating
-    periods with excessive internal price variation.
-
     This function is called incrementally after each relaxation phase:
     - Phase 1: existing = baseline, new = first relaxation
     - Phase 2: existing = baseline + phase 1, new = second relaxation
@@ -297,7 +144,7 @@ def resolve_period_overlaps(
         - new_periods_count: Number of new periods added (some may have been merged)
 
     """
-    _LOGGER_DETAILS.debug(
+    _LOGGER.debug(
         "%sresolve_period_overlaps called: existing=%d, new=%d",
         INDENT_L0,
         len(existing_periods),
@@ -319,60 +166,74 @@ def resolve_period_overlaps(
         relaxed_end = relaxed["end"]
 
         # Check if this period is duplicate (exact match within tolerance)
-        if _is_duplicate_period(relaxed, merged):
+        tolerance_seconds = 60  # 1 minute tolerance
-            continue
+        is_duplicate = False
+        for existing in merged:
+            if (
+                abs((relaxed_start - existing["start"]).total_seconds()) < tolerance_seconds
+                and abs((relaxed_end - existing["end"]).total_seconds()) < tolerance_seconds
+            ):
+                is_duplicate = True
+                _LOGGER.debug(
+                    "%sSkipping duplicate period %s-%s (already exists)",
+                    INDENT_L1,
+                    relaxed_start.strftime("%H:%M"),
+                    relaxed_end.strftime("%H:%M"),
+                )
+                break
 
-        # Check if this period would "swallow" an existing smaller period
+        if is_duplicate:
-        # In that case, skip it - the smaller existing period is more homogeneous
-        if _would_swallow_existing(relaxed, merged):
             continue
 
         # Find periods that are adjacent or overlapping (should be merged)
-        periods_to_merge = _find_adjacent_or_overlapping(relaxed, merged)
+        periods_to_merge = []
+        for idx, existing in enumerate(merged):
+            existing_start = existing["start"]
+            existing_end = existing["end"]
+
+            # Check if adjacent (no gap) or overlapping
+            is_adjacent = relaxed_end == existing_start or relaxed_start == existing_end
+            is_overlapping = relaxed_start < existing_end and relaxed_end > existing_start
+
+            if is_adjacent or is_overlapping:
+                periods_to_merge.append((idx, existing))
+                _LOGGER.debug(
+                    "%sPeriod %s-%s %s with existing period %s-%s",
+                    INDENT_L1,
+                    relaxed_start.strftime("%H:%M"),
+                    relaxed_end.strftime("%H:%M"),
+                    "overlaps" if is_overlapping else "is adjacent to",
+                    existing_start.strftime("%H:%M"),
+                    existing_end.strftime("%H:%M"),
+                )
 
         if not periods_to_merge:
             # No merge needed - add as new period
             merged.append(relaxed)
             periods_added += 1
-            _LOGGER_DETAILS.debug(
+            _LOGGER.debug(
                 "%sAdded new period %s-%s (no overlap/adjacency)",
                 INDENT_L1,
                 relaxed_start.strftime("%H:%M"),
                 relaxed_end.strftime("%H:%M"),
             )
-            continue
+        else:
+            # Merge with all adjacent/overlapping periods
+            # Start with the new relaxed period
+            merged_period = relaxed.copy()
 
-        # Quality Gate: Check if merging would create a period that's too heterogeneous
+            # Remove old periods (in reverse order to maintain indices)
-        should_merge = _check_merge_quality_gate(periods_to_merge, relaxed)
+            for idx, existing in reversed(periods_to_merge):
+                merged_period = merge_adjacent_periods(existing, merged_period)
+                merged.pop(idx)
 
-        if not should_merge:
+            # Add the merged result
-            # Don't merge - add as separate period instead
+            merged.append(merged_period)
-            merged.append(relaxed)
-            periods_added += 1
-            _LOGGER_DETAILS.debug(
-                "%sAdded new period %s-%s separately (merge blocked by CV gate)",
-                INDENT_L1,
-                relaxed_start.strftime("%H:%M"),
-                relaxed_end.strftime("%H:%M"),
-            )
-            continue
 
-        # Merge with all adjacent/overlapping periods
+            # Count as added if we merged exactly one existing period
-        # Start with the new relaxed period
+            # (means we extended/merged, not replaced multiple)
-        merged_period = relaxed.copy()
+            if len(periods_to_merge) == 1:
-
+                periods_added += 1
-        # Remove old periods (in reverse order to maintain indices)
-        for idx, existing in reversed(periods_to_merge):
-            merged_period = merge_adjacent_periods(existing, merged_period)
-            merged.pop(idx)
-
-        # Add the merged result
-        merged.append(merged_period)
-
-        # Count as added if we merged exactly one existing period
-        # (means we extended/merged, not replaced multiple)
-        if len(periods_to_merge) == 1:
-            periods_added += 1
 
     # Sort all periods by start time
     merged.sort(key=lambda p: p["start"])

custom_components/tibber_prices/coordinator/period_handlers/period_statistics.py

@@ -14,18 +14,15 @@ if TYPE_CHECKING:
         TibberPricesPeriodStatistics,
         TibberPricesThresholdConfig,
     )
-
-from custom_components.tibber_prices.utils.average import calculate_median
 from custom_components.tibber_prices.utils.price import (
     aggregate_period_levels,
     aggregate_period_ratings,
-    calculate_coefficient_of_variation,
     calculate_volatility_level,
 )
 
 
 def calculate_period_price_diff(
-    price_mean: float,
+    price_avg: float,
     start_time: datetime,
     price_context: dict[str, Any],
 ) -> tuple[float | None, float | None]:
@@ -34,11 +31,6 @@ def calculate_period_price_diff(
 
     Uses reference price from start day of the period for consistency.
 
-    Args:
-        price_mean: Mean price of the period (in base currency).
-        start_time: Start time of the period.
-        price_context: Dictionary with ref_prices per day.
-
     Returns:
         Tuple of (period_price_diff, period_price_diff_pct) or (None, None) if no reference available.
 
@@ -53,14 +45,12 @@ def calculate_period_price_diff(
     if ref_price is None:
         return None, None
 
-    # Both prices are in base currency, no conversion needed
+    # Convert reference price to minor units (ct/øre)
-    ref_price_display = round(ref_price, 4)
+    ref_price_minor = round(ref_price * 100, 2)
-    period_price_diff = round(price_mean - ref_price_display, 4)
+    period_price_diff = round(price_avg - ref_price_minor, 2)
     period_price_diff_pct = None
-    if ref_price_display != 0:
+    if ref_price_minor != 0:
-        # CRITICAL: Use abs() for negative prices (same logic as calculate_difference_percentage)
+        period_price_diff_pct = round((period_price_diff / ref_price_minor) * 100, 2)
-        # Example: avg=-10, ref=-20 → diff=10, pct=10/abs(-20)*100=+50% (correctly shows more expensive)
-        period_price_diff_pct = round((period_price_diff / abs(ref_price_display)) * 100, 2)
 
     return period_price_diff, period_price_diff_pct
 
@@ -90,44 +80,34 @@ def calculate_aggregated_rating_difference(period_price_data: list[dict]) -> flo
     return round(sum(differences) / len(differences), 2)
 
 
-def calculate_period_price_statistics(
+def calculate_period_price_statistics(period_price_data: list[dict]) -> dict[str, float]:
-    period_price_data: list[dict],
-) -> dict[str, float]:
     """
     Calculate price statistics for a period.
 
     Args:
-        period_price_data: List of price data dictionaries with "total" field.
+        period_price_data: List of price data dictionaries with "total" field
 
     Returns:
-        Dictionary with price_mean, price_median, price_min, price_max, price_spread (all in base currency).
+        Dictionary with price_avg, price_min, price_max, price_spread (all in minor units: ct/øre)
-        Note: price_spread is calculated based on price_mean (max - min range as percentage of mean).
 
     """
-    # Keep prices in base currency (Euro/NOK/SEK) for internal storage
+    prices_minor = [round(float(p["total"]) * 100, 2) for p in period_price_data]
-    # Conversion to display units (ct/øre) happens in services/formatting layer
-    factor = 1  # Always use base currency for storage
-    prices_display = [round(float(p["total"]) * factor, 4) for p in period_price_data]
 
-    if not prices_display:
+    if not prices_minor:
         return {
-            "price_mean": 0.0,
+            "price_avg": 0.0,
-            "price_median": 0.0,
             "price_min": 0.0,
             "price_max": 0.0,
             "price_spread": 0.0,
         }
 
-    price_mean = round(sum(prices_display) / len(prices_display), 4)
+    price_avg = round(sum(prices_minor) / len(prices_minor), 2)
-    median_value = calculate_median(prices_display)
+    price_min = round(min(prices_minor), 2)
-    price_median = round(median_value, 4) if median_value is not None else 0.0
+    price_max = round(max(prices_minor), 2)
-    price_min = round(min(prices_display), 4)
+    price_spread = round(price_max - price_min, 2)
-    price_max = round(max(prices_display), 4)
-    price_spread = round(price_max - price_min, 4)
 
     return {
-        "price_mean": price_mean,
+        "price_avg": price_avg,
-        "price_median": price_median,
         "price_min": price_min,
         "price_max": price_max,
         "price_spread": price_spread,
@@ -139,7 +119,6 @@ def build_period_summary_dict(
     stats: TibberPricesPeriodStatistics,
     *,
     reverse_sort: bool,
-    price_context: dict[str, Any] | None = None,
 ) -> dict:
     """
     Build the complete period summary dictionary.
@@ -148,7 +127,6 @@ def build_period_summary_dict(
         period_data: Period timing and position data
         stats: Calculated period statistics
         reverse_sort: True for peak price, False for best price (keyword-only)
-        price_context: Optional dict with ref_prices, avg_prices, intervals_by_day for day statistics
 
     Returns:
         Complete period summary dictionary following attribute ordering
@@ -165,12 +143,10 @@ def build_period_summary_dict(
         "rating_level": stats.aggregated_rating,
         "rating_difference_%": stats.rating_difference_pct,
         # 3. Price statistics (how much does it cost?)
-        "price_mean": stats.price_mean,
+        "price_avg": stats.price_avg,
-        "price_median": stats.price_median,
         "price_min": stats.price_min,
         "price_max": stats.price_max,
         "price_spread": stats.price_spread,
-        "price_coefficient_variation_%": stats.coefficient_of_variation,
         "volatility": stats.volatility,
         # 4. Price differences will be added below if available
         # 5. Detail information (additional context)
@@ -193,30 +169,6 @@ def build_period_summary_dict(
             if stats.period_price_diff_pct is not None:
                 summary["period_price_diff_from_daily_min_%"] = stats.period_price_diff_pct
 
-    # Add day volatility and price statistics (for understanding midnight classification changes)
-    if price_context:
-        period_start_date = period_data.start_time.date()
-        intervals_by_day = price_context.get("intervals_by_day", {})
-        avg_prices = price_context.get("avg_prices", {})
-
-        day_intervals = intervals_by_day.get(period_start_date, [])
-        if day_intervals:
-            # Calculate day price statistics (in EUR major units from API)
-            day_prices = [float(p["total"]) for p in day_intervals]
-            day_min = min(day_prices)
-            day_max = max(day_prices)
-            day_span = day_max - day_min
-            day_avg = avg_prices.get(period_start_date, sum(day_prices) / len(day_prices))
-
-            # Calculate volatility percentage (span / avg * 100)
-            day_volatility_pct = round((day_span / day_avg * 100), 1) if day_avg > 0 else 0.0
-
-            # Convert to minor units (ct/øre) for consistency with other price attributes
-            summary["day_volatility_%"] = day_volatility_pct
-            summary["day_price_min"] = round(day_min * 100, 2)
-            summary["day_price_max"] = round(day_max * 100, 2)
-            summary["day_price_span"] = round(day_span * 100, 2)
-
     return summary
 
 
@@ -233,7 +185,7 @@ def extract_period_summaries(
 
     Returns sensor-ready period summaries with:
     - Timestamps and positioning (start, end, hour, minute, time)
-    - Aggregated price statistics (price_mean, price_median, price_min, price_max, price_spread)
+    - Aggregated price statistics (price_avg, price_min, price_max, price_spread)
     - Volatility categorization (low/moderate/high/very_high based on coefficient of variation)
     - Rating difference percentage (aggregated from intervals)
     - Period price differences (period_price_diff_from_daily_min/max)
@@ -243,11 +195,11 @@ def extract_period_summaries(
     All data is pre-calculated and ready for display - no further processing needed.
 
     Args:
-        periods: List of periods, where each period is a list of interval dictionaries.
+        periods: List of periods, where each period is a list of interval dictionaries
-        all_prices: All price data from the API (enriched with level, difference, rating_level).
+        all_prices: All price data from the API (enriched with level, difference, rating_level)
-        price_context: Dictionary with ref_prices and avg_prices per day.
+        price_context: Dictionary with ref_prices and avg_prices per day
-        thresholds: Threshold configuration for calculations.
+        thresholds: Threshold configuration for calculations
-        time: TibberPricesTimeService instance (required).
+        time: TibberPricesTimeService instance (required)
 
     """
     from .types import (  # noqa: PLC0415 - Avoid circular import
@@ -305,21 +257,18 @@ def extract_period_summaries(
                     thresholds.threshold_high,
                 )
 
-        # Calculate price statistics (in base currency, conversion happens in presentation layer)
+        # Calculate price statistics (in minor units: ct/øre)
         price_stats = calculate_period_price_statistics(period_price_data)
 
         # Calculate period price difference from daily reference
         period_price_diff, period_price_diff_pct = calculate_period_price_diff(
-            price_stats["price_mean"], start_time, price_context
+            price_stats["price_avg"], start_time, price_context
         )
 
         # Extract prices for volatility calculation (coefficient of variation)
         prices_for_volatility = [float(p["total"]) for p in period_price_data if "total" in p]
 
-        # Calculate CV (numeric) for quality gate checks
+        # Calculate volatility (categorical) and aggregated rating difference (numeric)
-        period_cv = calculate_coefficient_of_variation(prices_for_volatility)
-
-        # Calculate volatility (categorical) using thresholds
         volatility = calculate_volatility_level(
             prices_for_volatility,
             threshold_moderate=thresholds.threshold_volatility_moderate,
@@ -347,21 +296,17 @@ def extract_period_summaries(
             aggregated_level=aggregated_level,
             aggregated_rating=aggregated_rating,
             rating_difference_pct=rating_difference_pct,
-            price_mean=price_stats["price_mean"],
+            price_avg=price_stats["price_avg"],
-            price_median=price_stats["price_median"],
             price_min=price_stats["price_min"],
             price_max=price_stats["price_max"],
             price_spread=price_stats["price_spread"],
             volatility=volatility,
-            coefficient_of_variation=round(period_cv, 1) if period_cv is not None else None,
             period_price_diff=period_price_diff,
             period_price_diff_pct=period_price_diff_pct,
         )
 
         # Build complete period summary
-        summary = build_period_summary_dict(
+        summary = build_period_summary_dict(period_data, stats, reverse_sort=thresholds.reverse_sort)
-            period_data, stats, reverse_sort=thresholds.reverse_sort, price_context=price_context
-        )
 
         # Add smoothing information if any intervals benefited from smoothing
         if smoothed_impactful_count > 0:

custom_components/tibber_prices/coordinator/period_handlers/relaxation.py

@@ -11,7 +11,7 @@ if TYPE_CHECKING:
 
     from custom_components.tibber_prices.coordinator.time_service import TibberPricesTimeService
 
-from custom_components.tibber_prices.utils.price import calculate_coefficient_of_variation
+    from .types import TibberPricesPeriodConfig
 
 from .period_overlap import (
     recalculate_period_metadata,
@@ -21,12 +21,9 @@ from .types import (
     INDENT_L0,
     INDENT_L1,
     INDENT_L2,
-    PERIOD_MAX_CV,
-    TibberPricesPeriodConfig,
 )
 
 _LOGGER = logging.getLogger(__name__)
-_LOGGER_DETAILS = logging.getLogger(__name__ + ".details")
 
 # Flex thresholds for warnings (see docs/development/period-calculation-theory.md)
 # With relaxation active, high base flex is counterproductive (reduces relaxation effectiveness)
@@ -34,204 +31,35 @@ FLEX_WARNING_THRESHOLD_RELAXATION = 0.25  # 25% - INFO: suggest lowering to 15-2
 MAX_FLEX_HARD_LIMIT = 0.50  # 50% - hard maximum flex value
 FLEX_HIGH_THRESHOLD_RELAXATION = 0.30  # 30% - WARNING: base flex too high for relaxation mode
 
-# Min duration fallback constants
-# When all relaxation phases are exhausted and still no periods found,
-# gradually reduce min_period_length to find at least something
-MIN_DURATION_FALLBACK_MINIMUM = 30  # Minimum period length to try (30 min = 2 intervals)
-MIN_DURATION_FALLBACK_STEP = 15  # Reduce by 15 min (1 interval) each step
-
-
-def _check_period_quality(
-    period: dict, all_prices: list[dict], *, time: TibberPricesTimeService
-) -> tuple[bool, float | None]:
-    """
-    Check if a period passes the quality gate (internal CV not too high).
-
-    The Quality Gate prevents relaxation from creating periods with too much
-    internal price variation. A "best price period" with prices ranging from
-    0.5 to 1.0 kr/kWh is not useful - user can't trust it's actually "best".
-
-    Args:
-        period: Period summary dict with "start" and "end" datetime
-        all_prices: All price intervals (to look up prices for CV calculation)
-        time: Time service for interval time parsing
-
-    Returns:
-        Tuple of (passes_quality_gate, cv_value)
-        - passes_quality_gate: True if CV <= PERIOD_MAX_CV
-        - cv_value: Calculated CV as percentage, or None if not calculable
-
-    """
-    start_time = period.get("start")
-    end_time = period.get("end")
-
-    if not start_time or not end_time:
-        return True, None  # Can't check, assume OK
-
-    # Build lookup for prices
-    price_lookup: dict[str, float] = {}
-    for price_data in all_prices:
-        interval_time = time.get_interval_time(price_data)
-        if interval_time:
-            price_lookup[interval_time.isoformat()] = float(price_data["total"])
-
-    # Collect prices within the period
-    period_prices: list[float] = []
-    interval_duration = time.get_interval_duration()
-
-    current = start_time
-    while current < end_time:
-        price = price_lookup.get(current.isoformat())
-        if price is not None:
-            period_prices.append(price)
-        current = current + interval_duration
-
-    # Need at least 2 prices to calculate CV (same as MIN_PRICES_FOR_VOLATILITY in price.py)
-    min_prices_for_cv = 2
-    if len(period_prices) < min_prices_for_cv:
-        return True, None  # Too few prices to calculate CV
-
-    cv = calculate_coefficient_of_variation(period_prices)
-    if cv is None:
-        return True, None
-
-    passes = cv <= PERIOD_MAX_CV
-    return passes, cv
-
-
-def _count_quality_periods(
-    periods: list[dict],
-    all_prices: list[dict],
-    prices_by_day: dict[date, list[dict]],
-    min_periods: int,
-    *,
-    time: TibberPricesTimeService,
-) -> tuple[int, int]:
-    """
-    Count days meeting requirement when considering quality gate.
-
-    Only periods passing the quality gate (CV <= PERIOD_MAX_CV) are counted
-    towards meeting the min_periods requirement.
-
-    Args:
-        periods: List of all periods
-        all_prices: All price intervals
-        prices_by_day: Price intervals grouped by day
-        min_periods: Target periods per day
-        time: Time service
-
-    Returns:
-        Tuple of (days_meeting_requirement, total_quality_periods)
-
-    """
-    periods_by_day = group_periods_by_day(periods)
-    days_meeting_requirement = 0
-    total_quality_periods = 0
-
-    for day in sorted(prices_by_day.keys()):
-        day_periods = periods_by_day.get(day, [])
-        quality_count = 0
-
-        for period in day_periods:
-            passes, cv = _check_period_quality(period, all_prices, time=time)
-            if passes:
-                quality_count += 1
-            else:
-                _LOGGER_DETAILS.debug(
-                    "%s      Day %s: Period %s-%s REJECTED by quality gate (CV=%.1f%% > %.1f%%)",
-                    INDENT_L2,
-                    day,
-                    period.get("start", "?").strftime("%H:%M") if hasattr(period.get("start"), "strftime") else "?",
-                    period.get("end", "?").strftime("%H:%M") if hasattr(period.get("end"), "strftime") else "?",
-                    cv or 0,
-                    PERIOD_MAX_CV,
-                )
-
-        total_quality_periods += quality_count
-        if quality_count >= min_periods:
-            days_meeting_requirement += 1
-
-    return days_meeting_requirement, total_quality_periods
-
 
 def group_periods_by_day(periods: list[dict]) -> dict[date, list[dict]]:
     """
-    Group periods by ALL days they span (including midnight crossings).
+    Group periods by the day they end in.
 
-    Periods crossing midnight are assigned to ALL affected days.
+    This ensures periods crossing midnight are counted towards the day they end,
-    Example: Period 23:00 yesterday - 02:00 today appears in BOTH days.
+    not the day they start. Example: Period 23:00 yesterday - 02:00 today counts
-
+    as "today" since it ends today.
-    This ensures that:
-    1. For min_periods checking: A midnight-crossing period counts towards both days
-    2. For binary sensors: Each day shows all relevant periods (including those starting/ending in other days)
 
     Args:
         periods: List of period summary dicts with "start" and "end" datetime
 
     Returns:
-        Dict mapping date to list of periods spanning that date
+        Dict mapping date to list of periods ending on that date
 
     """
     periods_by_day: dict[date, list[dict]] = {}
 
     for period in periods:
-        start_time = period.get("start")
+        # Use end time for grouping so periods crossing midnight are counted
+        # towards the day they end (more relevant for min_periods check)
         end_time = period.get("end")
-
+        if end_time:
-        if not start_time or not end_time:
+            day = end_time.date()
-            continue
+            periods_by_day.setdefault(day, []).append(period)
-
-        # Assign period to ALL days it spans
-        start_date = start_time.date()
-        end_date = end_time.date()
-
-        # Handle single-day and multi-day periods
-        current_date = start_date
-        while current_date <= end_date:
-            periods_by_day.setdefault(current_date, []).append(period)
-            # Move to next day
-            from datetime import timedelta  # noqa: PLC0415
-
-            current_date = current_date + timedelta(days=1)
 
     return periods_by_day
 
 
-def mark_periods_with_relaxation(
-    periods: list[dict],
-    relaxation_level: str,
-    original_threshold: float,
-    applied_threshold: float,
-    *,
-    reverse_sort: bool = False,
-) -> None:
-    """
-    Mark periods with relaxation information (mutates period dicts in-place).
-
-    Uses consistent 'relaxation_*' prefix for all relaxation-related attributes.
-    These attributes are read by period_overlap.py and binary_sensor/attributes.py.
-
-    For Peak Price periods (reverse_sort=True), thresholds are stored as negative
-    values to match the user's configuration semantics (negative flex = below maximum).
-
-    Args:
-        periods: List of period dicts to mark
-        relaxation_level: String describing the relaxation level (e.g., "flex=18.0% +level_any")
-        original_threshold: Original flex threshold value (decimal, e.g., 0.15 for 15%)
-        applied_threshold: Actually applied threshold value (decimal, e.g., 0.18 for 18%)
-        reverse_sort: True for Peak Price (negative values), False for Best Price (positive values)
-
-    """
-    for period in periods:
-        period["relaxation_active"] = True
-        period["relaxation_level"] = relaxation_level
-        # Convert decimal to percentage for display
-        # For Peak Prices: Store as negative to match user's config semantics
-        sign = -1 if reverse_sort else 1
-        period["relaxation_threshold_original_%"] = round(original_threshold * 100 * sign, 1)
-        period["relaxation_threshold_applied_%"] = round(applied_threshold * 100 * sign, 1)
-
-
 def group_prices_by_day(all_prices: list[dict], *, time: TibberPricesTimeService) -> dict[date, list[dict]]:
     """
     Group price intervals by the day they belong to (today and future only).
@@ -258,167 +86,89 @@ def group_prices_by_day(all_prices: list[dict], *, time: TibberPricesTimeService
     return prices_by_day
 
 
-def _try_min_duration_fallback(
+def check_min_periods_per_day(
-    *,
+    periods: list[dict], min_periods: int, all_prices: list[dict], *, time: TibberPricesTimeService
-    config: TibberPricesPeriodConfig,
+) -> bool:
-    existing_periods: list[dict],
-    prices_by_day: dict[date, list[dict]],
-    time: TibberPricesTimeService,
-) -> tuple[dict[str, Any] | None, dict[str, Any]]:
     """
-    Try reducing min_period_length to find periods when relaxation is exhausted.
+    Check if minimum periods requirement is met for each day individually.
 
-    This is a LAST RESORT mechanism. It only activates when:
+    Returns True if we should STOP relaxation (enough periods found per day).
-    1. All relaxation phases have been tried
+    Returns False if we should CONTINUE relaxation (not enough periods yet).
-    2. Some days STILL have zero periods (not just below min_periods)
-
-    The fallback progressively reduces min_period_length:
-    - 60 min (default) → 45 min → 30 min (minimum)
-
-    It does NOT reduce below 30 min (2 intervals) because a single 15-min
-    interval is essentially just the daily min/max price - not a "period".
 
     Args:
-        config: Period configuration
+        periods: List of period summary dicts
-        existing_periods: Periods found so far (from relaxation)
+        min_periods: Minimum number of periods required per day
-        prices_by_day: Price intervals grouped by day
+        all_prices: All available price intervals (used to determine which days have data)
-        time: Time service instance
+        time: TibberPricesTimeService instance (required)
 
     Returns:
-        Tuple of (result dict with periods, metadata dict) or (None, empty metadata)
+        True if every day with price data has at least min_periods, False otherwise
 
     """
-    from .core import calculate_periods  # noqa: PLC0415 - Avoid circular import
+    if not periods:
+        return False  # No periods at all, continue relaxation
 
-    metadata: dict[str, Any] = {"phases_used": [], "fallback_active": False}
+    # Get all days that have price data (today and future only, not yesterday)
+    today = time.now().date()
+    available_days = set()
+    for price in all_prices:
+        starts_at = time.get_interval_time(price)
+        if starts_at:
+            price_date = starts_at.date()
+            # Only count today and future days (not yesterday)
+            if price_date >= today:
+                available_days.add(price_date)
 
-    # Only try fallback if current min_period_length > minimum
+    if not available_days:
-    if config.min_period_length <= MIN_DURATION_FALLBACK_MINIMUM:
+        return False  # No price data for today/future, continue relaxation
-        return None, metadata
 
-    # Check which days have ZERO periods (not just below target)
+    # Group found periods by day
-    existing_by_day = group_periods_by_day(existing_periods)
+    periods_by_day = group_periods_by_day(periods)
-    days_with_zero_periods = [day for day in prices_by_day if not existing_by_day.get(day)]
 
-    if not days_with_zero_periods:
+    # Check each day with price data: ALL must have at least min_periods
-        _LOGGER_DETAILS.debug(
+    for day in available_days:
-            "%sMin duration fallback: All days have at least one period - no fallback needed",
+        day_periods = periods_by_day.get(day, [])
-            INDENT_L1,
+        period_count = len(day_periods)
-        )
+        if period_count < min_periods:
-        return None, metadata
+            _LOGGER.debug(
+                "Day %s has only %d periods (need %d) - continuing relaxation",
+                day,
+                period_count,
+                min_periods,
+            )
+            return False  # This day doesn't have enough, continue relaxation
 
-    _LOGGER.info(
+    # All days with price data have enough periods, stop relaxation
-        "Min duration fallback: %d day(s) have zero periods, trying shorter min_period_length...",
+    return True
-        len(days_with_zero_periods),
-    )
-
-    # Try progressively shorter min_period_length
-    current_min_duration = config.min_period_length
-    fallback_periods: list[dict] = []
-
-    while current_min_duration > MIN_DURATION_FALLBACK_MINIMUM:
-        current_min_duration = max(
-            current_min_duration - MIN_DURATION_FALLBACK_STEP,
-            MIN_DURATION_FALLBACK_MINIMUM,
-        )
-
-        _LOGGER_DETAILS.debug(
-            "%sTrying min_period_length=%d min for days with zero periods",
-            INDENT_L2,
-            current_min_duration,
-        )
-
-        # Create modified config with shorter min_period_length
-        # Use maxed-out flex (50%) since we're in fallback mode
-        fallback_config = TibberPricesPeriodConfig(
-            reverse_sort=config.reverse_sort,
-            flex=MAX_FLEX_HARD_LIMIT,  # Max flex
-            min_distance_from_avg=0,  # Disable min_distance in fallback
-            min_period_length=current_min_duration,
-            threshold_low=config.threshold_low,
-            threshold_high=config.threshold_high,
-            threshold_volatility_moderate=config.threshold_volatility_moderate,
-            threshold_volatility_high=config.threshold_volatility_high,
-            threshold_volatility_very_high=config.threshold_volatility_very_high,
-            level_filter=None,  # Disable level filter
-            gap_count=config.gap_count,
-        )
-
-        # Try to find periods for days with zero periods
-        for day in days_with_zero_periods:
-            day_prices = prices_by_day.get(day, [])
-            if not day_prices:
-                continue
-
-            try:
-                day_result = calculate_periods(
-                    day_prices,
-                    config=fallback_config,
-                    time=time,
-                )
-
-                day_periods = day_result.get("periods", [])
-                if day_periods:
-                    # Mark periods with fallback metadata
-                    for period in day_periods:
-                        period["duration_fallback_active"] = True
-                        period["duration_fallback_min_length"] = current_min_duration
-                        period["relaxation_active"] = True
-                        period["relaxation_level"] = f"duration_fallback={current_min_duration}min"
-
-                    fallback_periods.extend(day_periods)
-                    _LOGGER.info(
-                        "Min duration fallback: Found %d period(s) for %s at min_length=%d min",
-                        len(day_periods),
-                        day,
-                        current_min_duration,
-                    )
-
-            except (KeyError, ValueError, TypeError) as err:
-                _LOGGER.warning(
-                    "Error during min duration fallback for %s: %s",
-                    day,
-                    err,
-                )
-                continue
-
-        # If we found periods for all zero-period days, we can stop
-        if fallback_periods:
-            # Remove days that now have periods from the list
-            fallback_by_day = group_periods_by_day(fallback_periods)
-            days_with_zero_periods = [day for day in days_with_zero_periods if not fallback_by_day.get(day)]
-
-            if not days_with_zero_periods:
-                break
-
-    if fallback_periods:
-        # Merge with existing periods
-        # resolve_period_overlaps merges adjacent/overlapping periods
-        merged_periods, _new_count = resolve_period_overlaps(
-            existing_periods,
-            fallback_periods,
-        )
-        recalculate_period_metadata(merged_periods, time=time)
-
-        metadata["fallback_active"] = True
-        metadata["phases_used"] = [f"duration_fallback (min_length={current_min_duration}min)"]
-
-        _LOGGER.info(
-            "Min duration fallback complete: Added %d period(s), total now %d",
-            len(fallback_periods),
-            len(merged_periods),
-        )
-
-        return {"periods": merged_periods}, metadata
-
-    _LOGGER.warning(
-        "Min duration fallback: Still %d day(s) with zero periods after trying all durations",
-        len(days_with_zero_periods),
-    )
-    return None, metadata
 
 
-def calculate_periods_with_relaxation(  # noqa: PLR0912, PLR0913, PLR0915 - Per-day relaxation requires many parameters and branches
+def mark_periods_with_relaxation(
+    periods: list[dict],
+    relaxation_level: str,
+    original_threshold: float,
+    applied_threshold: float,
+) -> None:
+    """
+    Mark periods with relaxation information (mutates period dicts in-place).
+
+    Uses consistent 'relaxation_*' prefix for all relaxation-related attributes.
+
+    Args:
+        periods: List of period dicts to mark
+        relaxation_level: String describing the relaxation level
+        original_threshold: Original flex threshold value (decimal, e.g., 0.19 for 19%)
+        applied_threshold: Actually applied threshold value (decimal, e.g., 0.25 for 25%)
+
+    """
+    for period in periods:
+        period["relaxation_active"] = True
+        period["relaxation_level"] = relaxation_level
+        # Convert decimal to percentage for display (0.19 → 19.0)
+        period["relaxation_threshold_original_%"] = round(original_threshold * 100, 1)
+        period["relaxation_threshold_applied_%"] = round(applied_threshold * 100, 1)
+
+
+def calculate_periods_with_relaxation(  # noqa: PLR0913, PLR0915 - Per-day relaxation requires many parameters and statements
     all_prices: list[dict],
     *,
     config: TibberPricesPeriodConfig,
@@ -427,8 +177,7 @@ def calculate_periods_with_relaxation(  # noqa: PLR0912, PLR0913, PLR0915 - Per-
     max_relaxation_attempts: int,
     should_show_callback: Callable[[str | None], bool],
     time: TibberPricesTimeService,
-    config_entry: Any,  # ConfigEntry type
+) -> tuple[dict[str, Any], dict[str, Any]]:
-) -> dict[str, Any]:
     """
     Calculate periods with optional per-day filter relaxation.
 
@@ -452,23 +201,18 @@ def calculate_periods_with_relaxation(  # noqa: PLR0912, PLR0913, PLR0915 - Per-
         should_show_callback: Callback function(level_override) -> bool
             Returns True if periods should be shown with given filter overrides. Pass None
             to use original configured filter values.
-        time: TibberPricesTimeService instance (required).
+        time: TibberPricesTimeService instance (required)
-        config_entry: Config entry to get display unit configuration.
 
     Returns:
-        Dict with same format as calculate_periods() output:
+        Tuple of (periods_result, relaxation_metadata):
-        - periods: List of period summaries
+        - periods_result: Same format as calculate_periods() output, with periods from all days
-        - metadata: Config and statistics (includes relaxation info)
+        - relaxation_metadata: Dict with relaxation information (aggregated across all days)
-        - reference_data: Daily min/max/avg prices
 
     """
     # Import here to avoid circular dependency
     from .core import (  # noqa: PLC0415
         calculate_periods,
     )
-    from .period_building import (  # noqa: PLC0415
-        filter_superseded_periods,
-    )
 
     # Compact INFO-level summary
     period_type = "PEAK PRICE" if config.reverse_sort else "BEST PRICE"
@@ -491,61 +235,63 @@ def calculate_periods_with_relaxation(  # noqa: PLR0912, PLR0913, PLR0915 - Per-
 
     # Detailed DEBUG-level context header
     period_type_full = "PEAK PRICE (most expensive)" if config.reverse_sort else "BEST PRICE (cheapest)"
-    _LOGGER_DETAILS.debug(
+    _LOGGER.debug(
         "%s========== %s PERIODS ==========",
         INDENT_L0,
         period_type_full,
     )
-    _LOGGER_DETAILS.debug(
+    _LOGGER.debug(
         "%sRelaxation: %s",
         INDENT_L0,
         "ENABLED (user setting: ON)" if enable_relaxation else "DISABLED by user configuration",
     )
-    _LOGGER_DETAILS.debug(
+    _LOGGER.debug(
         "%sBase config: flex=%.1f%%, min_length=%d min",
         INDENT_L0,
         abs(config.flex) * 100,
         config.min_period_length,
     )
     if enable_relaxation:
-        _LOGGER_DETAILS.debug(
+        _LOGGER.debug(
             "%sRelaxation target: %d periods per day",
             INDENT_L0,
             min_periods,
         )
-        _LOGGER_DETAILS.debug(
+        _LOGGER.debug(
             "%sRelaxation strategy: 3%% fixed flex increment per step (%d flex levels x 2 filter combinations)",
             INDENT_L0,
             max_relaxation_attempts,
         )
-        _LOGGER_DETAILS.debug(
+        _LOGGER.debug(
             "%sEarly exit: After EACH filter combination when target reached",
             INDENT_L0,
         )
-    _LOGGER_DETAILS.debug(
+    _LOGGER.debug(
         "%s=============================================",
         INDENT_L0,
     )
 
-    # Validate we have price data
+    # Validate we have price data for today/future
-    if not all_prices:
+    today = time.now().date()
+    future_prices = [
+        p
+        for p in all_prices
+        if (interval_time := time.get_interval_time(p)) is not None and interval_time.date() >= today
+    ]
+
+    if not future_prices:
+        # No price data for today/future
         _LOGGER.warning(
-            "No price data available - cannot calculate periods",
+            "No price data available for today/future - cannot calculate periods",
         )
-        return {
+        return {"periods": [], "metadata": {}, "reference_data": {}}, {
-            "periods": [],
+            "relaxation_active": False,
-            "metadata": {
+            "relaxation_attempted": False,
-                "relaxation": {
+            "min_periods_requested": min_periods if enable_relaxation else 0,
-                    "relaxation_active": False,
+            "periods_found": 0,
-                    "relaxation_attempted": False,
-                    "min_periods_requested": min_periods if enable_relaxation else 0,
-                    "periods_found": 0,
-                },
-            },
-            "reference_data": {},
         }
 
-    # Count available days for logging (today and future only)
+    # Count available days for logging
     prices_by_day = group_prices_by_day(all_prices, time=time)
     total_days = len(prices_by_day)
 
@@ -553,16 +299,14 @@ def calculate_periods_with_relaxation(  # noqa: PLR0912, PLR0913, PLR0915 - Per-
         "Calculating baseline periods for %d days...",
         total_days,
     )
-    _LOGGER_DETAILS.debug(
+    _LOGGER.debug(
-        "%sProcessing ALL %d price intervals together (yesterday+today+tomorrow, allows midnight crossing)",
+        "%sProcessing ALL %d price intervals together (allows midnight crossing)",
         INDENT_L1,
-        len(all_prices),
+        len(future_prices),
     )
 
-    # === BASELINE CALCULATION (process ALL prices together, including yesterday) ===
+    # === BASELINE CALCULATION (process ALL prices together) ===
-    # Periods that ended before yesterday will be filtered out later by filter_periods_by_end_date()
+    baseline_result = calculate_periods(future_prices, config=config, time=time)
-    # This keeps yesterday/today/tomorrow periods in the cache
-    baseline_result = calculate_periods(all_prices, config=config, time=time)
     all_periods = baseline_result["periods"]
 
     # Count periods per day for min_periods check
@@ -573,7 +317,7 @@ def calculate_periods_with_relaxation(  # noqa: PLR0912, PLR0913, PLR0915 - Per-
         day_periods = periods_by_day.get(day, [])
         period_count = len(day_periods)
 
-        _LOGGER_DETAILS.debug(
+        _LOGGER.debug(
             "%sDay %s baseline: Found %d periods%s",
             INDENT_L1,
             day,
@@ -590,7 +334,7 @@ def calculate_periods_with_relaxation(  # noqa: PLR0912, PLR0913, PLR0915 - Per-
 
     if enable_relaxation and days_meeting_requirement < total_days:
         # At least one day doesn't have enough periods
-        _LOGGER_DETAILS.debug(
+        _LOGGER.debug(
             "%sBaseline insufficient (%d/%d days met target) - starting relaxation",
             INDENT_L1,
             days_meeting_requirement,
@@ -598,16 +342,15 @@ def calculate_periods_with_relaxation(  # noqa: PLR0912, PLR0913, PLR0915 - Per-
         )
         relaxation_was_needed = True
 
-        # Run relaxation on ALL prices together (including yesterday)
+        # Run relaxation on ALL prices together
         relaxed_result, relax_metadata = relax_all_prices(
-            all_prices=all_prices,
+            all_prices=future_prices,
             config=config,
             min_periods=min_periods,
             max_relaxation_attempts=max_relaxation_attempts,
             should_show_callback=should_show_callback,
             baseline_periods=all_periods,
             time=time,
-            config_entry=config_entry,
         )
 
         all_periods = relaxed_result["periods"]
@@ -622,39 +365,8 @@ def calculate_periods_with_relaxation(  # noqa: PLR0912, PLR0913, PLR0915 - Per-
             period_count = len(day_periods)
             if period_count >= min_periods:
                 days_meeting_requirement += 1
-
-        # === MIN DURATION FALLBACK ===
-        # If still no periods after relaxation, try reducing min_period_length
-        # This is a last resort to ensure users always get SOME period
-        if days_meeting_requirement < total_days and config.min_period_length > MIN_DURATION_FALLBACK_MINIMUM:
-            _LOGGER.info(
-                "Relaxation incomplete (%d/%d days). Trying min_duration fallback...",
-                days_meeting_requirement,
-                total_days,
-            )
-
-            fallback_result, fallback_metadata = _try_min_duration_fallback(
-                config=config,
-                existing_periods=all_periods,
-                prices_by_day=prices_by_day,
-                time=time,
-            )
-
-            if fallback_result:
-                all_periods = fallback_result["periods"]
-                all_phases_used.extend(fallback_metadata.get("phases_used", []))
-
-                # Recount after fallback
-                periods_by_day = group_periods_by_day(all_periods)
-                days_meeting_requirement = 0
-                for day in sorted(prices_by_day.keys()):
-                    day_periods = periods_by_day.get(day, [])
-                    period_count = len(day_periods)
-                    if period_count >= min_periods:
-                        days_meeting_requirement += 1
-
     elif enable_relaxation:
-        _LOGGER_DETAILS.debug(
+        _LOGGER.debug(
             "%sAll %d days met target with baseline - no relaxation needed",
             INDENT_L1,
             total_days,
@@ -666,24 +378,13 @@ def calculate_periods_with_relaxation(  # noqa: PLR0912, PLR0913, PLR0915 - Per-
     # Recalculate metadata for combined periods
     recalculate_period_metadata(all_periods, time=time)
 
-    # Apply cross-day supersession filter (only for best-price periods)
-    # This removes late-night today periods that are superseded by better tomorrow alternatives
-    all_periods = filter_superseded_periods(
-        all_periods,
-        time=time,
-        reverse_sort=config.reverse_sort,
-    )
-
     # Build final result
     final_result = baseline_result.copy()
     final_result["periods"] = all_periods
 
     total_periods = len(all_periods)
 
-    # Add relaxation info to metadata
+    return final_result, {
-    if "metadata" not in final_result:
-        final_result["metadata"] = {}
-    final_result["metadata"]["relaxation"] = {
         "relaxation_active": relaxation_was_needed,
         "relaxation_attempted": relaxation_was_needed,
         "min_periods_requested": min_periods,
@@ -694,8 +395,6 @@ def calculate_periods_with_relaxation(  # noqa: PLR0912, PLR0913, PLR0915 - Per-
         "relaxation_incomplete": days_meeting_requirement < total_days,
     }
 
-    return final_result
-
 
 def relax_all_prices(  # noqa: PLR0913 - Comprehensive filter relaxation requires many parameters and statements
     all_prices: list[dict],
@@ -706,25 +405,22 @@ def relax_all_prices(  # noqa: PLR0913 - Comprehensive filter relaxation require
     baseline_periods: list[dict],
     *,
     time: TibberPricesTimeService,
-    config_entry: Any,  # ConfigEntry type
 ) -> tuple[dict[str, Any], dict[str, Any]]:
     """
     Relax filters for all prices until min_periods per day is reached.
 
     Strategy: Try increasing flex by 3% increments, then relax level filter.
-    Processes all prices together (yesterday+today+tomorrow), allowing periods
+    Processes all prices together, allowing periods to cross midnight boundaries.
-    to cross midnight boundaries. Returns when ALL days have min_periods
+    Returns when ALL days have min_periods (or max attempts exhausted).
-    (or max attempts exhausted).
 
     Args:
-        all_prices: All price intervals (yesterday+today+tomorrow).
+        all_prices: All price intervals (today + future)
-        config: Base period configuration.
+        config: Base period configuration
-        min_periods: Target number of periods PER DAY.
+        min_periods: Target number of periods PER DAY
-        max_relaxation_attempts: Maximum flex levels to try.
+        max_relaxation_attempts: Maximum flex levels to try
-        should_show_callback: Callback to check if a flex level should be shown.
+        should_show_callback: Callback to check if a flex level should be shown
-        baseline_periods: Baseline periods (before relaxation).
+        baseline_periods: Baseline periods (before relaxation)
-        time: TibberPricesTimeService instance.
+        time: TibberPricesTimeService instance
-        config_entry: Config entry to get display unit configuration.
 
     Returns:
         Tuple of (result_dict, metadata_dict)
@@ -752,7 +448,7 @@ def relax_all_prices(  # noqa: PLR0913 - Comprehensive filter relaxation require
 
         # Stop if we exceed hard maximum
         if current_flex > MAX_FLEX_HARD_LIMIT:
-            _LOGGER_DETAILS.debug(
+            _LOGGER.debug(
                 "%s    Reached 50%% flex hard limit",
                 INDENT_L2,
             )
@@ -766,21 +462,20 @@ def relax_all_prices(  # noqa: PLR0913 - Comprehensive filter relaxation require
 
         # Try current flex with level="any" (in relaxation mode)
         if original_level_filter != "any":
-            _LOGGER_DETAILS.debug(
+            _LOGGER.debug(
                 "%s    Flex=%.1f%%: OVERRIDING level_filter: %s → ANY",
                 INDENT_L2,
                 current_flex * 100,
                 original_level_filter,
             )
 
-        # NOTE: config.flex is already normalized to positive by get_period_config()
         relaxed_config = config._replace(
-            flex=current_flex,  # Already positive from normalization
+            flex=current_flex if config.flex >= 0 else -current_flex,
             level_filter="any",
         )
 
         phase_label_full = f"flex={current_flex * 100:.1f}% +level_any"
-        _LOGGER_DETAILS.debug(
+        _LOGGER.debug(
             "%s    Trying %s: config has %d intervals (all days together), level_filter=%s",
             INDENT_L2,
             phase_label_full,
@@ -792,36 +487,39 @@ def relax_all_prices(  # noqa: PLR0913 - Comprehensive filter relaxation require
         result = calculate_periods(all_prices, config=relaxed_config, time=time)
         new_periods = result["periods"]
 
-        _LOGGER_DETAILS.debug(
+        _LOGGER.debug(
             "%s    %s: calculate_periods returned %d periods",
             INDENT_L2,
             phase_label_full,
             len(new_periods),
         )
 
-        # Mark newly found periods with relaxation metadata BEFORE merging
-        mark_periods_with_relaxation(
-            new_periods,
-            relaxation_level=phase_label_full,
-            original_threshold=base_flex,
-            applied_threshold=current_flex,
-            reverse_sort=config.reverse_sort,
-        )
-
         # Resolve overlaps between existing and new periods
         combined, standalone_count = resolve_period_overlaps(
             existing_periods=existing_periods,
             new_relaxed_periods=new_periods,
         )
 
-        # Count periods per day with QUALITY GATE check
+        # Count periods per day to check if requirement met
-        # Only periods with CV <= PERIOD_MAX_CV count towards min_periods requirement
+        periods_by_day = group_periods_by_day(combined)
-        days_meeting_requirement, quality_period_count = _count_quality_periods(
+        days_meeting_requirement = 0
-            combined, all_prices, prices_by_day, min_periods, time=time
+
-        )
+        for day in sorted(prices_by_day.keys()):
+            day_periods = periods_by_day.get(day, [])
+            period_count = len(day_periods)
+            if period_count >= min_periods:
+                days_meeting_requirement += 1
+
+            _LOGGER.debug(
+                "%s      Day %s: %d periods%s",
+                INDENT_L2,
+                day,
+                period_count,
+                " ✓" if period_count >= min_periods else f" (need {min_periods})",
+            )
 
         total_periods = len(combined)
-        _LOGGER_DETAILS.debug(
+        _LOGGER.debug(
             "%s    %s: found %d periods total, %d/%d days meet requirement",
             INDENT_L2,
             phase_label_full,

custom_components/tibber_prices/coordinator/period_handlers/types.py

@@ -15,24 +15,6 @@ from custom_components.tibber_prices.const import (
     DEFAULT_VOLATILITY_THRESHOLD_VERY_HIGH,
 )
 
-# Quality Gate: Maximum coefficient of variation (CV) allowed within a period
-# Periods with internal CV above this are considered too heterogeneous for "best price"
-# A 25% CV means the std dev is 25% of the mean - beyond this, prices vary too much
-# Example: Period with prices 0.7-0.99 kr has ~15% CV which is acceptable
-#          Period with prices 0.5-1.0 kr has ~30% CV which would be rejected
-PERIOD_MAX_CV = 25.0  # 25% max coefficient of variation within a period
-
-# Cross-Day Extension: Time window constants
-# When a period ends late in the day and tomorrow data is available,
-# we can extend it past midnight if prices remain favorable
-CROSS_DAY_LATE_PERIOD_START_HOUR = 20  # Consider periods starting at 20:00 or later for extension
-CROSS_DAY_MAX_EXTENSION_HOUR = 8  # Don't extend beyond 08:00 next day (covers typical night low)
-
-# Cross-Day Supersession: When tomorrow data arrives, late-night periods that are
-# worse than early-morning tomorrow periods become obsolete
-# A today period is "superseded" if tomorrow has a significantly better alternative
-SUPERSESSION_PRICE_IMPROVEMENT_PCT = 10.0  # Tomorrow must be at least 10% cheaper to supersede
-
 # Log indentation levels for visual hierarchy
 INDENT_L0 = ""  # Top level (calculate_periods_with_relaxation)
 INDENT_L1 = "  "  # Per-day loop
@@ -74,13 +56,11 @@ class TibberPricesPeriodStatistics(NamedTuple):
     aggregated_level: str | None
     aggregated_rating: str | None
     rating_difference_pct: float | None
-    price_mean: float
+    price_avg: float
-    price_median: float
     price_min: float
     price_max: float
     price_spread: float
     volatility: str
-    coefficient_of_variation: float | None  # CV as percentage (e.g., 15.0 for 15%)
     period_price_diff: float | None
     period_price_diff_pct: float | None
 

custom_components/tibber_prices/coordinator/periods.py

@@ -13,17 +13,16 @@ from typing import TYPE_CHECKING, Any
 from custom_components.tibber_prices import const as _const
 
 if TYPE_CHECKING:
-    from collections.abc import Callable
-
     from custom_components.tibber_prices.coordinator.time_service import TibberPricesTimeService
-    from homeassistant.config_entries import ConfigEntry
 
-from .helpers import get_intervals_for_day_offsets
 from .period_handlers import (
     TibberPricesPeriodConfig,
     calculate_periods_with_relaxation,
 )
 
+if TYPE_CHECKING:
+    from homeassistant.config_entries import ConfigEntry
+
 _LOGGER = logging.getLogger(__name__)
 
 
@@ -34,7 +33,6 @@ class TibberPricesPeriodCalculator:
         self,
         config_entry: ConfigEntry,
         log_prefix: str,
-        get_config_override_fn: Callable[[str, str], Any | None] | None = None,
     ) -> None:
         """Initialize the period calculator."""
         self.config_entry = config_entry
@@ -42,40 +40,11 @@ class TibberPricesPeriodCalculator:
         self.time: TibberPricesTimeService  # Set by coordinator before first use
         self._config_cache: dict[str, dict[str, Any]] | None = None
         self._config_cache_valid = False
-        self._get_config_override = get_config_override_fn
 
         # Period calculation cache
         self._cached_periods: dict[str, Any] | None = None
         self._last_periods_hash: str | None = None
 
-    def _get_option(
-        self,
-        config_key: str,
-        config_section: str,
-        default: Any,
-    ) -> Any:
-        """
-        Get a config option, checking overrides first.
-
-        Args:
-            config_key: The configuration key
-            config_section: The section in options (e.g., "flexibility_settings")
-            default: Default value if not set
-
-        Returns:
-            Override value if set, otherwise options value, otherwise default
-
-        """
-        # Check overrides first
-        if self._get_config_override is not None:
-            override = self._get_config_override(config_key, config_section)
-            if override is not None:
-                return override
-
-        # Fall back to options
-        section = self.config_entry.options.get(config_section, {})
-        return section.get(config_key, default)
-
     def _log(self, level: str, message: str, *args: object, **kwargs: object) -> None:
         """Log with calculator-specific prefix."""
         prefixed_message = f"{self._log_prefix} {message}"
@@ -95,7 +64,7 @@ class TibberPricesPeriodCalculator:
         Compute hash of price data and config for period calculation caching.
 
         Only includes data that affects period calculation:
-        - All interval timestamps and enriched rating levels (yesterday/today/tomorrow)
+        - Today's interval timestamps and enriched rating levels
         - Period calculation config (flex, min_distance, min_period_length)
         - Level filter overrides
 
@@ -103,20 +72,9 @@ class TibberPricesPeriodCalculator:
             Hash string for cache key comparison.
 
         """
-        # Get today and tomorrow intervals for hash calculation
+        # Get relevant price data
-        # CRITICAL: Only today+tomorrow needed in hash because:
+        today = price_info.get("today", [])
-        # 1. Mitternacht: "today" startsAt changes → cache invalidates
+        today_signature = tuple((interval.get("startsAt"), interval.get("rating_level")) for interval in today)
-        # 2. Tomorrow arrival: "tomorrow" startsAt changes from None → cache invalidates
-        # 3. Yesterday/day-before-yesterday are static (rating_levels don't change retroactively)
-        # 4. Using first startsAt as representative (changes → entire day changed)
-        coordinator_data = {"priceInfo": price_info}
-        today_intervals = get_intervals_for_day_offsets(coordinator_data, [0])
-        tomorrow_intervals = get_intervals_for_day_offsets(coordinator_data, [1])
-
-        # Use first startsAt of each day as representative for entire day's data
-        # If day is empty, use None (detects data availability changes)
-        today_start = today_intervals[0].get("startsAt") if today_intervals else None
-        tomorrow_start = tomorrow_intervals[0].get("startsAt") if tomorrow_intervals else None
 
         # Get period configs (both best and peak)
         best_config = self.get_period_config(reverse_sort=False)
@@ -124,14 +82,12 @@ class TibberPricesPeriodCalculator:
 
         # Get level filter overrides from options
         options = self.config_entry.options
-        period_settings = options.get("period_settings", {})
+        best_level_filter = options.get(_const.CONF_BEST_PRICE_MAX_LEVEL, _const.DEFAULT_BEST_PRICE_MAX_LEVEL)
-        best_level_filter = period_settings.get(_const.CONF_BEST_PRICE_MAX_LEVEL, _const.DEFAULT_BEST_PRICE_MAX_LEVEL)
+        peak_level_filter = options.get(_const.CONF_PEAK_PRICE_MIN_LEVEL, _const.DEFAULT_PEAK_PRICE_MIN_LEVEL)
-        peak_level_filter = period_settings.get(_const.CONF_PEAK_PRICE_MIN_LEVEL, _const.DEFAULT_PEAK_PRICE_MIN_LEVEL)
 
         # Compute hash from all relevant data
         hash_data = (
-            today_start,  # Representative for today's data (changes at midnight)
+            today_signature,
-            tomorrow_start,  # Representative for tomorrow's data (changes when data arrives)
             tuple(best_config.items()),
             tuple(peak_config.items()),
             best_level_filter,
@@ -144,7 +100,7 @@ class TibberPricesPeriodCalculator:
         Get period calculation configuration from config options.
 
         Uses cached config to avoid multiple options.get() calls.
-        Cache is invalidated when config_entry.options change or override entities update.
+        Cache is invalidated when config_entry.options change.
         """
         cache_key = "peak" if reverse_sort else "best"
 
@@ -156,72 +112,45 @@ class TibberPricesPeriodCalculator:
         if self._config_cache is None:
             self._config_cache = {}
 
-        # Get config values, checking overrides first
+        options = self.config_entry.options
-        # CRITICAL: Best/Peak price settings are stored in nested sections:
+        data = self.config_entry.data
-        # - period_settings: min_period_length, max_level, gap_count
-        # - flexibility_settings: flex, min_distance_from_avg
-        # Override entities can override any of these values at runtime
 
         if reverse_sort:
             # Peak price configuration
-            flex = self._get_option(
+            flex = options.get(
-                _const.CONF_PEAK_PRICE_FLEX,
+                _const.CONF_PEAK_PRICE_FLEX, data.get(_const.CONF_PEAK_PRICE_FLEX, _const.DEFAULT_PEAK_PRICE_FLEX)
-                "flexibility_settings",
-                _const.DEFAULT_PEAK_PRICE_FLEX,
             )
-            min_distance_from_avg = self._get_option(
+            min_distance_from_avg = options.get(
                 _const.CONF_PEAK_PRICE_MIN_DISTANCE_FROM_AVG,
-                "flexibility_settings",
+                data.get(_const.CONF_PEAK_PRICE_MIN_DISTANCE_FROM_AVG, _const.DEFAULT_PEAK_PRICE_MIN_DISTANCE_FROM_AVG),
-                _const.DEFAULT_PEAK_PRICE_MIN_DISTANCE_FROM_AVG,
             )
-            min_period_length = self._get_option(
+            min_period_length = options.get(
                 _const.CONF_PEAK_PRICE_MIN_PERIOD_LENGTH,
-                "period_settings",
+                data.get(_const.CONF_PEAK_PRICE_MIN_PERIOD_LENGTH, _const.DEFAULT_PEAK_PRICE_MIN_PERIOD_LENGTH),
-                _const.DEFAULT_PEAK_PRICE_MIN_PERIOD_LENGTH,
             )
         else:
             # Best price configuration
-            flex = self._get_option(
+            flex = options.get(
-                _const.CONF_BEST_PRICE_FLEX,
+                _const.CONF_BEST_PRICE_FLEX, data.get(_const.CONF_BEST_PRICE_FLEX, _const.DEFAULT_BEST_PRICE_FLEX)
-                "flexibility_settings",
-                _const.DEFAULT_BEST_PRICE_FLEX,
             )
-            min_distance_from_avg = self._get_option(
+            min_distance_from_avg = options.get(
                 _const.CONF_BEST_PRICE_MIN_DISTANCE_FROM_AVG,
-                "flexibility_settings",
+                data.get(_const.CONF_BEST_PRICE_MIN_DISTANCE_FROM_AVG, _const.DEFAULT_BEST_PRICE_MIN_DISTANCE_FROM_AVG),
-                _const.DEFAULT_BEST_PRICE_MIN_DISTANCE_FROM_AVG,
             )
-            min_period_length = self._get_option(
+            min_period_length = options.get(
                 _const.CONF_BEST_PRICE_MIN_PERIOD_LENGTH,
-                "period_settings",
+                data.get(_const.CONF_BEST_PRICE_MIN_PERIOD_LENGTH, _const.DEFAULT_BEST_PRICE_MIN_PERIOD_LENGTH),
-                _const.DEFAULT_BEST_PRICE_MIN_PERIOD_LENGTH,
             )
 
         # Convert flex from percentage to decimal (e.g., 5 -> 0.05)
-        # CRITICAL: Normalize to absolute value for internal calculations
-        # User-facing values use sign convention:
-        # - Best price: positive (e.g., +15% above minimum)
-        # - Peak price: negative (e.g., -20% below maximum)
-        # Internal calculations always use positive values with reverse_sort flag
         try:
-            flex = abs(float(flex)) / 100  # Always positive internally
+            flex = float(flex) / 100
         except (TypeError, ValueError):
-            flex = (
+            flex = _const.DEFAULT_BEST_PRICE_FLEX / 100 if not reverse_sort else _const.DEFAULT_PEAK_PRICE_FLEX / 100
-                abs(_const.DEFAULT_BEST_PRICE_FLEX) / 100
-                if not reverse_sort
-                else abs(_const.DEFAULT_PEAK_PRICE_FLEX) / 100
-            )
-
-        # CRITICAL: Normalize min_distance_from_avg to absolute value
-        # User-facing values use sign convention:
-        # - Best price: negative (e.g., -5% below average)
-        # - Peak price: positive (e.g., +5% above average)
-        # Internal calculations always use positive values with reverse_sort flag
-        min_distance_from_avg_normalized = abs(float(min_distance_from_avg))
 
         config = {
             "flex": flex,
-            "min_distance_from_avg": min_distance_from_avg_normalized,
+            "min_distance_from_avg": float(min_distance_from_avg),
             "min_period_length": int(min_period_length),
         }
 
@@ -400,14 +329,13 @@ class TibberPricesPeriodCalculator:
 
         # Normal check failed - try splitting at gap clusters as fallback
         # Get minimum period length from config (convert minutes to intervals)
-        period_settings = self.config_entry.options.get("period_settings", {})
         if reverse_sort:
-            min_period_minutes = period_settings.get(
+            min_period_minutes = self.config_entry.options.get(
                 _const.CONF_PEAK_PRICE_MIN_PERIOD_LENGTH,
                 _const.DEFAULT_PEAK_PRICE_MIN_PERIOD_LENGTH,
             )
         else:
-            min_period_minutes = period_settings.get(
+            min_period_minutes = self.config_entry.options.get(
                 _const.CONF_BEST_PRICE_MIN_PERIOD_LENGTH,
                 _const.DEFAULT_BEST_PRICE_MIN_PERIOD_LENGTH,
             )
@@ -532,15 +460,13 @@ class TibberPricesPeriodCalculator:
         # Get appropriate config based on sensor type
         elif reverse_sort:
             # Peak price: minimum level filter (lower bound)
-            period_settings = self.config_entry.options.get("period_settings", {})
+            level_config = self.config_entry.options.get(
-            level_config = period_settings.get(
                 _const.CONF_PEAK_PRICE_MIN_LEVEL,
                 _const.DEFAULT_PEAK_PRICE_MIN_LEVEL,
             )
         else:
             # Best price: maximum level filter (upper bound)
-            period_settings = self.config_entry.options.get("period_settings", {})
+            level_config = self.config_entry.options.get(
-            level_config = period_settings.get(
                 _const.CONF_BEST_PRICE_MAX_LEVEL,
                 _const.DEFAULT_BEST_PRICE_MAX_LEVEL,
             )
@@ -549,23 +475,20 @@ class TibberPricesPeriodCalculator:
         if level_config == "any":
             return True
 
-        # Get today's intervals from flat list
+        # Get today's intervals
-        # Build minimal coordinator_data structure for get_intervals_for_day_offsets
+        today_intervals = price_info.get("today", [])
-        coordinator_data = {"priceInfo": price_info}
-        today_intervals = get_intervals_for_day_offsets(coordinator_data, [0])
 
         if not today_intervals:
             return True  # If no data, don't filter
 
         # Get gap tolerance configuration
-        period_settings = self.config_entry.options.get("period_settings", {})
         if reverse_sort:
-            max_gap_count = period_settings.get(
+            max_gap_count = self.config_entry.options.get(
                 _const.CONF_PEAK_PRICE_MAX_LEVEL_GAP_COUNT,
                 _const.DEFAULT_PEAK_PRICE_MAX_LEVEL_GAP_COUNT,
             )
         else:
-            max_gap_count = period_settings.get(
+            max_gap_count = self.config_entry.options.get(
                 _const.CONF_BEST_PRICE_MAX_LEVEL_GAP_COUNT,
                 _const.DEFAULT_BEST_PRICE_MAX_LEVEL_GAP_COUNT,
             )
@@ -616,14 +539,12 @@ class TibberPricesPeriodCalculator:
 
         self._log("debug", "Calculating periods (cache miss or hash mismatch)")
 
-        # Get all intervals at once (day before yesterday + yesterday + today + tomorrow)
+        yesterday_prices = price_info.get("yesterday", [])
-        # CRITICAL: 4 days ensure stable historical period calculations
+        today_prices = price_info.get("today", [])
-        # (periods calculated today for yesterday match periods calculated yesterday)
+        tomorrow_prices = price_info.get("tomorrow", [])
-        coordinator_data = {"priceInfo": price_info}
+        all_prices = yesterday_prices + today_prices + tomorrow_prices
-        all_prices = get_intervals_for_day_offsets(coordinator_data, [-2, -1, 0, 1])
 
-        # Get rating thresholds from config (flat in options, not in sections)
+        # Get rating thresholds from config
-        # CRITICAL: Price rating thresholds are stored FLAT in options (no sections)
         threshold_low = self.config_entry.options.get(
             _const.CONF_PRICE_RATING_THRESHOLD_LOW,
             _const.DEFAULT_PRICE_RATING_THRESHOLD_LOW,
@@ -633,8 +554,7 @@ class TibberPricesPeriodCalculator:
             _const.DEFAULT_PRICE_RATING_THRESHOLD_HIGH,
         )
 
-        # Get volatility thresholds from config (flat in options, not in sections)
+        # Get volatility thresholds from config
-        # CRITICAL: Volatility thresholds are stored FLAT in options (no sections)
         threshold_volatility_moderate = self.config_entry.options.get(
             _const.CONF_VOLATILITY_THRESHOLD_MODERATE,
             _const.DEFAULT_VOLATILITY_THRESHOLD_MODERATE,
@@ -649,11 +569,8 @@ class TibberPricesPeriodCalculator:
         )
 
         # Get relaxation configuration for best price
-        # CRITICAL: Relaxation settings are stored in nested section 'relaxation_and_target_periods'
+        enable_relaxation_best = self.config_entry.options.get(
-        # Override entities can override any of these values at runtime
-        enable_relaxation_best = self._get_option(
             _const.CONF_ENABLE_MIN_PERIODS_BEST,
-            "relaxation_and_target_periods",
             _const.DEFAULT_ENABLE_MIN_PERIODS_BEST,
         )
 
@@ -664,30 +581,25 @@ class TibberPricesPeriodCalculator:
             show_best_price = bool(all_prices)
         else:
             show_best_price = self.should_show_periods(price_info, reverse_sort=False) if all_prices else False
-        min_periods_best = self._get_option(
+        min_periods_best = self.config_entry.options.get(
             _const.CONF_MIN_PERIODS_BEST,
-            "relaxation_and_target_periods",
             _const.DEFAULT_MIN_PERIODS_BEST,
         )
-        relaxation_attempts_best = self._get_option(
+        relaxation_attempts_best = self.config_entry.options.get(
             _const.CONF_RELAXATION_ATTEMPTS_BEST,
-            "relaxation_and_target_periods",
             _const.DEFAULT_RELAXATION_ATTEMPTS_BEST,
         )
 
         # Calculate best price periods (or return empty if filtered)
         if show_best_price:
             best_config = self.get_period_config(reverse_sort=False)
-            # Get level filter configuration from period_settings section
+            # Get level filter configuration
-            # CRITICAL: max_level and gap_count are stored in nested section 'period_settings'
+            max_level_best = self.config_entry.options.get(
-            max_level_best = self._get_option(
                 _const.CONF_BEST_PRICE_MAX_LEVEL,
-                "period_settings",
                 _const.DEFAULT_BEST_PRICE_MAX_LEVEL,
             )
-            gap_count_best = self._get_option(
+            gap_count_best = self.config_entry.options.get(
                 _const.CONF_BEST_PRICE_MAX_LEVEL_GAP_COUNT,
-                "period_settings",
                 _const.DEFAULT_BEST_PRICE_MAX_LEVEL_GAP_COUNT,
             )
             best_period_config = TibberPricesPeriodConfig(
@@ -703,7 +615,7 @@ class TibberPricesPeriodCalculator:
                 level_filter=max_level_best,
                 gap_count=gap_count_best,
             )
-            best_periods = calculate_periods_with_relaxation(
+            best_periods, best_relaxation = calculate_periods_with_relaxation(
                 all_prices,
                 config=best_period_config,
                 enable_relaxation=enable_relaxation_best,
@@ -715,26 +627,18 @@ class TibberPricesPeriodCalculator:
                     level_override=lvl,
                 ),
                 time=self.time,
-                config_entry=self.config_entry,
             )
         else:
             best_periods = {
                 "periods": [],
                 "intervals": [],
-                "metadata": {
+                "metadata": {"total_intervals": 0, "total_periods": 0, "config": {}},
-                    "total_intervals": 0,
-                    "total_periods": 0,
-                    "config": {},
-                    "relaxation": {"relaxation_active": False, "relaxation_attempted": False},
-                },
             }
+            best_relaxation = {"relaxation_active": False, "relaxation_attempted": False}
 
         # Get relaxation configuration for peak price
-        # CRITICAL: Relaxation settings are stored in nested section 'relaxation_and_target_periods'
+        enable_relaxation_peak = self.config_entry.options.get(
-        # Override entities can override any of these values at runtime
-        enable_relaxation_peak = self._get_option(
             _const.CONF_ENABLE_MIN_PERIODS_PEAK,
-            "relaxation_and_target_periods",
             _const.DEFAULT_ENABLE_MIN_PERIODS_PEAK,
         )
 
@@ -745,30 +649,25 @@ class TibberPricesPeriodCalculator:
             show_peak_price = bool(all_prices)
         else:
             show_peak_price = self.should_show_periods(price_info, reverse_sort=True) if all_prices else False
-        min_periods_peak = self._get_option(
+        min_periods_peak = self.config_entry.options.get(
             _const.CONF_MIN_PERIODS_PEAK,
-            "relaxation_and_target_periods",
             _const.DEFAULT_MIN_PERIODS_PEAK,
         )
-        relaxation_attempts_peak = self._get_option(
+        relaxation_attempts_peak = self.config_entry.options.get(
             _const.CONF_RELAXATION_ATTEMPTS_PEAK,
-            "relaxation_and_target_periods",
             _const.DEFAULT_RELAXATION_ATTEMPTS_PEAK,
         )
 
         # Calculate peak price periods (or return empty if filtered)
         if show_peak_price:
             peak_config = self.get_period_config(reverse_sort=True)
-            # Get level filter configuration from period_settings section
+            # Get level filter configuration
-            # CRITICAL: min_level and gap_count are stored in nested section 'period_settings'
+            min_level_peak = self.config_entry.options.get(
-            min_level_peak = self._get_option(
                 _const.CONF_PEAK_PRICE_MIN_LEVEL,
-                "period_settings",
                 _const.DEFAULT_PEAK_PRICE_MIN_LEVEL,
             )
-            gap_count_peak = self._get_option(
+            gap_count_peak = self.config_entry.options.get(
                 _const.CONF_PEAK_PRICE_MAX_LEVEL_GAP_COUNT,
-                "period_settings",
                 _const.DEFAULT_PEAK_PRICE_MAX_LEVEL_GAP_COUNT,
             )
             peak_period_config = TibberPricesPeriodConfig(
@@ -784,7 +683,7 @@ class TibberPricesPeriodCalculator:
                 level_filter=min_level_peak,
                 gap_count=gap_count_peak,
             )
-            peak_periods = calculate_periods_with_relaxation(
+            peak_periods, peak_relaxation = calculate_periods_with_relaxation(
                 all_prices,
                 config=peak_period_config,
                 enable_relaxation=enable_relaxation_peak,
@@ -796,23 +695,20 @@ class TibberPricesPeriodCalculator:
                     level_override=lvl,
                 ),
                 time=self.time,
-                config_entry=self.config_entry,
             )
         else:
             peak_periods = {
                 "periods": [],
                 "intervals": [],
-                "metadata": {
+                "metadata": {"total_intervals": 0, "total_periods": 0, "config": {}},
-                    "total_intervals": 0,
-                    "total_periods": 0,
-                    "config": {},
-                    "relaxation": {"relaxation_active": False, "relaxation_attempted": False},
-                },
             }
+            peak_relaxation = {"relaxation_active": False, "relaxation_attempted": False}
 
         result = {
             "best_price": best_periods,
+            "best_price_relaxation": best_relaxation,
             "peak_price": peak_periods,
+            "peak_price_relaxation": peak_relaxation,
         }
 
         # Cache the result

custom_components/tibber_prices/coordinator/price_data_manager.py

@@ -1,631 +0,0 @@
-"""
-Price data management for the coordinator.
-
-This module manages all price-related data for the Tibber Prices integration:
-
-**User Data** (fetched directly via API):
-- Home metadata (name, address, timezone)
-- Account info (subscription status)
-- Currency settings
-- Refreshed daily (24h interval)
-
-**Price Data** (fetched via IntervalPool):
-- Quarter-hourly price intervals
-- Yesterday/today/tomorrow coverage
-- The IntervalPool handles actual API fetching, deduplication, and caching
-- This manager coordinates the data flow and user data refresh
-
-Data flow:
-    Tibber API → IntervalPool → PriceDataManager → Coordinator → Sensors
-                     ↑                  ↓
-              (actual fetching)   (orchestration + user data)
-
-Note: Price data is NOT cached in this module - IntervalPool is the single
-source of truth. This module only caches user_data for daily refresh cycle.
-"""
-
-from __future__ import annotations
-
-import logging
-from datetime import timedelta
-from typing import TYPE_CHECKING, Any
-
-from custom_components.tibber_prices.api import (
-    TibberPricesApiClientAuthenticationError,
-    TibberPricesApiClientCommunicationError,
-    TibberPricesApiClientError,
-)
-from homeassistant.exceptions import ConfigEntryAuthFailed
-from homeassistant.helpers.update_coordinator import UpdateFailed
-
-from . import cache, helpers
-
-if TYPE_CHECKING:
-    from collections.abc import Callable
-    from datetime import datetime
-
-    from custom_components.tibber_prices.api import TibberPricesApiClient
-    from custom_components.tibber_prices.interval_pool import TibberPricesIntervalPool
-
-    from .time_service import TibberPricesTimeService
-
-_LOGGER = logging.getLogger(__name__)
-
-# Hour when Tibber publishes tomorrow's prices (around 13:00 local time)
-# Before this hour, requesting tomorrow data will always fail → wasted API call
-TOMORROW_DATA_AVAILABLE_HOUR = 13
-
-
-class TibberPricesPriceDataManager:
-    """
-    Manages price and user data for the coordinator.
-
-    Responsibilities:
-    - User data: Fetches directly via API, validates, caches with persistence
-    - Price data: Coordinates with IntervalPool (which does actual API fetching)
-    - Cache management: Loads/stores both data types to HA persistent storage
-    - Update decisions: Determines when fresh data is needed
-
-    Note: Despite the name, this class does NOT do the actual price fetching.
-    The IntervalPool handles API calls, deduplication, and interval management.
-    This class orchestrates WHEN to fetch and processes the results.
-    """
-
-    def __init__(  # noqa: PLR0913
-        self,
-        api: TibberPricesApiClient,
-        store: Any,
-        log_prefix: str,
-        user_update_interval: timedelta,
-        time: TibberPricesTimeService,
-        home_id: str,
-        interval_pool: TibberPricesIntervalPool,
-    ) -> None:
-        """
-        Initialize the price data manager.
-
-        Args:
-            api: API client for direct requests (user data only).
-            store: Home Assistant storage for persistence.
-            log_prefix: Prefix for log messages (e.g., "[Home Name]").
-            user_update_interval: How often to refresh user data (default: 1 day).
-            time: TimeService for time operations.
-            home_id: Home ID this manager is responsible for.
-            interval_pool: IntervalPool for price data (handles actual fetching).
-
-        """
-        self.api = api
-        self._store = store
-        self._log_prefix = log_prefix
-        self._user_update_interval = user_update_interval
-        self.time: TibberPricesTimeService = time
-        self.home_id = home_id
-        self._interval_pool = interval_pool
-
-        # Cached data (user data only - price data is in IntervalPool)
-        self._cached_user_data: dict[str, Any] | None = None
-        self._last_user_update: datetime | None = None
-
-    def _log(self, level: str, message: str, *args: object, **kwargs: object) -> None:
-        """Log with coordinator-specific prefix."""
-        prefixed_message = f"{self._log_prefix} {message}"
-        getattr(_LOGGER, level)(prefixed_message, *args, **kwargs)
-
-    async def load_cache(self) -> None:
-        """Load cached user data from storage (price data is in IntervalPool)."""
-        cache_data = await cache.load_cache(self._store, self._log_prefix, time=self.time)
-
-        self._cached_user_data = cache_data.user_data
-        self._last_user_update = cache_data.last_user_update
-
-    def should_fetch_tomorrow_data(
-        self,
-        current_price_info: list[dict[str, Any]] | None,
-    ) -> bool:
-        """
-        Determine if tomorrow's data should be requested from the API.
-
-        This is the key intelligence that prevents API spam:
-        - Tibber publishes tomorrow's prices around 13:00 each day
-        - Before 13:00, requesting tomorrow data will always fail → wasted API call
-        - If we already have tomorrow data, no need to request it again
-
-        The decision logic:
-        1. Before 13:00 local time → Don't fetch (data not available yet)
-        2. After 13:00 AND tomorrow data already present → Don't fetch (already have it)
-        3. After 13:00 AND tomorrow data missing → Fetch (data should be available)
-
-        Args:
-            current_price_info: List of price intervals from current coordinator data.
-                               Used to check if tomorrow data already exists.
-
-        Returns:
-            True if tomorrow data should be requested, False otherwise.
-
-        """
-        now = self.time.now()
-        now_local = self.time.as_local(now)
-        current_hour = now_local.hour
-
-        # Before TOMORROW_DATA_AVAILABLE_HOUR - tomorrow data not available yet
-        if current_hour < TOMORROW_DATA_AVAILABLE_HOUR:
-            self._log("debug", "Before %d:00 - not requesting tomorrow data", TOMORROW_DATA_AVAILABLE_HOUR)
-            return False
-
-        # After TOMORROW_DATA_AVAILABLE_HOUR - check if we already have tomorrow data
-        if current_price_info:
-            has_tomorrow = self.has_tomorrow_data(current_price_info)
-            if has_tomorrow:
-                self._log(
-                    "debug", "After %d:00 but already have tomorrow data - not requesting", TOMORROW_DATA_AVAILABLE_HOUR
-                )
-                return False
-            self._log("debug", "After %d:00 and tomorrow data missing - will request", TOMORROW_DATA_AVAILABLE_HOUR)
-            return True
-
-        # No current data - request tomorrow data if after TOMORROW_DATA_AVAILABLE_HOUR
-        self._log(
-            "debug", "After %d:00 with no current data - will request tomorrow data", TOMORROW_DATA_AVAILABLE_HOUR
-        )
-        return True
-
-    async def store_cache(self, last_midnight_check: datetime | None = None) -> None:
-        """Store cache data (user metadata only, price data is in IntervalPool)."""
-        cache_data = cache.TibberPricesCacheData(
-            user_data=self._cached_user_data,
-            last_user_update=self._last_user_update,
-            last_midnight_check=last_midnight_check,
-        )
-        await cache.save_cache(self._store, cache_data, self._log_prefix)
-
-    def _validate_user_data(self, user_data: dict, home_id: str) -> bool:  # noqa: PLR0911
-        """
-        Validate user data completeness.
-
-        Rejects incomplete/invalid data from API to prevent caching temporary errors.
-        Currency information is critical - if missing, we cannot safely calculate prices.
-
-        Args:
-            user_data: User data dict from API.
-            home_id: Home ID to validate against.
-
-        Returns:
-            True if data is valid and complete, False otherwise.
-
-        """
-        if not user_data:
-            self._log("warning", "User data validation failed: Empty data")
-            return False
-
-        viewer = user_data.get("viewer")
-        if not viewer or not isinstance(viewer, dict):
-            self._log("warning", "User data validation failed: Missing or invalid viewer")
-            return False
-
-        homes = viewer.get("homes")
-        if not homes or not isinstance(homes, list) or len(homes) == 0:
-            self._log("warning", "User data validation failed: No homes found")
-            return False
-
-        # Find our home and validate it has required data
-        home_found = False
-        for home in homes:
-            if home.get("id") == home_id:
-                home_found = True
-
-                # Validate home has timezone (required for cursor calculation)
-                if not home.get("timeZone"):
-                    self._log("warning", "User data validation failed: Home %s missing timezone", home_id)
-                    return False
-
-                # Currency is REQUIRED - we cannot function without it
-                # The currency is nested in currentSubscription.priceInfo.current.currency
-                subscription = home.get("currentSubscription")
-                if not subscription:
-                    self._log(
-                        "warning",
-                        "User data validation failed: Home %s has no active subscription",
-                        home_id,
-                    )
-                    return False
-
-                price_info = subscription.get("priceInfo")
-                if not price_info:
-                    self._log(
-                        "warning",
-                        "User data validation failed: Home %s subscription has no priceInfo",
-                        home_id,
-                    )
-                    return False
-
-                current = price_info.get("current")
-                if not current:
-                    self._log(
-                        "warning",
-                        "User data validation failed: Home %s priceInfo has no current data",
-                        home_id,
-                    )
-                    return False
-
-                currency = current.get("currency")
-                if not currency:
-                    self._log(
-                        "warning",
-                        "User data validation failed: Home %s has no currency",
-                        home_id,
-                    )
-                    return False
-
-                break
-
-        if not home_found:
-            self._log("warning", "User data validation failed: Home %s not found in homes list", home_id)
-            return False
-
-        self._log("debug", "User data validation passed for home %s", home_id)
-        return True
-
-    async def update_user_data_if_needed(self, current_time: datetime) -> bool:
-        """
-        Update user data if needed (daily check).
-
-        Only accepts complete and valid data. If API returns incomplete data
-        (e.g., during maintenance), keeps existing cached data and retries later.
-
-        Returns:
-            True if user data was updated, False otherwise
-
-        """
-        if self._last_user_update is None or current_time - self._last_user_update >= self._user_update_interval:
-            try:
-                self._log("debug", "Updating user data")
-                user_data = await self.api.async_get_viewer_details()
-
-                # Validate before caching
-                if not self._validate_user_data(user_data, self.home_id):
-                    self._log(
-                        "warning",
-                        "Rejecting incomplete user data from API - keeping existing cached data",
-                    )
-                    return False  # Keep existing data, don't update timestamp
-
-                # Data is valid, cache it
-                self._cached_user_data = user_data
-                self._last_user_update = current_time
-                self._log("debug", "User data updated successfully")
-            except (
-                TibberPricesApiClientError,
-                TibberPricesApiClientCommunicationError,
-            ) as ex:
-                self._log("warning", "Failed to update user data: %s", ex)
-                return False  # Update failed
-            else:
-                return True  # User data was updated
-        return False  # No update needed
-
-    async def fetch_home_data(
-        self,
-        home_id: str,
-        current_time: datetime,
-        *,
-        include_tomorrow: bool = True,
-    ) -> tuple[dict[str, Any], bool]:
-        """
-        Fetch data for a single home via pool.
-
-        Args:
-            home_id: Home ID to fetch data for.
-            current_time: Current time for timestamp in result.
-            include_tomorrow: If True, request tomorrow's data too. If False,
-                             only request up to end of today.
-
-        Returns:
-            Tuple of (data_dict, api_called):
-            - data_dict: Dictionary with timestamp, home_id, price_info, currency.
-            - api_called: True if API was called to fetch missing data.
-
-        """
-        if not home_id:
-            self._log("warning", "No home ID provided - cannot fetch price data")
-            return (
-                {
-                    "timestamp": current_time,
-                    "home_id": "",
-                    "price_info": [],
-                    "currency": "EUR",
-                },
-                False,  # No API call made
-            )
-
-        # Ensure we have user_data before fetching price data
-        # This is critical for timezone-aware cursor calculation
-        if not self._cached_user_data:
-            self._log("info", "User data not cached, fetching before price data")
-            try:
-                user_data = await self.api.async_get_viewer_details()
-
-                # Validate data before accepting it (especially on initial setup)
-                if not self._validate_user_data(user_data, self.home_id):
-                    msg = "Received incomplete user data from API - cannot proceed with price fetching"
-                    self._log("error", msg)
-                    raise TibberPricesApiClientError(msg)  # noqa: TRY301
-
-                self._cached_user_data = user_data
-                self._last_user_update = current_time
-            except (
-                TibberPricesApiClientError,
-                TibberPricesApiClientCommunicationError,
-            ) as ex:
-                msg = f"Failed to fetch user data (required for price fetching): {ex}"
-                self._log("error", msg)
-                raise TibberPricesApiClientError(msg) from ex
-
-        # At this point, _cached_user_data is guaranteed to be not None (checked above)
-        if not self._cached_user_data:
-            msg = "User data unexpectedly None after fetch attempt"
-            raise TibberPricesApiClientError(msg)
-
-        # Retrieve price data via IntervalPool (single source of truth)
-        price_info, api_called = await self._fetch_via_pool(home_id, include_tomorrow=include_tomorrow)
-
-        # Extract currency for this home from user_data
-        currency = self._get_currency_for_home(home_id)
-
-        self._log(
-            "debug",
-            "Successfully fetched data for home %s (%d intervals, api_called=%s)",
-            home_id,
-            len(price_info),
-            api_called,
-        )
-
-        return (
-            {
-                "timestamp": current_time,
-                "home_id": home_id,
-                "price_info": price_info,
-                "currency": currency,
-            },
-            api_called,
-        )
-
-    async def _fetch_via_pool(
-        self,
-        home_id: str,
-        *,
-        include_tomorrow: bool = True,
-    ) -> tuple[list[dict[str, Any]], bool]:
-        """
-        Retrieve price data via IntervalPool.
-
-        The IntervalPool is the single source of truth for price data:
-        - Handles actual API calls to Tibber
-        - Manages deduplication and caching
-        - Provides intervals from day-before-yesterday to end-of-today/tomorrow
-
-        This method delegates to the Pool's get_sensor_data() which returns
-        all relevant intervals for sensor display.
-
-        Args:
-            home_id: Home ID (currently unused, Pool knows its home).
-            include_tomorrow: If True, request tomorrow's data too. If False,
-                             only request up to end of today. This prevents
-                             API spam before 13:00 when Tibber doesn't have
-                             tomorrow data yet.
-
-        Returns:
-            Tuple of (intervals, api_called):
-            - intervals: List of price interval dicts.
-            - api_called: True if API was called to fetch missing data.
-
-        """
-        # user_data is guaranteed by fetch_home_data(), but needed for type narrowing
-        if self._cached_user_data is None:
-            return [], False  # No data, no API call
-
-        self._log(
-            "debug",
-            "Retrieving price data for home %s via interval pool (include_tomorrow=%s)",
-            home_id,
-            include_tomorrow,
-        )
-        intervals, api_called = await self._interval_pool.get_sensor_data(
-            api_client=self.api,
-            user_data=self._cached_user_data,
-            include_tomorrow=include_tomorrow,
-        )
-
-        return intervals, api_called
-
-    def _get_currency_for_home(self, home_id: str) -> str:
-        """
-        Get currency for a specific home from cached user_data.
-
-        Note: The cached user_data is validated before storage, so if we have
-        cached data it should contain valid currency. This method extracts
-        the currency from the nested structure.
-
-        Returns:
-            Currency code (e.g., "EUR", "NOK", "SEK").
-
-        Raises:
-            TibberPricesApiClientError: If currency cannot be determined.
-
-        """
-        if not self._cached_user_data:
-            msg = "No user data cached - cannot determine currency"
-            self._log("error", msg)
-            raise TibberPricesApiClientError(msg)
-
-        viewer = self._cached_user_data.get("viewer", {})
-        homes = viewer.get("homes", [])
-
-        for home in homes:
-            if home.get("id") == home_id:
-                # Extract currency from nested structure
-                # Use 'or {}' to handle None values (homes without active subscription)
-                subscription = home.get("currentSubscription") or {}
-                price_info = subscription.get("priceInfo") or {}
-                current = price_info.get("current") or {}
-                currency = current.get("currency")
-
-                if not currency:
-                    # This should not happen if validation worked correctly
-                    msg = f"Home {home_id} has no active subscription - currency unavailable"
-                    self._log("error", msg)
-                    raise TibberPricesApiClientError(msg)
-
-                self._log("debug", "Extracted currency %s for home %s", currency, home_id)
-                return currency
-
-        # Home not found in cached data - data validation should have caught this
-        msg = f"Home {home_id} not found in user data - data validation failed"
-        self._log("error", msg)
-        raise TibberPricesApiClientError(msg)
-
-    def _check_home_exists(self, home_id: str) -> bool:
-        """
-        Check if a home ID exists in cached user data.
-
-        Args:
-            home_id: The home ID to check.
-
-        Returns:
-            True if home exists, False otherwise.
-
-        """
-        if not self._cached_user_data:
-            # No user data yet - assume home exists (will be checked on next update)
-            return True
-
-        viewer = self._cached_user_data.get("viewer", {})
-        homes = viewer.get("homes", [])
-
-        return any(home.get("id") == home_id for home in homes)
-
-    async def handle_main_entry_update(
-        self,
-        current_time: datetime,
-        home_id: str,
-        transform_fn: Callable[[dict[str, Any]], dict[str, Any]],
-        *,
-        current_price_info: list[dict[str, Any]] | None = None,
-    ) -> tuple[dict[str, Any], bool]:
-        """
-        Handle update for main entry - fetch data for this home.
-
-        The IntervalPool is the single source of truth for price data:
-        - It handles API fetching, deduplication, and caching internally
-        - We decide WHEN to fetch tomorrow data (after 13:00, if not already present)
-        - This prevents API spam before 13:00 when Tibber doesn't have tomorrow data
-
-        This method:
-        1. Updates user data if needed (daily)
-        2. Determines if tomorrow data should be requested
-        3. Fetches price data via IntervalPool
-        4. Transforms result for coordinator
-
-        Args:
-            current_time: Current time for update decisions.
-            home_id: Home ID to fetch data for.
-            transform_fn: Function to transform raw data for coordinator.
-            current_price_info: Current price intervals (from coordinator.data["priceInfo"]).
-                               Used to check if tomorrow data already exists.
-
-        Returns:
-            Tuple of (transformed_data, api_called):
-            - transformed_data: Transformed data dict for coordinator.
-            - api_called: True if API was called to fetch missing data.
-
-        """
-        # Update user data if needed (daily check)
-        user_data_updated = await self.update_user_data_if_needed(current_time)
-
-        # Check if this home still exists in user data after update
-        # This detects when a home was removed from the Tibber account
-        home_exists = self._check_home_exists(home_id)
-        if not home_exists:
-            self._log("warning", "Home ID %s not found in Tibber account", home_id)
-            # Return a special marker in the result that coordinator can check
-            result = transform_fn({})
-            result["_home_not_found"] = True  # Special marker for coordinator
-            return result, False  # No API call made (home doesn't exist)
-
-        # Determine if we should request tomorrow data
-        include_tomorrow = self.should_fetch_tomorrow_data(current_price_info)
-
-        # Fetch price data via IntervalPool
-        self._log(
-            "debug",
-            "Fetching price data for home %s via interval pool (include_tomorrow=%s)",
-            home_id,
-            include_tomorrow,
-        )
-        raw_data, api_called = await self.fetch_home_data(home_id, current_time, include_tomorrow=include_tomorrow)
-
-        # Parse timestamps immediately after fetch
-        raw_data = helpers.parse_all_timestamps(raw_data, time=self.time)
-
-        # Store user data cache (price data persisted by IntervalPool)
-        if user_data_updated:
-            await self.store_cache()
-
-        # Transform for main entry
-        return transform_fn(raw_data), api_called
-
-    async def handle_api_error(
-        self,
-        error: Exception,
-    ) -> None:
-        """
-        Handle API errors - re-raise appropriate exceptions.
-
-        Note: With IntervalPool as source of truth, there's no local price cache
-        to fall back to. The Pool has its own persistence, so on next update
-        it will use its cached intervals if API is unavailable.
-        """
-        if isinstance(error, TibberPricesApiClientAuthenticationError):
-            msg = "Invalid access token"
-            raise ConfigEntryAuthFailed(msg) from error
-
-        msg = f"Error communicating with API: {error}"
-        raise UpdateFailed(msg) from error
-
-    @property
-    def cached_user_data(self) -> dict[str, Any] | None:
-        """Get cached user data."""
-        return self._cached_user_data
-
-    def has_tomorrow_data(self, price_info: list[dict[str, Any]]) -> bool:
-        """
-        Check if tomorrow's price data is available.
-
-        Args:
-            price_info: List of price intervals from coordinator data.
-
-        Returns:
-            True if at least one interval from tomorrow is present.
-
-        """
-        if not price_info:
-            return False
-
-        # Get tomorrow's date
-        now = self.time.now()
-        tomorrow = (self.time.as_local(now) + timedelta(days=1)).date()
-
-        # Check if any interval is from tomorrow
-        for interval in price_info:
-            if "startsAt" not in interval:
-                continue
-
-            # startsAt is already a datetime object after _transform_data()
-            interval_time = interval["startsAt"]
-            if isinstance(interval_time, str):
-                # Fallback: parse if still string (shouldn't happen with transformed data)
-                interval_time = self.time.parse_datetime(interval_time)
-
-            if interval_time and self.time.as_local(interval_time).date() == tomorrow:
-                return True
-
-        return False

custom_components/tibber_prices/coordinator/repairs.py

@@ -1,228 +0,0 @@
-"""
-Repair issue management for Tibber Prices integration.
-
-This module handles creation and cleanup of repair issues that notify users
-about problems requiring attention in the Home Assistant UI.
-
-Repair Types:
-1. Tomorrow Data Missing - Warns when tomorrow's price data is unavailable after 18:00
-2. Persistent Rate Limits - Warns when API rate limiting persists after multiple errors
-3. Home Not Found - Warns when a home no longer exists in the Tibber account
-"""
-
-from __future__ import annotations
-
-import logging
-from typing import TYPE_CHECKING
-
-from custom_components.tibber_prices.const import DOMAIN
-from homeassistant.helpers import issue_registry as ir
-
-if TYPE_CHECKING:
-    from datetime import datetime
-
-    from homeassistant.core import HomeAssistant
-
-_LOGGER = logging.getLogger(__name__)
-
-# Repair issue tracking thresholds
-TOMORROW_DATA_WARNING_HOUR = 18  # Warn after 18:00 if tomorrow data missing
-RATE_LIMIT_WARNING_THRESHOLD = 3  # Warn after 3 consecutive rate limit errors
-
-
-class TibberPricesRepairManager:
-    """Manage repair issues for Tibber Prices integration."""
-
-    def __init__(self, hass: HomeAssistant, entry_id: str, home_name: str) -> None:
-        """
-        Initialize repair manager.
-
-        Args:
-            hass: Home Assistant instance
-            entry_id: Config entry ID for this home
-            home_name: Display name of the home (for user-friendly messages)
-
-        """
-        self._hass = hass
-        self._entry_id = entry_id
-        self._home_name = home_name
-
-        # Track consecutive rate limit errors
-        self._rate_limit_error_count = 0
-
-        # Track if repairs are currently active
-        self._tomorrow_data_repair_active = False
-        self._rate_limit_repair_active = False
-        self._home_not_found_repair_active = False
-
-    async def check_tomorrow_data_availability(
-        self,
-        has_tomorrow_data: bool,  # noqa: FBT001 - Clear meaning in context
-        current_time: datetime,
-    ) -> None:
-        """
-        Check if tomorrow data is available and create/clear repair as needed.
-
-        Creates repair if:
-        - Current hour >= 18:00 (after expected data availability)
-        - Tomorrow's data is missing
-
-        Clears repair if:
-        - Tomorrow's data is now available
-
-        Args:
-            has_tomorrow_data: Whether tomorrow's data is available
-            current_time: Current local datetime for hour check
-
-        """
-        should_warn = current_time.hour >= TOMORROW_DATA_WARNING_HOUR and not has_tomorrow_data
-
-        if should_warn and not self._tomorrow_data_repair_active:
-            await self._create_tomorrow_data_repair()
-        elif not should_warn and self._tomorrow_data_repair_active:
-            await self._clear_tomorrow_data_repair()
-
-    async def track_rate_limit_error(self) -> None:
-        """
-        Track rate limit error and create repair if threshold exceeded.
-
-        Increments rate limit error counter and creates repair issue
-        if threshold (3 consecutive errors) is reached.
-        """
-        self._rate_limit_error_count += 1
-
-        if self._rate_limit_error_count >= RATE_LIMIT_WARNING_THRESHOLD and not self._rate_limit_repair_active:
-            await self._create_rate_limit_repair()
-
-    async def clear_rate_limit_tracking(self) -> None:
-        """
-        Clear rate limit error tracking after successful API call.
-
-        Resets counter and clears any active repair issue.
-        """
-        self._rate_limit_error_count = min(self._rate_limit_error_count, 0)
-
-        if self._rate_limit_repair_active:
-            await self._clear_rate_limit_repair()
-
-    async def create_home_not_found_repair(self) -> None:
-        """
-        Create repair for home no longer found in Tibber account.
-
-        This indicates the home was deleted from the user's Tibber account
-        but the config entry still exists in Home Assistant.
-        """
-        if self._home_not_found_repair_active:
-            return
-
-        _LOGGER.warning(
-            "Home '%s' not found in Tibber account - creating repair issue",
-            self._home_name,
-        )
-
-        ir.async_create_issue(
-            self._hass,
-            DOMAIN,
-            f"home_not_found_{self._entry_id}",
-            is_fixable=True,
-            severity=ir.IssueSeverity.ERROR,
-            translation_key="home_not_found",
-            translation_placeholders={
-                "home_name": self._home_name,
-                "entry_id": self._entry_id,
-            },
-        )
-        self._home_not_found_repair_active = True
-
-    async def clear_home_not_found_repair(self) -> None:
-        """Clear home not found repair (home is available again or entry removed)."""
-        if not self._home_not_found_repair_active:
-            return
-
-        _LOGGER.debug("Clearing home not found repair for '%s'", self._home_name)
-
-        ir.async_delete_issue(
-            self._hass,
-            DOMAIN,
-            f"home_not_found_{self._entry_id}",
-        )
-        self._home_not_found_repair_active = False
-
-    async def clear_all_repairs(self) -> None:
-        """
-        Clear all active repair issues.
-
-        Called during coordinator shutdown or entry removal.
-        """
-        if self._tomorrow_data_repair_active:
-            await self._clear_tomorrow_data_repair()
-        if self._rate_limit_repair_active:
-            await self._clear_rate_limit_repair()
-        if self._home_not_found_repair_active:
-            await self.clear_home_not_found_repair()
-
-    async def _create_tomorrow_data_repair(self) -> None:
-        """Create repair issue for missing tomorrow data."""
-        _LOGGER.warning(
-            "Tomorrow's price data missing after %d:00 for home '%s' - creating repair issue",
-            TOMORROW_DATA_WARNING_HOUR,
-            self._home_name,
-        )
-
-        ir.async_create_issue(
-            self._hass,
-            DOMAIN,
-            f"tomorrow_data_missing_{self._entry_id}",
-            is_fixable=False,
-            severity=ir.IssueSeverity.WARNING,
-            translation_key="tomorrow_data_missing",
-            translation_placeholders={
-                "home_name": self._home_name,
-                "warning_hour": str(TOMORROW_DATA_WARNING_HOUR),
-            },
-        )
-        self._tomorrow_data_repair_active = True
-
-    async def _clear_tomorrow_data_repair(self) -> None:
-        """Clear tomorrow data repair issue."""
-        _LOGGER.debug("Tomorrow's data now available for '%s' - clearing repair issue", self._home_name)
-
-        ir.async_delete_issue(
-            self._hass,
-            DOMAIN,
-            f"tomorrow_data_missing_{self._entry_id}",
-        )
-        self._tomorrow_data_repair_active = False
-
-    async def _create_rate_limit_repair(self) -> None:
-        """Create repair issue for persistent rate limiting."""
-        _LOGGER.warning(
-            "Persistent API rate limiting detected for home '%s' (%d consecutive errors) - creating repair issue",
-            self._home_name,
-            self._rate_limit_error_count,
-        )
-
-        ir.async_create_issue(
-            self._hass,
-            DOMAIN,
-            f"rate_limit_exceeded_{self._entry_id}",
-            is_fixable=False,
-            severity=ir.IssueSeverity.WARNING,
-            translation_key="rate_limit_exceeded",
-            translation_placeholders={
-                "home_name": self._home_name,
-                "error_count": str(self._rate_limit_error_count),
-            },
-        )
-        self._rate_limit_repair_active = True
-
-    async def _clear_rate_limit_repair(self) -> None:
-        """Clear rate limit repair issue."""
-        _LOGGER.debug("Rate limiting resolved for '%s' - clearing repair issue", self._home_name)
-
-        ir.async_delete_issue(
-            self._hass,
-            DOMAIN,
-            f"rate_limit_exceeded_{self._entry_id}",
-        )
-        self._rate_limit_repair_active = False

custom_components/tibber_prices/coordinator/time_service.py

@@ -12,33 +12,6 @@ All datetime operations MUST go through TimeService to ensure:
 - Proper timezone handling (local time, not UTC)
 - Testability (mock time in one place)
 - Future time-travel feature support
-
-TIMER ARCHITECTURE:
-
-This integration uses three distinct timer mechanisms:
-
-1. **Timer #1: API Polling (DataUpdateCoordinator)**
-   - Runs every 15 minutes at a RANDOM offset (e.g., 10:04:37, 10:19:37, 10:34:37)
-   - Offset determined by when last API call completed
-   - Tracked via _last_coordinator_update for next poll prediction
-   - NO tolerance needed - offset variation is INTENTIONAL
-   - Purpose: Spread API load, avoid thundering herd problem
-
-2. **Timer #2: Entity Updates (quarter-hour boundaries)**
-   - Must trigger at EXACT boundaries (00, 15, 30, 45 minutes)
-   - Uses _BOUNDARY_TOLERANCE_SECONDS for HA scheduling jitter correction
-   - Scheduled via async_track_utc_time_change(minute=[0,15,30,45], second=0)
-   - If HA triggers at 15:00:01 → round to 15:00:00 (within ±2s tolerance)
-   - Purpose: Entity state updates reflect correct quarter-hour interval
-
-3. **Timer #3: Timing Sensors (30-second boundaries)**
-   - Must trigger at EXACT boundaries (0, 30 seconds)
-   - Uses _BOUNDARY_TOLERANCE_SECONDS for HA scheduling jitter correction
-   - Scheduled via async_track_utc_time_change(second=[0,30])
-   - Purpose: Update countdown/time-to sensors
-
-CRITICAL: The tolerance is ONLY for Timer #2 and #3 to correct HA's
-scheduling delays. It is NOT used for Timer #1's offset tracking.
 """
 
 from __future__ import annotations
@@ -69,13 +42,6 @@ _INTERVALS_PER_HOUR = 60 // _DEFAULT_INTERVAL_MINUTES  # 4
 _INTERVALS_PER_DAY = 24 * _INTERVALS_PER_HOUR  # 96
 
 # Rounding tolerance for boundary detection (±2 seconds)
-# This handles Home Assistant's scheduling jitter for Timer #2 (entity updates)
-# and Timer #3 (timing sensors). When HA schedules a callback for exactly
-# 15:00:00 but actually triggers it at 15:00:01, this tolerance ensures we
-# still recognize it as the 15:00:00 boundary.
-#
-# NOT used for Timer #1 (API polling), which intentionally runs at random
-# offsets determined by last API call completion time.
 _BOUNDARY_TOLERANCE_SECONDS = 2
 
 
@@ -532,17 +498,16 @@ class TibberPricesTimeService:
             rounded_seconds = interval_start_seconds
         elif distance_to_next <= _BOUNDARY_TOLERANCE_SECONDS:
             # Near next interval start → use it
-            # CRITICAL: If rounding to next interval and it wraps to midnight (index 0),
-            # we need to increment to next day, not stay on same day!
-            if next_interval_index == 0:
-                # Rounding to midnight of NEXT day
-                return (target + timedelta(days=1)).replace(hour=0, minute=0, second=0, microsecond=0)
             rounded_seconds = next_interval_start_seconds
         else:
             # Not near any boundary → floor to current interval
             rounded_seconds = interval_start_seconds
 
-        # Build rounded datetime (no midnight wrap needed here - handled above)
+        # Handle midnight wrap
+        if rounded_seconds >= 24 * 3600:
+            rounded_seconds = 0
+
+        # Build rounded datetime
         hours = int(rounded_seconds // 3600)
         minutes = int((rounded_seconds % 3600) // 60)
 

custom_components/tibber_prices/custom_translations/de.json

@@ -1,20 +1,7 @@
 {
     "apexcharts": {
         "title_rating_level": "Preisphasen Tagesverlauf",
-        "title_level": "Preisniveau",
+        "title_level": "Preisniveau"
-        "hourly_suffix": "(Ø stündlich)",
-        "best_price_period_name": "Bestpreis-Zeitraum",
-        "peak_price_period_name": "Spitzenpreis-Zeitraum",
-        "notification": {
-            "metadata_sensor_unavailable": {
-                "title": "Tibber Prices: ApexCharts YAML mit eingeschränkter Funktionalität generiert",
-                "message": "Du hast gerade eine ApexCharts-Card-Konfiguration über die Entwicklerwerkzeuge generiert. Der Chart-Metadaten-Sensor ist aktuell deaktiviert, daher zeigt das generierte YAML nur **Basisfunktionalität** (Auto-Skalierung, fester Gradient bei 50%).\n\n**Für volle Funktionalität** (optimierte Skalierung, dynamische Verlaufsfarben):\n1. [Tibber Prices Integration öffnen](https://my.home-assistant.io/redirect/integration/?domain=tibber_prices)\n2. Aktiviere den 'Chart Metadata' Sensor\n3. **Generiere das YAML erneut** über die Entwicklerwerkzeuge\n4. **Ersetze den alten YAML-Code** in deinem Dashboard durch die neue Version\n\n⚠️ Nur den Sensor zu aktivieren reicht nicht - du musst das YAML neu generieren und ersetzen!"
-            },
-            "missing_cards": {
-                "title": "Tibber Prices: ApexCharts YAML kann nicht verwendet werden",
-                "message": "Du hast gerade eine ApexCharts-Card-Konfiguration über die Entwicklerwerkzeuge generiert, aber das generierte YAML **funktioniert nicht**, weil erforderliche Custom Cards fehlen.\n\n**Fehlende Cards:**\n{cards}\n\n**Um das generierte YAML zu nutzen:**\n1. Klicke auf die obigen Links, um die fehlenden Cards über HACS zu installieren\n2. Starte Home Assistant neu (manchmal erforderlich)\n3. **Generiere das YAML erneut** über die Entwicklerwerkzeuge\n4. Füge das YAML zu deinem Dashboard hinzu\n\n⚠️ Der aktuelle YAML-Code funktioniert nicht, bis alle Cards installiert sind!"
-            }
-        }
     },
     "sensor": {
         "current_interval_price": {
@@ -22,7 +9,7 @@
             "long_description": "Zeigt den aktuellen Preis pro kWh von deinem Tibber-Abonnement an",
             "usage_tips": "Nutze dies, um Preise zu verfolgen oder Automatisierungen zu erstellen, die bei günstigem Strom ausgeführt werden"
         },
-        "current_interval_price_base": {
+        "current_interval_price_major": {
             "description": "Aktueller Strompreis in Hauptwährung (EUR/kWh, NOK/kWh, etc.) für Energie-Dashboard",
             "long_description": "Zeigt den aktuellen Preis pro kWh in Hauptwährungseinheiten an (z.B. EUR/kWh statt ct/kWh, NOK/kWh statt øre/kWh). Dieser Sensor ist speziell für die Verwendung mit dem Energie-Dashboard von Home Assistant konzipiert, das Preise in Standard-Währungseinheiten benötigt.",
             "usage_tips": "Verwende diesen Sensor beim Konfigurieren des Energie-Dashboards unter Einstellungen → Dashboards → Energie. Wähle diesen Sensor als 'Entität mit dem aktuellen Preis' aus, um deine Energiekosten automatisch zu berechnen. Das Energie-Dashboard multipliziert deinen Energieverbrauch (kWh) mit diesem Preis, um die Gesamtkosten anzuzeigen."
@@ -58,9 +45,9 @@
             "usage_tips": "Nutze dies, um den Betrieb von Geräten während Spitzenpreiszeiten zu vermeiden"
         },
         "average_price_today": {
-            "description": "Der typische Strompreis für heute pro kWh (konfigurierbares Anzeigeformat)",
+            "description": "Der durchschnittliche Strompreis für heute pro kWh",
-            "long_description": "Zeigt den typischen Preis pro kWh für heute. **Standardmäßig zeigt der Status den Median** (resistent gegen extreme Preisspitzen, zeigt was du generell erwarten kannst). Du kannst dies in den Integrationsoptionen ändern, um stattdessen das arithmetische Mittel anzuzeigen. Der alternative Wert ist immer als Attribut `price_mean` oder `price_median` für Automatisierungen verfügbar.",
+            "long_description": "Zeigt den durchschnittlichen Preis pro kWh für den aktuellen Tag von deinem Tibber-Abonnement an",
-            "usage_tips": "Nutze den Status-Wert für die Anzeige. Für exakte Kostenberechnungen in Automatisierungen nutze: {{ state_attr('sensor.average_price_today', 'price_mean') }}"
+            "usage_tips": "Nutze dies als Grundlage für den Vergleich mit aktuellen Preisen"
         },
         "lowest_price_tomorrow": {
             "description": "Der niedrigste Strompreis für morgen pro kWh",
@@ -73,9 +60,9 @@
             "usage_tips": "Nutze dies, um den Betrieb von Geräten während der teuersten Stunden morgen zu vermeiden. Plane nicht-essentielle Lasten außerhalb dieser Spitzenpreiszeiten."
         },
         "average_price_tomorrow": {
-            "description": "Der typische Strompreis für morgen pro kWh (konfigurierbares Anzeigeformat)",
+            "description": "Der durchschnittliche Strompreis für morgen pro kWh",
-            "long_description": "Zeigt den typischen Preis pro kWh für morgen. **Standardmäßig zeigt der Status den Median** (resistent gegen extreme Preisspitzen). Du kannst dies in den Integrationsoptionen ändern, um stattdessen das arithmetische Mittel anzuzeigen. Der alternative Wert ist als Attribut verfügbar. Dieser Sensor wird nicht verfügbar, bis die Preise für morgen von Tibber veröffentlicht werden (typischerweise zwischen 13:00 und 14:00 Uhr MEZ).",
+            "long_description": "Zeigt den durchschnittlichen Preis pro kWh für den morgigen Tag von deinem Tibber-Abonnement an. Dieser Sensor wird nicht verfügbar, bis die Preise für morgen von Tibber veröffentlicht werden (typischerweise zwischen 13:00 und 14:00 Uhr MEZ).",
-            "usage_tips": "Nutze den Status-Wert für Anzeige und schnelle Vergleiche. Für Automatisierungen, die exakte Kostenberechnungen benötigen, nutze das Attribut `price_mean`: {{ state_attr('sensor.average_price_tomorrow', 'price_mean') }}"
+            "usage_tips": "Nutze dies als Grundlinie für den Vergleich mit den morgigen Preisen und zur Verbrauchsplanung. Vergleiche mit dem heutigen Durchschnitt, um zu sehen, ob morgen insgesamt teurer oder günstiger wird."
         },
         "yesterday_price_level": {
             "description": "Aggregiertes Preisniveau für gestern",
@@ -108,14 +95,14 @@
             "usage_tips": "Nutze dies, um den morgigen Energieverbrauch basierend auf deinen persönlichen Preisschwellenwerten zu planen. Vergleiche mit heute, um zu entscheiden, ob du den Verbrauch auf morgen verschieben oder heute nutzen solltest."
         },
         "trailing_price_average": {
-            "description": "Der typische Strompreis der letzten 24 Stunden pro kWh (konfigurierbares Anzeigeformat)",
+            "description": "Der durchschnittliche Strompreis für die letzten 24 Stunden pro kWh",
-            "long_description": "Zeigt den typischen Preis pro kWh der letzten 24 Stunden. **Standardmäßig zeigt der Status den Median** (resistent gegen extreme Spitzen, zeigt welches Preisniveau typisch war). Du kannst dies in den Integrationsoptionen ändern, um stattdessen das arithmetische Mittel anzuzeigen. Der alternative Wert ist als Attribut verfügbar. Wird alle 15 Minuten aktualisiert.",
+            "long_description": "Zeigt den durchschnittlichen Preis pro kWh berechnet aus den letzten 24 Stunden (nachlaufender Durchschnitt) von deinem Tibber-Abonnement an. Dies bietet einen gleitenden Durchschnitt, der alle 15 Minuten basierend auf historischen Daten aktualisiert wird.",
-            "usage_tips": "Nutze den Status-Wert, um das typische aktuelle Preisniveau zu sehen. Für Kostenberechnungen nutze: {{ state_attr('sensor.trailing_price_average', 'price_mean') }}"
+            "usage_tips": "Nutze dies, um aktuelle Preise mit den jüngsten Trends zu vergleichen. Ein aktueller Preis deutlich über diesem Durchschnitt kann ein guter Zeitpunkt sein, um den Verbrauch zu reduzieren."
         },
         "leading_price_average": {
-            "description": "Der typische Strompreis für die nächsten 24 Stunden pro kWh (konfigurierbares Anzeigeformat)",
+            "description": "Der durchschnittliche Strompreis für die nächsten 24 Stunden pro kWh",
-            "long_description": "Zeigt den typischen Preis pro kWh für die nächsten 24 Stunden. **Standardmäßig zeigt der Status den Median** (resistent gegen extreme Spitzen, zeigt welches Preisniveau zu erwarten ist). Du kannst dies in den Integrationsoptionen ändern, um stattdessen das arithmetische Mittel anzuzeigen. Der alternative Wert ist als Attribut verfügbar.",
+            "long_description": "Zeigt den durchschnittlichen Preis pro kWh berechnet aus den nächsten 24 Stunden (vorlaufender Durchschnitt) von deinem Tibber-Abonnement an. Dies bietet einen vorausschauenden Durchschnitt basierend auf verfügbaren Prognosedaten.",
-            "usage_tips": "Nutze den Status-Wert, um das typische kommende Preisniveau zu sehen. Für Kostenberechnungen nutze: {{ state_attr('sensor.leading_price_average', 'price_mean') }}"
+            "usage_tips": "Nutze dies zur Energieverbrauchsplanung. Wenn der aktuelle Preis unter dem vorlaufenden Durchschnitt liegt, kann es ein guter Zeitpunkt sein, um energieintensive Geräte zu betreiben."
         },
         "trailing_price_min": {
             "description": "Der niedrigste Strompreis für die letzten 24 Stunden pro kWh",
@@ -289,27 +276,27 @@
         },
         "data_timestamp": {
             "description": "Zeitstempel des letzten verfügbaren Preisintervalls",
-            "long_description": "Zeigt den Zeitstempel des letzten verfügbaren Preisdatenintervalls von deinem Tibber-Abonnement"
+            "long_description": "Zeigt den Zeitstempel des letzten verfügbaren Preisdatenintervalls von Ihrem Tibber-Abonnement"
         },
         "today_volatility": {
-            "description": "Wie stark sich die Strompreise heute verändern",
+            "description": "Preisvolatilitätsklassifizierung für heute",
-            "long_description": "Zeigt, ob die heutigen Preise stabil bleiben oder stark schwanken. Niedrige Volatilität bedeutet recht konstante Preise – Timing ist kaum wichtig. Hohe Volatilität bedeutet spürbare Preisunterschiede über den Tag – gute Chance, den Verbrauch auf günstigere Zeiten zu verschieben. `price_coefficient_variation_%` zeigt den Prozentwert, `price_spread` die absolute Preisspanne.",
+            "long_description": "Zeigt, wie stark die Strompreise im Laufe des heutigen Tages variieren, basierend auf der Spannweite (Differenz zwischen höchstem und niedrigstem Preis). Klassifizierung: NIEDRIG = Spannweite < 5ct, MODERAT = 5-15ct, HOCH = 15-30ct, SEHR HOCH = >30ct.",
-            "usage_tips": "Nutze dies, um zu entscheiden, ob Optimierung sich lohnt. Bei niedriger Volatilität kannst du Geräte jederzeit laufen lassen. Bei hoher Volatilität sparst du spürbar, wenn du Best-Price-Perioden nutzt."
+            "usage_tips": "Verwenden Sie dies, um zu entscheiden, ob preisbasierte Optimierung lohnenswert ist. Zum Beispiel lohnt sich bei einer Balkonbatterie mit 15% Effizienzverlusten die Optimierung nur, wenn die Volatilität mindestens MODERAT ist. Erstellen Sie Automatisierungen, die die Volatilität prüfen, bevor Lade-/Entladezyklen geplant werden."
         },
         "tomorrow_volatility": {
-            "description": "Wie stark sich die Strompreise morgen verändern werden",
+            "description": "Preisvolatilitätsklassifizierung für morgen",
-            "long_description": "Zeigt, ob die Preise morgen stabil bleiben oder stark schwanken. Verfügbar, sobald die morgigen Daten veröffentlicht sind (typischerweise 13:00–14:00 MEZ). Niedrige Volatilität bedeutet recht konstante Preise – Timing ist nicht kritisch. Hohe Volatilität bedeutet deutliche Preisunterschiede über den Tag – gute Gelegenheit, energieintensive Aufgaben zu planen. `price_coefficient_variation_%` zeigt den Prozentwert, `price_spread` die absolute Preisspanne.",
+            "long_description": "Zeigt, wie stark die Strompreise im Laufe des morgigen Tages variieren werden, basierend auf der Spannweite (Differenz zwischen höchstem und niedrigstem Preis). Wird nicht verfügbar, bis morgige Daten veröffentlicht sind (typischerweise 13:00-14:00 MEZ).",
-            "usage_tips": "Nutze dies für die Planung des morgigen Energieverbrauchs. Hohe Volatilität? Plane flexible Lasten in Best-Price-Perioden. Niedrige Volatilität? Lass Geräte laufen, wann es dir passt."
+            "usage_tips": "Verwenden Sie dies zur Vorausplanung des morgigen Energieverbrauchs. Bei HOHER oder SEHR HOHER Volatilität morgen lohnt sich die Optimierung des Energieverbrauchs. Bei NIEDRIGER Volatilität können Sie Geräte jederzeit ohne wesentliche Kostenunterschiede betreiben."
         },
         "next_24h_volatility": {
-            "description": "Wie stark sich die Preise in den nächsten 24 Stunden verändern",
+            "description": "Preisvolatilitätsklassifizierung für die rollierenden nächsten 24 Stunden",
-            "long_description": "Zeigt die Preisvolatilität für ein rollierendes 24-Stunden-Fenster ab jetzt (aktualisiert alle 15 Minuten). Niedrige Volatilität bedeutet recht konstante Preise. Hohe Volatilität bedeutet spürbare Preisschwankungen und damit Chancen zur Optimierung. Im Unterschied zu Heute/Morgen-Sensoren überschreitet dieser Tagesgrenzen und liefert eine durchgängige Vorhersage. `price_coefficient_variation_%` zeigt den Prozentwert, `price_spread` die absolute Preisspanne.",
+            "long_description": "Zeigt, wie stark die Strompreise in den nächsten 24 Stunden ab jetzt variieren (rollierendes Fenster). Dies überschreitet Tagesgrenzen und aktualisiert sich alle 15 Minuten, wodurch eine vorausschauende Volatilitätsbewertung unabhängig von Kalendertagen bereitgestellt wird.",
-            "usage_tips": "Am besten für Entscheidungen in Echtzeit. Nutze dies für Batterieladestrategien oder andere flexible Lasten, die über Mitternacht laufen könnten. Bietet eine konsistente 24h-Perspektive unabhängig vom Kalendertag."
+            "usage_tips": "Bester Sensor für Echtzeitoptimierungsentscheidungen. Im Gegensatz zu Heute/Morgen-Sensoren, die um Mitternacht wechseln, bietet dies eine kontinuierliche 24h-Volatilitätsbewertung. Verwenden Sie dies für Batterielade-Strategien, die Tagesgrenzen überschreiten."
         },
         "today_tomorrow_volatility": {
-            "description": "Kombinierte Preisvolatilität für heute und morgen",
+            "description": "Kombinierte Preisvolatilitätsklassifizierung für heute und morgen",
-            "long_description": "Zeigt die Gesamtvolatilität, wenn heute und morgen gemeinsam betrachtet werden (sobald die morgigen Daten verfügbar sind). Zeigt, ob über die Tagesgrenze hinweg deutliche Preisunterschiede bestehen. Fällt auf nur-heute zurück, wenn morgige Daten noch fehlen. Hilfreich für mehrtägige Optimierung. `price_coefficient_variation_%` zeigt den Prozentwert, `price_spread` die absolute Preisspanne.",
+            "long_description": "Zeigt die Volatilität über heute und morgen zusammen (wenn morgige Daten verfügbar sind). Bietet eine erweiterte Ansicht der Preisvariation über bis zu 48 Stunden. Fällt auf Nur-Heute zurück, wenn morgige Daten noch nicht verfügbar sind.",
-            "usage_tips": "Nutze dies für Aufgaben, die sich über mehrere Tage erstrecken. Prüfe, ob die Preisunterschiede groß genug für eine Planung sind. Die einzelnen Tages-Sensoren zeigen die Beiträge pro Tag, falls du mehr Details brauchst."
+            "usage_tips": "Verwenden Sie dies für Mehrtagsplanung und um zu verstehen, ob Preismöglichkeiten über die Tagesgrenze hinweg bestehen. Die Attribute 'today_volatility' und 'tomorrow_volatility' zeigen individuelle Tagesbeiträge. Nützlich für die Planung von Ladesitzungen, die Mitternacht überschreiten könnten."
         },
         "data_lifecycle_status": {
             "description": "Aktueller Status des Preisdaten-Lebenszyklus und der Zwischenspeicherung",
@@ -322,14 +309,14 @@
             "usage_tips": "Nutze dies, um einen Countdown wie 'Günstiger Zeitraum endet in 2 Stunden' (wenn aktiv) oder 'Nächster günstiger Zeitraum endet um 14:00' (wenn inaktiv) anzuzeigen. Home Assistant zeigt automatisch relative Zeit für Zeitstempel-Sensoren an."
         },
         "best_price_period_duration": {
-            "description": "Gesamtlänge des aktuellen oder nächsten günstigen Zeitraums",
+            "description": "Gesamtlänge des aktuellen oder nächsten günstigen Zeitraums in Minuten",
-            "long_description": "Zeigt, wie lange der günstige Zeitraum insgesamt dauert. Der State wird in Stunden angezeigt (z. B. 1,5 h) für eine einfache Lesbarkeit in der UI, während das Attribut `period_duration_minutes` denselben Wert in Minuten bereitstellt (z. B. 90) für Automationen. Während eines aktiven Zeitraums zeigt dies die Dauer des aktuellen Zeitraums. Wenn kein Zeitraum aktiv ist, zeigt dies die Dauer des nächsten kommenden Zeitraums. Gibt nur 'Unbekannt' zurück, wenn keine Zeiträume ermittelt wurden.",
+            "long_description": "Zeigt, wie lange der günstige Zeitraum insgesamt dauert. Während eines aktiven Zeitraums zeigt dies die Dauer des aktuellen Zeitraums. Wenn kein Zeitraum aktiv ist, zeigt dies die Dauer des nächsten kommenden Zeitraums. Gibt nur 'Unbekannt' zurück, wenn keine Zeiträume ermittelt wurden.",
-            "usage_tips": "Für Anzeige: State-Wert (Stunden) in Dashboards nutzen. Für Automationen: Attribut `period_duration_minutes` verwenden, um zu prüfen, ob genug Zeit für langläufige Geräte ist (z. B. 'Wenn period_duration_minutes >= 90, starte Waschmaschine')."
+            "usage_tips": "Nützlich für Planung: 'Der nächste günstige Zeitraum dauert 90 Minuten' oder 'Der aktuelle günstige Zeitraum ist 120 Minuten lang'. Kombiniere mit remaining_minutes, um zu berechnen, wann langlaufende Geräte gestartet werden sollten."
         },
         "best_price_remaining_minutes": {
-            "description": "Verbleibende Zeit im aktuellen günstigen Zeitraum",
+            "description": "Verbleibende Minuten im aktuellen günstigen Zeitraum (0 wenn inaktiv)",
-            "long_description": "Zeigt, wie viel Zeit im aktuellen günstigen Zeitraum noch verbleibt. Der State wird in Stunden angezeigt (z. B. 0,5 h) für eine einfache Lesbarkeit, während das Attribut `remaining_minutes` Minuten bereitstellt (z. B. 30) für Automationslogik. Gibt 0 zurück, wenn kein Zeitraum aktiv ist. Aktualisiert sich jede Minute. Prüfe binary_sensor.best_price_period, um zu sehen, ob ein Zeitraum aktuell aktiv ist.",
+            "long_description": "Zeigt, wie viele Minuten im aktuellen günstigen Zeitraum noch verbleiben. Gibt 0 zurück, wenn kein Zeitraum aktiv ist. Aktualisiert sich jede Minute. Prüfe binary_sensor.best_price_period, um zu sehen, ob ein Zeitraum aktuell aktiv ist.",
-            "usage_tips": "Für Automationen: Attribut `remaining_minutes` mit numerischen Vergleichen nutzen wie 'Wenn remaining_minutes > 0 UND remaining_minutes < 30, starte Waschmaschine jetzt'. Der Wert 0 macht es einfach zu prüfen, ob ein Zeitraum aktiv ist (Wert > 0) oder nicht (Wert = 0)."
+            "usage_tips": "Perfekt für Automatisierungen: 'Wenn remaining_minutes > 0 UND remaining_minutes < 30, starte Waschmaschine jetzt'. Der Wert 0 macht es einfach zu prüfen, ob ein Zeitraum aktiv ist (Wert > 0) oder nicht (Wert = 0)."
         },
         "best_price_progress": {
             "description": "Fortschritt durch aktuellen günstigen Zeitraum (0% wenn inaktiv)",
@@ -342,9 +329,9 @@
             "usage_tips": "Immer nützlich für Vorausplanung: 'Nächster günstiger Zeitraum startet in 3 Stunden' (egal, ob du gerade in einem Zeitraum bist oder nicht). Kombiniere mit Automatisierungen: 'Wenn nächste Startzeit in 10 Minuten ist, sende Benachrichtigung zur Vorbereitung der Waschmaschine'."
         },
         "best_price_next_in_minutes": {
-            "description": "Zeit bis zum nächsten günstigen Zeitraum",
+            "description": "Minuten bis nächster günstiger Zeitraum startet (0 beim Übergang)",
-            "long_description": "Zeigt, wie lange es bis zum nächsten günstigen Zeitraum dauert. Der State wird in Stunden angezeigt (z. B. 2,25 h) für Dashboards, während das Attribut `next_in_minutes` Minuten bereitstellt (z. B. 135) für Automationsbedingungen. Während eines aktiven Zeitraums zeigt dies die Zeit bis zum Zeitraum nach dem aktuellen. Gibt 0 während kurzer Übergangsphasen zurück. Aktualisiert sich jede Minute.",
+            "long_description": "Zeigt Minuten bis der nächste günstige Zeitraum startet. Während eines aktiven Zeitraums zeigt dies die Zeit bis zum Zeitraum nach dem aktuellen. Gibt 0 während kurzer Übergangsphasen zurück. Aktualisiert sich jede Minute.",
-            "usage_tips": "Für Automationen: Attribut `next_in_minutes` nutzen wie 'Wenn next_in_minutes > 0 UND next_in_minutes < 15, warte, bevor du die Geschirrspülmaschine startest'. Wert > 0 zeigt immer an, dass ein zukünftiger Zeitraum geplant ist."
+            "usage_tips": "Perfekt für 'warte bis günstiger Zeitraum' Automatisierungen: 'Wenn next_in_minutes > 0 UND next_in_minutes < 15, warte, bevor du die Geschirrspülmaschine startest'. Wert > 0 zeigt immer an, dass ein zukünftiger Zeitraum geplant ist."
         },
         "peak_price_end_time": {
             "description": "Wann der aktuelle oder nächste teure Zeitraum endet",
@@ -352,14 +339,14 @@
             "usage_tips": "Nutze dies, um 'Teurer Zeitraum endet in 1 Stunde' (wenn aktiv) oder 'Nächster teurer Zeitraum endet um 18:00' (wenn inaktiv) anzuzeigen. Kombiniere mit Automatisierungen, um den Betrieb nach der Spitzenzeit fortzusetzen."
         },
         "peak_price_period_duration": {
-            "description": "Länge des aktuellen/nächsten teuren Zeitraums",
+            "description": "Gesamtlänge des aktuellen oder nächsten teuren Zeitraums in Minuten",
-            "long_description": "Gesamtdauer des aktuellen oder nächsten teuren Zeitraums. Der State wird in Stunden angezeigt (z. B. 1,5 h) für leichtes Ablesen in der UI, während das Attribut `period_duration_minutes` denselben Wert in Minuten bereitstellt (z. B. 90) für Automationen. Dieser Wert repräsentiert die **volle geplante Dauer** des Zeitraums und ist konstant während des gesamten Zeitraums, auch wenn die verbleibende Zeit (remaining_minutes) abnimmt.",
+            "long_description": "Zeigt, wie lange der teure Zeitraum insgesamt dauert. Während eines aktiven Zeitraums zeigt dies die Dauer des aktuellen Zeitraums. Wenn kein Zeitraum aktiv ist, zeigt dies die Dauer des nächsten kommenden Zeitraums. Gibt nur 'Unbekannt' zurück, wenn keine Zeiträume ermittelt wurden.",
-            "usage_tips": "Kombiniere mit remaining_minutes, um zu berechnen, wann langlaufende Geräte gestoppt werden sollen: Zeitraum begann vor `period_duration_minutes - remaining_minutes` Minuten. Dieses Attribut unterstützt Energiespar-Strategien, indem es hilft, Hochverbrauchsaktivitäten außerhalb teurer Perioden zu planen."
+            "usage_tips": "Nützlich für Planung: 'Der nächste teure Zeitraum dauert 60 Minuten' oder 'Der aktuelle Spitzenzeitraum ist 90 Minuten lang'. Kombiniere mit remaining_minutes, um zu entscheiden, ob die Spitze abgewartet oder der Betrieb fortgesetzt werden soll."
         },
         "peak_price_remaining_minutes": {
-            "description": "Verbleibende Zeit im aktuellen teuren Zeitraum",
+            "description": "Verbleibende Minuten im aktuellen teuren Zeitraum (0 wenn inaktiv)",
-            "long_description": "Zeigt, wie viel Zeit im aktuellen teuren Zeitraum noch verbleibt. Der State wird in Stunden angezeigt (z. B. 0,75 h) für einfaches Ablesen in Dashboards, während das Attribut `remaining_minutes` dieselbe Zeit in Minuten liefert (z. B. 45) für Automationsbedingungen. **Countdown-Timer**: Dieser Wert dekrementiert jede Minute während eines aktiven Zeitraums. Gibt 0 zurück, wenn kein teurer Zeitraum aktiv ist. Aktualisiert sich minütlich.",
+            "long_description": "Zeigt, wie viele Minuten im aktuellen teuren Zeitraum noch verbleiben. Gibt 0 zurück, wenn kein Zeitraum aktiv ist. Aktualisiert sich jede Minute. Prüfe binary_sensor.peak_price_period, um zu sehen, ob ein Zeitraum aktuell aktiv ist.",
-            "usage_tips": "Für Automationen: Nutze Attribut `remaining_minutes` wie 'Wenn remaining_minutes > 60, setze Heizung auf Energiesparmodus' oder 'Wenn remaining_minutes < 15, erhöhe Temperatur wieder'. UI zeigt benutzerfreundliche Stunden (z. B. 1,25 h). Wert 0 zeigt an, dass kein teurer Zeitraum aktiv ist."
+            "usage_tips": "Nutze in Automatisierungen: 'Wenn remaining_minutes > 60, breche aufgeschobene Ladesitzung ab'. Wert 0 macht es einfach zu unterscheiden zwischen aktivem (Wert > 0) und inaktivem (Wert = 0) Zeitraum."
         },
         "peak_price_progress": {
             "description": "Fortschritt durch aktuellen teuren Zeitraum (0% wenn inaktiv)",
@@ -372,9 +359,9 @@
             "usage_tips": "Immer nützlich für Planung: 'Nächster teurer Zeitraum startet in 2 Stunden'. Automatisierung: 'Wenn nächste Startzeit in 30 Minuten ist, reduziere Heiztemperatur vorsorglich'."
         },
         "peak_price_next_in_minutes": {
-            "description": "Zeit bis zum nächsten teuren Zeitraum",
+            "description": "Minuten bis nächster teurer Zeitraum startet (0 beim Übergang)",
-            "long_description": "Zeigt, wie lange es bis zum nächsten teuren Zeitraum dauert. Der State wird in Stunden angezeigt (z. B. 2,25 h) für Dashboards, während das Attribut `next_in_minutes` Minuten bereitstellt (z. B. 135) für Automationsbedingungen. Während eines aktiven Zeitraums zeigt dies die Zeit bis zum Zeitraum nach dem aktuellen. Gibt 0 während kurzer Übergangsphasen zurück. Aktualisiert sich jede Minute.",
+            "long_description": "Zeigt Minuten bis der nächste teure Zeitraum startet. Während eines aktiven Zeitraums zeigt dies die Zeit bis zum Zeitraum nach dem aktuellen. Gibt 0 während kurzer Übergangsphasen zurück. Aktualisiert sich jede Minute.",
-            "usage_tips": "Für Automationen: Attribut `next_in_minutes` nutzen wie 'Wenn next_in_minutes > 0 UND next_in_minutes < 10, reduziere Heizung vorsorglich bevor der teure Zeitraum beginnt'. Wert > 0 zeigt immer an, dass ein zukünftiger teurer Zeitraum geplant ist."
+            "usage_tips": "Präventive Automatisierung: 'Wenn next_in_minutes > 0 UND next_in_minutes < 10, beende aktuellen Ladezyklus jetzt, bevor die Preise steigen'."
         },
         "home_type": {
             "description": "Art der Wohnung (Wohnung, Haus usw.)",
@@ -450,11 +437,6 @@
             "description": "Datenexport für Dashboard-Integrationen",
             "long_description": "Dieser Sensor ruft den get_chartdata-Service mit deiner konfigurierten YAML-Konfiguration auf und stellt das Ergebnis als Entity-Attribute bereit. Der Status zeigt 'ready' wenn Daten verfügbar sind, 'error' bei Fehlern, oder 'pending' vor dem ersten Aufruf. Perfekt für Dashboard-Integrationen wie ApexCharts, die Preisdaten aus Entity-Attributen lesen.",
             "usage_tips": "Konfiguriere die YAML-Parameter in den Integrationsoptionen entsprechend deinem get_chartdata-Service-Aufruf. Der Sensor aktualisiert automatisch bei Preisdaten-Updates (typischerweise nach Mitternacht und wenn morgige Daten eintreffen). Greife auf die Service-Response-Daten direkt über die Entity-Attribute zu - die Struktur entspricht exakt dem, was get_chartdata zurückgibt."
-        },
-        "chart_metadata": {
-            "description": "Leichtgewichtige Metadaten für Diagrammkonfiguration",
-            "long_description": "Liefert wesentliche Diagrammkonfigurationswerte als Sensor-Attribute. Nützlich für jede Diagrammkarte, die Y-Achsen-Grenzen benötigt. Der Sensor ruft get_chartdata im Nur-Metadaten-Modus auf (keine Datenverarbeitung) und extrahiert: yaxis_min, yaxis_max (vorgeschlagener Y-Achsenbereich für optimale Skalierung). Der Status spiegelt das Service-Call-Ergebnis wider: 'ready' bei Erfolg, 'error' bei Fehler, 'pending' während der Initialisierung.",
-            "usage_tips": "Konfiguriere über configuration.yaml unter tibber_prices.chart_metadata_config (optional: day, subunit_currency, resolution). Der Sensor aktualisiert sich automatisch bei Preisdatenänderungen. Greife auf Metadaten aus Attributen zu: yaxis_min, yaxis_max. Verwende mit config-template-card oder jedem Tool, das Entity-Attribute liest - perfekt für dynamische Diagrammkonfiguration ohne manuelle Berechnungen."
         }
     },
     "binary_sensor": {
@@ -489,95 +471,11 @@
             "usage_tips": "Verwende dies, um zu überprüfen, ob Echtzeit-Verbrauchsdaten verfügbar sind. Aktiviere Benachrichtigungen, falls dies unerwartet auf 'Aus' wechselt, was auf potenzielle Hardware- oder Verbindungsprobleme hinweist."
         }
     },
-    "number": {
-        "best_price_flex_override": {
-            "description": "Maximaler Prozentsatz über dem Tagesminimumpreis, den Intervalle haben können und trotzdem als 'Bestpreis' gelten. Empfohlen: 15-20 mit Lockerung aktiviert (Standard), oder 25-35 ohne Lockerung. Maximum: 50 (Obergrenze für zuverlässige Periodenerkennung).",
-            "long_description": "Wenn diese Entität aktiviert ist, überschreibt ihr Wert die Einstellung 'Flexibilität' aus dem Optionen-Dialog für die Bestpreis-Periodenberechnung.",
-            "usage_tips": "Aktiviere diese Entität, um die Bestpreiserkennung dynamisch über Automatisierungen anzupassen, z.B. höhere Flexibilität bei kritischen Lasten oder engere Anforderungen für flexible Geräte."
-        },
-        "best_price_min_distance_override": {
-            "description": "Minimaler prozentualer Abstand unter dem Tagesdurchschnitt. Intervalle müssen so weit unter dem Durchschnitt liegen, um als 'Bestpreis' zu gelten. Hilft, echte Niedrigpreis-Perioden von durchschnittlichen Preisen zu unterscheiden.",
-            "long_description": "Wenn diese Entität aktiviert ist, überschreibt ihr Wert die Einstellung 'Mindestabstand' aus dem Optionen-Dialog für die Bestpreis-Periodenberechnung.",
-            "usage_tips": "Erhöhe den Wert, wenn du strengere Bestpreis-Kriterien möchtest. Verringere ihn, wenn zu wenige Perioden erkannt werden."
-        },
-        "best_price_min_period_length_override": {
-            "description": "Minimale Periodenl\u00e4nge in 15-Minuten-Intervallen. Perioden kürzer als diese werden nicht gemeldet. Beispiel: 2 = mindestens 30 Minuten.",
-            "long_description": "Wenn diese Entität aktiviert ist, überschreibt ihr Wert die Einstellung 'Mindestperiodenlänge' aus dem Optionen-Dialog für die Bestpreis-Periodenberechnung.",
-            "usage_tips": "Passe an die typische Laufzeit deiner Geräte an: 2 (30 Min) für Schnellprogramme, 4-8 (1-2 Std) für normale Zyklen, 8+ für lange ECO-Programme."
-        },
-        "best_price_min_periods_override": {
-            "description": "Minimale Anzahl an Bestpreis-Perioden, die täglich gefunden werden sollen. Wenn Lockerung aktiviert ist, wird das System die Kriterien automatisch anpassen, um diese Zahl zu erreichen.",
-            "long_description": "Wenn diese Entität aktiviert ist, überschreibt ihr Wert die Einstellung 'Mindestperioden' aus dem Optionen-Dialog für die Bestpreis-Periodenberechnung.",
-            "usage_tips": "Setze dies auf die Anzahl zeitkritischer Aufgaben, die du täglich hast. Beispiel: 2 für zwei Waschmaschinenladungen."
-        },
-        "best_price_relaxation_attempts_override": {
-            "description": "Anzahl der Versuche, die Kriterien schrittweise zu lockern, um die Mindestperiodenanzahl zu erreichen. Jeder Versuch erhöht die Flexibilität um 3 Prozent. Bei 0 werden nur Basis-Kriterien verwendet.",
-            "long_description": "Wenn diese Entität aktiviert ist, überschreibt ihr Wert die Einstellung 'Lockerungsversuche' aus dem Optionen-Dialog für die Bestpreis-Periodenberechnung.",
-            "usage_tips": "Höhere Werte machen die Periodenerkennung anpassungsfähiger an Tage mit stabilen Preisen. Setze auf 0, um strenge Kriterien ohne Lockerung zu erzwingen."
-        },
-        "best_price_gap_count_override": {
-            "description": "Maximale Anzahl teurerer Intervalle, die zwischen günstigen Intervallen erlaubt sind und trotzdem als eine zusammenhängende Periode gelten. Bei 0 müssen günstige Intervalle aufeinander folgen.",
-            "long_description": "Wenn diese Entität aktiviert ist, überschreibt ihr Wert die Einstellung 'Lückentoleranz' aus dem Optionen-Dialog für die Bestpreis-Periodenberechnung.",
-            "usage_tips": "Erhöhe dies für Geräte mit variabler Last (z.B. Wärmepumpen), die kurze teurere Intervalle tolerieren können. Setze auf 0 für kontinuierliche günstige Perioden."
-        },
-        "peak_price_flex_override": {
-            "description": "Maximaler Prozentsatz unter dem Tagesmaximumpreis, den Intervalle haben können und trotzdem als 'Spitzenpreis' gelten. Gleiche Empfehlungen wie für Bestpreis-Flexibilität.",
-            "long_description": "Wenn diese Entität aktiviert ist, überschreibt ihr Wert die Einstellung 'Flexibilität' aus dem Optionen-Dialog für die Spitzenpreis-Periodenberechnung.",
-            "usage_tips": "Nutze dies, um den Spitzenpreis-Schwellenwert zur Laufzeit für Automatisierungen anzupassen, die den Verbrauch während teurer Stunden vermeiden."
-        },
-        "peak_price_min_distance_override": {
-            "description": "Minimaler prozentualer Abstand über dem Tagesdurchschnitt. Intervalle müssen so weit über dem Durchschnitt liegen, um als 'Spitzenpreis' zu gelten.",
-            "long_description": "Wenn diese Entität aktiviert ist, überschreibt ihr Wert die Einstellung 'Mindestabstand' aus dem Optionen-Dialog für die Spitzenpreis-Periodenberechnung.",
-            "usage_tips": "Erhöhe den Wert, um nur extreme Preisspitzen zu erfassen. Verringere ihn, um mehr Hochpreiszeiten einzubeziehen."
-        },
-        "peak_price_min_period_length_override": {
-            "description": "Minimale Periodenl\u00e4nge in 15-Minuten-Intervallen für Spitzenpreise. Kürzere Preisspitzen werden nicht als Perioden gemeldet.",
-            "long_description": "Wenn diese Entität aktiviert ist, überschreibt ihr Wert die Einstellung 'Mindestperiodenlänge' aus dem Optionen-Dialog für die Spitzenpreis-Periodenberechnung.",
-            "usage_tips": "Kürzere Werte erfassen kurze Preisspitzen. Längere Werte fokussieren auf anhaltende Hochpreisphasen."
-        },
-        "peak_price_min_periods_override": {
-            "description": "Minimale Anzahl an Spitzenpreis-Perioden, die täglich gefunden werden sollen.",
-            "long_description": "Wenn diese Entität aktiviert ist, überschreibt ihr Wert die Einstellung 'Mindestperioden' aus dem Optionen-Dialog für die Spitzenpreis-Periodenberechnung.",
-            "usage_tips": "Setze dies basierend darauf, wie viele Hochpreisphasen du pro Tag für Automatisierungen erfassen möchtest."
-        },
-        "peak_price_relaxation_attempts_override": {
-            "description": "Anzahl der Versuche, die Kriterien zu lockern, um die Mindestanzahl an Spitzenpreis-Perioden zu erreichen.",
-            "long_description": "Wenn diese Entität aktiviert ist, überschreibt ihr Wert die Einstellung 'Lockerungsversuche' aus dem Optionen-Dialog für die Spitzenpreis-Periodenberechnung.",
-            "usage_tips": "Erhöhe dies, wenn an Tagen mit stabilen Preisen keine Perioden gefunden werden. Setze auf 0, um strenge Kriterien zu erzwingen."
-        },
-        "peak_price_gap_count_override": {
-            "description": "Maximale Anzahl günstigerer Intervalle, die zwischen teuren Intervallen erlaubt sind und trotzdem als eine Spitzenpreis-Periode gelten.",
-            "long_description": "Wenn diese Entität aktiviert ist, überschreibt ihr Wert die Einstellung 'Lückentoleranz' aus dem Optionen-Dialog für die Spitzenpreis-Periodenberechnung.",
-            "usage_tips": "Höhere Werte erfassen längere Hochpreisphasen auch mit kurzen Preiseinbrüchen. Setze auf 0, um strikt zusammenhängende Spitzenpreise zu erfassen."
-        }
-    },
-    "switch": {
-        "best_price_enable_relaxation_override": {
-            "description": "Wenn aktiviert, werden die Kriterien automatisch gelockert, um die Mindestperiodenanzahl zu erreichen. Wenn deaktiviert, werden nur Perioden gemeldet, die die strengen Kriterien erfüllen (möglicherweise null Perioden bei stabilen Preisen).",
-            "long_description": "Wenn diese Entität aktiviert ist, überschreibt ihr Wert die Einstellung 'Mindestanzahl erreichen' aus dem Optionen-Dialog für die Bestpreis-Periodenberechnung.",
-            "usage_tips": "Aktiviere dies für garantierte tägliche Automatisierungsmöglichkeiten. Deaktiviere es, wenn du nur wirklich günstige Zeiträume willst, auch wenn das bedeutet, dass an manchen Tagen keine Perioden gefunden werden."
-        },
-        "peak_price_enable_relaxation_override": {
-            "description": "Wenn aktiviert, werden die Kriterien automatisch gelockert, um die Mindestperiodenanzahl zu erreichen. Wenn deaktiviert, werden nur echte Preisspitzen gemeldet.",
-            "long_description": "Wenn diese Entität aktiviert ist, überschreibt ihr Wert die Einstellung 'Mindestanzahl erreichen' aus dem Optionen-Dialog für die Spitzenpreis-Periodenberechnung.",
-            "usage_tips": "Aktiviere dies für konsistente Spitzenpreis-Warnungen. Deaktiviere es, um nur extreme Preisspitzen zu erfassen."
-        }
-    },
     "home_types": {
         "APARTMENT": "Wohnung",
         "ROWHOUSE": "Reihenhaus",
         "HOUSE": "Haus",
         "COTTAGE": "Ferienhaus"
     },
-    "time_units": {
-        "day": "{count} Tag",
-        "days": "{count} Tagen",
-        "hour": "{count} Stunde",
-        "hours": "{count} Stunden",
-        "minute": "{count} Minute",
-        "minutes": "{count} Minuten",
-        "ago": "vor {parts}",
-        "now": "jetzt"
-    },
     "attribution": "Daten bereitgestellt von Tibber"
 }

custom_components/tibber_prices/custom_translations/en.json

@@ -1,20 +1,7 @@
 {
     "apexcharts": {
         "title_rating_level": "Price Phases Daily Progress",
-        "title_level": "Price Level",
+        "title_level": "Price Level"
-        "hourly_suffix": "(Ø hourly)",
-        "best_price_period_name": "Best Price Period",
-        "peak_price_period_name": "Peak Price Period",
-        "notification": {
-            "metadata_sensor_unavailable": {
-                "title": "Tibber Prices: ApexCharts YAML Generated with Limited Functionality",
-                "message": "You just generated an ApexCharts card configuration via Developer Tools. The Chart Metadata sensor is currently disabled, so the generated YAML will only show **basic functionality** (auto-scale axis, fixed gradient at 50%).\n\n**To enable full functionality** (optimized scaling, dynamic gradient colors):\n1. [Open Tibber Prices Integration](https://my.home-assistant.io/redirect/integration/?domain=tibber_prices)\n2. Enable the 'Chart Metadata' sensor\n3. **Generate the YAML again** via Developer Tools\n4. **Replace the old YAML** in your dashboard with the new version\n\n⚠️ Simply enabling the sensor is not enough - you must regenerate and replace the YAML code!"
-            },
-            "missing_cards": {
-                "title": "Tibber Prices: ApexCharts YAML Cannot Be Used",
-                "message": "You just generated an ApexCharts card configuration via Developer Tools, but the generated YAML **will not work** because required custom cards are missing.\n\n**Missing cards:**\n{cards}\n\n**To use the generated YAML:**\n1. Click the links above to install the missing cards from HACS\n2. Restart Home Assistant (sometimes needed)\n3. **Generate the YAML again** via Developer Tools\n4. Add the YAML to your dashboard\n\n⚠️ The current YAML code will not work until all cards are installed!"
-            }
-        }
     },
     "sensor": {
         "current_interval_price": {
@@ -22,9 +9,9 @@
             "long_description": "Shows the current price per kWh from your Tibber subscription",
             "usage_tips": "Use this to track prices or to create automations that run when electricity is cheap"
         },
-        "current_interval_price_base": {
+        "current_interval_price_major": {
-            "description": "Current electricity price in base currency (EUR/kWh, NOK/kWh, etc.) for Energy Dashboard",
+            "description": "Current electricity price in major currency (EUR/kWh, NOK/kWh, etc.) for Energy Dashboard",
-            "long_description": "Shows the current price per kWh in base currency units (e.g., EUR/kWh instead of ct/kWh, NOK/kWh instead of øre/kWh). This sensor is specifically designed for use with Home Assistant's Energy Dashboard, which requires prices in standard currency units.",
+            "long_description": "Shows the current price per kWh in major currency units (e.g., EUR/kWh instead of ct/kWh, NOK/kWh instead of øre/kWh). This sensor is specifically designed for use with Home Assistant's Energy Dashboard, which requires prices in standard currency units.",
             "usage_tips": "Use this sensor when configuring the Energy Dashboard under Settings → Dashboards → Energy. Select this sensor as the 'Entity with current price' to automatically calculate your energy costs. The Energy Dashboard multiplies your energy consumption (kWh) by this price to show total costs."
         },
         "next_interval_price": {
@@ -58,9 +45,9 @@
             "usage_tips": "Use this to avoid running appliances during peak price times"
         },
         "average_price_today": {
-            "description": "The typical electricity price for today per kWh (configurable display format)",
+            "description": "The average electricity price for today per kWh",
-            "long_description": "Shows the typical price per kWh for today. **By default, the state displays the median** (resistant to extreme spikes, showing what you can generally expect). You can change this in the integration options to show the arithmetic mean instead. The alternate value is always available as attribute `price_mean` or `price_median` for automations.",
+            "long_description": "Shows the average price per kWh for the current day from your Tibber subscription",
-            "usage_tips": "Use the state value for display. For exact cost calculations in automations, use: {{ state_attr('sensor.average_price_today', 'price_mean') }}"
+            "usage_tips": "Use this as a baseline for comparing current prices"
         },
         "lowest_price_tomorrow": {
             "description": "The lowest electricity price for tomorrow per kWh",
@@ -73,9 +60,9 @@
             "usage_tips": "Use this to avoid running appliances during tomorrow's peak price times. Helpful for planning around expensive periods."
         },
         "average_price_tomorrow": {
-            "description": "The typical electricity price for tomorrow per kWh (configurable display format)",
+            "description": "The average electricity price for tomorrow per kWh",
-            "long_description": "Shows the typical price per kWh for tomorrow. **By default, the state displays the median** (resistant to extreme spikes). You can change this in the integration options to show the arithmetic mean instead. The alternate value is available as attribute. This sensor becomes unavailable until tomorrow's data is published by Tibber (typically around 13:00-14:00 CET).",
+            "long_description": "Shows the average price per kWh for tomorrow from your Tibber subscription. This sensor becomes unavailable until tomorrow's data is published by Tibber (typically around 13:00-14:00 CET).",
-            "usage_tips": "Use this to plan tomorrow's energy consumption. For cost calculations, use: {{ state_attr('sensor.average_price_tomorrow', 'price_mean') }}"
+            "usage_tips": "Use this as a baseline for comparing tomorrow's prices and planning consumption. Compare with today's average to see if tomorrow will be more or less expensive overall."
         },
         "yesterday_price_level": {
             "description": "Aggregated price level for yesterday",
@@ -108,14 +95,14 @@
             "usage_tips": "Use this to plan tomorrow's energy consumption based on your personalized price thresholds. Compare with today to decide if you should shift consumption to tomorrow or use energy today."
         },
         "trailing_price_average": {
-            "description": "The typical electricity price for the past 24 hours per kWh (configurable display format)",
+            "description": "The average electricity price for the past 24 hours per kWh",
-            "long_description": "Shows the typical price per kWh for the past 24 hours. **By default, the state displays the median** (resistant to extreme spikes, showing what price level was typical). You can change this in the integration options to show the arithmetic mean instead. The alternate value is available as attribute. Updates every 15 minutes.",
+            "long_description": "Shows the average price per kWh calculated from the past 24 hours (trailing average) from your Tibber subscription. This provides a rolling average that updates every 15 minutes based on historical data.",
-            "usage_tips": "Use the state value to see the typical recent price level. For cost calculations, use: {{ state_attr('sensor.trailing_price_average', 'price_mean') }}"
+            "usage_tips": "Use this to compare current prices against recent trends. A current price significantly above this average may indicate a good time to reduce consumption."
         },
         "leading_price_average": {
-            "description": "The typical electricity price for the next 24 hours per kWh (configurable display format)",
+            "description": "The average electricity price for the next 24 hours per kWh",
-            "long_description": "Shows the typical price per kWh for the next 24 hours. **By default, the state displays the median** (resistant to extreme spikes, showing what price level to expect). You can change this in the integration options to show the arithmetic mean instead. The alternate value is available as attribute.",
+            "long_description": "Shows the average price per kWh calculated from the next 24 hours (leading average) from your Tibber subscription. This provides a forward-looking average based on available forecast data.",
-            "usage_tips": "Use the state value to see the typical upcoming price level. For cost calculations, use: {{ state_attr('sensor.leading_price_average', 'price_mean') }}"
+            "usage_tips": "Use this to plan energy usage. If the current price is below the leading average, it may be a good time to run energy-intensive appliances."
         },
         "trailing_price_min": {
             "description": "The minimum electricity price for the past 24 hours per kWh",
@@ -292,24 +279,24 @@
             "long_description": "Shows the timestamp of the latest available price data interval from your Tibber subscription"
         },
         "today_volatility": {
-            "description": "How much electricity prices change throughout today",
+            "description": "Price volatility classification for today",
-            "long_description": "Indicates whether today's prices are stable or have big swings. Low volatility means prices stay fairly consistent—timing doesn't matter much. High volatility means significant price differences throughout the day—great opportunity to shift consumption to cheaper periods. Check `price_coefficient_variation_%` for the variance percentage and `price_spread` for the absolute price span.",
+            "long_description": "Shows how much electricity prices vary throughout today based on the spread (difference between highest and lowest price). Classification: LOW = spread < 5ct, MODERATE = 5-15ct, HIGH = 15-30ct, VERY HIGH = >30ct.",
-            "usage_tips": "Use this to decide if optimization is worth your effort. On low-volatility days, you can run devices anytime. On high-volatility days, following Best Price periods saves meaningful money."
+            "usage_tips": "Use this to decide if price-based optimization is worthwhile. For example, with a balcony battery that has 15% efficiency losses, optimization only makes sense when volatility is at least MODERATE. Create automations that check volatility before scheduling charging/discharging cycles."
         },
         "tomorrow_volatility": {
-            "description": "How much electricity prices will change tomorrow",
+            "description": "Price volatility classification for tomorrow",
-            "long_description": "Indicates whether tomorrow's prices will be stable or have big swings. Available once tomorrow's data is published (typically 13:00-14:00 CET). Low volatility means prices stay fairly consistent—timing isn't critical. High volatility means significant price differences throughout the day—good opportunity for scheduling energy-intensive activities. Check `price_coefficient_variation_%` for the variance percentage and `price_spread` for the absolute price span.",
+            "long_description": "Shows how much electricity prices will vary throughout tomorrow based on the spread (difference between highest and lowest price). Becomes unavailable until tomorrow's data is published (typically 13:00-14:00 CET).",
-            "usage_tips": "Use for planning tomorrow's energy consumption. High volatility? Schedule flexible loads during Best Price periods. Low volatility? Run devices whenever is convenient."
+            "usage_tips": "Use this for advance planning of tomorrow's energy usage. If tomorrow has HIGH or VERY HIGH volatility, it's worth optimizing energy consumption timing. If LOW, you can run devices anytime without significant cost differences."
         },
         "next_24h_volatility": {
-            "description": "How much prices will change over the next 24 hours",
+            "description": "Price volatility classification for the rolling next 24 hours",
-            "long_description": "Indicates price volatility for a rolling 24-hour window from now (updates every 15 minutes). Low volatility means prices stay fairly consistent. High volatility means significant price swings offer optimization opportunities. Unlike today/tomorrow sensors, this crosses day boundaries and provides a continuous forward-looking assessment. Check `price_coefficient_variation_%` for the variance percentage and `price_spread` for the absolute price span.",
+            "long_description": "Shows how much electricity prices vary in the next 24 hours from now (rolling window). This crosses day boundaries and updates every 15 minutes, providing a forward-looking volatility assessment independent of calendar days.",
-            "usage_tips": "Best for real-time decisions. Use when planning battery charging strategies or other flexible loads that might span across midnight. Provides consistent 24h perspective regardless of calendar day."
+            "usage_tips": "Best sensor for real-time optimization decisions. Unlike today/tomorrow sensors that switch at midnight, this provides continuous 24h volatility assessment. Use for battery charging strategies that span across day boundaries."
         },
         "today_tomorrow_volatility": {
-            "description": "Combined price volatility across today and tomorrow",
+            "description": "Combined price volatility classification for today and tomorrow",
-            "long_description": "Shows overall price volatility when considering both today and tomorrow together (when available). Indicates whether there are significant price differences across the day boundary. Falls back to today-only when tomorrow's data isn't available yet. Useful for understanding multi-day optimization opportunities. Check `price_coefficient_variation_%` for the variance percentage and `price_spread` for the absolute price span.",
+            "long_description": "Shows volatility across both today and tomorrow combined (when tomorrow's data is available). Provides an extended view of price variation spanning up to 48 hours. Falls back to today-only when tomorrow's data isn't available yet.",
-            "usage_tips": "Use for planning tasks that span multiple days. Check if prices vary enough to make scheduling worthwhile. The individual day volatility sensors show breakdown per day if you need more detail."
+            "usage_tips": "Use this for multi-day planning and to understand if price opportunities exist across the day boundary. The 'today_volatility' and 'tomorrow_volatility' breakdown attributes show individual day contributions. Useful for scheduling charging sessions that might span midnight."
         },
         "data_lifecycle_status": {
             "description": "Current state of price data lifecycle and caching",
@@ -322,14 +309,14 @@
             "usage_tips": "Use this to display a countdown like 'Cheap period ends in 2 hours' (when active) or 'Next cheap period ends at 14:00' (when inactive). Home Assistant automatically shows relative time for timestamp sensors."
         },
         "best_price_period_duration": {
-            "description": "Total length of current or next best price period",
+            "description": "Total length of current or next best price period in minutes",
-            "long_description": "Shows how long the best price period lasts in total. The state is displayed in hours (e.g., 1.5 h) for easy reading in the UI, while the `period_duration_minutes` attribute provides the same value in minutes (e.g., 90) for use in automations. During an active period, shows the duration of the current period. When no period is active, shows the duration of the next upcoming period. Returns 'Unknown' only when no periods are configured.",
+            "long_description": "Shows how long the best price period lasts in total. During an active period, shows the duration of the current period. When no period is active, shows the duration of the next upcoming period. Returns 'Unknown' only when no periods are configured.",
-            "usage_tips": "For display: Use the state value (hours) in dashboards. For automations: Use `period_duration_minutes` attribute to check if there's enough time for long-running tasks (e.g., 'If period_duration_minutes >= 90, start washing machine')."
+            "usage_tips": "Useful for planning: 'The next cheap period lasts 90 minutes' or 'Current cheap period is 120 minutes long'. Combine with remaining_minutes to calculate when to start long-running appliances."
         },
         "best_price_remaining_minutes": {
-            "description": "Time remaining in current best price period",
+            "description": "Minutes remaining in current best price period (0 when inactive)",
-            "long_description": "Shows how much time is left in the current best price period. The state displays in hours (e.g., 0.5 h) for easy reading, while the `remaining_minutes` attribute provides minutes (e.g., 30) for automation logic. Returns 0 when no period is active. Updates every minute. Check binary_sensor.best_price_period to see if a period is currently active.",
+            "long_description": "Shows how many minutes are left in the current best price period. Returns 0 when no period is active. Updates every minute. Check binary_sensor.best_price_period to see if a period is currently active.",
-            "usage_tips": "For automations: Use `remaining_minutes` attribute with numeric comparisons like 'If remaining_minutes > 0 AND remaining_minutes < 30, start washing machine now'. The value 0 makes it easy to check if a period is active (value > 0) or not (value = 0)."
+            "usage_tips": "Perfect for automations: 'If remaining_minutes > 0 AND remaining_minutes < 30, start washing machine now'. The value 0 makes it easy to check if a period is active (value > 0) or not (value = 0)."
         },
         "best_price_progress": {
             "description": "Progress through current best price period (0% when inactive)",
@@ -342,9 +329,9 @@
             "usage_tips": "Always useful for planning ahead: 'Next cheap period starts in 3 hours' (whether you're in a period now or not). Combine with automations: 'When next start time is in 10 minutes, send notification to prepare washing machine'."
         },
         "best_price_next_in_minutes": {
-            "description": "Time until next best price period starts",
+            "description": "Minutes until next best price period starts (0 when in transition)",
-            "long_description": "Shows how long until the next best price period starts. The state displays in hours (e.g., 2.25 h) for dashboards, while the `next_in_minutes` attribute provides minutes (e.g., 135) for automation conditions. During an active period, shows time until the period AFTER the current one. Returns 0 during brief transition moments. Updates every minute.",
+            "long_description": "Shows minutes until the next best price period starts. During an active period, shows time until the period AFTER the current one. Returns 0 during brief transition moments. Updates every minute.",
-            "usage_tips": "For automations: Use `next_in_minutes` attribute like 'If next_in_minutes > 0 AND next_in_minutes < 15, wait before starting dishwasher'. Value > 0 always indicates a future period is scheduled."
+            "usage_tips": "Perfect for 'wait until cheap period' automations: 'If next_in_minutes > 0 AND next_in_minutes < 15, wait before starting dishwasher'. Value > 0 always indicates a future period is scheduled."
         },
         "peak_price_end_time": {
             "description": "When the current or next peak price period ends",
@@ -352,14 +339,14 @@
             "usage_tips": "Use this to display 'Expensive period ends in 1 hour' (when active) or 'Next expensive period ends at 18:00' (when inactive). Combine with automations to resume operations after peak."
         },
         "peak_price_period_duration": {
-            "description": "Total length of current or next peak price period",
+            "description": "Total length of current or next peak price period in minutes",
-            "long_description": "Shows how long the peak price period lasts in total. The state is displayed in hours (e.g., 0.75 h) for easy reading in the UI, while the `period_duration_minutes` attribute provides the same value in minutes (e.g., 45) for use in automations. During an active period, shows the duration of the current period. When no period is active, shows the duration of the next upcoming period. Returns 'Unknown' only when no periods are configured.",
+            "long_description": "Shows how long the peak price period lasts in total. During an active period, shows the duration of the current period. When no period is active, shows the duration of the next upcoming period. Returns 'Unknown' only when no periods are configured.",
-            "usage_tips": "For display: Use the state value (hours) in dashboards. For automations: Use `period_duration_minutes` attribute to decide whether to wait out the peak or proceed (e.g., 'If period_duration_minutes <= 60, pause operations')."
+            "usage_tips": "Useful for planning: 'The next expensive period lasts 60 minutes' or 'Current peak is 90 minutes long'. Combine with remaining_minutes to decide whether to wait out the peak or proceed with operations."
         },
         "peak_price_remaining_minutes": {
-            "description": "Time remaining in current peak price period",
+            "description": "Minutes remaining in current peak price period (0 when inactive)",
-            "long_description": "Shows how much time is left in the current peak price period. The state displays in hours (e.g., 1.0 h) for easy reading, while the `remaining_minutes` attribute provides minutes (e.g., 60) for automation logic. Returns 0 when no period is active. Updates every minute. Check binary_sensor.peak_price_period to see if a period is currently active.",
+            "long_description": "Shows how many minutes are left in the current peak price period. Returns 0 when no period is active. Updates every minute. Check binary_sensor.peak_price_period to see if a period is currently active.",
-            "usage_tips": "For automations: Use `remaining_minutes` attribute like 'If remaining_minutes > 60, cancel deferred charging session'. Value 0 makes it easy to distinguish active (value > 0) from inactive (value = 0) periods."
+            "usage_tips": "Use in automations: 'If remaining_minutes > 60, cancel deferred charging session'. Value 0 makes it easy to distinguish active (value > 0) from inactive (value = 0) periods."
         },
         "peak_price_progress": {
             "description": "Progress through current peak price period (0% when inactive)",
@@ -372,9 +359,9 @@
             "usage_tips": "Always useful for planning: 'Next expensive period starts in 2 hours'. Automation: 'When next start time is in 30 minutes, reduce heating temperature preemptively'."
         },
         "peak_price_next_in_minutes": {
-            "description": "Time until next peak price period starts",
+            "description": "Minutes until next peak price period starts (0 when in transition)",
-            "long_description": "Shows how long until the next peak price period starts. The state displays in hours (e.g., 0.5 h) for dashboards, while the `next_in_minutes` attribute provides minutes (e.g., 30) for automation conditions. During an active period, shows time until the period AFTER the current one. Returns 0 during brief transition moments. Updates every minute.",
+            "long_description": "Shows minutes until the next peak price period starts. During an active period, shows time until the period AFTER the current one. Returns 0 during brief transition moments. Updates every minute.",
-            "usage_tips": "For automations: Use `next_in_minutes` attribute like 'If next_in_minutes > 0 AND next_in_minutes < 10, complete current charging cycle now before prices increase'."
+            "usage_tips": "Pre-emptive automation: 'If next_in_minutes > 0 AND next_in_minutes < 10, complete current charging cycle now before prices increase'."
         },
         "home_type": {
             "description": "Type of home (apartment, house, etc.)",
@@ -445,16 +432,6 @@
             "description": "Status of your Tibber subscription",
             "long_description": "Shows whether your Tibber subscription is currently running, has ended, or is pending activation. A status of 'running' means you're actively receiving electricity through Tibber.",
             "usage_tips": "Use this to monitor your subscription status. Set up alerts if status changes from 'running' to ensure uninterrupted service."
-        },
-        "chart_data_export": {
-            "description": "Data export for dashboard integrations",
-            "long_description": "This binary sensor calls the get_chartdata service with your configured YAML parameters and exposes the result as entity attributes. The state is 'on' when the service call succeeds and data is available, 'off' when the call fails or no configuration is set. Perfect for dashboard integrations like ApexCharts that need to read price data from entity attributes.",
-            "usage_tips": "Configure the YAML parameters in the integration options to match your get_chartdata service call. The sensor will automatically refresh when price data updates (typically after midnight and when tomorrow's data arrives). Access the service response data directly from the entity's attributes - the structure matches exactly what get_chartdata returns."
-        },
-        "chart_metadata": {
-            "description": "Lightweight metadata for chart configuration",
-            "long_description": "Provides essential chart configuration values as sensor attributes. Useful for any chart card that needs Y-axis bounds. The sensor calls get_chartdata with metadata-only mode (no data processing) and extracts: yaxis_min, yaxis_max (suggested Y-axis range for optimal scaling). The state reflects the service call result: 'ready' when successful, 'error' on failure, 'pending' during initialization.",
-            "usage_tips": "Configure via configuration.yaml under tibber_prices.chart_metadata_config (optional: day, subunit_currency, resolution). The sensor automatically refreshes when price data updates. Access metadata from attributes: yaxis_min, yaxis_max. Use with config-template-card or any tool that reads entity attributes - perfect for dynamic chart configuration without manual calculations."
         }
     },
     "binary_sensor": {
@@ -487,80 +464,11 @@
             "description": "Whether realtime consumption monitoring is active",
             "long_description": "Indicates if realtime electricity consumption monitoring is enabled and active for your Tibber home. This requires compatible metering hardware (e.g., Tibber Pulse) and an active subscription.",
             "usage_tips": "Use this to verify that realtime consumption data is available. Enable notifications if this changes to 'off' unexpectedly, indicating potential hardware or connectivity issues."
-        }
-    },
-    "number": {
-        "best_price_flex_override": {
-            "description": "Maximum above the daily minimum price that intervals can be and still qualify as 'best price'. Recommended: 15-20 with relaxation enabled (default), or 25-35 without relaxation. Maximum: 50 (hard cap for reliable period detection).",
-            "long_description": "When this entity is enabled, its value overrides the 'Flexibility' setting from the options flow for best price period calculations.",
-            "usage_tips": "Enable this entity to dynamically adjust best price detection via automations. Higher values create longer periods, lower values are stricter."
         },
-        "best_price_min_distance_override": {
+        "chart_data_export": {
-            "description": "Ensures periods are significantly cheaper than the daily average, not just marginally below it. This filters out noise and prevents marking slightly-below-average periods as 'best price' on days with flat prices. Higher values = stricter filtering (only truly cheap periods qualify).",
+            "description": "Data export for dashboard integrations",
-            "long_description": "When this entity is enabled, its value overrides the 'Minimum Distance' setting from the options flow for best price period calculations.",
+            "long_description": "This binary sensor calls the get_chartdata service with your configured YAML parameters and exposes the result as entity attributes. The state is 'on' when the service call succeeds and data is available, 'off' when the call fails or no configuration is set. Perfect for dashboard integrations like ApexCharts that need to read price data from entity attributes.",
-            "usage_tips": "Use in automations to adjust how much better than average the best price periods must be. Higher values require prices to be further below average."
+            "usage_tips": "Configure the YAML parameters in the integration options to match your get_chartdata service call. The sensor will automatically refresh when price data updates (typically after midnight and when tomorrow's data arrives). Access the service response data directly from the entity's attributes - the structure matches exactly what get_chartdata returns."
-        },
-        "best_price_min_period_length_override": {
-            "description": "Minimum duration for a period to be considered as 'best price'. Longer periods are more practical for running appliances like dishwashers or heat pumps. Best price periods require 60 minutes minimum (vs. 30 minutes for peak price warnings) because they should provide meaningful time windows for consumption planning.",
-            "long_description": "When this entity is enabled, its value overrides the 'Minimum Period Length' setting from the options flow for best price period calculations.",
-            "usage_tips": "Increase when your appliances need longer uninterrupted run times (e.g., washing machines, dishwashers)."
-        },
-        "best_price_min_periods_override": {
-            "description": "Minimum number of best price periods to aim for per day. Filters will be relaxed step-by-step to try achieving this count. Only active when 'Achieve Minimum Count' is enabled.",
-            "long_description": "When this entity is enabled, its value overrides the 'Minimum Periods' setting from the options flow for best price period calculations.",
-            "usage_tips": "Adjust dynamically based on how many times per day you need cheap electricity windows."
-        },
-        "best_price_relaxation_attempts_override": {
-            "description": "How many flex levels (attempts) to try before giving up. Each attempt runs all filter combinations at the new flex level. More attempts increase the chance of finding additional periods at the cost of longer processing time.",
-            "long_description": "When this entity is enabled, its value overrides the 'Relaxation Attempts' setting from the options flow for best price period calculations.",
-            "usage_tips": "Increase when periods are hard to find. Decrease for stricter price filtering."
-        },
-        "best_price_gap_count_override": {
-            "description": "Maximum number of consecutive intervals allowed that deviate by exactly one level step from the required level. This prevents periods from being split by occasional level deviations. Gap tolerance requires periods ≥90 minutes (6 intervals) to detect outliers effectively.",
-            "long_description": "When this entity is enabled, its value overrides the 'Gap Tolerance' setting from the options flow for best price period calculations.",
-            "usage_tips": "Increase to allow longer periods with occasional price spikes. Keep low for stricter continuous cheap periods."
-        },
-        "peak_price_flex_override": {
-            "description": "Maximum below the daily maximum price that intervals can be and still qualify as 'peak price'. Recommended: -15 to -20 with relaxation enabled (default), or -25 to -35 without relaxation. Maximum: -50 (hard cap for reliable period detection). Note: Negative values indicate distance below maximum.",
-            "long_description": "When this entity is enabled, its value overrides the 'Flexibility' setting from the options flow for peak price period calculations.",
-            "usage_tips": "Enable this entity to dynamically adjust peak price detection via automations. Higher values create longer peak periods."
-        },
-        "peak_price_min_distance_override": {
-            "description": "Ensures periods are significantly more expensive than the daily average, not just marginally above it. This filters out noise and prevents marking slightly-above-average periods as 'peak price' on days with flat prices. Higher values = stricter filtering (only truly expensive periods qualify).",
-            "long_description": "When this entity is enabled, its value overrides the 'Minimum Distance' setting from the options flow for peak price period calculations.",
-            "usage_tips": "Use in automations to adjust how much higher than average the peak price periods must be."
-        },
-        "peak_price_min_period_length_override": {
-            "description": "Minimum duration for a period to be considered as 'peak price'. Peak price warnings are allowed for shorter periods (30 minutes minimum vs. 60 minutes for best price) because brief expensive spikes are worth alerting about, even if they're too short for consumption planning.",
-            "long_description": "When this entity is enabled, its value overrides the 'Minimum Period Length' setting from the options flow for peak price period calculations.",
-            "usage_tips": "Increase to filter out brief price spikes, focusing on sustained expensive periods."
-        },
-        "peak_price_min_periods_override": {
-            "description": "Minimum number of peak price periods to aim for per day. Filters will be relaxed step-by-step to try achieving this count. Only active when 'Achieve Minimum Count' is enabled.",
-            "long_description": "When this entity is enabled, its value overrides the 'Minimum Periods' setting from the options flow for peak price period calculations.",
-            "usage_tips": "Adjust based on how many peak periods you want to identify and avoid."
-        },
-        "peak_price_relaxation_attempts_override": {
-            "description": "How many flex levels (attempts) to try before giving up. Each attempt runs all filter combinations at the new flex level. More attempts increase the chance of finding additional peak periods at the cost of longer processing time.",
-            "long_description": "When this entity is enabled, its value overrides the 'Relaxation Attempts' setting from the options flow for peak price period calculations.",
-            "usage_tips": "Increase when peak periods are hard to detect. Decrease for stricter peak price filtering."
-        },
-        "peak_price_gap_count_override": {
-            "description": "Maximum number of consecutive intervals allowed that deviate by exactly one level step from the required level. This prevents periods from being split by occasional level deviations. Gap tolerance requires periods ≥90 minutes (6 intervals) to detect outliers effectively.",
-            "long_description": "When this entity is enabled, its value overrides the 'Gap Tolerance' setting from the options flow for peak price period calculations.",
-            "usage_tips": "Increase to identify sustained expensive periods with brief dips. Keep low for stricter continuous peak detection."
-        }
-    },
-    "switch": {
-        "best_price_enable_relaxation_override": {
-            "description": "When enabled, filters will be gradually relaxed if not enough periods are found. This attempts to reach the desired minimum number of periods, which may include less optimal time windows as best-price periods.",
-            "long_description": "When this entity is enabled, its value overrides the 'Achieve Minimum Count' setting from the options flow for best price period calculations.",
-            "usage_tips": "Turn OFF to disable relaxation and use strict filtering only. Turn ON to allow the algorithm to relax criteria to find more periods."
-        },
-        "peak_price_enable_relaxation_override": {
-            "description": "When enabled, filters will be gradually relaxed if not enough periods are found. This attempts to reach the desired minimum number of periods to ensure you're warned about expensive periods even on days with unusual price patterns.",
-            "long_description": "When this entity is enabled, its value overrides the 'Achieve Minimum Count' setting from the options flow for peak price period calculations.",
-            "usage_tips": "Turn OFF to disable relaxation and use strict filtering only. Turn ON to allow the algorithm to relax criteria to find more peak periods."
         }
     },
     "home_types": {
@@ -569,15 +477,5 @@
         "HOUSE": "House",
         "COTTAGE": "Cottage"
     },
-    "time_units": {
-        "day": "{count} day",
-        "days": "{count} days",
-        "hour": "{count} hour",
-        "hours": "{count} hours",
-        "minute": "{count} minute",
-        "minutes": "{count} minutes",
-        "ago": "{parts} ago",
-        "now": "now"
-    },
     "attribution": "Data provided by Tibber"
 }

custom_components/tibber_prices/custom_translations/nb.json

@@ -1,20 +1,7 @@
 {
     "apexcharts": {
-        "title_rating_level": "Prisfaser dagsfremdrift",
+        "title_rating_level": "Prisfaser daglig fremgang",
-        "title_level": "Prisnivå",
+        "title_level": "Prisnivå"
-        "hourly_suffix": "(Ø per time)",
-        "best_price_period_name": "Beste prisperiode",
-        "peak_price_period_name": "Toppprisperiode",
-        "notification": {
-            "metadata_sensor_unavailable": {
-                "title": "Tibber Prices: ApexCharts YAML generert med begrenset funksjonalitet",
-                "message": "Du har nettopp generert en ApexCharts-kort-konfigurasjon via Utviklerverktøy. Diagram-metadata-sensoren er deaktivert, så den genererte YAML-en vil bare vise **grunnleggende funksjonalitet** (auto-skalering, fast gradient på 50%).\n\n**For full funksjonalitet** (optimert skalering, dynamiske gradientfarger):\n1. [Åpne Tibber Prices-integrasjonen](https://my.home-assistant.io/redirect/integration/?domain=tibber_prices)\n2. Aktiver 'Chart Metadata'-sensoren\n3. **Generer YAML-en på nytt** via Utviklerverktøy\n4. **Erstatt den gamle YAML-en** i dashbordet ditt med den nye versjonen\n\n⚠️ Det er ikke nok å bare aktivere sensoren - du må regenerere og erstatte YAML-koden!"
-            },
-            "missing_cards": {
-                "title": "Tibber Prices: ApexCharts YAML kan ikke brukes",
-                "message": "Du har nettopp generert en ApexCharts-kort-konfigurasjon via Utviklerverktøy, men den genererte YAML-en **vil ikke fungere** fordi nødvendige tilpassede kort mangler.\n\n**Manglende kort:**\n{cards}\n\n**For å bruke den genererte YAML-en:**\n1. Klikk på lenkene ovenfor for å installere de manglende kortene fra HACS\n2. Start Home Assistant på nytt (noen ganger nødvendig)\n3. **Generer YAML-en på nytt** via Utviklerverktøy\n4. Legg til YAML-en i dashbordet ditt\n\n⚠️ Den nåværende YAML-koden vil ikke fungere før alle kort er installert!"
-            }
-        }
     },
     "sensor": {
         "current_interval_price": {
@@ -22,7 +9,7 @@
             "long_description": "Viser nåværende pris per kWh fra ditt Tibber-abonnement",
             "usage_tips": "Bruk dette til å spore priser eller lage automatiseringer som kjører når strøm er billig"
         },
-        "current_interval_price_base": {
+        "current_interval_price_major": {
             "description": "Nåværende elektrisitetspris i hovedvaluta (EUR/kWh, NOK/kWh, osv.) for Energi-dashboard",
             "long_description": "Viser nåværende pris per kWh i hovedvalutaenheter (f.eks. EUR/kWh i stedet for ct/kWh, NOK/kWh i stedet for øre/kWh). Denne sensoren er spesielt designet for bruk med Home Assistants Energi-dashboard, som krever priser i standard valutaenheter.",
             "usage_tips": "Bruk denne sensoren når du konfigurerer Energi-dashboardet under Innstillinger → Dashbord → Energi. Velg denne sensoren som 'Entitet med nåværende pris' for automatisk å beregne energikostnadene. Energi-dashboardet multipliserer energiforbruket ditt (kWh) med denne prisen for å vise totale kostnader."
@@ -58,9 +45,9 @@
             "usage_tips": "Bruk dette til å unngå å kjøre apparater i toppristider"
         },
         "average_price_today": {
-            "description": "Typisk elektrisitetspris i dag per kWh (konfigurerbart visningsformat)",
+            "description": "Den gjennomsnittlige elektrisitetsprisen i dag per kWh",
-            "long_description": "Viser prisen per kWh for gjeldende dag fra ditt Tibber-abonnement. **Som standard viser statusen medianen** (motstandsdyktig mot ekstreme prisspiss, viser typisk prisnivå). Du kan endre dette i integrasjonsinnstillingene for å vise det aritmetiske gjennomsnittet i stedet. Den alternative verdien er tilgjengelig som attributt.",
+            "long_description": "Viser gjennomsnittsprisen per kWh for gjeldende dag fra ditt Tibber-abonnement",
-            "usage_tips": "Bruk dette som baseline for å sammenligne nåværende priser. For beregninger bruk: {{ state_attr('sensor.average_price_today', 'price_mean') }}"
+            "usage_tips": "Bruk dette som en baseline for å sammenligne nåværende priser"
         },
         "lowest_price_tomorrow": {
             "description": "Den laveste elektrisitetsprisen i morgen per kWh",
@@ -73,9 +60,9 @@
             "usage_tips": "Bruk dette til å unngå å kjøre apparater i morgendagens toppristider. Nyttig for å planlegge rundt dyre perioder."
         },
         "average_price_tomorrow": {
-            "description": "Typisk elektrisitetspris i morgen per kWh (konfigurerbart visningsformat)",
+            "description": "Den gjennomsnittlige elektrisitetsprisen i morgen per kWh",
-            "long_description": "Viser prisen per kWh for morgendagen fra ditt Tibber-abonnement. **Som standard viser statusen medianen** (motstandsdyktig mot ekstreme prisspiss). Du kan endre dette i integrasjonsinnstillingene for å vise det aritmetiske gjennomsnittet i stedet. Den alternative verdien er tilgjengelig som attributt. Denne sensoren blir utilgjengelig inntil morgendagens data er publisert av Tibber (vanligvis rundt 13:00-14:00 CET).",
+            "long_description": "Viser gjennomsnittsprisen per kWh for morgendagen fra ditt Tibber-abonnement. Denne sensoren blir utilgjengelig inntil morgendagens data er publisert av Tibber (vanligvis rundt 13:00-14:00 CET).",
-            "usage_tips": "Bruk dette som baseline for å sammenligne morgendagens priser og planlegge forbruk. Sammenlign med dagens median for å se om morgendagen vil være mer eller mindre dyr totalt sett."
+            "usage_tips": "Bruk dette som en baseline for å sammenligne morgendagens priser og planlegge forbruk. Sammenlign med dagens gjennomsnitt for å se om morgendagen vil være mer eller mindre dyr totalt sett."
         },
         "yesterday_price_level": {
             "description": "Aggregert prisnivå for i går",
@@ -108,14 +95,14 @@
             "usage_tips": "Bruk dette for å planlegge morgendagens energiforbruk basert på dine personlige pristerskelverdier. Sammenlign med i dag for å bestemme om du skal flytte forbruk til i morgen eller bruke energi i dag."
         },
         "trailing_price_average": {
-            "description": "Typisk elektrisitetspris for de siste 24 timene per kWh (konfigurerbart visningsformat)",
+            "description": "Den gjennomsnittlige elektrisitetsprisen for de siste 24 timene per kWh",
-            "long_description": "Viser prisen per kWh beregnet fra de siste 24 timene. **Som standard viser statusen medianen** (motstandsdyktig mot ekstreme prisspiss, viser typisk prisnivå). Du kan endre dette i integrasjonsinnstillingene for å vise det aritmetiske gjennomsnittet i stedet. Den alternative verdien er tilgjengelig som attributt. Oppdateres hvert 15. minutt.",
+            "long_description": "Viser gjennomsnittsprisen per kWh beregnet fra de siste 24 timene (glidende gjennomsnitt) fra ditt Tibber-abonnement. Dette gir et rullende gjennomsnitt som oppdateres hvert 15. minutt basert på historiske data.",
-            "usage_tips": "Bruk statusverdien for å se det typiske nåværende prisnivået. For kostnadsberegninger bruk: {{ state_attr('sensor.trailing_price_average', 'price_mean') }}"
+            "usage_tips": "Bruk dette til å sammenligne nåværende priser mot nylige trender. En nåværende pris betydelig over dette gjennomsnittet kan indikere et godt tidspunkt å redusere forbruket."
         },
         "leading_price_average": {
-            "description": "Typisk elektrisitetspris for de neste 24 timene per kWh (konfigurerbart visningsformat)",
+            "description": "Den gjennomsnittlige elektrisitetsprisen for de neste 24 timene per kWh",
-            "long_description": "Viser prisen per kWh beregnet fra de neste 24 timene. **Som standard viser statusen medianen** (motstandsdyktig mot ekstreme prisspiss, viser forventet prisnivå). Du kan endre dette i integrasjonsinnstillingene for å vise det aritmetiske gjennomsnittet i stedet. Den alternative verdien er tilgjengelig som attributt.",
+            "long_description": "Viser gjennomsnittsprisen per kWh beregnet fra de neste 24 timene (fremtidsrettet gjennomsnitt) fra ditt Tibber-abonnement. Dette gir et fremtidsrettet gjennomsnitt basert på tilgjengelige prognosedata.",
-            "usage_tips": "Bruk statusverdien for å se det typiske kommende prisnivået. For kostnadsberegninger bruk: {{ state_attr('sensor.leading_price_average', 'price_mean') }}"
+            "usage_tips": "Bruk dette til å planlegge energibruk. Hvis nåværende pris er under det fremtidsrettede gjennomsnittet, kan det være et godt tidspunkt å kjøre energikrevende apparater."
         },
         "trailing_price_min": {
             "description": "Den minste elektrisitetsprisen for de siste 24 timene per kWh",
@@ -292,24 +279,24 @@
             "long_description": "Viser tidsstempelet for siste tilgjengelige prisdataintervall fra ditt Tibber-abonnement"
         },
         "today_volatility": {
-            "description": "Hvor mye strømprisene endrer seg i dag",
+            "description": "Prisvolatilitetsklassifisering for i dag",
-            "long_description": "Viser om dagens priser er stabile eller har store svingninger. Lav volatilitet betyr ganske jevne priser – timing betyr lite. Høy volatilitet betyr tydelige prisforskjeller gjennom dagen – en god sjanse til å flytte forbruk til billigere perioder. `price_coefficient_variation_%` viser prosentverdien, `price_spread` viser den absolutte prisspennet.",
+            "long_description": "Viser hvor mye strømprisene varierer gjennom dagen basert på spredningen (forskjellen mellom høyeste og laveste pris). Klassifisering: LOW = spredning < 5øre, MODERATE = 5-15øre, HIGH = 15-30øre, VERY HIGH = >30øre.",
-            "usage_tips": "Bruk dette for å avgjøre om optimalisering er verdt innsatsen. Ved lav volatilitet kan du kjøre enheter når som helst. Ved høy volatilitet sparer du merkbart ved å følge Best Price-perioder."
+            "usage_tips": "Bruk dette til å bestemme om prisbasert optimalisering er verdt det. For eksempel, med et balkongbatteri som har 15% effektivitetstap, er optimalisering kun meningsfull når volatiliteten er minst MODERATE. Opprett automatiseringer som sjekker volatilitet før planlegging av lade-/utladingssykluser."
         },
         "tomorrow_volatility": {
-            "description": "Hvor mye strømprisene vil endre seg i morgen",
+            "description": "Prisvolatilitetsklassifisering for i morgen",
-            "long_description": "Viser om prisene i morgen blir stabile eller får store svingninger. Tilgjengelig når morgendagens data er publisert (vanligvis 13:00–14:00 CET). Lav volatilitet betyr jevne priser – timing er ikke kritisk. Høy volatilitet betyr tydelige prisforskjeller gjennom dagen – en god mulighet til å planlegge energikrevende oppgaver. `price_coefficient_variation_%` viser prosentverdien, `price_spread` viser den absolutte prisspennet.",
+            "long_description": "Viser hvor mye strømprisene vil variere gjennom morgendagen basert på spredningen (forskjellen mellom høyeste og laveste pris). Blir utilgjengelig til morgendagens data er publisert (typisk 13:00-14:00 CET).",
-            "usage_tips": "Bruk dette til å planlegge morgendagens forbruk. Høy volatilitet? Planlegg fleksible laster i Best Price-perioder. Lav volatilitet? Kjør enheter når det passer deg."
+            "usage_tips": "Bruk dette til forhåndsplanlegging av morgendagens energiforbruk. Hvis morgendagen har HIGH eller VERY HIGH volatilitet, er det verdt å optimalisere tidspunktet for energiforbruk. Hvis LOW, kan du kjøre enheter når som helst uten betydelige kostnadsforskjeller."
         },
         "next_24h_volatility": {
-            "description": "Hvor mye prisene endrer seg de neste 24 timene",
+            "description": "Prisvolatilitetsklassifisering for de rullerende neste 24 timene",
-            "long_description": "Viser prisvolatilitet for et rullerende 24-timers vindu fra nå (oppdateres hvert 15. minutt). Lav volatilitet betyr jevne priser. Høy volatilitet betyr merkbare prissvingninger og mulighet for optimalisering. I motsetning til i dag/i morgen-sensorer krysser denne daggrenser og gir en kontinuerlig fremoverskuende vurdering. `price_coefficient_variation_%` viser prosentverdien, `price_spread` viser den absolutte prisspennet.",
+            "long_description": "Viser hvor mye strømprisene varierer i de neste 24 timene fra nå (rullerende vindu). Dette krysser daggrenser og oppdateres hvert 15. minutt, og gir en fremoverskuende volatilitetsvurdering uavhengig av kalenderdager.",
-            "usage_tips": "Best for beslutninger i sanntid. Bruk når du planlegger batterilading eller andre fleksible laster som kan gå over midnatt. Gir et konsistent 24t-bilde uavhengig av kalenderdag."
+            "usage_tips": "Beste sensor for sanntids optimaliseringsbeslutninger. I motsetning til dagens/morgendagens sensorer som bytter ved midnatt, gir denne kontinuerlig 24t volatilitetsvurdering. Bruk til batteriladingsstrategier som spenner over daggrenser."
         },
         "today_tomorrow_volatility": {
-            "description": "Kombinert prisvolatilitet for i dag og i morgen",
+            "description": "Kombinert prisvolatilitetsklassifisering for i dag og i morgen",
-            "long_description": "Viser samlet volatilitet når i dag og i morgen sees sammen (når morgendata er tilgjengelig). Viser om det finnes klare prisforskjeller over dagsgrensen. Faller tilbake til kun i dag hvis morgendata mangler. Nyttig for flerdagers optimalisering. `price_coefficient_variation_%` viser prosentverdien, `price_spread` viser den absolutte prisspennet.",
+            "long_description": "Viser volatilitet på tvers av både i dag og i morgen kombinert (når morgendagens data er tilgjengelig). Gir en utvidet visning av prisvariasjoner som spenner over opptil 48 timer. Faller tilbake til bare i dag når morgendagens data ikke er tilgjengelig ennå.",
-            "usage_tips": "Bruk for oppgaver som går over flere dager. Sjekk om prisforskjellene er store nok til å planlegge etter. De enkelte dagssensorene viser bidrag per dag om du trenger mer detalj."
+            "usage_tips": "Bruk dette for flersdagers planlegging og for å forstå om prismuligheter eksisterer på tvers av dags grensen. Attributtene 'today_volatility' og 'tomorrow_volatility' viser individuelle dagbidrag. Nyttig for planlegging av ladeøkter som kan strekke seg over midnatt."
         },
         "data_lifecycle_status": {
             "description": "Gjeldende tilstand for prisdatalivssyklus og hurtigbufring",
@@ -317,49 +304,39 @@
             "usage_tips": "Bruk denne diagnosesensoren for å forstå dataferskhet og API-anropsmønstre. Sjekk 'cache_age'-attributtet for å se hvor gamle de nåværende dataene er. Overvåk 'next_api_poll' for å vite når neste oppdatering er planlagt. Bruk 'data_completeness' for å se om data for i går/i dag/i morgen er tilgjengelig. 'api_calls_today'-telleren hjelper med å spore API-bruk. Perfekt for feilsøking eller forståelse av integrasjonens oppførsel."
         },
         "best_price_end_time": {
-            "description": "Total lengde på nåværende eller neste billigperiode (state i timer, attributt i minutter)",
+            "description": "Når gjeldende eller neste billigperiode slutter",
-            "long_description": "Viser hvor lenge billigperioden varer. State bruker timer (desimal) for lesbar UI; attributtet `period_duration_minutes` beholder avrundede minutter for automasjoner. Aktiv → varighet for gjeldende periode, ellers neste.",
+            "long_description": "Viser sluttidspunktet for gjeldende billigperiode når aktiv, eller slutten av neste periode når ingen periode er aktiv. Viser alltid en nyttig tidsreferanse for planlegging. Returnerer 'Ukjent' bare når ingen perioder er konfigurert.",
-            "usage_tips": "UI kan vise 1,5 t mens `period_duration_minutes` = 90 for automasjoner."
+            "usage_tips": "Bruk dette til å vise en nedtelling som 'Billigperiode slutter om 2 timer' (når aktiv) eller 'Neste billigperiode slutter kl 14:00' (når inaktiv). Home Assistant viser automatisk relativ tid for tidsstempelsensorer."
-        },
-        "best_price_period_duration": {
-            "description": "Lengde på gjeldende/neste billigperiode",
-            "long_description": "Total varighet av gjeldende eller neste billigperiode. State vises i timer (f.eks. 1,5 t) for enkel lesing i UI, mens attributtet `period_duration_minutes` gir samme verdi i minutter (f.eks. 90) for automasjoner. Denne verdien representerer den **fulle planlagte varigheten** av perioden og er konstant gjennom hele perioden, selv om gjenværende tid (remaining_minutes) reduseres.",
-            "usage_tips": "Kombiner med remaining_minutes for å beregne når langvarige enheter skal stoppes: Perioden startet for `period_duration_minutes - remaining_minutes` minutter siden. Dette attributtet støtter energioptimeringsstrategier ved å hjelpe til med å planlegge høyforbruksaktiviteter innenfor billige perioder."
         },
         "best_price_remaining_minutes": {
-            "description": "Gjenværende tid i gjeldende billigperiode",
+            "description": "Gjenværende minutter i gjeldende billigperiode (0 når inaktiv)",
-            "long_description": "Viser hvor mye tid som gjenstår i gjeldende billigperiode. State vises i timer (f.eks. 0,75 t) for enkel lesing i dashboards, mens attributtet `remaining_minutes` gir samme tid i minutter (f.eks. 45) for automasjonsbetingelser. **Nedtellingstimer**: Denne verdien reduseres hvert minutt under en aktiv periode. Returnerer 0 når ingen billigperiode er aktiv. Oppdateres hvert minutt.",
+            "long_description": "Viser hvor mange minutter som er igjen i gjeldende billigperiode. Returnerer 0 når ingen periode er aktiv. Oppdateres hvert minutt. Sjekk binary_sensor.best_price_period for å se om en periode er aktiv.",
-            "usage_tips": "For automasjoner: Bruk attributtet `remaining_minutes` som 'Hvis remaining_minutes > 60, start oppvaskmaskinen nå (nok tid til å fullføre)' eller 'Hvis remaining_minutes < 15, fullfør gjeldende syklus snart'. UI viser brukervennlige timer (f.eks. 1,25 t). Verdi 0 indikerer ingen aktiv billigperiode."
+            "usage_tips": "Perfekt for automatiseringer: 'Hvis remaining_minutes > 0 OG remaining_minutes < 30, start vaskemaskin nå'. Verdien 0 gjør det enkelt å sjekke om en periode er aktiv (verdi > 0) eller ikke (verdi = 0)."
         },
         "best_price_progress": {
             "description": "Fremdrift gjennom gjeldende billigperiode (0% når inaktiv)",
-            "long_description": "Viser fremdrift gjennom gjeldende billigperiode som 0-100%. Returnerer 0% når ingen periode er aktiv. Oppdateres hvert minutt. 0% betyr perioden nettopp startet, 100% betyr den slutter snart.",
+            "long_description": "Viser fremdrift gjennom gjeldende billigperiode som 0-100%. Returnerer 0% når ingen periode er aktiv. Oppdateres hvert minutt. 0% betyr periode nettopp startet, 100% betyr den snart slutter.",
-            "usage_tips": "Flott for visuelle fremgangsindikatorer. Bruk i automatiseringer: 'Hvis progress > 0 OG progress > 75, send varsel om at billigperioden snart slutter'. Verdi 0 indikerer ingen aktiv periode."
+            "usage_tips": "Flott for visuelle fremdriftslinjer. Bruk i automatiseringer: 'Hvis progress > 0 OG progress > 75, send varsel om at billigperiode snart slutter'. Verdi 0 indikerer ingen aktiv periode."
         },
         "best_price_next_start_time": {
-            "description": "Total lengde på nåværende eller neste dyr-periode (state i timer, attributt i minutter)",
+            "description": "Når neste billigperiode starter",
-            "long_description": "Viser hvor lenge den dyre perioden varer. State bruker timer (desimal) for UI; attributtet `period_duration_minutes` beholder avrundede minutter for automasjoner. Aktiv → varighet for gjeldende periode, ellers neste.",
+            "long_description": "Viser når neste kommende billigperiode starter. Under en aktiv periode viser dette starten av NESTE periode etter den gjeldende. Returnerer 'Ukjent' bare når ingen fremtidige perioder er konfigurert.",
-            "usage_tips": "UI kan vise 0,75 t mens `period_duration_minutes` = 45 for automasjoner."
+            "usage_tips": "Alltid nyttig for planlegging: 'Neste billigperiode starter om 3 timer' (enten du er i en periode nå eller ikke). Kombiner med automatiseringer: 'Når neste starttid er om 10 minutter, send varsel for å forberede vaskemaskin'."
         },
         "best_price_next_in_minutes": {
-            "description": "Tid til neste billigperiode",
+            "description": "Minutter til neste billigperiode starter (0 ved overgang)",
-            "long_description": "Viser hvor lenge til neste billigperiode. State vises i timer (f.eks. 2,25 t) for dashboards, mens attributtet `next_in_minutes` gir minutter (f.eks. 135) for automasjonsbetingelser. Under en aktiv periode viser dette tiden til perioden ETTER den gjeldende. Returnerer 0 under korte overgangsmomenter. Oppdateres hvert minutt.",
+            "long_description": "Viser minutter til neste billigperiode starter. Under en aktiv periode viser dette tiden til perioden ETTER den gjeldende. Returnerer 0 under korte overgangsmomenter. Oppdateres hvert minutt.",
-            "usage_tips": "For automasjoner: Bruk attributtet `next_in_minutes` som 'Hvis next_in_minutes > 0 OG next_in_minutes < 15, vent før start av oppvaskmaskin'. Verdi > 0 indikerer alltid at en fremtidig periode er planlagt."
+            "usage_tips": "Perfekt for 'vent til billigperiode' automatiseringer: 'Hvis next_in_minutes > 0 OG next_in_minutes < 15, vent før oppvaskmaskin startes'. Verdi > 0 indikerer alltid at en fremtidig periode er planlagt."
         },
         "peak_price_end_time": {
-            "description": "Tid til neste dyr-periode (state i timer, attributt i minutter)",
+            "description": "Når gjeldende eller neste dyrperiode slutter",
-            "long_description": "Viser hvor lenge til neste dyre periode starter. State bruker timer (desimal); attributtet `next_in_minutes` beholder avrundede minutter for automasjoner. Under aktiv periode viser dette tiden til perioden etter den nåværende. 0 i korte overgangsøyeblikk. Oppdateres hvert minutt.",
+            "long_description": "Viser sluttidspunktet for gjeldende dyrperiode når aktiv, eller slutten av neste periode når ingen periode er aktiv. Viser alltid en nyttig tidsreferanse for planlegging. Returnerer 'Ukjent' bare når ingen perioder er konfigurert.",
-            "usage_tips": "Bruk `next_in_minutes` i automasjoner (f.eks. < 10) mens state er lett å lese i timer."
+            "usage_tips": "Bruk dette til å vise 'Dyrperiode slutter om 1 time' (når aktiv) eller 'Neste dyrperiode slutter kl 18:00' (når inaktiv). Kombiner med automatiseringer for å gjenoppta drift etter topp."
-        },
-        "peak_price_period_duration": {
-            "description": "Lengde på gjeldende/neste dyr periode",
-            "long_description": "Total varighet av gjeldende eller neste dyre periode. State vises i timer (f.eks. 1,5 t) for enkel lesing i UI, mens attributtet `period_duration_minutes` gir samme verdi i minutter (f.eks. 90) for automasjoner. Denne verdien representerer den **fulle planlagte varigheten** av perioden og er konstant gjennom hele perioden, selv om gjenværende tid (remaining_minutes) reduseres.",
-            "usage_tips": "Kombiner med remaining_minutes for å beregne når langvarige enheter skal stoppes: Perioden startet for `period_duration_minutes - remaining_minutes` minutter siden. Dette attributtet støtter energisparingsstrategier ved å hjelpe til med å planlegge høyforbruksaktiviteter utenfor dyre perioder."
         },
         "peak_price_remaining_minutes": {
-            "description": "Gjenværende tid i gjeldende dyre periode",
+            "description": "Gjenværende minutter i gjeldende dyrperiode (0 når inaktiv)",
-            "long_description": "Viser hvor mye tid som gjenstår i gjeldende dyre periode. State vises i timer (f.eks. 0,75 t) for enkel lesing i dashboards, mens attributtet `remaining_minutes` gir samme tid i minutter (f.eks. 45) for automasjonsbetingelser. **Nedtellingstimer**: Denne verdien reduseres hvert minutt under en aktiv periode. Returnerer 0 når ingen dyr periode er aktiv. Oppdateres hvert minutt.",
+            "long_description": "Viser hvor mange minutter som er igjen i gjeldende dyrperiode. Returnerer 0 når ingen periode er aktiv. Oppdateres hvert minutt. Sjekk binary_sensor.peak_price_period for å se om en periode er aktiv.",
-            "usage_tips": "For automasjoner: Bruk attributtet `remaining_minutes` som 'Hvis remaining_minutes > 60, avbryt utsatt ladeøkt' eller 'Hvis remaining_minutes < 15, fortsett normal drift snart'. UI viser brukervennlige timer (f.eks. 1,0 t). Verdi 0 indikerer ingen aktiv dyr periode."
+            "usage_tips": "Bruk i automatiseringer: 'Hvis remaining_minutes > 60, avbryt utsatt ladeøkt'. Verdi 0 gjør det enkelt å skille mellom aktive (verdi > 0) og inaktive (verdi = 0) perioder."
         },
         "peak_price_progress": {
             "description": "Fremdrift gjennom gjeldende dyrperiode (0% når inaktiv)",
@@ -372,9 +349,19 @@
             "usage_tips": "Alltid nyttig for planlegging: 'Neste dyrperiode starter om 2 timer'. Automatisering: 'Når neste starttid er om 30 minutter, reduser varmetemperatur forebyggende'."
         },
         "peak_price_next_in_minutes": {
-            "description": "Tid til neste dyre periode",
+            "description": "Minutter til neste dyrperiode starter (0 ved overgang)",
-            "long_description": "Viser hvor lenge til neste dyre periode starter. State vises i timer (f.eks. 0,5 t) for dashboards, mens attributtet `next_in_minutes` gir minutter (f.eks. 30) for automasjonsbetingelser. Under en aktiv periode viser dette tiden til perioden ETTER den gjeldende. Returnerer 0 under korte overgangsmomenter. Oppdateres hvert minutt.",
+            "long_description": "Viser minutter til neste dyrperiode starter. Under en aktiv periode viser dette tiden til perioden ETTER den gjeldende. Returnerer 0 under korte overgangsmomenter. Oppdateres hvert minutt.",
-            "usage_tips": "For automasjoner: Bruk attributtet `next_in_minutes` som 'Hvis next_in_minutes > 0 OG next_in_minutes < 10, fullfør gjeldende ladesyklus nå før prisene øker'. Verdi > 0 indikerer alltid at en fremtidig dyr periode er planlagt."
+            "usage_tips": "Forebyggende automatisering: 'Hvis next_in_minutes > 0 OG next_in_minutes < 10, fullfør gjeldende ladesyklus nå før prisene øker'."
+        },
+        "best_price_period_duration": {
+            "description": "Total varighet av gjeldende eller neste billigperiode i minutter",
+            "long_description": "Viser den totale varigheten av billigperioden i minutter. Under en aktiv periode viser dette hele varigheten av gjeldende periode. Når ingen periode er aktiv, viser dette varigheten av neste kommende periode. Eksempel: '90 minutter' for en 1,5-timers periode.",
+            "usage_tips": "Kombiner med remaining_minutes for å planlegge oppgaver: 'Hvis duration = 120 OG remaining_minutes > 90, start vaskemaskin (nok tid til å fullføre)'. Nyttig for å forstå om perioder er lange nok for strømkrevende oppgaver."
+        },
+        "peak_price_period_duration": {
+            "description": "Total varighet av gjeldende eller neste dyrperiode i minutter",
+            "long_description": "Viser den totale varigheten av dyrperioden i minutter. Under en aktiv periode viser dette hele varigheten av gjeldende periode. Når ingen periode er aktiv, viser dette varigheten av neste kommende periode. Eksempel: '60 minutter' for en 1-times periode.",
+            "usage_tips": "Bruk til å planlegge energibesparelsestiltak: 'Hvis duration > 120, reduser varmetemperatur mer aggressivt (lang dyr periode)'. Hjelper med å vurdere hvor mye energiforbruk må reduseres."
         },
         "home_type": {
             "description": "Type bolig (leilighet, hus osv.)",
@@ -450,11 +437,6 @@
             "description": "Dataeksport for dashboardintegrasjoner",
             "long_description": "Denne sensoren kaller get_chartdata-tjenesten med din konfigurerte YAML-konfigurasjon og eksponerer resultatet som entitetsattributter. Status viser 'ready' når data er tilgjengelig, 'error' ved feil, eller 'pending' før første kall. Perfekt for dashboardintegrasjoner som ApexCharts som trenger å lese prisdata fra entitetsattributter.",
             "usage_tips": "Konfigurer YAML-parametrene i integrasjonsinnstillingene for å matche get_chartdata-tjenestekallet ditt. Sensoren vil automatisk oppdatere når prisdata oppdateres (typisk etter midnatt og når morgendagens data ankommer). Få tilgang til tjenesteresponsdataene direkte fra entitetens attributter - strukturen matcher nøyaktig det get_chartdata returnerer."
-        },
-        "chart_metadata": {
-            "description": "Lettvekts metadata for diagramkonfigurasjon",
-            "long_description": "Gir essensielle diagramkonfigurasjonsverdier som sensorattributter. Nyttig for ethvert diagramkort som trenger Y-aksegrenser. Sensoren kaller get_chartdata med kun-metadata-modus (ingen databehandling) og trekker ut: yaxis_min, yaxis_max (foreslått Y-akseområde for optimal skalering). Status reflekterer tjenestekallresultatet: 'ready' ved suksess, 'error' ved feil, 'pending' under initialisering.",
-            "usage_tips": "Konfigurer via configuration.yaml under tibber_prices.chart_metadata_config (valgfritt: day, subunit_currency, resolution). Sensoren oppdateres automatisk når prisdata endres. Få tilgang til metadata fra attributter: yaxis_min, yaxis_max. Bruk med config-template-card eller ethvert verktøy som leser entitetsattributter - perfekt for dynamisk diagramkonfigurasjon uten manuelle beregninger."
         }
     },
     "binary_sensor": {
@@ -487,80 +469,11 @@
             "description": "Om sanntidsforbruksovervåking er aktiv",
             "long_description": "Indikerer om sanntidsovervåking av strømforbruk er aktivert og aktiv for ditt Tibber-hjem. Dette krever kompatibel målehardware (f.eks. Tibber Pulse) og et aktivt abonnement.",
             "usage_tips": "Bruk dette for å bekrefte at sanntidsforbruksdata er tilgjengelig. Aktiver varsler hvis dette endres til 'av' uventet, noe som indikerer potensielle maskinvare- eller tilkoblingsproblemer."
-        }
-    },
-    "number": {
-        "best_price_flex_override": {
-            "description": "Maksimal prosent over daglig minimumspris som intervaller kan ha og fortsatt kvalifisere som 'beste pris'. Anbefalt: 15-20 med lemping aktivert (standard), eller 25-35 uten lemping. Maksimum: 50 (tak for pålitelig periodedeteksjon).",
-            "long_description": "Når denne entiteten er aktivert, overstyrer verdien 'Fleksibilitet'-innstillingen fra alternativer-dialogen for beste pris-periodeberegninger.",
-            "usage_tips": "Aktiver denne entiteten for å dynamisk justere beste pris-deteksjon via automatiseringer, f.eks. høyere fleksibilitet for kritiske laster eller strengere krav for fleksible apparater."
         },
-        "best_price_min_distance_override": {
+        "chart_data_export": {
-            "description": "Minimum prosentavstand under daglig gjennomsnitt. Intervaller må være så langt under gjennomsnittet for å kvalifisere som 'beste pris'. Hjelper med å skille ekte lavprisperioder fra gjennomsnittspriser.",
+            "description": "Dataeksport for dashboardintegrasjoner",
-            "long_description": "Når denne entiteten er aktivert, overstyrer verdien 'Minimumsavstand'-innstillingen fra alternativer-dialogen for beste pris-periodeberegninger.",
+            "long_description": "Denne binærsensoren kaller get_chartdata-tjenesten for å eksportere prisdata i formater som er kompatible med ApexCharts og andre dashboardverktøy. Dataeksporten inkluderer historiske og fremtidsrettede prisdata strukturert for visualisering.",
-            "usage_tips": "Øk verdien for strengere beste pris-kriterier. Reduser hvis for få perioder blir oppdaget."
+            "usage_tips": "Konfigurer YAML-parametrene i integrasjonsalternativene. Bruk denne sensoren til å trigge dataeksporthendelser for dashboards. Når den slås på, eksporteres data til en fil eller tjeneste som er konfigurert for integrering med ApexCharts eller tilsvarende visualiseringsverktøy."
-        },
-        "best_price_min_period_length_override": {
-            "description": "Minimum periodelengde i 15-minutters intervaller. Perioder kortere enn dette blir ikke rapportert. Eksempel: 2 = minimum 30 minutter.",
-            "long_description": "Når denne entiteten er aktivert, overstyrer verdien 'Minimum periodelengde'-innstillingen fra alternativer-dialogen for beste pris-periodeberegninger.",
-            "usage_tips": "Juster til typisk apparatkjøretid: 2 (30 min) for hurtigprogrammer, 4-8 (1-2 timer) for normale sykluser, 8+ for lange ECO-programmer."
-        },
-        "best_price_min_periods_override": {
-            "description": "Minimum antall beste pris-perioder å finne daglig. Når lemping er aktivert, vil systemet automatisk justere kriterier for å oppnå dette antallet.",
-            "long_description": "Når denne entiteten er aktivert, overstyrer verdien 'Minimum perioder'-innstillingen fra alternativer-dialogen for beste pris-periodeberegninger.",
-            "usage_tips": "Sett dette til antall tidskritiske oppgaver du har daglig. Eksempel: 2 for to vaskemaskinkjøringer."
-        },
-        "best_price_relaxation_attempts_override": {
-            "description": "Antall forsøk på å gradvis lempe kriteriene for å oppnå minimum periodeantall. Hvert forsøk øker fleksibiliteten med 3 prosent. Ved 0 brukes kun basiskriterier.",
-            "long_description": "Når denne entiteten er aktivert, overstyrer verdien 'Lemping forsøk'-innstillingen fra alternativer-dialogen for beste pris-periodeberegninger.",
-            "usage_tips": "Høyere verdier gjør periodedeteksjon mer adaptiv for dager med stabile priser. Sett til 0 for å tvinge strenge kriterier uten lemping."
-        },
-        "best_price_gap_count_override": {
-            "description": "Maksimalt antall dyrere intervaller som kan tillates mellom billige intervaller mens de fortsatt regnes som en sammenhengende periode. Ved 0 må billige intervaller være påfølgende.",
-            "long_description": "Når denne entiteten er aktivert, overstyrer verdien 'Gaptoleranse'-innstillingen fra alternativer-dialogen for beste pris-periodeberegninger.",
-            "usage_tips": "Øk dette for apparater med variabel last (f.eks. varmepumper) som kan tåle korte dyrere intervaller. Sett til 0 for kontinuerlige billige perioder."
-        },
-        "peak_price_flex_override": {
-            "description": "Maksimal prosent under daglig maksimumspris som intervaller kan ha og fortsatt kvalifisere som 'topppris'. Samme anbefalinger som for beste pris-fleksibilitet.",
-            "long_description": "Når denne entiteten er aktivert, overstyrer verdien 'Fleksibilitet'-innstillingen fra alternativer-dialogen for topppris-periodeberegninger.",
-            "usage_tips": "Bruk dette for å justere topppris-terskelen ved kjøretid for automatiseringer som unngår forbruk under dyre timer."
-        },
-        "peak_price_min_distance_override": {
-            "description": "Minimum prosentavstand over daglig gjennomsnitt. Intervaller må være så langt over gjennomsnittet for å kvalifisere som 'topppris'.",
-            "long_description": "Når denne entiteten er aktivert, overstyrer verdien 'Minimumsavstand'-innstillingen fra alternativer-dialogen for topppris-periodeberegninger.",
-            "usage_tips": "Øk verdien for kun å fange ekstreme pristopper. Reduser for å inkludere flere høypristider."
-        },
-        "peak_price_min_period_length_override": {
-            "description": "Minimum periodelengde i 15-minutters intervaller for topppriser. Kortere pristopper rapporteres ikke som perioder.",
-            "long_description": "Når denne entiteten er aktivert, overstyrer verdien 'Minimum periodelengde'-innstillingen fra alternativer-dialogen for topppris-periodeberegninger.",
-            "usage_tips": "Kortere verdier fanger korte pristopper. Lengre verdier fokuserer på vedvarende høyprisperioder."
-        },
-        "peak_price_min_periods_override": {
-            "description": "Minimum antall topppris-perioder å finne daglig.",
-            "long_description": "Når denne entiteten er aktivert, overstyrer verdien 'Minimum perioder'-innstillingen fra alternativer-dialogen for topppris-periodeberegninger.",
-            "usage_tips": "Sett dette basert på hvor mange høyprisperioder du vil fange per dag for automatiseringer."
-        },
-        "peak_price_relaxation_attempts_override": {
-            "description": "Antall forsøk på å lempe kriteriene for å oppnå minimum antall topppris-perioder.",
-            "long_description": "Når denne entiteten er aktivert, overstyrer verdien 'Lemping forsøk'-innstillingen fra alternativer-dialogen for topppris-periodeberegninger.",
-            "usage_tips": "Øk dette hvis ingen perioder blir funnet på dager med stabile priser. Sett til 0 for å tvinge strenge kriterier."
-        },
-        "peak_price_gap_count_override": {
-            "description": "Maksimalt antall billigere intervaller som kan tillates mellom dyre intervaller mens de fortsatt regnes som en topppris-periode.",
-            "long_description": "Når denne entiteten er aktivert, overstyrer verdien 'Gaptoleranse'-innstillingen fra alternativer-dialogen for topppris-periodeberegninger.",
-            "usage_tips": "Høyere verdier fanger lengre høyprisperioder selv med korte prisdykk. Sett til 0 for strengt sammenhengende topppriser."
-        }
-    },
-    "switch": {
-        "best_price_enable_relaxation_override": {
-            "description": "Når aktivert, lempes kriteriene automatisk for å oppnå minimum periodeantall. Når deaktivert, rapporteres kun perioder som oppfyller strenge kriterier (muligens null perioder på dager med stabile priser).",
-            "long_description": "Når denne entiteten er aktivert, overstyrer verdien 'Oppnå minimumsantall'-innstillingen fra alternativer-dialogen for beste pris-periodeberegninger.",
-            "usage_tips": "Aktiver dette for garanterte daglige automatiseringsmuligheter. Deaktiver hvis du kun vil ha virkelig billige perioder, selv om det betyr ingen perioder på noen dager."
-        },
-        "peak_price_enable_relaxation_override": {
-            "description": "Når aktivert, lempes kriteriene automatisk for å oppnå minimum periodeantall. Når deaktivert, rapporteres kun ekte pristopper.",
-            "long_description": "Når denne entiteten er aktivert, overstyrer verdien 'Oppnå minimumsantall'-innstillingen fra alternativer-dialogen for topppris-periodeberegninger.",
-            "usage_tips": "Aktiver dette for konsistente topppris-varsler. Deaktiver for kun å fange ekstreme pristopper."
         }
     },
     "home_types": {
@@ -569,15 +482,5 @@
         "HOUSE": "Hus",
         "COTTAGE": "Hytte"
     },
-    "time_units": {
-        "day": "{count} dag",
-        "days": "{count} dager",
-        "hour": "{count} time",
-        "hours": "{count} timer",
-        "minute": "{count} minutt",
-        "minutes": "{count} minutter",
-        "ago": "{parts} siden",
-        "now": "nå"
-    },
     "attribution": "Data levert av Tibber"
 }

custom_components/tibber_prices/custom_translations/nl.json

@@ -1,40 +1,27 @@
 {
     "apexcharts": {
-        "title_rating_level": "Prijsfasen dagverloop",
+        "title_rating_level": "Prijsfasen dagelijkse voortgang",
-        "title_level": "Prijsniveau",
+        "title_level": "Prijsniveau"
-        "hourly_suffix": "(Ø per uur)",
-        "best_price_period_name": "Beste prijsperiode",
-        "peak_price_period_name": "Piekprijsperiode",
-        "notification": {
-            "metadata_sensor_unavailable": {
-                "title": "Tibber Prices: ApexCharts YAML gegenereerd met beperkte functionaliteit",
-                "message": "Je hebt zojuist een ApexCharts-kaartconfiguratie gegenereerd via Ontwikkelaarstools. De grafiek-metadata-sensor is momenteel uitgeschakeld, dus de gegenereerde YAML toont alleen **basisfunctionaliteit** (auto-schaal as, vaste verloop op 50%).\n\n**Voor volledige functionaliteit** (geoptimaliseerde schaling, dynamische verloopkleuren):\n1. [Open Tibber Prices-integratie](https://my.home-assistant.io/redirect/integration/?domain=tibber_prices)\n2. Schakel de 'Chart Metadata'-sensor in\n3. **Genereer de YAML opnieuw** via Ontwikkelaarstools\n4. **Vervang de oude YAML** in je dashboard door de nieuwe versie\n\n⚠️ Alleen de sensor inschakelen is niet genoeg - je moet de YAML opnieuw genereren en vervangen!"
-            },
-            "missing_cards": {
-                "title": "Tibber Prices: ApexCharts YAML kan niet worden gebruikt",
-                "message": "Je hebt zojuist een ApexCharts-kaartconfiguratie gegenereerd via Ontwikkelaarstools, maar de gegenereerde YAML **zal niet werken** omdat vereiste aangepaste kaarten ontbreken.\n\n**Ontbrekende kaarten:**\n{cards}\n\n**Om de gegenereerde YAML te gebruiken:**\n1. Klik op de bovenstaande links om de ontbrekende kaarten te installeren vanuit HACS\n2. Herstart Home Assistant (soms nodig)\n3. **Genereer de YAML opnieuw** via Ontwikkelaarstools\n4. Voeg de YAML toe aan je dashboard\n\n⚠️ De huidige YAML-code werkt niet totdat alle kaarten zijn geïnstalleerd!"
-            }
-        }
     },
     "sensor": {
         "current_interval_price": {
             "description": "De huidige elektriciteitsprijs per kWh",
-            "long_description": "Toont de huidige prijs per kWh van je Tibber-abonnement",
+            "long_description": "Toont de huidige prijs per kWh van uw Tibber-abonnement",
             "usage_tips": "Gebruik dit om prijzen bij te houden of om automatiseringen te maken die worden uitgevoerd wanneer elektriciteit goedkoop is"
         },
-        "current_interval_price_base": {
+        "current_interval_price_major": {
             "description": "Huidige elektriciteitsprijs in hoofdvaluta (EUR/kWh, NOK/kWh, enz.) voor Energie-dashboard",
             "long_description": "Toont de huidige prijs per kWh in hoofdvaluta-eenheden (bijv. EUR/kWh in plaats van ct/kWh, NOK/kWh in plaats van øre/kWh). Deze sensor is speciaal ontworpen voor gebruik met het Energie-dashboard van Home Assistant, dat prijzen in standaard valuta-eenheden vereist.",
             "usage_tips": "Gebruik deze sensor bij het configureren van het Energie-dashboard onder Instellingen → Dashboards → Energie. Selecteer deze sensor als 'Entiteit met huidige prijs' om automatisch je energiekosten te berekenen. Het Energie-dashboard vermenigvuldigt je energieverbruik (kWh) met deze prijs om totale kosten weer te geven."
         },
         "next_interval_price": {
             "description": "De volgende interval elektriciteitsprijs per kWh",
-            "long_description": "Toont de prijs voor het volgende 15-minuten interval van je Tibber-abonnement",
+            "long_description": "Toont de prijs voor het volgende 15-minuten interval van uw Tibber-abonnement",
-            "usage_tips": "Gebruik dit om je voor te bereiden op aanstaande prijswijzigingen of om apparaten te plannen om tijdens goedkopere intervallen te draaien"
+            "usage_tips": "Gebruik dit om u voor te bereiden op aanstaande prijswijzigingen of om apparaten te plannen om tijdens goedkopere intervallen te draaien"
         },
         "previous_interval_price": {
             "description": "De vorige interval elektriciteitsprijs per kWh",
-            "long_description": "Toont de prijs voor het vorige 15-minuten interval van je Tibber-abonnement",
+            "long_description": "Toont de prijs voor het vorige 15-minuten interval van uw Tibber-abonnement",
             "usage_tips": "Gebruik dit om eerdere prijswijzigingen te bekijken of prijsgeschiedenis bij te houden"
         },
         "current_hour_average_price": {
@@ -49,33 +36,33 @@
         },
         "lowest_price_today": {
             "description": "De laagste elektriciteitsprijs voor vandaag per kWh",
-            "long_description": "Toont de laagste prijs per kWh voor de huidige dag van je Tibber-abonnement",
+            "long_description": "Toont de laagste prijs per kWh voor de huidige dag van uw Tibber-abonnement",
             "usage_tips": "Gebruik dit om huidige prijzen te vergelijken met de goedkoopste tijd van de dag"
         },
         "highest_price_today": {
             "description": "De hoogste elektriciteitsprijs voor vandaag per kWh",
-            "long_description": "Toont de hoogste prijs per kWh voor de huidige dag van je Tibber-abonnement",
+            "long_description": "Toont de hoogste prijs per kWh voor de huidige dag van uw Tibber-abonnement",
-            "usage_tips": "Gebruik dit om te voorkomen dat je apparaten draait tijdens piekprijstijden"
+            "usage_tips": "Gebruik dit om te voorkomen dat u apparaten draait tijdens piekprijstijden"
         },
         "average_price_today": {
-            "description": "Typische elektriciteitsprijs voor vandaag per kWh (configureerbare weergave)",
+            "description": "De gemiddelde elektriciteitsprijs voor vandaag per kWh",
-            "long_description": "Toont de prijs per kWh voor de huidige dag van je Tibber-abonnement. **Standaard toont de status de mediaan** (resistent tegen extreme prijspieken, toont typisch prijsniveau). Je kunt dit wijzigen in de integratie-instellingen om het rekenkundig gemiddelde te tonen. De alternatieve waarde is beschikbaar als attribuut.",
+            "long_description": "Toont de gemiddelde prijs per kWh voor de huidige dag van uw Tibber-abonnement",
-            "usage_tips": "Gebruik dit als basislijn voor het vergelijken van huidige prijzen. Voor berekeningen gebruik: {{ state_attr('sensor.average_price_today', 'price_mean') }}"
+            "usage_tips": "Gebruik dit als basislijn voor het vergelijken van huidige prijzen"
         },
         "lowest_price_tomorrow": {
             "description": "De laagste elektriciteitsprijs voor morgen per kWh",
-            "long_description": "Toont de laagste prijs per kWh voor morgen van je Tibber-abonnement. Deze sensor wordt niet beschikbaar totdat de gegevens van morgen door Tibber worden gepubliceerd (meestal rond 13:00-14:00 CET).",
+            "long_description": "Toont de laagste prijs per kWh voor morgen van uw Tibber-abonnement. Deze sensor wordt niet beschikbaar totdat de gegevens van morgen door Tibber worden gepubliceerd (meestal rond 13:00-14:00 CET).",
             "usage_tips": "Gebruik dit om energie-intensieve activiteiten te plannen voor de goedkoopste tijd van morgen. Perfect voor vooraf plannen van verwarming, EV-laden of apparaten."
         },
         "highest_price_tomorrow": {
             "description": "De hoogste elektriciteitsprijs voor morgen per kWh",
-            "long_description": "Toont de hoogste prijs per kWh voor morgen van je Tibber-abonnement. Deze sensor wordt niet beschikbaar totdat de gegevens van morgen door Tibber worden gepubliceerd (meestal rond 13:00-14:00 CET).",
+            "long_description": "Toont de hoogste prijs per kWh voor morgen van uw Tibber-abonnement. Deze sensor wordt niet beschikbaar totdat de gegevens van morgen door Tibber worden gepubliceerd (meestal rond 13:00-14:00 CET).",
-            "usage_tips": "Gebruik dit om te voorkomen dat je apparaten draait tijdens de piekprijstijden van morgen. Handig voor het plannen rond dure perioden."
+            "usage_tips": "Gebruik dit om te voorkomen dat u apparaten draait tijdens de piekprijstijden van morgen. Handig voor het plannen rond dure perioden."
         },
         "average_price_tomorrow": {
-            "description": "Typische elektriciteitsprijs voor morgen per kWh (configureerbare weergave)",
+            "description": "De gemiddelde elektriciteitsprijs voor morgen per kWh",
-            "long_description": "Toont de prijs per kWh voor morgen van je Tibber-abonnement. **Standaard toont de status de mediaan** (resistent tegen extreme prijspieken). Je kunt dit wijzigen in de integratie-instellingen om het rekenkundig gemiddelde te tonen. De alternatieve waarde is beschikbaar als attribuut. Deze sensor wordt niet beschikbaar totdat de gegevens van morgen door Tibber worden gepubliceerd (meestal rond 13:00-14:00 CET).",
+            "long_description": "Toont de gemiddelde prijs per kWh voor morgen van uw Tibber-abonnement. Deze sensor wordt niet beschikbaar totdat de gegevens van morgen door Tibber worden gepubliceerd (meestal rond 13:00-14:00 CET).",
-            "usage_tips": "Gebruik dit als basislijn voor het vergelijken van prijzen van morgen en het plannen van verbruik. Vergelijk met de mediaan van vandaag om te zien of morgen over het algemeen duurder of goedkoper wordt."
+            "usage_tips": "Gebruik dit als basislijn voor het vergelijken van prijzen van morgen en het plannen van verbruik. Vergelijk met het gemiddelde van vandaag om te zien of morgen over het algemeen duurder of goedkoper wordt."
         },
         "yesterday_price_level": {
             "description": "Geaggregeerd prijsniveau voor gisteren",
@@ -94,48 +81,48 @@
         },
         "yesterday_price_rating": {
             "description": "Geaggregeerde prijsbeoordeling voor gisteren",
-            "long_description": "Toont de geaggregeerde prijsbeoordeling (laag/normaal/hoog) voor alle intervallen van gisteren, gebaseerd op jouw geconfigureerde drempelwaarden. Gebruikt dezelfde logica als de uursensoren om de totale beoordeling voor de hele dag te bepalen.",
+            "long_description": "Toont de geaggregeerde prijsbeoordeling (laag/normaal/hoog) voor alle intervallen van gisteren, gebaseerd op uw geconfigureerde drempelwaarden. Gebruikt dezelfde logica als de uursensoren om de totale beoordeling voor de hele dag te bepalen.",
-            "usage_tips": "Gebruik dit om de prijssituatie van gisteren te begrijpen ten opzichte van jouw persoonlijke drempelwaarden. Vergelijk met vandaag voor trendanalyse."
+            "usage_tips": "Gebruik dit om de prijssituatie van gisteren te begrijpen ten opzichte van uw persoonlijke drempelwaarden. Vergelijk met vandaag voor trendanalyse."
         },
         "today_price_rating": {
             "description": "Geaggregeerde prijsbeoordeling voor vandaag",
-            "long_description": "Toont de geaggregeerde prijsbeoordeling (laag/normaal/hoog) voor alle intervallen van vandaag, gebaseerd op jouw geconfigureerde drempelwaarden. Gebruikt dezelfde logica als de uursensoren om de totale beoordeling voor de hele dag te bepalen.",
+            "long_description": "Toont de geaggregeerde prijsbeoordeling (laag/normaal/hoog) voor alle intervallen van vandaag, gebaseerd op uw geconfigureerde drempelwaarden. Gebruikt dezelfde logica als de uursensoren om de totale beoordeling voor de hele dag te bepalen.",
-            "usage_tips": "Gebruik dit om snel de prijssituatie van vandaag te beoordelen ten opzichte van jouw persoonlijke drempelwaarden. Helpt bij het nemen van verbruiksbeslissingen voor de huidige dag."
+            "usage_tips": "Gebruik dit om snel de prijssituatie van vandaag te beoordelen ten opzichte van uw persoonlijke drempelwaarden. Helpt bij het nemen van verbruiksbeslissingen voor de huidige dag."
         },
         "tomorrow_price_rating": {
             "description": "Geaggregeerde prijsbeoordeling voor morgen",
-            "long_description": "Toont de geaggregeerde prijsbeoordeling (laag/normaal/hoog) voor alle intervallen van morgen, gebaseerd op jouw geconfigureerde drempelwaarden. Gebruikt dezelfde logica als de uursensoren om de totale beoordeling voor de hele dag te bepalen. Deze sensor wordt niet beschikbaar totdat de gegevens van morgen door Tibber worden gepubliceerd (meestal rond 13:00-14:00 CET).",
+            "long_description": "Toont de geaggregeerde prijsbeoordeling (laag/normaal/hoog) voor alle intervallen van morgen, gebaseerd op uw geconfigureerde drempelwaarden. Gebruikt dezelfde logica als de uursensoren om de totale beoordeling voor de hele dag te bepalen. Deze sensor wordt niet beschikbaar totdat de gegevens van morgen door Tibber worden gepubliceerd (meestal rond 13:00-14:00 CET).",
-            "usage_tips": "Gebruik dit om het energieverbruik van morgen te plannen op basis van jouw persoonlijke prijsdrempelwaarden. Vergelijk met vandaag om te beslissen of je verbruik naar morgen moet verschuiven of vandaag energie moet gebruiken."
+            "usage_tips": "Gebruik dit om het energieverbruik van morgen te plannen op basis van uw persoonlijke prijsdrempelwaarden. Vergelijk met vandaag om te beslissen of u verbruik naar morgen moet verschuiven of vandaag energie moet gebruiken."
         },
         "trailing_price_average": {
-            "description": "Typische elektriciteitsprijs voor de afgelopen 24 uur per kWh (configureerbare weergave)",
+            "description": "De gemiddelde elektriciteitsprijs voor de afgelopen 24 uur per kWh",
-            "long_description": "Toont de prijs per kWh berekend uit de afgelopen 24 uur. **Standaard toont de status de mediaan** (resistent tegen extreme prijspieken, toont typisch prijsniveau). Je kunt dit wijzigen in de integratie-instellingen om het rekenkundig gemiddelde te tonen. De alternatieve waarde is beschikbaar als attribuut. Wordt elke 15 minuten bijgewerkt.",
+            "long_description": "Toont de gemiddelde prijs per kWh berekend uit de afgelopen 24 uur (voortschrijdend gemiddelde) van uw Tibber-abonnement. Dit biedt een voortschrijdend gemiddelde dat elke 15 minuten wordt bijgewerkt op basis van historische gegevens.",
-            "usage_tips": "Gebruik de statuswaarde om het typische huidige prijsniveau te zien. Voor kostenberekeningen gebruik: {{ state_attr('sensor.trailing_price_average', 'price_mean') }}"
+            "usage_tips": "Gebruik dit om huidige prijzen te vergelijken met recente trends. Een huidige prijs die aanzienlijk boven dit gemiddelde ligt, kan aangeven dat het een goed moment is om het verbruik te verminderen."
         },
         "leading_price_average": {
-            "description": "Typische elektriciteitsprijs voor de komende 24 uur per kWh (configureerbare weergave)",
+            "description": "De gemiddelde elektriciteitsprijs voor de komende 24 uur per kWh",
-            "long_description": "Toont de prijs per kWh berekend uit de komende 24 uur. **Standaard toont de status de mediaan** (resistent tegen extreme prijspieken, toont verwacht prijsniveau). Je kunt dit wijzigen in de integratie-instellingen om het rekenkundig gemiddelde te tonen. De alternatieve waarde is beschikbaar als attribuut.",
+            "long_description": "Toont de gemiddelde prijs per kWh berekend uit de komende 24 uur (vooruitlopend gemiddelde) van uw Tibber-abonnement. Dit biedt een vooruitkijkend gemiddelde op basis van beschikbare prognosegegevens.",
-            "usage_tips": "Gebruik de statuswaarde om het typische toekomstige prijsniveau te zien. Voor kostenberekeningen gebruik: {{ state_attr('sensor.leading_price_average', 'price_mean') }}"
+            "usage_tips": "Gebruik dit om energieverbruik te plannen. Als de huidige prijs onder het vooruitlopende gemiddelde ligt, kan het een goed moment zijn om energie-intensieve apparaten te laten draaien."
         },
         "trailing_price_min": {
             "description": "De minimale elektriciteitsprijs voor de afgelopen 24 uur per kWh",
-            "long_description": "Toont de minimumprijs per kWh van de afgelopen 24 uur (voortschrijdend minimum) van je Tibber-abonnement. Dit geeft de laagste prijs die in de afgelopen 24 uur is gezien.",
+            "long_description": "Toont de minimumprijs per kWh van de afgelopen 24 uur (voortschrijdend minimum) van uw Tibber-abonnement. Dit geeft de laagste prijs die in de afgelopen 24 uur is gezien.",
-            "usage_tips": "Gebruik dit om de beste prijsmogelijkheid te zien die je in de afgelopen 24 uur had en vergelijk deze met huidige prijzen."
+            "usage_tips": "Gebruik dit om de beste prijsmogelijkheid te zien die u in de afgelopen 24 uur had en vergelijk deze met huidige prijzen."
         },
         "trailing_price_max": {
             "description": "De maximale elektriciteitsprijs voor de afgelopen 24 uur per kWh",
-            "long_description": "Toont de maximumprijs per kWh van de afgelopen 24 uur (voortschrijdend maximum) van je Tibber-abonnement. Dit geeft de hoogste prijs die in de afgelopen 24 uur is gezien.",
+            "long_description": "Toont de maximumprijs per kWh van de afgelopen 24 uur (voortschrijdend maximum) van uw Tibber-abonnement. Dit geeft de hoogste prijs die in de afgelopen 24 uur is gezien.",
             "usage_tips": "Gebruik dit om de piekprijs in de afgelopen 24 uur te zien en prijsvolatiliteit te beoordelen."
         },
         "leading_price_min": {
             "description": "De minimale elektriciteitsprijs voor de komende 24 uur per kWh",
-            "long_description": "Toont de minimumprijs per kWh van de komende 24 uur (vooruitlopend minimum) van je Tibber-abonnement. Dit geeft de laagste prijs die wordt verwacht in de komende 24 uur op basis van prognosegegevens.",
+            "long_description": "Toont de minimumprijs per kWh van de komende 24 uur (vooruitlopend minimum) van uw Tibber-abonnement. Dit geeft de laagste prijs die wordt verwacht in de komende 24 uur op basis van prognosegegevens.",
             "usage_tips": "Gebruik dit om de beste prijsmogelijkheid te identificeren die eraan komt en plan energie-intensieve taken dienovereenkomstig."
         },
         "leading_price_max": {
             "description": "De maximale elektriciteitsprijs voor de komende 24 uur per kWh",
-            "long_description": "Toont de maximumprijs per kWh van de komende 24 uur (vooruitlopend maximum) van je Tibber-abonnement. Dit geeft de hoogste prijs die wordt verwacht in de komende 24 uur op basis van prognosegegevens.",
+            "long_description": "Toont de maximumprijs per kWh van de komende 24 uur (vooruitlopend maximum) van uw Tibber-abonnement. Dit geeft de hoogste prijs die wordt verwacht in de komende 24 uur op basis van prognosegegevens.",
-            "usage_tips": "Gebruik dit om te voorkomen dat je apparaten draait tijdens aanstaande piekprijsperioden."
+            "usage_tips": "Gebruik dit om te voorkomen dat u apparaten draait tijdens aanstaande piekprijsperioden."
         },
         "current_interval_price_level": {
             "description": "De huidige prijsniveauclassificatie",
@@ -155,7 +142,7 @@
         "current_hour_price_level": {
             "description": "Geaggregeerd prijsniveau voor huidig voortschrijdend uur (5 intervallen)",
             "long_description": "Toont het mediane prijsniveau over 5 intervallen (2 ervoor, huidig, 2 erna) dat ongeveer 75 minuten beslaat. Biedt een stabielere prijsniveauindicator die kortetermijnschommelingen afvlakt.",
-            "usage_tips": "Gebruik voor planningsbeslissingen op middellange termijn waarbij je niet wilt reageren op korte prijspieken of -dalingen."
+            "usage_tips": "Gebruik voor planningsbeslissingen op middellange termijn waarbij u niet wilt reageren op korte prijspieken of -dalingen."
         },
         "next_hour_price_level": {
             "description": "Geaggregeerd prijsniveau voor volgend voortschrijdend uur (5 intervallen vooruit)",
@@ -185,22 +172,22 @@
         "next_hour_price_rating": {
             "description": "Geaggregeerde prijsbeoordeling voor volgend voortschrijdend uur (5 intervallen vooruit)",
             "long_description": "Toont de gemiddelde beoordeling voor 5 intervallen gecentreerd één uur vooruit. Helpt te begrijpen of het volgende uur over het algemeen boven of onder gemiddelde prijzen zal liggen.",
-            "usage_tips": "Gebruik om te beslissen of je een uur moet wachten voordat je activiteiten met hoog verbruik start."
+            "usage_tips": "Gebruik om te beslissen of u een uur moet wachten voordat u activiteiten met hoog verbruik start."
         },
         "next_avg_1h": {
             "description": "Gemiddelde prijs voor het volgende 1 uur (alleen vooruit vanaf volgend interval)",
             "long_description": "Vooruitkijkend gemiddelde: Toont gemiddelde van volgende 4 intervallen (1 uur) vanaf het VOLGENDE 15-minuten interval (niet inclusief huidig). Verschilt van current_hour_average_price die vorige intervallen omvat. Gebruik voor absolute prijsdrempelplanning.",
-            "usage_tips": "Absolute prijsdrempel: Start apparaten alleen wanneer het gemiddelde onder je maximaal acceptabele prijs blijft (bijv. onder 0,25 EUR/kWh). Combineer met trendsensor voor optimale timing. Let op: Dit is GEEN vervanging voor uurprijzen - gebruik current_hour_average_price daarvoor."
+            "usage_tips": "Absolute prijsdrempel: Start apparaten alleen wanneer het gemiddelde onder uw maximaal acceptabele prijs blijft (bijv. onder 0,25 EUR/kWh). Combineer met trendsensor voor optimale timing. Let op: Dit is GEEN vervanging voor uurprijzen - gebruik current_hour_average_price daarvoor."
         },
         "next_avg_2h": {
             "description": "Gemiddelde prijs voor de volgende 2 uur",
             "long_description": "Toont de gemiddelde prijs voor de volgende 8 intervallen (2 uur) vanaf het volgende 15-minuten interval.",
-            "usage_tips": "Absolute prijsdrempel: Stel een maximaal acceptabele gemiddelde prijs in voor standaard apparaten zoals wasmachines. Zorgt ervoor dat je nooit meer betaalt dan je limiet."
+            "usage_tips": "Absolute prijsdrempel: Stel een maximaal acceptabele gemiddelde prijs in voor standaard apparaten zoals wasmachines. Zorgt ervoor dat u nooit meer betaalt dan uw limiet."
         },
         "next_avg_3h": {
             "description": "Gemiddelde prijs voor de volgende 3 uur",
             "long_description": "Toont de gemiddelde prijs voor de volgende 12 intervallen (3 uur) vanaf het volgende 15-minuten interval.",
-            "usage_tips": "Absolute prijsdrempel: Voor EU Eco-programma's (vaatwassers, 3-4u looptijd). Start alleen wanneer 3u gemiddelde onder je prijslimiet is. Gebruik met trendsensor om beste moment binnen acceptabel prijsbereik te vinden."
+            "usage_tips": "Absolute prijsdrempel: Voor EU Eco-programma's (vaatwassers, 3-4u looptijd). Start alleen wanneer 3u gemiddelde onder uw prijslimiet is. Gebruik met trendsensor om beste moment binnen acceptabel prijsbereik te vinden."
         },
         "next_avg_4h": {
             "description": "Gemiddelde prijs voor de volgende 4 uur",
@@ -215,32 +202,32 @@
         "next_avg_6h": {
             "description": "Gemiddelde prijs voor de volgende 6 uur",
             "long_description": "Toont de gemiddelde prijs voor de volgende 24 intervallen (6 uur) vanaf het volgende 15-minuten interval.",
-            "usage_tips": "Absolute prijsdrempel: Avondplanning met prijslimieten. Plan taken alleen als 6u gemiddelde onder je maximaal acceptabele kosten blijft."
+            "usage_tips": "Absolute prijsdrempel: Avondplanning met prijslimieten. Plan taken alleen als 6u gemiddelde onder uw maximaal acceptabele kosten blijft."
         },
         "next_avg_8h": {
             "description": "Gemiddelde prijs voor de volgende 8 uur",
             "long_description": "Toont de gemiddelde prijs voor de volgende 32 intervallen (8 uur) vanaf het volgende 15-minuten interval.",
-            "usage_tips": "Absolute prijsdrempel: Nachtelijke bedieningsbeslissingen. Stel harde prijslimieten in voor nachtelijke belastingen (batterij opladen, thermische opslag). Overschrijd nooit je budget."
+            "usage_tips": "Absolute prijsdrempel: Nachtelijke bedieningsbeslissingen. Stel harde prijslimieten in voor nachtelijke belastingen (batterij opladen, thermische opslag). Overschrijd nooit uw budget."
         },
         "next_avg_12h": {
             "description": "Gemiddelde prijs voor de volgende 12 uur",
             "long_description": "Toont de gemiddelde prijs voor de volgende 48 intervallen (12 uur) vanaf het volgende 15-minuten interval.",
-            "usage_tips": "Absolute prijsdrempel: Strategische beslissingen met prijslimieten. Ga alleen door als 12u gemiddelde onder je maximaal acceptabele prijs is. Goed voor uitgestelde grote belastingen."
+            "usage_tips": "Absolute prijsdrempel: Strategische beslissingen met prijslimieten. Ga alleen door als 12u gemiddelde onder uw maximaal acceptabele prijs is. Goed voor uitgestelde grote belastingen."
         },
         "price_trend_1h": {
             "description": "Prijstrend voor het volgende uur",
             "long_description": "Vergelijkt huidige intervalprijs met gemiddelde van volgend 1 uur (4 intervallen). Stijgend als toekomst >5% hoger is, dalend als >5% lager, anders stabiel.",
-            "usage_tips": "Relatieve optimalisatie: 'dalend' = wacht, prijzen dalen. 'stijgend' = handel nu of je betaalt meer. 'stabiel' = prijs maakt nu niet veel uit. Werkt onafhankelijk van absoluut prijsniveau."
+            "usage_tips": "Relatieve optimalisatie: 'dalend' = wacht, prijzen dalen. 'stijgend' = handel nu of u betaalt meer. 'stabiel' = prijs maakt nu niet veel uit. Werkt onafhankelijk van absoluut prijsniveau."
         },
         "price_trend_2h": {
             "description": "Prijstrend voor de volgende 2 uur",
             "long_description": "Vergelijkt huidige intervalprijs met gemiddelde van volgende 2 uur (8 intervallen). Stijgend als toekomst >5% hoger is, dalend als >5% lager, anders stabiel.",
-            "usage_tips": "Relatieve optimalisatie: Ideaal voor apparaten. 'dalend' betekent betere prijzen komen over 2u - stel uit indien mogelijk. Vindt beste timing binnen je beschikbare venster, ongeacht seizoen."
+            "usage_tips": "Relatieve optimalisatie: Ideaal voor apparaten. 'dalend' betekent betere prijzen komen over 2u - stel uit indien mogelijk. Vindt beste timing binnen uw beschikbare venster, ongeacht seizoen."
         },
         "price_trend_3h": {
             "description": "Prijstrend voor de volgende 3 uur",
             "long_description": "Vergelijkt huidige intervalprijs met gemiddelde van volgende 3 uur (12 intervallen). Stijgend als toekomst >5% hoger is, dalend als >5% lager, anders stabiel.",
-            "usage_tips": "Relatieve optimalisatie: Voor Eco-programma's. 'dalend' betekent prijzen dalen >5% - het wachten waard. Werkt in elk seizoen. Combineer met avg-sensor voor prijslimiet: alleen wanneer avg < je limiet EN trend niet 'dalend'."
+            "usage_tips": "Relatieve optimalisatie: Voor Eco-programma's. 'dalend' betekent prijzen dalen >5% - het wachten waard. Werkt in elk seizoen. Combineer met avg-sensor voor prijslimiet: alleen wanneer avg < uw limiet EN trend niet 'dalend'."
         },
         "price_trend_4h": {
             "description": "Prijstrend voor de volgende 4 uur",
@@ -250,12 +237,12 @@
         "price_trend_5h": {
             "description": "Prijstrend voor de volgende 5 uur",
             "long_description": "Vergelijkt huidige intervalprijs met gemiddelde van volgende 5 uur (20 intervallen). Stijgend als toekomst >5% hoger is, dalend als >5% lager, anders stabiel.",
-            "usage_tips": "Relatieve optimalisatie: Uitgebreide operaties. Past zich aan de markt aan - vindt beste relatieve timing in elke prijsomgeving. 'stabiel/stijgend' = goed moment om te starten binnen je planningsvenster."
+            "usage_tips": "Relatieve optimalisatie: Uitgebreide operaties. Past zich aan de markt aan - vindt beste relatieve timing in elke prijsomgeving. 'stabiel/stijgend' = goed moment om te starten binnen uw planningsvenster."
         },
         "price_trend_6h": {
             "description": "Prijstrend voor de volgende 6 uur",
             "long_description": "Vergelijkt huidige intervalprijs met gemiddelde van volgende 6 uur (24 intervallen). Stijgend als toekomst >5% hoger is, dalend als >5% lager, anders stabiel.",
-            "usage_tips": "Relatieve optimalisatie: Avandbeslissingen. 'dalend' = prijzen verbeteren aanzienlijk als je wacht. Geen vaste drempels nodig - past automatisch aan winter/zomer prijsniveaus."
+            "usage_tips": "Relatieve optimalisatie: Avandbeslissingen. 'dalend' = prijzen verbeteren aanzienlijk als u wacht. Geen vaste drempels nodig - past automatisch aan winter/zomer prijsniveaus."
         },
         "price_trend_8h": {
             "description": "Prijstrend voor de volgende 8 uur",
@@ -289,27 +276,27 @@
         },
         "data_timestamp": {
             "description": "Tijdstempel van het laatst beschikbare prijsgegevensinterval",
-            "long_description": "Toont het tijdstempel van het laatst beschikbare prijsgegevensinterval van je Tibber-abonnement"
+            "long_description": "Toont het tijdstempel van het laatst beschikbare prijsgegevensinterval van uw Tibber-abonnement"
         },
         "today_volatility": {
-            "description": "Hoeveel de stroomprijzen vandaag schommelen",
+            "description": "Prijsvolatiliteitsclassificatie voor vandaag",
-            "long_description": "Geeft aan of de prijzen vandaag stabiel blijven of grote schommelingen hebben. Lage volatiliteit betekent vrij constante prijzen – timing maakt weinig uit. Hoge volatiliteit betekent duidelijke prijsverschillen gedurende de dag – goede kans om verbruik naar goedkopere periodes te verschuiven. `price_coefficient_variation_%` toont het percentage, `price_spread` de absolute prijsspanne.",
+            "long_description": "Toont hoeveel elektriciteitsprijzen variëren gedurende vandaag op basis van de spreiding (verschil tussen hoogste en laagste prijs). Classificatie: LOW = spreiding < 5ct, MODERATE = 5-15ct, HIGH = 15-30ct, VERY HIGH = >30ct.",
-            "usage_tips": "Gebruik dit om te beslissen of optimaliseren de moeite waard is. Bij lage volatiliteit kun je apparaten op elk moment laten draaien. Bij hoge volatiliteit bespaar je merkbaar door Best Price-periodes te volgen."
+            "usage_tips": "Gebruik dit om te bepalen of prijsgebaseerde optimalisatie de moeite waard is. Bijvoorbeeld, met een balkonbatterij met 15% efficiëntieverlies is optimalisatie alleen zinvol wanneer volatiliteit ten minste MODERATE is. Maak automatiseringen die volatiliteit controleren voordat u laad-/ontlaadcycli plant."
         },
         "tomorrow_volatility": {
-            "description": "Hoeveel de stroomprijzen morgen zullen schommelen",
+            "description": "Prijsvolatiliteitsclassificatie voor morgen",
-            "long_description": "Geeft aan of de prijzen morgen stabiel blijven of grote schommelingen hebben. Beschikbaar zodra de gegevens voor morgen zijn gepubliceerd (meestal 13:00–14:00 CET). Lage volatiliteit betekent vrij constante prijzen – timing is niet kritisch. Hoge volatiliteit betekent duidelijke prijsverschillen gedurende de dag – goede kans om energie-intensieve taken te plannen. `price_coefficient_variation_%` toont het percentage, `price_spread` de absolute prijsspanne.",
+            "long_description": "Toont hoeveel elektriciteitsprijzen zullen variëren gedurende morgen op basis van de spreiding (verschil tussen hoogste en laagste prijs). Wordt onbeschikbaar totdat de gegevens van morgen zijn gepubliceerd (meestal 13:00-14:00 CET).",
-            "usage_tips": "Gebruik dit om het verbruik van morgen te plannen. Hoge volatiliteit? Plan flexibele lasten in Best Price-periodes. Lage volatiliteit? Laat apparaten draaien wanneer het jou uitkomt."
+            "usage_tips": "Gebruik dit voor vooruitplanning van het energieverbruik van morgen. Als morgen HIGH of VERY HIGH volatiliteit heeft, is het de moeite waard om de timing van energieverbruik te optimaliseren. Bij LOW kunt u apparaten op elk moment gebruiken zonder significante kostenverschillen."
         },
         "next_24h_volatility": {
-            "description": "Hoeveel de prijzen de komende 24 uur zullen schommelen",
+            "description": "Prijsvolatiliteitsclassificatie voor de rollende volgende 24 uur",
-            "long_description": "Geeft de prijsvolatiliteit aan voor een rollend 24-uursvenster vanaf nu (wordt elke 15 minuten bijgewerkt). Lage volatiliteit betekent vrij constante prijzen. Hoge volatiliteit betekent merkbare prijsschommelingen en dus optimalisatiemogelijkheden. In tegenstelling tot vandaag/morgen-sensoren overschrijdt deze daggrenzen en geeft een doorlopende vooruitblik. `price_coefficient_variation_%` toont het percentage, `price_spread` de absolute prijsspanne.",
+            "long_description": "Toont hoeveel elektriciteitsprijzen variëren in de volgende 24 uur vanaf nu (rollend venster). Dit overschrijdt daggrenzen en wordt elke 15 minuten bijgewerkt, wat een vooruitkijkende volatiliteitsbeoordeling biedt onafhankelijk van kalenderdagen.",
-            "usage_tips": "Het beste voor beslissingen in real-time. Gebruik bij het plannen van batterijladen of andere flexibele lasten die over middernacht kunnen lopen. Biedt een consistent 24-uurs beeld, los van de kalenderdag."
+            "usage_tips": "Beste sensor voor realtime optimalisatiebeslissingen. In tegenstelling tot vandaag/morgen-sensoren die om middernacht wisselen, biedt deze een continue 24-uurs volatiliteitsbeoordeling. Gebruik voor batterijlaadstrategieën die over daggrenzen heen gaan."
         },
         "today_tomorrow_volatility": {
-            "description": "Gecombineerde prijsvolatiliteit voor vandaag en morgen",
+            "description": "Gecombineerde prijsvolatiliteitsclassificatie voor vandaag en morgen",
-            "long_description": "Geeft de totale volatiliteit weer wanneer vandaag en morgen samen worden bekeken (zodra morgengegevens beschikbaar zijn). Toont of er duidelijke prijsverschillen over de daggrens heen zijn. Valt terug naar alleen vandaag als morgengegevens ontbreken. Handig voor meerdaagse optimalisatie. `price_coefficient_variation_%` toont het percentage, `price_spread` de absolute prijsspanne.",
+            "long_description": "Toont volatiliteit over zowel vandaag als morgen gecombineerd (wanneer de gegevens van morgen beschikbaar zijn). Biedt een uitgebreid overzicht van prijsvariatie over maximaal 48 uur. Valt terug op alleen vandaag wanneer de gegevens van morgen nog niet beschikbaar zijn.",
-            "usage_tips": "Gebruik voor taken die meerdere dagen beslaan. Kijk of de prijsverschillen groot genoeg zijn om plannen op te baseren. De afzonderlijke dag-sensoren tonen per-dag bijdragen als je meer detail wilt."
+            "usage_tips": "Gebruik dit voor meerdaagse planning en om te begrijpen of prijskansen bestaan over de daggrenzen heen. De attributen 'today_volatility' en 'tomorrow_volatility' tonen individuele dagbijdragen. Handig voor het plannen van laadsessies die middernacht kunnen overschrijden."
         },
         "data_lifecycle_status": {
             "description": "Huidige status van prijsgegevenslevenscyclus en caching",
@@ -317,49 +304,39 @@
             "usage_tips": "Gebruik deze diagnostische sensor om gegevensfrisheid en API-aanroeppatronen te begrijpen. Controleer het 'cache_age'-attribuut om te zien hoe oud de huidige gegevens zijn. Monitor 'next_api_poll' om te weten wanneer de volgende update is gepland. Gebruik 'data_completeness' om te zien of gisteren/vandaag/morgen gegevens beschikbaar zijn. De 'api_calls_today'-teller helpt API-gebruik bij te houden. Perfect voor probleemoplossing of begrip van integratiegedrag."
         },
         "best_price_end_time": {
-            "description": "Totale lengte van huidige of volgende voordelige periode (state in uren, attribuut in minuten)",
+            "description": "Wanneer de huidige of volgende goedkope periode eindigt",
-            "long_description": "Toont hoe lang de voordelige periode duurt. State gebruikt uren (float) voor een leesbare UI; attribuut `period_duration_minutes` behoudt afgeronde minuten voor automatiseringen. Actief → duur van de huidige periode, anders de volgende.",
+            "long_description": "Toont het eindtijdstempel van de huidige goedkope periode wanneer actief, of het einde van de volgende periode wanneer geen periode actief is. Toont altijd een nuttige tijdreferentie voor planning. Geeft alleen 'Onbekend' terug wanneer geen periodes zijn geconfigureerd.",
-            "usage_tips": "UI kan 1,5 u tonen terwijl `period_duration_minutes` = 90 voor automatiseringen blijft."
+            "usage_tips": "Gebruik dit om een aftelling weer te geven zoals 'Goedkope periode eindigt over 2 uur' (wanneer actief) of 'Volgende goedkope periode eindigt om 14:00' (wanneer inactief). Home Assistant toont automatisch relatieve tijd voor tijdstempelsensoren."
-        },
-        "best_price_period_duration": {
-            "description": "Lengte van huidige/volgende goedkope periode",
-            "long_description": "Totale duur van huidige of volgende goedkope periode. De state wordt weergegeven in uren (bijv. 1,5 u) voor gemakkelijk aflezen in de UI, terwijl het attribuut `period_duration_minutes` dezelfde waarde in minuten levert (bijv. 90) voor automatiseringen. Deze waarde vertegenwoordigt de **volledige geplande duur** van de periode en is constant gedurende de gehele periode, zelfs als de resterende tijd (remaining_minutes) afneemt.",
-            "usage_tips": "Combineer met remaining_minutes om te berekenen wanneer langlopende apparaten moeten worden gestopt: Periode is `period_duration_minutes - remaining_minutes` minuten geleden gestart. Dit attribuut ondersteunt energie-optimalisatiestrategieën door te helpen bij het plannen van hoog-verbruiksactiviteiten binnen goedkope periodes."
         },
         "best_price_remaining_minutes": {
-            "description": "Resterende tijd in huidige goedkope periode",
+            "description": "Resterende minuten in huidige goedkope periode (0 wanneer inactief)",
-            "long_description": "Toont hoeveel tijd er nog overblijft in de huidige goedkope periode. De state wordt weergegeven in uren (bijv. 0,75 u) voor gemakkelijk aflezen in dashboards, terwijl het attribuut `remaining_minutes` dezelfde tijd in minuten levert (bijv. 45) voor automatiseringsvoorwaarden. **Afteltimer**: Deze waarde neemt elke minuut af tijdens een actieve periode. Geeft 0 terug wanneer geen goedkope periode actief is. Werkt elke minuut bij.",
+            "long_description": "Toont hoeveel minuten er nog over zijn in de huidige goedkope periode. Geeft 0 terug wanneer geen periode actief is. Werkt elke minuut bij. Controleer binary_sensor.best_price_period om te zien of een periode momenteel actief is.",
-            "usage_tips": "Voor automatiseringen: Gebruik attribuut `remaining_minutes` zoals 'Als remaining_minutes > 60, start vaatwasser nu (genoeg tijd om te voltooien)' of 'Als remaining_minutes < 15, rond huidige cyclus binnenkort af'. UI toont gebruiksvriendelijke uren (bijv. 1,25 u). Waarde 0 geeft aan dat geen goedkope periode actief is."
+            "usage_tips": "Perfect voor automatiseringen: 'Als remaining_minutes > 0 EN remaining_minutes < 30, start wasmachine nu'. De waarde 0 maakt het gemakkelijk om te controleren of een periode actief is (waarde > 0) of niet (waarde = 0)."
         },
         "best_price_progress": {
             "description": "Voortgang door huidige goedkope periode (0% wanneer inactief)",
-            "long_description": "Toont voortgang door de huidige goedkope periode als 0-100%. Geeft 0% terug wanneer geen periode actief is. Werkt elke minuut bij. 0% betekent periode net gestart, 100% betekent dat deze bijna eindigt.",
+            "long_description": "Toont de voortgang door de huidige goedkope periode als 0-100%. Geeft 0% terug wanneer geen periode actief is. Werkt elke minuut bij. 0% betekent periode net gestart, 100% betekent het eindigt bijna.",
-            "usage_tips": "Geweldig voor visuele voortgangsbalken. Gebruik in automatiseringen: 'Als progress > 0 EN progress > 75, stuur melding dat goedkope periode bijna eindigt'. Waarde 0 geeft aan dat geen periode actief is."
+            "usage_tips": "Geweldig voor visuele voortgangsbalken. Gebruik in automatiseringen: 'Als progress > 0 EN progress > 75, stuur melding dat goedkope periode bijna eindigt'. Waarde 0 geeft aan dat er geen actieve periode is."
         },
         "best_price_next_start_time": {
-            "description": "Totale lengte van huidige of volgende dure periode (state in uren, attribuut in minuten)",
+            "description": "Wanneer de volgende goedkope periode begint",
-            "long_description": "Toont hoe lang de dure periode duurt. State gebruikt uren (float) voor de UI; attribuut `period_duration_minutes` behoudt afgeronde minuten voor automatiseringen. Actief → duur van de huidige periode, anders de volgende.",
+            "long_description": "Toont wanneer de volgende komende goedkope periode begint. Tijdens een actieve periode toont dit de start van de VOLGENDE periode na de huidige. Geeft alleen 'Onbekend' terug wanneer geen toekomstige periodes zijn geconfigureerd.",
-            "usage_tips": "UI kan 0,75 u tonen terwijl `period_duration_minutes` = 45 voor automatiseringen blijft."
+            "usage_tips": "Altijd nuttig voor vooruitplanning: 'Volgende goedkope periode begint over 3 uur' (of je nu in een periode zit of niet). Combineer met automatiseringen: 'Wanneer volgende starttijd over 10 minuten is, stuur melding om wasmachine voor te bereiden'."
         },
         "best_price_next_in_minutes": {
-            "description": "Resterende tijd in huidige dure periode (state in uren, attribuut in minuten)",
+            "description": "Minuten tot volgende goedkope periode begint (0 bij overgang)",
-            "long_description": "Toont hoeveel tijd er nog over is. State gebruikt uren (float); attribuut `remaining_minutes` behoudt afgeronde minuten voor automatiseringen. Geeft 0 terug wanneer er geen periode actief is. Werkt elke minuut bij.",
+            "long_description": "Toont minuten tot de volgende goedkope periode begint. Tijdens een actieve periode toont dit de tijd tot de periode NA de huidige. Geeft 0 terug tijdens korte overgangsmomenten. Werkt elke minuut bij.",
-            "usage_tips": "Gebruik `remaining_minutes` voor drempels (bijv. > 60) terwijl de state in uren goed leesbaar blijft."
+            "usage_tips": "Perfect voor 'wacht tot goedkope periode' automatiseringen: 'Als next_in_minutes > 0 EN next_in_minutes < 15, wacht voordat vaatwasser wordt gestart'. Waarde > 0 geeft altijd aan dat een toekomstige periode is gepland."
         },
         "peak_price_end_time": {
-            "description": "Tijd tot volgende dure periode (state in uren, attribuut in minuten)",
+            "description": "Wanneer de huidige of volgende dure periode eindigt",
-            "long_description": "Toont hoe lang het duurt tot de volgende dure periode start. State gebruikt uren (float); attribuut `next_in_minutes` behoudt afgeronde minuten voor automatiseringen. Tijdens een actieve periode is dit de tijd tot de periode na de huidige. 0 tijdens korte overgangen. Werkt elke minuut bij.",
+            "long_description": "Toont het eindtijdstempel van de huidige dure periode wanneer actief, of het einde van de volgende periode wanneer geen periode actief is. Toont altijd een nuttige tijdreferentie voor planning. Geeft alleen 'Onbekend' terug wanneer geen periodes zijn geconfigureerd.",
-            "usage_tips": "Gebruik `next_in_minutes` in automatiseringen (bijv. < 10) terwijl de state in uren leesbaar blijft."
+            "usage_tips": "Gebruik dit om 'Dure periode eindigt over 1 uur' weer te geven (wanneer actief) of 'Volgende dure periode eindigt om 18:00' (wanneer inactief). Combineer met automatiseringen om activiteiten te hervatten na piek."
-        },
-        "peak_price_period_duration": {
-            "description": "Totale duur van huidige of volgende dure periode in minuten",
-            "long_description": "Toont de totale duur van de dure periode in minuten. Tijdens een actieve periode toont dit de volledige lengte van de huidige periode. Wanneer geen periode actief is, toont dit de duur van de volgende komende periode. Voorbeeld: '60 minuten' voor een 1-uur periode.",
-            "usage_tips": "Gebruik om energiebesparende maatregelen te plannen: 'Als duration > 120, verlaag verwarmingstemperatuur agressiever (lange dure periode)'. Helpt bij het inschatten hoeveel energieverbruik moet worden verminderd."
         },
         "peak_price_remaining_minutes": {
-            "description": "Resterende tijd in huidige dure periode",
+            "description": "Resterende minuten in huidige dure periode (0 wanneer inactief)",
-            "long_description": "Toont hoeveel tijd er nog overblijft in de huidige dure periode. De state wordt weergegeven in uren (bijv. 0,75 u) voor gemakkelijk aflezen in dashboards, terwijl het attribuut `remaining_minutes` dezelfde tijd in minuten levert (bijv. 45) voor automatiseringsvoorwaarden. **Afteltimer**: Deze waarde neemt elke minuut af tijdens een actieve periode. Geeft 0 terug wanneer geen dure periode actief is. Werkt elke minuut bij.",
+            "long_description": "Toont hoeveel minuten er nog over zijn in de huidige dure periode. Geeft 0 terug wanneer geen periode actief is. Werkt elke minuut bij. Controleer binary_sensor.peak_price_period om te zien of een periode momenteel actief is.",
-            "usage_tips": "Voor automatiseringen: Gebruik attribuut `remaining_minutes` zoals 'Als remaining_minutes > 60, annuleer uitgestelde laadronde' of 'Als remaining_minutes < 15, hervat normaal gebruik binnenkort'. UI toont gebruiksvriendelijke uren (bijv. 1,0 u). Waarde 0 geeft aan dat geen dure periode actief is."
+            "usage_tips": "Gebruik in automatiseringen: 'Als remaining_minutes > 60, annuleer uitgestelde laadronde'. Waarde 0 maakt het gemakkelijk om onderscheid te maken tussen actieve (waarde > 0) en inactieve (waarde = 0) periodes."
         },
         "peak_price_progress": {
             "description": "Voortgang door huidige dure periode (0% wanneer inactief)",
@@ -372,9 +349,19 @@
             "usage_tips": "Altijd nuttig voor planning: 'Volgende dure periode begint over 2 uur'. Automatisering: 'Wanneer volgende starttijd over 30 minuten is, verlaag verwarmingstemperatuur preventief'."
         },
         "peak_price_next_in_minutes": {
-            "description": "Tijd tot volgende dure periode",
+            "description": "Minuten tot volgende dure periode begint (0 bij overgang)",
-            "long_description": "Toont hoe lang het duurt tot de volgende dure periode. De state wordt weergegeven in uren (bijv. 0,5 u) voor dashboards, terwijl het attribuut `next_in_minutes` minuten levert (bijv. 30) voor automatiseringsvoorwaarden. Tijdens een actieve periode toont dit de tijd tot de periode NA de huidige. Geeft 0 terug tijdens korte overgangsmomenten. Werkt elke minuut bij.",
+            "long_description": "Toont minuten tot de volgende dure periode begint. Tijdens een actieve periode toont dit de tijd tot de periode NA de huidige. Geeft 0 terug tijdens korte overgangsmomenten. Werkt elke minuut bij.",
-            "usage_tips": "Voor automatiseringen: Gebruik attribuut `next_in_minutes` zoals 'Als next_in_minutes > 0 EN next_in_minutes < 10, voltooi huidige laadcyclus nu voordat prijzen stijgen'. Waarde > 0 geeft altijd aan dat een toekomstige dure periode is gepland."
+            "usage_tips": "Preventieve automatisering: 'Als next_in_minutes > 0 EN next_in_minutes < 10, voltooi huidige laadcyclus nu voordat prijzen stijgen'."
+        },
+        "best_price_period_duration": {
+            "description": "Totale duur van huidige of volgende goedkope periode in minuten",
+            "long_description": "Toont de totale duur van de goedkope periode in minuten. Tijdens een actieve periode toont dit de volledige lengte van de huidige periode. Wanneer geen periode actief is, toont dit de duur van de volgende komende periode. Voorbeeld: '90 minuten' voor een 1,5-uur periode.",
+            "usage_tips": "Combineer met remaining_minutes voor taakplanning: 'Als duration = 120 EN remaining_minutes > 90, start wasmachine (genoeg tijd om te voltooien)'. Nuttig om te begrijpen of periodes lang genoeg zijn voor energie-intensieve taken."
+        },
+        "peak_price_period_duration": {
+            "description": "Totale duur van huidige of volgende dure periode in minuten",
+            "long_description": "Toont de totale duur van de dure periode in minuten. Tijdens een actieve periode toont dit de volledige lengte van de huidige periode. Wanneer geen periode actief is, toont dit de duur van de volgende komende periode. Voorbeeld: '60 minuten' voor een 1-uur periode.",
+            "usage_tips": "Gebruik om energiebesparende maatregelen te plannen: 'Als duration > 120, verlaag verwarmingstemperatuur agressiever (lange dure periode)'. Helpt bij het inschatten hoeveel energieverbruik moet worden verminderd."
         },
         "home_type": {
             "description": "Type woning (appartement, huis enz.)",
@@ -450,11 +437,6 @@
             "description": "Data-export voor dashboard-integraties",
             "long_description": "Deze sensor roept de get_chartdata-service aan met jouw geconfigureerde YAML-configuratie en stelt het resultaat beschikbaar als entiteitsattributen. De status toont 'ready' wanneer data beschikbaar is, 'error' bij fouten, of 'pending' voor de eerste aanroep. Perfekt voor dashboard-integraties zoals ApexCharts die prijsgegevens uit entiteitsattributen moeten lezen.",
             "usage_tips": "Configureer de YAML-parameters in de integratie-opties om overeen te komen met jouw get_chartdata-service-aanroep. De sensor wordt automatisch bijgewerkt wanneer prijsgegevens worden bijgewerkt (typisch na middernacht en wanneer gegevens van morgen binnenkomen). Krijg toegang tot de service-responsgegevens direct vanuit de entiteitsattributen - de structuur komt exact overeen met wat get_chartdata retourneert."
-        },
-        "chart_metadata": {
-            "description": "Lichtgewicht metadata voor diagramconfiguratie",
-            "long_description": "Biedt essentiële diagramconfiguratiewaarden als sensorattributen. Nuttig voor elke grafiekkaart die Y-as-grenzen nodig heeft. De sensor roept get_chartdata aan in alleen-metadata-modus (geen dataverwerking) en extraheert: yaxis_min, yaxis_max (gesuggereerd Y-asbereik voor optimale schaling). De status weerspiegelt het service-aanroepresultaat: 'ready' bij succes, 'error' bij fouten, 'pending' tijdens initialisatie.",
-            "usage_tips": "Configureer via configuration.yaml onder tibber_prices.chart_metadata_config (optioneel: day, subunit_currency, resolution). De sensor wordt automatisch bijgewerkt bij prijsgegevenswijzigingen. Krijg toegang tot metadata vanuit attributen: yaxis_min, yaxis_max. Gebruik met config-template-card of elk hulpmiddel dat entiteitsattributen leest - perfect voor dynamische diagramconfiguratie zonder handmatige berekeningen."
         }
     },
     "binary_sensor": {
@@ -466,7 +448,7 @@
         "peak_price_period": {
             "description": "Of het huidige interval tot de duurste van de dag behoort",
             "long_description": "Wordt geactiveerd wanneer de huidige prijs in de top 20% van de prijzen van vandaag ligt",
-            "usage_tips": "Gebruik dit om te voorkomen dat je apparaten met hoog verbruik draait tijdens dure intervallen"
+            "usage_tips": "Gebruik dit om te voorkomen dat u apparaten met hoog verbruik draait tijdens dure intervallen"
         },
         "best_price_period": {
             "description": "Of het huidige interval tot de goedkoopste van de dag behoort",
@@ -487,80 +469,11 @@
             "description": "Of realtime verbruiksmonitoring actief is",
             "long_description": "Geeft aan of realtime elektriciteitsverbruikmonitoring is ingeschakeld en actief voor je Tibber-woning. Dit vereist compatibele meethardware (bijv. Tibber Pulse) en een actief abonnement.",
             "usage_tips": "Gebruik dit om te verifiëren dat realtimeverbruiksgegevens beschikbaar zijn. Schakel meldingen in als dit onverwacht verandert naar 'uit', wat wijst op mogelijke hardware- of verbindingsproblemen."
-        }
-    },
-    "number": {
-        "best_price_flex_override": {
-            "description": "Maximaal percentage boven de dagelijkse minimumprijs dat intervallen kunnen hebben en nog steeds als 'beste prijs' kwalificeren. Aanbevolen: 15-20 met versoepeling ingeschakeld (standaard), of 25-35 zonder versoepeling. Maximum: 50 (harde limiet voor betrouwbare periodedetectie).",
-            "long_description": "Wanneer deze entiteit is ingeschakeld, overschrijft de waarde de 'Flexibiliteit'-instelling uit de opties-dialoog voor beste prijs-periodeberekeningen.",
-            "usage_tips": "Schakel deze entiteit in om beste prijs-detectie dynamisch aan te passen via automatiseringen, bijv. hogere flexibiliteit voor kritieke lasten of strengere eisen voor flexibele apparaten."
         },
-        "best_price_min_distance_override": {
+        "chart_data_export": {
-            "description": "Minimale procentuele afstand onder het daggemiddelde. Intervallen moeten zo ver onder het gemiddelde liggen om als 'beste prijs' te kwalificeren. Helpt echte lage prijsperioden te onderscheiden van gemiddelde prijzen.",
+            "description": "Gegevensexport voor dashboardintegraties",
-            "long_description": "Wanneer deze entiteit is ingeschakeld, overschrijft de waarde de 'Minimale afstand'-instelling uit de opties-dialoog voor beste prijs-periodeberekeningen.",
+            "long_description": "Deze binaire sensor roept de get_chartdata-service aan om gegevens voor dashboard-widgets te exporteren. Ondersteunt ApexCharts en andere dashboardoplossingen die prijsgegevens willen visualiseren.",
-            "usage_tips": "Verhoog de waarde voor strengere beste prijs-criteria. Verlaag als te weinig perioden worden gedetecteerd."
+            "usage_tips": "Configureer de YAML-parameters in de integratieopties onder 'Geavanceerd'. Deze sensor biedt meestal geen praktische waarde in automatiseringen - hij dient hoofdzakelijk als servicecontainer voor dashboardgebruik. Raadpleeg de documentatie voor specifieke parameterformat."
-        },
-        "best_price_min_period_length_override": {
-            "description": "Minimale periodelengte in 15-minuten intervallen. Perioden korter dan dit worden niet gerapporteerd. Voorbeeld: 2 = minimaal 30 minuten.",
-            "long_description": "Wanneer deze entiteit is ingeschakeld, overschrijft de waarde de 'Minimale periodelengte'-instelling uit de opties-dialoog voor beste prijs-periodeberekeningen.",
-            "usage_tips": "Pas aan op typische apparaatlooptijd: 2 (30 min) voor snelle programma's, 4-8 (1-2 uur) voor normale cycli, 8+ voor lange ECO-programma's."
-        },
-        "best_price_min_periods_override": {
-            "description": "Minimum aantal beste prijs-perioden om dagelijks te vinden. Wanneer versoepeling is ingeschakeld, past het systeem automatisch de criteria aan om dit aantal te bereiken.",
-            "long_description": "Wanneer deze entiteit is ingeschakeld, overschrijft de waarde de 'Minimum periodes'-instelling uit de opties-dialoog voor beste prijs-periodeberekeningen.",
-            "usage_tips": "Stel dit in op het aantal tijdkritieke taken dat je dagelijks hebt. Voorbeeld: 2 voor twee wasladingen."
-        },
-        "best_price_relaxation_attempts_override": {
-            "description": "Aantal pogingen om de criteria geleidelijk te versoepelen om het minimum aantal perioden te bereiken. Elke poging verhoogt de flexibiliteit met 3 procent. Bij 0 worden alleen basiscriteria gebruikt.",
-            "long_description": "Wanneer deze entiteit is ingeschakeld, overschrijft de waarde de 'Versoepeling pogingen'-instelling uit de opties-dialoog voor beste prijs-periodeberekeningen.",
-            "usage_tips": "Hogere waarden maken periodedetectie adaptiever voor dagen met stabiele prijzen. Stel in op 0 om strikte criteria af te dwingen zonder versoepeling."
-        },
-        "best_price_gap_count_override": {
-            "description": "Maximum aantal duurdere intervallen dat mag worden toegestaan tussen goedkope intervallen terwijl ze nog steeds als één aaneengesloten periode tellen. Bij 0 moeten goedkope intervallen opeenvolgend zijn.",
-            "long_description": "Wanneer deze entiteit is ingeschakeld, overschrijft de waarde de 'Gap tolerantie'-instelling uit de opties-dialoog voor beste prijs-periodeberekeningen.",
-            "usage_tips": "Verhoog dit voor apparaten met variabele belasting (bijv. warmtepompen) die korte duurdere intervallen kunnen tolereren. Stel in op 0 voor continu goedkope perioden."
-        },
-        "peak_price_flex_override": {
-            "description": "Maximaal percentage onder de dagelijkse maximumprijs dat intervallen kunnen hebben en nog steeds als 'piekprijs' kwalificeren. Dezelfde aanbevelingen als voor beste prijs-flexibiliteit.",
-            "long_description": "Wanneer deze entiteit is ingeschakeld, overschrijft de waarde de 'Flexibiliteit'-instelling uit de opties-dialoog voor piekprijs-periodeberekeningen.",
-            "usage_tips": "Gebruik dit om de piekprijs-drempel tijdens runtime aan te passen voor automatiseringen die verbruik tijdens dure uren vermijden."
-        },
-        "peak_price_min_distance_override": {
-            "description": "Minimale procentuele afstand boven het daggemiddelde. Intervallen moeten zo ver boven het gemiddelde liggen om als 'piekprijs' te kwalificeren.",
-            "long_description": "Wanneer deze entiteit is ingeschakeld, overschrijft de waarde de 'Minimale afstand'-instelling uit de opties-dialoog voor piekprijs-periodeberekeningen.",
-            "usage_tips": "Verhoog de waarde om alleen extreme prijspieken te vangen. Verlaag om meer dure tijden mee te nemen."
-        },
-        "peak_price_min_period_length_override": {
-            "description": "Minimale periodelengte in 15-minuten intervallen voor piekprijzen. Kortere prijspieken worden niet als perioden gerapporteerd.",
-            "long_description": "Wanneer deze entiteit is ingeschakeld, overschrijft de waarde de 'Minimale periodelengte'-instelling uit de opties-dialoog voor piekprijs-periodeberekeningen.",
-            "usage_tips": "Kortere waarden vangen korte prijspieken. Langere waarden focussen op aanhoudende dure perioden."
-        },
-        "peak_price_min_periods_override": {
-            "description": "Minimum aantal piekprijs-perioden om dagelijks te vinden.",
-            "long_description": "Wanneer deze entiteit is ingeschakeld, overschrijft de waarde de 'Minimum periodes'-instelling uit de opties-dialoog voor piekprijs-periodeberekeningen.",
-            "usage_tips": "Stel dit in op basis van hoeveel dure perioden je per dag wilt vangen voor automatiseringen."
-        },
-        "peak_price_relaxation_attempts_override": {
-            "description": "Aantal pogingen om de criteria te versoepelen om het minimum aantal piekprijs-perioden te bereiken.",
-            "long_description": "Wanneer deze entiteit is ingeschakeld, overschrijft de waarde de 'Versoepeling pogingen'-instelling uit de opties-dialoog voor piekprijs-periodeberekeningen.",
-            "usage_tips": "Verhoog dit als geen perioden worden gevonden op dagen met stabiele prijzen. Stel in op 0 om strikte criteria af te dwingen."
-        },
-        "peak_price_gap_count_override": {
-            "description": "Maximum aantal goedkopere intervallen dat mag worden toegestaan tussen dure intervallen terwijl ze nog steeds als één piekprijs-periode tellen.",
-            "long_description": "Wanneer deze entiteit is ingeschakeld, overschrijft de waarde de 'Gap tolerantie'-instelling uit de opties-dialoog voor piekprijs-periodeberekeningen.",
-            "usage_tips": "Hogere waarden vangen langere dure perioden zelfs met korte prijsdips. Stel in op 0 voor strikt aaneengesloten piekprijzen."
-        }
-    },
-    "switch": {
-        "best_price_enable_relaxation_override": {
-            "description": "Indien ingeschakeld, worden criteria automatisch versoepeld om het minimum aantal perioden te bereiken. Indien uitgeschakeld, worden alleen perioden gerapporteerd die aan strikte criteria voldoen (mogelijk nul perioden op dagen met stabiele prijzen).",
-            "long_description": "Wanneer deze entiteit is ingeschakeld, overschrijft de waarde de 'Minimum aantal bereiken'-instelling uit de opties-dialoog voor beste prijs-periodeberekeningen.",
-            "usage_tips": "Schakel dit in voor gegarandeerde dagelijkse automatiseringsmogelijkheden. Schakel uit als je alleen echt goedkope perioden wilt, ook als dat betekent dat er op sommige dagen geen perioden zijn."
-        },
-        "peak_price_enable_relaxation_override": {
-            "description": "Indien ingeschakeld, worden criteria automatisch versoepeld om het minimum aantal perioden te bereiken. Indien uitgeschakeld, worden alleen echte prijspieken gerapporteerd.",
-            "long_description": "Wanneer deze entiteit is ingeschakeld, overschrijft de waarde de 'Minimum aantal bereiken'-instelling uit de opties-dialoog voor piekprijs-periodeberekeningen.",
-            "usage_tips": "Schakel dit in voor consistente piekprijs-waarschuwingen. Schakel uit om alleen extreme prijspieken te vangen."
         }
     },
     "home_types": {
@@ -569,15 +482,5 @@
         "HOUSE": "Huis",
         "COTTAGE": "Huisje"
     },
-    "time_units": {
-        "day": "{count} dag",
-        "days": "{count} dagen",
-        "hour": "{count} uur",
-        "hours": "{count} uur",
-        "minute": "{count} minuut",
-        "minutes": "{count} minuten",
-        "ago": "{parts} geleden",
-        "now": "nu"
-    },
     "attribution": "Gegevens geleverd door Tibber"
 }

custom_components/tibber_prices/custom_translations/sv.json

@@ -1,20 +1,7 @@
 {
     "apexcharts": {
-        "title_rating_level": "Prisfaser dagsprogress",
+        "title_rating_level": "Prisfaser daglig framsteg",
-        "title_level": "Prisnivå",
+        "title_level": "Prisnivå"
-        "hourly_suffix": "(Ø per timme)",
-        "best_price_period_name": "Bästa prisperiod",
-        "peak_price_period_name": "Toppprisperiod",
-        "notification": {
-            "metadata_sensor_unavailable": {
-                "title": "Tibber Prices: ApexCharts YAML genererad med begränsad funktionalitet",
-                "message": "Du har precis genererat en ApexCharts-kortkonfiguration via Utvecklarverktyg. Diagram-metadata-sensorn är inaktiverad, så den genererade YAML:en visar bara **grundläggande funktionalitet** (auto-skalning, fast gradient vid 50%).\n\n**För full funktionalitet** (optimerad skalning, dynamiska gradientfärger):\n1. [Öppna Tibber Prices-integrationen](https://my.home-assistant.io/redirect/integration/?domain=tibber_prices)\n2. Aktivera 'Chart Metadata'-sensorn\n3. **Generera YAML:en igen** via Utvecklarverktyg\n4. **Ersätt den gamla YAML:en** i din instrumentpanel med den nya versionen\n\n⚠️ Det räcker inte att bara aktivera sensorn - du måste regenerera och ersätta YAML-koden!"
-            },
-            "missing_cards": {
-                "title": "Tibber Prices: ApexCharts YAML kan inte användas",
-                "message": "Du har precis genererat en ApexCharts-kortkonfiguration via Utvecklarverktyg, men den genererade YAML:en **kommer inte att fungera** eftersom nödvändiga anpassade kort saknas.\n\n**Saknade kort:**\n{cards}\n\n**För att använda den genererade YAML:en:**\n1. Klicka på länkarna ovan för att installera de saknade korten från HACS\n2. Starta om Home Assistant (ibland nödvändigt)\n3. **Generera YAML:en igen** via Utvecklarverktyg\n4. Lägg till YAML:en i din instrumentpanel\n\n⚠️ Den nuvarande YAML-koden fungerar inte förrän alla kort är installerade!"
-            }
-        }
     },
     "sensor": {
         "current_interval_price": {
@@ -22,7 +9,7 @@
             "long_description": "Visar nuvarande pris per kWh från ditt Tibber-abonnemang",
             "usage_tips": "Använd detta för att spåra priser eller skapa automationer som körs när el är billig"
         },
-        "current_interval_price_base": {
+        "current_interval_price_major": {
             "description": "Nuvarande elpris i huvudvaluta (EUR/kWh, NOK/kWh, osv.) för Energipanelen",
             "long_description": "Visar nuvarande pris per kWh i huvudvaluta-enheter (t.ex. EUR/kWh istället för ct/kWh, NOK/kWh istället för øre/kWh). Denna sensor är speciellt utformad för användning med Home Assistants Energipanel, som kräver priser i standardvalutaenheter.",
             "usage_tips": "Använd denna sensor när du konfigurerar Energipanelen under Inställningar → Instrumentpaneler → Energi. Välj denna sensor som 'Entitet med nuvarande pris' för att automatiskt beräkna dina energikostnader. Energipanelen multiplicerar din energiförbrukning (kWh) med detta pris för att visa totala kostnader."
@@ -58,9 +45,9 @@
             "usage_tips": "Använd detta för att undvika att köra apparater under topppristider"
         },
         "average_price_today": {
-            "description": "Typiskt elpris för idag per kWh (konfigurerbart visningsformat)",
+            "description": "Det genomsnittliga elpriset för idag per kWh",
-            "long_description": "Visar priset per kWh för nuvarande dag från ditt Tibber-abonnemang. **Som standard visar statusen medianen** (motståndskraftig mot extrema prispikar, visar typisk prisnåvå). Du kan ändra detta i integrationsinstllningarna för att visa det aritmetiska medelvärdet istället. Det alternativa värdet är tillgängligt som attribut.",
+            "long_description": "Visar genomsnittspriset per kWh för nuvarande dag från ditt Tibber-abonnemang",
-            "usage_tips": "Använd detta som baslinje för att jämföra nuvarande priser. För beräkningar använd: {{ state_attr('sensor.average_price_today', 'price_mean') }}"
+            "usage_tips": "Använd detta som baslinje för att jämföra nuvarande priser"
         },
         "lowest_price_tomorrow": {
             "description": "Det lägsta elpriset för imorgon per kWh",
@@ -73,9 +60,9 @@
             "usage_tips": "Använd detta för att undvika att köra apparater under morgondagens topppristider. Användbart för att planera runt dyra perioder."
         },
         "average_price_tomorrow": {
-            "description": "Typiskt elpris för imorgon per kWh (konfigurerbart visningsformat)",
+            "description": "Det genomsnittliga elpriset för imorgon per kWh",
-            "long_description": "Visar priset per kWh för morgondagen från ditt Tibber-abonnemang. **Som standard visar statusen medianen** (motståndskraftig mot extrema prispikar). Du kan ändra detta i integrationsinstllningarna för att visa det aritmetiska medelvärdet istället. Det alternativa värdet är tillgängligt som attribut. Denna sensor blir otillgänglig tills morgondagens data publiceras av Tibber (vanligtvis runt 13:00-14:00 CET).",
+            "long_description": "Visar genomsnittspriset per kWh för morgondagen från ditt Tibber-abonnemang. Denna sensor blir otillgänglig tills morgondagens data publiceras av Tibber (vanligtvis runt 13:00-14:00 CET).",
-            "usage_tips": "Använd detta som baslinje för att jämföra morgondagens priser och planera konsumtion. Jämför med dagens median för att se om morgondagen kommer att bli dyrare eller billigare totalt sett."
+            "usage_tips": "Använd detta som baslinje för att jämföra morgondagens priser och planera konsumtion. Jämför med dagens genomsnitt för att se om morgondagen kommer att bli dyrare eller billigare totalt sett."
         },
         "yesterday_price_level": {
             "description": "Aggregerad prisnivå för igår",
@@ -108,14 +95,14 @@
             "usage_tips": "Använd detta för att planera imorgonens energiförbrukning baserat på dina personliga priströskelvärden. Jämför med idag för att avgöra om du ska skjuta upp förbrukning till imorgon eller använda energi idag."
         },
         "trailing_price_average": {
-            "description": "Typiskt elpris för de senaste 24 timmarna per kWh (konfigurerbart visningsformat)",
+            "description": "Det genomsnittliga elpriset för de senaste 24 timmarna per kWh",
-            "long_description": "Visar priset per kWh beräknat från de senaste 24 timmarna. **Som standard visar statusen medianen** (motståndskraftig mot extrema prispikar, visar typisk prisnåvå). Du kan ändra detta i integrationsinstllningarna för att visa det aritmetiska medelvärdet istället. Det alternativa värdet är tillgängligt som attribut. Uppdateras var 15:e minut.",
+            "long_description": "Visar genomsnittspriset per kWh beräknat från de senaste 24 timmarna (rullande genomsnitt) från ditt Tibber-abonnemang. Detta ger ett rullande genomsnitt som uppdateras var 15:e minut baserat på historiska data.",
-            "usage_tips": "Använd statusvärdet för att se den typiska nuvarande prisnåvån. För kostnadsberäkningar använd: {{ state_attr('sensor.trailing_price_average', 'price_mean') }}"
+            "usage_tips": "Använd detta för att jämföra nuvarande priser mot senaste trender. Ett nuvarande pris som ligger väsentligt över detta genomsnitt kan indikera ett bra tillfälle att minska konsumtionen."
         },
         "leading_price_average": {
-            "description": "Typiskt elpris för nästa 24 timmar per kWh (konfigurerbart visningsformat)",
+            "description": "Det genomsnittliga elpriset för nästa 24 timmar per kWh",
-            "long_description": "Visar priset per kWh beräknat från nästa 24 timmar. **Som standard visar statusen medianen** (motståndskraftig mot extrema prispikar, visar förväntad prisnåvå). Du kan ändra detta i integrationsinstllningarna för att visa det aritmetiska medelvärdet istället. Det alternativa värdet är tillgängligt som attribut.",
+            "long_description": "Visar genomsnittspriset per kWh beräknat från nästa 24 timmar (framåtblickande genomsnitt) från ditt Tibber-abonnemang. Detta ger ett framåtblickande genomsnitt baserat på tillgängliga prognosdata.",
-            "usage_tips": "Använd statusvärdet för att se den typiska kommande prisnåvån. För kostnadsberäkningar använd: {{ state_attr('sensor.leading_price_average', 'price_mean') }}"
+            "usage_tips": "Använd detta för att planera energianvändning. Om nuvarande pris är under det framåtblickande genomsnittet kan det vara ett bra tillfälle att köra energikrävande apparater."
         },
         "trailing_price_min": {
             "description": "Det minsta elpriset för de senaste 24 timmarna per kWh",
@@ -292,74 +279,64 @@
             "long_description": "Visar tidsstämpeln för det senaste tillgängliga prisdataintervallet från ditt Tibber-abonnemang"
         },
         "today_volatility": {
-            "description": "Hur mycket elpriserna varierar idag",
+            "description": "Prisvolatilitetsklassificering för idag",
-            "long_description": "Visar om dagens priser är stabila eller har stora svängningar. Låg volatilitet innebär ganska jämna priser – timing spelar liten roll. Hög volatilitet innebär tydliga prisskillnader under dagen – bra tillfälle att flytta förbrukning till billigare perioder. `price_coefficient_variation_%` visar procentvärdet, `price_spread` visar den absoluta prisspannet.",
+            "long_description": "Visar hur mycket elpriserna varierar under dagen baserat på spridningen (skillnaden mellan högsta och lägsta pris). Klassificering: LÅG = spridning < 5 öre, MÅTTLIG = 5-15 öre, HÖG = 15-30 öre, MYCKET HÖG = >30 öre.",
-            "usage_tips": "Använd detta för att avgöra om optimering är värt besväret. Vid låg volatilitet kan du köra enheter när som helst. Vid hög volatilitet sparar du märkbart genom att följa Best Price-perioder."
+            "usage_tips": "Använd detta för att avgöra om prisbaserad optimering är värt besväret. Till exempel, med ett balkongbatteri som har 15% effektivitetsförlust är optimering endast meningsfull när volatiliteten är åtminstone MÅTTLIG. Skapa automationer som kontrollerar volatiliteten innan laddnings-/urladdningscykler planeras."
         },
         "tomorrow_volatility": {
-            "description": "Hur mycket elpriserna kommer att variera i morgon",
+            "description": "Prisvolatilitetsklassificering för imorgon",
-            "long_description": "Visar om priserna i morgon blir stabila eller får stora svängningar. Tillgänglig när morgondagens data är publicerad (vanligen 13:00–14:00 CET). Låg volatilitet innebär ganska jämna priser – timing är inte kritisk. Hög volatilitet innebär tydliga prisskillnader under dagen – bra läge att planera energikrävande uppgifter. `price_coefficient_variation_%` visar procentvärdet, `price_spread` visar den absoluta prisspannet.",
+            "long_description": "Visar hur mycket elpriserna kommer att variera under morgondagen baserat på spridningen (skillnaden mellan högsta och lägsta pris). Blir otillgänglig tills morgondagens data publiceras (vanligtvis 13:00-14:00 CET).",
-            "usage_tips": "Använd för att planera morgondagens förbrukning. Hög volatilitet? Planera flexibla laster i Best Price-perioder. Låg volatilitet? Kör enheter när det passar dig."
+            "usage_tips": "Använd detta för förhandsplanering av morgondagens energianvändning. Om morgondagen har HÖG eller MYCKET HÖG volatilitet är det värt att optimera energiförbrukningstiming. Vid LÅG volatilitet kan du köra enheter när som helst utan betydande kostnadsskillnader."
         },
         "next_24h_volatility": {
-            "description": "Hur mycket priserna varierar de kommande 24 timmarna",
+            "description": "Prisvolatilitetsklassificering för rullande nästa 24 timmar",
-            "long_description": "Visar prisvolatilitet för ett rullande 24-timmarsfönster från nu (uppdateras var 15:e minut). Låg volatilitet innebär ganska jämna priser. Hög volatilitet innebär märkbara prissvängningar och därmed optimeringsmöjligheter. Till skillnad från idag/i morgon-sensorer korsar den här dagsgränser och ger en kontinuerlig framåtblickande bedömning. `price_coefficient_variation_%` visar procentvärdet, `price_spread` visar den absoluta prisspannet.",
+            "long_description": "Visar hur mycket elpriserna varierar under de nästa 24 timmarna från nu (rullande fönster). Detta korsar daggränser och uppdateras var 15:e minut, vilket ger en framåtblickande volatilitetsbedömning oberoende av kalenderdagar.",
-            "usage_tips": "Bäst för beslut i realtid. Använd vid planering av batteriladdning eller andra flexibla laster som kan gå över midnatt. Ger en konsekvent 24h-bild oberoende av kalenderdag."
+            "usage_tips": "Bästa sensorn för realtidsoptimeringsbeslut. Till skillnad från idag/imorgon-sensorer som växlar vid midnatt ger detta en kontinuerlig 24t volatilitetsbedömning. Använd för batteriladningsstrategier som sträcker sig över daggränser."
         },
         "today_tomorrow_volatility": {
-            "description": "Kombinerad prisvolatilitet för idag och imorgon",
+            "description": "Kombinerad prisvolatilitetsklassificering för idag och imorgon",
-            "long_description": "Visar den samlade volatiliteten när idag och imorgon ses tillsammans (när morgondatan finns). Visar om det finns tydliga prisskillnader över dagsgränsen. Faller tillbaka till endast idag om morgondatan saknas. Nyttig för flerdagarsoptimering. `price_coefficient_variation_%` visar procentvärdet, `price_spread` visar den absoluta prisspannet.",
+            "long_description": "Visar volatilitet över både idag och imorgon kombinerat (när morgondagens data är tillgänglig). Ger en utökad vy av prisvariation över upp till 48 timmar. Faller tillbaka till endast idag när morgondagens data inte är tillgänglig ännu.",
-            "usage_tips": "Använd för uppgifter som sträcker sig över flera dagar. Kontrollera om prisskillnaderna är stora nog för att planera efter. De enskilda dag-sensorerna visar bidrag per dag om du behöver mer detaljer."
+            "usage_tips": "Använd detta för flerdagarsplanering och för att förstå om prismöjligheter existerar över dagsgränsen. Attributen 'today_volatility' och 'tomorrow_volatility' visar individuella dagsbidrag. Användbart för planering av laddningssessioner som kan sträcka sig över midnatt."
         },
         "data_lifecycle_status": {
-            "description": "Gjeldende tilstand for prisdatalivssyklus og hurtigbufring",
+            "description": "Aktuell status för prisdatalivscykel och cachning",
-            "long_description": "Viser om integrasjonen bruker hurtigbufrede data eller ferske data fra API-et. Viser gjeldende livssyklustilstand: 'cached' (bruker lagrede data), 'fresh' (nettopp hentet fra API), 'refreshing' (henter for øyeblikket), 'searching_tomorrow' (søker aktivt etter morgendagens data etter 13:00), 'turnover_pending' (innen 15 minutter før midnatt, 23:45-00:00), eller 'error' (henting mislyktes). Inkluderer omfattende attributter som cache-alder, neste API-spørring, datafullstendighet og API-anropsstatistikk.",
+            "long_description": "Visar om integrationen använder cachad data eller färsk data från API:et. Visar aktuell livscykelstatus: 'cached' (använder lagrad data), 'fresh' (nyss hämtad från API), 'refreshing' (hämtar för närvarande), 'searching_tomorrow' (söker aktivt efter morgondagens data efter 13:00), 'turnover_pending' (inom 15 minuter före midnatt, 23:45-00:00), eller 'error' (hämtning misslyckades). Inkluderar omfattande attribut som cache-ålder, nästa API-polling, datafullständighet och API-anropsstatistik.",
-            "usage_tips": "Bruk denne diagnosesensoren for å forstå dataferskhet og API-anropsmønstre. Sjekk 'cache_age'-attributtet for å se hvor gamle de nåværende dataene er. Overvåk 'next_api_poll' for å vite når neste oppdatering er planlagt. Bruk 'data_completeness' for å se om data for i går/i dag/i morgen er tilgjengelig. 'api_calls_today'-telleren hjelper med å spore API-bruk. Perfekt for feilsøking eller forståelse av integrasjonens oppførsel."
+            "usage_tips": "Använd denna diagnostiksensor för att förstå datafärskhet och API-anropsmönster. Kontrollera 'cache_age'-attributet för att se hur gammal den aktuella datan är. Övervaka 'next_api_poll' för att veta när nästa uppdatering är schemalagd. Använd 'data_completeness' för att se om data för igår/idag/imorgon är tillgänglig. Räknaren 'api_calls_today' hjälper till att spåra API-användning. Perfekt för felsökning eller förståelse av integrationens beteende."
         },
         "best_price_end_time": {
-            "description": "Total längd för nuvarande eller nästa billigperiod (state i timmar, attribut i minuter)",
+            "description": "När nuvarande eller nästa billigperiod slutar",
-            "long_description": "Visar hur länge billigperioden varar. State använder timmar (decimal) för en läsbar UI; attributet `period_duration_minutes` behåller avrundade minuter för automationer. Aktiv → varaktighet för aktuell period, annars nästa.",
+            "long_description": "Visar sluttidsstämpeln för nuvarande billigperiod när aktiv, eller slutet av nästa period när ingen period är aktiv. Visar alltid en användbar tidsreferens för planering. Returnerar 'Okänt' endast när inga perioder är konfigurerade.",
-            "usage_tips": "UI kan visa 1,5 h medan `period_duration_minutes` = 90 för automationer."
+            "usage_tips": "Använd detta för att visa en nedräkning som 'Billigperiod slutar om 2 timmar' (när aktiv) eller 'Nästa billigperiod slutar kl 14:00' (när inaktiv). Home Assistant visar automatiskt relativ tid för tidsstämpelsensorer."
-        },
-        "best_price_period_duration": {
-            "description": "Längd på nuvarande/nästa billigperiod",
-            "long_description": "Total längd av nuvarande eller nästa billigperiod. State visas i timmar (t.ex. 1,5 h) för enkel avläsning i UI, medan attributet `period_duration_minutes` ger samma värde i minuter (t.ex. 90) för automationer. Detta värde representerar den **fullständigt planerade längden** av perioden och är konstant under hela perioden, även när återstående tid (remaining_minutes) minskar.",
-            "usage_tips": "Kombinera med remaining_minutes för att beräkna när långvariga enheter ska stoppas: Perioden startade för `period_duration_minutes - remaining_minutes` minuter sedan. Detta attribut stöder energioptimeringsstrategier genom att hjälpa till med att planera högförbruksaktiviteter inom billiga perioder."
         },
         "best_price_remaining_minutes": {
-            "description": "Tid kvar i nuvarande billigperiod",
+            "description": "Återstående minuter i nuvarande billigperiod (0 när inaktiv)",
-            "long_description": "Visar hur mycket tid som återstår i nuvarande billigperiod. State visas i timmar (t.ex. 0,75 h) för enkel avläsning i instrumentpaneler, medan attributet `remaining_minutes` ger samma tid i minuter (t.ex. 45) för automationsvillkor. **Nedräkningstimer**: Detta värde minskar varje minut under en aktiv period. Returnerar 0 när ingen billigperiod är aktiv. Uppdateras varje minut.",
+            "long_description": "Visar hur många minuter som återstår i nuvarande billigperiod. Returnerar 0 när ingen period är aktiv. Uppdateras varje minut. Kontrollera binary_sensor.best_price_period för att se om en period är aktiv.",
-            "usage_tips": "För automationer: Använd attribut `remaining_minutes` som 'Om remaining_minutes > 60, starta diskmaskin nu (tillräckligt med tid för att slutföra)' eller 'Om remaining_minutes < 15, avsluta nuvarande cykel snart'. UI visar användarvänliga timmar (t.ex. 1,25 h). Värde 0 indikerar ingen aktiv billigperiod."
+            "usage_tips": "Perfekt för automationer: 'Om remaining_minutes > 0 OCH remaining_minutes < 30, starta tvättmaskin nu'. Värdet 0 gör det enkelt att kontrollera om en period är aktiv (värde > 0) eller inte (värde = 0)."
         },
         "best_price_progress": {
             "description": "Framsteg genom nuvarande billigperiod (0% när inaktiv)",
-            "long_description": "Visar framsteg genom nuvarande billigperiod som 0-100%. Returnerar 0% när ingen period är aktiv. Uppdateras varje minut. 0% betyder att perioden just startade, 100% betyder att den snart slutar.",
+            "long_description": "Visar framsteg genom nuvarande billigperiod som 0-100%. Returnerar 0% när ingen period är aktiv. Uppdateras varje minut. 0% betyder period just startad, 100% betyder den snart slutar.",
-            "usage_tips": "Perfekt för visuella framstegsindikatorer. Använd i automationer: 'Om progress > 0 OCH progress > 75, skicka avisering om att billigperioden snart slutar'. Värde 0 indikerar ingen aktiv period."
+            "usage_tips": "Bra för visuella framstegsstaplar. Använd i automationer: 'Om progress > 0 OCH progress > 75, skicka meddelande att billigperiod snart slutar'. Värde 0 indikerar ingen aktiv period."
         },
         "best_price_next_start_time": {
-            "description": "Total längd för nuvarande eller nästa dyrperiod (state i timmar, attribut i minuter)",
+            "description": "När nästa billigperiod startar",
-            "long_description": "Visar hur länge den dyra perioden varar. State använder timmar (decimal) för UI; attributet `period_duration_minutes` behåller avrundade minuter för automationer. Aktiv → varaktighet för aktuell period, annars nästa.",
+            "long_description": "Visar när nästa kommande billigperiod startar. Under en aktiv period visar detta starten av NÄSTA period efter den nuvarande. Returnerar 'Okänt' endast när inga framtida perioder är konfigurerade.",
-            "usage_tips": "UI kan visa 0,75 h medan `period_duration_minutes` = 45 för automationer."
+            "usage_tips": "Alltid användbart för framåtplanering: 'Nästa billigperiod startar om 3 timmar' (oavsett om du är i en period nu eller inte). Kombinera med automationer: 'När nästa starttid är om 10 minuter, skicka meddelande för att förbereda tvättmaskin'."
         },
         "best_price_next_in_minutes": {
-            "description": "Tid kvar i nuvarande dyrperiod (state i timmar, attribut i minuter)",
+            "description": "Minuter tills nästa billigperiod startar (0 vid övergång)",
-            "long_description": "Visar hur mycket tid som återstår. State använder timmar (decimal); attributet `remaining_minutes` behåller avrundade minuter för automationer. Returnerar 0 när ingen period är aktiv. Uppdateras varje minut.",
+            "long_description": "Visar minuter tills nästa billigperiod startar. Under en aktiv period visar detta tiden till perioden EFTER den nuvarande. Returnerar 0 under korta övergångsmoment. Uppdateras varje minut.",
-            "usage_tips": "Använd `remaining_minutes` för trösklar (t.ex. > 60) medan state är lätt att läsa i timmar."
+            "usage_tips": "Perfekt för 'vänta tills billigperiod' automationer: 'Om next_in_minutes > 0 OCH next_in_minutes < 15, vänta innan diskmaskin startas'. Värde > 0 indikerar alltid att en framtida period är planerad."
         },
         "peak_price_end_time": {
-            "description": "Tid tills nästa dyrperiod startar (state i timmar, attribut i minuter)",
+            "description": "När nuvarande eller nästa dyrperiod slutar",
-            "long_description": "Visar hur länge tills nästa dyrperiod startar. State använder timmar (decimal); attributet `next_in_minutes` behåller avrundade minuter för automationer. Under en aktiv period visar detta tiden till perioden efter den aktuella. 0 under korta övergångar. Uppdateras varje minut.",
+            "long_description": "Visar sluttidsstämpeln för nuvarande dyrperiod när aktiv, eller slutet av nästa period när ingen period är aktiv. Visar alltid en användbar tidsreferens för planering. Returnerar 'Okänt' endast när inga perioder är konfigurerade.",
-            "usage_tips": "Använd `next_in_minutes` i automationer (t.ex. < 10) medan state är lätt att läsa i timmar."
+            "usage_tips": "Använd detta för att visa 'Dyrperiod slutar om 1 timme' (när aktiv) eller 'Nästa dyrperiod slutar kl 18:00' (när inaktiv). Kombinera med automationer för att återuppta drift efter topp."
-        },
-        "peak_price_period_duration": {
-            "description": "Längd på nuvarande/nästa dyrperiod",
-            "long_description": "Total längd av nuvarande eller nästa dyrperiod. State visas i timmar (t.ex. 1,5 h) för enkel avläsning i UI, medan attributet `period_duration_minutes` ger samma värde i minuter (t.ex. 90) för automationer. Detta värde representerar den **fullständigt planerade längden** av perioden och är konstant under hela perioden, även när återstående tid (remaining_minutes) minskar.",
-            "usage_tips": "Kombinera med remaining_minutes för att beräkna när långvariga enheter ska stoppas: Perioden startade för `period_duration_minutes - remaining_minutes` minuter sedan. Detta attribut stöder energibesparingsstrategier genom att hjälpa till med att planera högförbruksaktiviteter utanför dyra perioder."
         },
         "peak_price_remaining_minutes": {
-            "description": "Tid kvar i nuvarande dyrperiod",
+            "description": "Återstående minuter i nuvarande dyrperiod (0 när inaktiv)",
-            "long_description": "Visar hur mycket tid som återstår i nuvarande dyrperiod. State visas i timmar (t.ex. 0,75 h) för enkel avläsning i instrumentpaneler, medan attributet `remaining_minutes` ger samma tid i minuter (t.ex. 45) för automationsvillkor. **Nedräkningstimer**: Detta värde minskar varje minut under en aktiv period. Returnerar 0 när ingen dyrperiod är aktiv. Uppdateras varje minut.",
+            "long_description": "Visar hur många minuter som återstår i nuvarande dyrperiod. Returnerar 0 när ingen period är aktiv. Uppdateras varje minut. Kontrollera binary_sensor.peak_price_period för att se om en period är aktiv.",
-            "usage_tips": "För automationer: Använd attribut `remaining_minutes` som 'Om remaining_minutes > 60, avbryt uppskjuten laddningssession' eller 'Om remaining_minutes < 15, återuppta normal drift snart'. UI visar användarvänliga timmar (t.ex. 1,0 h). Värde 0 indikerar ingen aktiv dyrperiod."
+            "usage_tips": "Använd i automationer: 'Om remaining_minutes > 60, avbryt uppskjuten laddningssession'. Värde 0 gör det enkelt att skilja mellan aktiva (värde > 0) och inaktiva (värde = 0) perioder."
         },
         "peak_price_progress": {
             "description": "Framsteg genom nuvarande dyrperiod (0% när inaktiv)",
@@ -372,9 +349,19 @@
             "usage_tips": "Alltid användbart för planering: 'Nästa dyrperiod startar om 2 timmar'. Automation: 'När nästa starttid är om 30 minuter, minska värmetemperatur förebyggande'."
         },
         "peak_price_next_in_minutes": {
-            "description": "Tid till nästa dyrperiod",
+            "description": "Minuter tills nästa dyrperiod startar (0 vid övergång)",
-            "long_description": "Visar hur länge till nästa dyrperiod. State visas i timmar (t.ex. 0,5 h) för instrumentpaneler, medan attributet `next_in_minutes` ger minuter (t.ex. 30) för automationsvillkor. Under en aktiv period visar detta tiden till perioden EFTER den nuvarande. Returnerar 0 under korta övergångsmoment. Uppdateras varje minut.",
+            "long_description": "Visar minuter tills nästa dyrperiod startar. Under en aktiv period visar detta tiden till perioden EFTER den nuvarande. Returnerar 0 under korta övergångsmoment. Uppdateras varje minut.",
-            "usage_tips": "För automationer: Använd attribut `next_in_minutes` som 'Om next_in_minutes > 0 OCH next_in_minutes < 10, slutför nuvarande laddcykel nu innan priserna ökar'. Värde > 0 indikerar alltid att en framtida dyrperiod är planerad."
+            "usage_tips": "Förebyggande automation: 'Om next_in_minutes > 0 OCH next_in_minutes < 10, slutför nuvarande laddcykel nu innan priserna ökar'."
+        },
+        "best_price_period_duration": {
+            "description": "Total längd på nuvarande eller nästa billigperiod i minuter",
+            "long_description": "Visar den totala längden på billigperioden i minuter. Under en aktiv period visar detta hela längden av nuvarande period. När ingen period är aktiv visar detta längden på nästa kommande period. Exempel: '90 minuter' för en 1,5-timmars period.",
+            "usage_tips": "Kombinera med remaining_minutes för att planera uppgifter: 'Om duration = 120 OCH remaining_minutes > 90, starta tvättmaskin (tillräckligt med tid för att slutföra)'. Användbart för att förstå om perioder är tillräckligt långa för energikrävande uppgifter."
+        },
+        "peak_price_period_duration": {
+            "description": "Total längd på nuvarande eller nästa dyrperiod i minuter",
+            "long_description": "Visar den totala längden på dyrperioden i minuter. Under en aktiv period visar detta hela längden av nuvarande period. När ingen period är aktiv visar detta längden på nästa kommande period. Exempel: '60 minuter' för en 1-timmars period.",
+            "usage_tips": "Använd för att planera energisparåtgärder: 'Om duration > 120, minska värmetemperatur mer aggressivt (lång dyr period)'. Hjälper till att bedöma hur mycket energiförbrukning måste minskas."
         },
         "home_type": {
             "description": "Bostadstyp (lägenhet, hus osv.)",
@@ -447,14 +434,9 @@
             "usage_tips": "Använd detta för att övervaka din abonnemangsstatus. Ställ in varningar om statusen ändras från 'Aktiv' för att säkerställa oavbruten service."
         },
         "chart_data_export": {
-            "description": "Dataexport för dashboard-integrationer",
+            "description": "Dataexport för instrumentpanelsintegrationer",
-            "long_description": "Denna sensor anropar get_chartdata-tjänsten med din konfigurerade YAML-konfiguration och exponerar resultatet som entitetsattribut. Statusen visar 'ready' när data är tillgänglig, 'error' vid fel, eller 'pending' före första anropet. Perfekt för dashboard-integrationer som ApexCharts som behöver läsa prisdata från entitetsattribut.",
+            "long_description": "Denna sensor anropar get_chartdata-tjänsten med din konfigurerade YAML-konfiguration och exponerar resultatet som entitetsattribut. Statusen visar 'ready' när data är tillgänglig, 'error' vid fel, eller 'pending' före första anropet. Perfekt för instrumentpanelsintegrationer som ApexCharts som behöver läsa prisdata från entitetsattribut.",
-            "usage_tips": "Konfigurera YAML-parametrarna i integrationsalternativen för att matcha ditt get_chartdata-tjänstanrop. Sensorn uppdateras automatiskt när prisdata uppdateras (vanligtvis efter midnatt och när morgondagens data anländer). Få tillgång till tjänstesvarsdata direkt från entitetens attribut - strukturen matchar exakt vad get_chartdata returnerar."
+            "usage_tips": "Konfigurera YAML-parametrarna i integrationsinställningarna för att matcha ditt get_chartdata-tjänsteanrop. Sensorn uppdateras automatiskt när prisdata uppdateras (vanligtvis efter midnatt och när morgondagens data anländer). Få åtkomst till tjänstesvarsdata direkt från entitetens attribut - strukturen matchar exakt vad get_chartdata returnerar."
-        },
-        "chart_metadata": {
-            "description": "Lättviktig metadata för diagramkonfiguration",
-            "long_description": "Tillhandahåller väsentliga diagramkonfigurationsvärden som sensorattribut. Användbart för vilket diagramkort som helst som behöver Y-axelgränser. Sensorn anropar get_chartdata med endast-metadata-läge (ingen databehandling) och extraherar: yaxis_min, yaxis_max (föreslagen Y-axelomfång för optimal skalning). Statusen återspeglar tjänstanropsresultatet: 'ready' vid framgång, 'error' vid fel, 'pending' under initialisering.",
-            "usage_tips": "Konfigurera via configuration.yaml under tibber_prices.chart_metadata_config (valfritt: day, subunit_currency, resolution). Sensorn uppdateras automatiskt vid pris dataändringar. Få tillgång till metadata från attribut: yaxis_min, yaxis_max. Använd med config-template-card eller vilket verktyg som helst som läser entitetsattribut - perfekt för dynamisk diagramkonfiguration utan manuella beräkningar."
         }
     },
     "binary_sensor": {
@@ -487,80 +469,11 @@
             "description": "Om realtidsförbrukningsövervakning är aktiv",
             "long_description": "Indikerar om realtidsövervakning av elförbrukning är aktiverad och aktiv för ditt Tibber-hem. Detta kräver kompatibel mätutrustning (t.ex. Tibber Pulse) och en aktiv prenumeration.",
             "usage_tips": "Använd detta för att verifiera att realtidsförbrukningen är tillgänglig. Aktivera meddelanden om detta oväntat ändras till 'av', vilket indikerar potentiella hårdvaru- eller anslutningsproblem."
-        }
-    },
-    "number": {
-        "best_price_flex_override": {
-            "description": "Maximal procent över daglig minimumpris som intervaller kan ha och fortfarande kvalificera som 'bästa pris'. Rekommenderas: 15-20 med lättnad aktiverad (standard), eller 25-35 utan lättnad. Maximum: 50 (hårt tak för tillförlitlig perioddetektering).",
-            "long_description": "När denna entitet är aktiverad överskriver värdet 'Flexibilitet'-inställningen från alternativ-dialogen för bästa pris-periodberäkningar.",
-            "usage_tips": "Aktivera denna entitet för att dynamiskt justera bästa pris-detektering via automatiseringar, t.ex. högre flexibilitet för kritiska laster eller striktare krav för flexibla apparater."
         },
-        "best_price_min_distance_override": {
+        "chart_data_export": {
-            "description": "Minsta procentuella avstånd under dagligt genomsnitt. Intervaller måste vara så långt under genomsnittet för att kvalificera som 'bästa pris'. Hjälper att skilja äkta lågprisperioder från genomsnittspriser.",
+            "description": "Dataexport för instrumentpanelsintegrationer",
-            "long_description": "När denna entitet är aktiverad överskriver värdet 'Minimiavstånd'-inställningen från alternativ-dialogen för bästa pris-periodberäkningar.",
+            "long_description": "Denna binär sensor anropar tjänsten get_chartdata för att exportera prissensordata i format som är kompatibelt med ApexCharts och andra instrumentpanelskomponenter. Använd denna tillsammans med custom:apexcharts-card för att visa prissensorer på din instrumentpanel.",
-            "usage_tips": "Öka värdet för striktare bästa pris-kriterier. Minska om för få perioder detekteras."
+            "usage_tips": "Konfigurera YAML-parametrarna i integrationens alternativ under 'ApexCharts-datakonfiguration'. Tjänsten kräver en giltig sensorenhet och returnerar formaterad data för kartrendring. Se dokumentationen för tillgängliga parametrar och anpassningsalternativ."
-        },
-        "best_price_min_period_length_override": {
-            "description": "Minsta periodlängd i 15-minuters intervaller. Perioder kortare än detta rapporteras inte. Exempel: 2 = minst 30 minuter.",
-            "long_description": "När denna entitet är aktiverad överskriver värdet 'Minsta periodlängd'-inställningen från alternativ-dialogen för bästa pris-periodberäkningar.",
-            "usage_tips": "Anpassa till typisk apparatkörtid: 2 (30 min) för snabbprogram, 4-8 (1-2 timmar) för normala cykler, 8+ för långa ECO-program."
-        },
-        "best_price_min_periods_override": {
-            "description": "Minsta antal bästa pris-perioder att hitta dagligen. När lättnad är aktiverad kommer systemet automatiskt att justera kriterierna för att uppnå detta antal.",
-            "long_description": "När denna entitet är aktiverad överskriver värdet 'Minsta antal perioder'-inställningen från alternativ-dialogen för bästa pris-periodberäkningar.",
-            "usage_tips": "Ställ in detta på antalet tidskritiska uppgifter du har dagligen. Exempel: 2 för två tvattmaskinskörningar."
-        },
-        "best_price_relaxation_attempts_override": {
-            "description": "Antal försök att gradvis lätta på kriterierna för att uppnå minsta periodantal. Varje försök ökar flexibiliteten med 3 procent. Vid 0 används endast baskriterier.",
-            "long_description": "När denna entitet är aktiverad överskriver värdet 'Lättnadsförsök'-inställningen från alternativ-dialogen för bästa pris-periodberäkningar.",
-            "usage_tips": "Högre värden gör perioddetektering mer adaptiv för dagar med stabila priser. Ställ in på 0 för att tvinga strikta kriterier utan lättnad."
-        },
-        "best_price_gap_count_override": {
-            "description": "Maximalt antal dyrare intervaller som kan tillåtas mellan billiga intervaller medan de fortfarande räknas som en sammanhängande period. Vid 0 måste billiga intervaller vara påföljande.",
-            "long_description": "När denna entitet är aktiverad överskriver värdet 'Glaptolerans'-inställningen från alternativ-dialogen för bästa pris-periodberäkningar.",
-            "usage_tips": "Öka detta för apparater med variabel last (t.ex. värmepumpar) som kan tolerera korta dyrare intervaller. Ställ in på 0 för kontinuerligt billiga perioder."
-        },
-        "peak_price_flex_override": {
-            "description": "Maximal procent under daglig maximumpris som intervaller kan ha och fortfarande kvalificera som 'topppris'. Samma rekommendationer som för bästa pris-flexibilitet.",
-            "long_description": "När denna entitet är aktiverad överskriver värdet 'Flexibilitet'-inställningen från alternativ-dialogen för topppris-periodberäkningar.",
-            "usage_tips": "Använd detta för att justera topppris-tröskeln vid körtid för automatiseringar som undviker förbrukning under dyra timmar."
-        },
-        "peak_price_min_distance_override": {
-            "description": "Minsta procentuella avstånd över dagligt genomsnitt. Intervaller måste vara så långt över genomsnittet för att kvalificera som 'topppris'.",
-            "long_description": "När denna entitet är aktiverad överskriver värdet 'Minimiavstånd'-inställningen från alternativ-dialogen för topppris-periodberäkningar.",
-            "usage_tips": "Öka värdet för att endast fånga extrema pristoppar. Minska för att inkludera fler högpristider."
-        },
-        "peak_price_min_period_length_override": {
-            "description": "Minsta periodlängd i 15-minuters intervaller för topppriser. Kortare pristoppar rapporteras inte som perioder.",
-            "long_description": "När denna entitet är aktiverad överskriver värdet 'Minsta periodlängd'-inställningen från alternativ-dialogen för topppris-periodberäkningar.",
-            "usage_tips": "Kortare värden fångar korta pristoppar. Längre värden fokuserar på ihållande högprisperioder."
-        },
-        "peak_price_min_periods_override": {
-            "description": "Minsta antal topppris-perioder att hitta dagligen.",
-            "long_description": "När denna entitet är aktiverad överskriver värdet 'Minsta antal perioder'-inställningen från alternativ-dialogen för topppris-periodberäkningar.",
-            "usage_tips": "Ställ in detta baserat på hur många högprisperioder du vill fånga per dag för automatiseringar."
-        },
-        "peak_price_relaxation_attempts_override": {
-            "description": "Antal försök att lätta på kriterierna för att uppnå minsta antal topppris-perioder.",
-            "long_description": "När denna entitet är aktiverad överskriver värdet 'Lättnadsförsök'-inställningen från alternativ-dialogen för topppris-periodberäkningar.",
-            "usage_tips": "Öka detta om inga perioder hittas på dagar med stabila priser. Ställ in på 0 för att tvinga strikta kriterier."
-        },
-        "peak_price_gap_count_override": {
-            "description": "Maximalt antal billigare intervaller som kan tillåtas mellan dyra intervaller medan de fortfarande räknas som en topppris-period.",
-            "long_description": "När denna entitet är aktiverad överskriver värdet 'Glaptolerans'-inställningen från alternativ-dialogen för topppris-periodberäkningar.",
-            "usage_tips": "Högre värden fångar längre högprisperioder även med korta prisdipp. Ställ in på 0 för strikt sammanhängande topppriser."
-        }
-    },
-    "switch": {
-        "best_price_enable_relaxation_override": {
-            "description": "När aktiverad lättas kriterierna automatiskt för att uppnå minsta periodantal. När inaktiverad rapporteras endast perioder som uppfyller strikta kriterier (möjligen noll perioder på dagar med stabila priser).",
-            "long_description": "När denna entitet är aktiverad överskriver värdet 'Uppnå minimiantal'-inställningen från alternativ-dialogen för bästa pris-periodberäkningar.",
-            "usage_tips": "Aktivera detta för garanterade dagliga automatiseringsmöjligheter. Inaktivera om du endast vill ha riktigt billiga perioder, även om det innebär inga perioder vissa dagar."
-        },
-        "peak_price_enable_relaxation_override": {
-            "description": "När aktiverad lättas kriterierna automatiskt för att uppnå minsta periodantal. När inaktiverad rapporteras endast äkta pristoppar.",
-            "long_description": "När denna entitet är aktiverad överskriver värdet 'Uppnå minimiantal'-inställningen från alternativ-dialogen för topppris-periodberäkningar.",
-            "usage_tips": "Aktivera detta för konsekventa topppris-varningar. Inaktivera för att endast fånga extrema pristoppar."
         }
     },
     "home_types": {
@@ -569,15 +482,5 @@
         "HOUSE": "Hus",
         "COTTAGE": "Stuga"
     },
-    "time_units": {
-        "day": "{count} dag",
-        "days": "{count} dagar",
-        "hour": "{count} timme",
-        "hours": "{count} timmar",
-        "minute": "{count} minut",
-        "minutes": "{count} minuter",
-        "ago": "{parts} sedan",
-        "now": "nu"
-    },
     "attribution": "Data tillhandahålls av Tibber"
 }

custom_components/tibber_prices/data.py

@@ -11,7 +11,6 @@ if TYPE_CHECKING:
 
     from .api import TibberPricesApiClient
     from .coordinator import TibberPricesDataUpdateCoordinator
-    from .interval_pool import TibberPricesIntervalPool
 
 
 @dataclass
@@ -21,7 +20,6 @@ class TibberPricesData:
     client: TibberPricesApiClient
     coordinator: TibberPricesDataUpdateCoordinator
     integration: Integration
-    interval_pool: TibberPricesIntervalPool  # Shared interval pool per config entry
 
 
 if TYPE_CHECKING:

custom_components/tibber_prices/diagnostics.py

@@ -22,9 +22,6 @@ async def async_get_config_entry_diagnostics(
     """Return diagnostics for a config entry."""
     coordinator = entry.runtime_data.coordinator
 
-    # Get period metadata from coordinator data
-    price_periods = coordinator.data.get("pricePeriods", {}) if coordinator.data else {}
-
     return {
         "entry": {
             "entry_id": entry.entry_id,
@@ -33,46 +30,16 @@ async def async_get_config_entry_diagnostics(
             "domain": entry.domain,
             "title": entry.title,
             "state": str(entry.state),
-            "home_id": entry.data.get("home_id", ""),
         },
         "coordinator": {
             "last_update_success": coordinator.last_update_success,
             "update_interval": str(coordinator.update_interval),
+            "is_main_entry": coordinator.is_main_entry(),
             "data": coordinator.data,
             "update_timestamps": {
                 "price": coordinator._last_price_update.isoformat() if coordinator._last_price_update else None,  # noqa: SLF001
                 "user": coordinator._last_user_update.isoformat() if coordinator._last_user_update else None,  # noqa: SLF001
-                "last_coordinator_update": coordinator._last_coordinator_update.isoformat()  # noqa: SLF001
-                if coordinator._last_coordinator_update  # noqa: SLF001
-                else None,
             },
-            "lifecycle": {
-                "state": coordinator._lifecycle_state,  # noqa: SLF001
-                "is_fetching": coordinator._is_fetching,  # noqa: SLF001
-                "api_calls_today": coordinator._api_calls_today,  # noqa: SLF001
-                "last_api_call_date": coordinator._last_api_call_date.isoformat()  # noqa: SLF001
-                if coordinator._last_api_call_date  # noqa: SLF001
-                else None,
-            },
-        },
-        "periods": {
-            "best_price": {
-                "count": len(price_periods.get("best_price", {}).get("periods", [])),
-                "metadata": price_periods.get("best_price", {}).get("metadata", {}),
-            },
-            "peak_price": {
-                "count": len(price_periods.get("peak_price", {}).get("periods", [])),
-                "metadata": price_periods.get("peak_price", {}).get("metadata", {}),
-            },
-        },
-        "config": {
-            "options": dict(entry.options),
-        },
-        "cache_status": {
-            "user_data_cached": coordinator._cached_user_data is not None,  # noqa: SLF001
-            "has_price_data": coordinator.data is not None and "priceInfo" in (coordinator.data or {}),
-            "transformer_cache_valid": coordinator._data_transformer._cached_transformed_data is not None,  # noqa: SLF001
-            "period_calculator_cache_valid": coordinator._period_calculator._cached_periods is not None,  # noqa: SLF001
         },
         "error": {
             "last_exception": str(coordinator.last_exception) if coordinator.last_exception else None,

custom_components/tibber_prices/entity.py

@@ -44,22 +44,6 @@ class TibberPricesEntity(CoordinatorEntity[TibberPricesDataUpdateCoordinator]):
             configuration_url="https://developer.tibber.com/explorer",
         )
 
-    @property
-    def available(self) -> bool:
-        """
-        Return if entity is available.
-
-        Entity is unavailable when:
-        - Coordinator has not completed first update (no data yet)
-        - Coordinator has encountered an error (last_update_success = False)
-
-        Note: Auth failures are handled by coordinator's update method,
-        which raises ConfigEntryAuthFailed and triggers reauth flow.
-        """
-        # Return False if coordinator not ready or has errors
-        # Return True if coordinator has data (bool conversion handles None/empty)
-        return self.coordinator.last_update_success and bool(self.coordinator.data)
-
     def _get_device_info(self) -> tuple[str, str | None, str | None]:
         """Get device name, ID and type."""
         user_profile = self.coordinator.get_user_profile()
@@ -118,10 +102,8 @@ class TibberPricesEntity(CoordinatorEntity[TibberPricesDataUpdateCoordinator]):
             return "Tibber Home", None
 
         try:
-            # Use 'or {}' to handle None values (API may return None during maintenance)
+            address1 = str(self.coordinator.data.get("address", {}).get("address1", ""))
-            address = self.coordinator.data.get("address") or {}
+            city = str(self.coordinator.data.get("address", {}).get("city", ""))
-            address1 = str(address.get("address1", ""))
-            city = str(address.get("city", ""))
             app_nickname = str(self.coordinator.data.get("appNickname", ""))
             home_type = str(self.coordinator.data.get("type", ""))
 

custom_components/tibber_prices/entity_utils/attributes.py

@@ -49,7 +49,7 @@ def build_period_attributes(period_data: dict) -> dict:
     }
 
 
-def add_description_attributes(  # noqa: PLR0913, PLR0912
+def add_description_attributes(  # noqa: PLR0913
     attributes: dict,
     platform: str,
     translation_key: str | None,
@@ -61,13 +61,8 @@ def add_description_attributes(  # noqa: PLR0913, PLR0912
     """
     Add description attributes from custom translations to an existing attributes dict.
 
-    The 'description' attribute is always present, but its content changes based on
+    Adds description (always), and optionally long_description and usage_tips if
-    CONF_EXTENDED_DESCRIPTIONS setting:
+    CONF_EXTENDED_DESCRIPTIONS is enabled in config.
-    - When disabled: Uses short 'description' from translations
-    - When enabled: Uses 'long_description' from translations (falls back to short if not available)
-
-    Additionally, when CONF_EXTENDED_DESCRIPTIONS is enabled, 'usage_tips' is added as
-    a separate attribute.
 
     This function modifies the attributes dict in-place. By default, descriptions are
     added at the END of the dict (after all other attributes). For special cases like
@@ -100,27 +95,20 @@ def add_description_attributes(  # noqa: PLR0913, PLR0912
     # Build description dict
     desc_attrs: dict[str, str] = {}
 
+    description = get_entity_description(platform, translation_key, language, "description")
+    if description:
+        desc_attrs["description"] = description
+
     extended_descriptions = config_entry.options.get(
         CONF_EXTENDED_DESCRIPTIONS,
         config_entry.data.get(CONF_EXTENDED_DESCRIPTIONS, DEFAULT_EXTENDED_DESCRIPTIONS),
     )
 
-    # Choose description based on extended_descriptions setting
     if extended_descriptions:
-        # Use long_description as description content (if available)
+        long_desc = get_entity_description(platform, translation_key, language, "long_description")
-        description = get_entity_description(platform, translation_key, language, "long_description")
+        if long_desc:
-        if not description:
+            desc_attrs["long_description"] = long_desc
-            # Fallback to short description if long_description not available
-            description = get_entity_description(platform, translation_key, language, "description")
-    else:
-        # Use short description
-        description = get_entity_description(platform, translation_key, language, "description")
 
-    if description:
-        desc_attrs["description"] = description
-
-    # Add usage_tips as separate attribute if extended_descriptions enabled
-    if extended_descriptions:
         usage_tips = get_entity_description(platform, translation_key, language, "usage_tips")
         if usage_tips:
             desc_attrs["usage_tips"] = usage_tips
@@ -152,7 +140,7 @@ def add_description_attributes(  # noqa: PLR0913, PLR0912
             attributes[key] = value
 
 
-async def async_add_description_attributes(  # noqa: PLR0913, PLR0912
+async def async_add_description_attributes(  # noqa: PLR0913
     attributes: dict,
     platform: str,
     translation_key: str | None,
@@ -191,45 +179,32 @@ async def async_add_description_attributes(  # noqa: PLR0913, PLR0912
     # Build description dict
     desc_attrs: dict[str, str] = {}
 
+    description = await async_get_entity_description(
+        hass,
+        platform,
+        translation_key,
+        language,
+        "description",
+    )
+    if description:
+        desc_attrs["description"] = description
+
     extended_descriptions = config_entry.options.get(
         CONF_EXTENDED_DESCRIPTIONS,
         config_entry.data.get(CONF_EXTENDED_DESCRIPTIONS, DEFAULT_EXTENDED_DESCRIPTIONS),
     )
 
-    # Choose description based on extended_descriptions setting
     if extended_descriptions:
-        # Use long_description as description content (if available)
+        long_desc = await async_get_entity_description(
-        description = await async_get_entity_description(
             hass,
             platform,
             translation_key,
             language,
             "long_description",
         )
-        if not description:
+        if long_desc:
-            # Fallback to short description if long_description not available
+            desc_attrs["long_description"] = long_desc
-            description = await async_get_entity_description(
-                hass,
-                platform,
-                translation_key,
-                language,
-                "description",
-            )
-    else:
-        # Use short description
-        description = await async_get_entity_description(
-            hass,
-            platform,
-            translation_key,
-            language,
-            "description",
-        )
 
-    if description:
-        desc_attrs["description"] = description
-
-    # Add usage_tips as separate attribute if extended_descriptions enabled
-    if extended_descriptions:
         usage_tips = await async_get_entity_description(
             hass,
             platform,

custom_components/tibber_prices/entity_utils/helpers.py

@@ -2,7 +2,7 @@
 Common helper functions for entities across platforms.
 
 This module provides utility functions used by both sensor and binary_sensor platforms:
-- Price value conversion (major/subunit currency units)
+- Price value conversion (major/minor currency units)
 - Translation helpers (price levels, ratings)
 - Time-based calculations (rolling hour center index)
 
@@ -14,52 +14,28 @@ from __future__ import annotations
 
 from typing import TYPE_CHECKING
 
-from custom_components.tibber_prices.const import get_display_unit_factor, get_price_level_translation
+from custom_components.tibber_prices.const import get_price_level_translation
 
 if TYPE_CHECKING:
     from datetime import datetime
 
     from custom_components.tibber_prices.coordinator.time_service import TibberPricesTimeService
-    from custom_components.tibber_prices.data import TibberPricesConfigEntry
-    from homeassistant.config_entries import ConfigEntry
     from homeassistant.core import HomeAssistant
 
 
-def get_price_value(
+def get_price_value(price: float, *, in_euro: bool) -> float:
-    price: float,
-    *,
-    in_euro: bool | None = None,
-    config_entry: ConfigEntry | TibberPricesConfigEntry | None = None,
-) -> float:
     """
     Convert price based on unit.
 
-    NOTE: This function supports two modes for backward compatibility:
-    1. Legacy mode: in_euro=True/False (hardcoded conversion)
-    2. New mode: config_entry (config-driven conversion)
-
-    New code should use get_display_unit_factor(config_entry) directly.
-
     Args:
-        price: Price value to convert.
+        price: Price value to convert
-        in_euro: (Legacy) If True, return in base currency; if False, in subunit currency.
+        in_euro: If True, return price in euros; if False, return in cents/øre
-        config_entry: (New) Config entry to get display unit configuration.
 
     Returns:
-        Price in requested unit (major or subunit currency units).
+        Price in requested unit (euros or minor currency units)
 
     """
-    # Legacy mode: use in_euro parameter
+    return price if in_euro else round((price * 100), 2)
-    if in_euro is not None:
-        return price if in_euro else round(price * 100, 2)
-
-    # New mode: use config_entry
-    if config_entry is not None:
-        factor = get_display_unit_factor(config_entry)
-        return round(price * factor, 2)
-
-    # Fallback: default to subunit currency (backward compatibility)
-    return round(price * 100, 2)
 
 
 def translate_level(hass: HomeAssistant, level: str) -> str:

custom_components/tibber_prices/entity_utils/icons.py

@@ -17,7 +17,6 @@ from custom_components.tibber_prices.const import (
     PRICE_RATING_ICON_MAPPING,
     VOLATILITY_ICON_MAPPING,
 )
-from custom_components.tibber_prices.coordinator.helpers import get_intervals_for_day_offsets
 from custom_components.tibber_prices.entity_utils.helpers import find_rolling_hour_center_index
 from custom_components.tibber_prices.sensor.helpers import aggregate_level_data
 from custom_components.tibber_prices.utils.price import find_price_data_for_interval
@@ -85,25 +84,19 @@ def get_dynamic_icon(
 
 
 def get_trend_icon(key: str, value: Any) -> str | None:
-    """Get icon for trend sensors using 5-level trend scale."""
+    """Get icon for trend sensors."""
     # Handle next_price_trend_change TIMESTAMP sensor differently
     # (icon based on attributes, not value which is a timestamp)
     if key == "next_price_trend_change":
         return None  # Will be handled by sensor's icon property using attributes
 
-    if not key.startswith("price_trend_") and key != "current_price_trend":
+    if not key.startswith("price_trend_") or not isinstance(value, str):
         return None
 
-    if not isinstance(value, str):
-        return None
-
-    # 5-level trend icons: strongly uses double arrows, normal uses single
     trend_icons = {
-        "strongly_rising": "mdi:chevron-double-up",  # Strong upward movement
+        "rising": "mdi:trending-up",
-        "rising": "mdi:trending-up",  # Normal upward trend
+        "falling": "mdi:trending-down",
-        "stable": "mdi:trending-neutral",  # No significant change
+        "stable": "mdi:trending-neutral",
-        "falling": "mdi:trending-down",  # Normal downward trend
-        "strongly_falling": "mdi:chevron-double-down",  # Strong downward movement
     }
     return trend_icons.get(value)
 
@@ -203,7 +196,7 @@ def get_price_sensor_icon(
         return None
 
     # Only current price sensors get dynamic icons
-    if key in ("current_interval_price", "current_interval_price_base"):
+    if key == "current_interval_price":
         level = get_price_level_for_icon(coordinator_data, interval_offset=0, time=time)
         if level:
             return PRICE_LEVEL_CASH_ICON_MAPPING.get(level.upper())
@@ -326,11 +319,12 @@ def get_price_level_for_icon(
     if not coordinator_data or interval_offset is None:
         return None
 
+    price_info = coordinator_data.get("priceInfo", {})
     now = time.now()
 
     # Interval-based lookup
     target_time = now + timedelta(minutes=_INTERVAL_MINUTES * interval_offset)
-    interval_data = find_price_data_for_interval(coordinator_data, target_time, time=time)
+    interval_data = find_price_data_for_interval(price_info, target_time, time=time)
 
     if not interval_data or "level" not in interval_data:
         return None
@@ -364,8 +358,8 @@ def get_rolling_hour_price_level_for_icon(
     if not coordinator_data:
         return None
 
-    # Get all intervals (yesterday, today, tomorrow) via helper
+    price_info = coordinator_data.get("priceInfo", {})
-    all_prices = get_intervals_for_day_offsets(coordinator_data, [-1, 0, 1])
+    all_prices = price_info.get("yesterday", []) + price_info.get("today", []) + price_info.get("tomorrow", [])
 
     if not all_prices:
         return None

custom_components/tibber_prices/icons.json

@@ -1,33 +0,0 @@
-{
-    "services": {
-        "get_price": {
-            "service": "mdi:table-search"
-        },
-        "get_chartdata": {
-            "service": "mdi:chart-bar",
-            "sections": {
-                "general": "mdi:identifier",
-                "selection": "mdi:calendar-range",
-                "filters": "mdi:filter-variant",
-                "transformation": "mdi:tune",
-                "format": "mdi:file-table",
-                "arrays_of_objects": "mdi:code-json",
-                "arrays_of_arrays": "mdi:code-brackets"
-            }
-        },
-        "get_apexcharts_yaml": {
-            "service": "mdi:chart-line",
-            "sections": {
-                "entry_id": "mdi:identifier",
-                "day": "mdi:calendar-range",
-                "level_type": "mdi:format-list-bulleted-type",
-                "resolution": "mdi:timer-sand",
-                "highlight_best_price": "mdi:battery-charging-low",
-                "highlight_peak_price": "mdi:battery-alert"
-            }
-        },
-        "refresh_user_data": {
-            "service": "mdi:refresh"
-        }
-    }
-}

custom_components/tibber_prices/interval_pool/__init__.py

@@ -1,21 +0,0 @@
-"""Interval Pool - Intelligent interval caching and routing."""
-
-from .manager import TibberPricesIntervalPool
-from .routing import get_price_intervals_for_range
-from .storage import (
-    INTERVAL_POOL_STORAGE_VERSION,
-    async_load_pool_state,
-    async_remove_pool_storage,
-    async_save_pool_state,
-    get_storage_key,
-)
-
-__all__ = [
-    "INTERVAL_POOL_STORAGE_VERSION",
-    "TibberPricesIntervalPool",
-    "async_load_pool_state",
-    "async_remove_pool_storage",
-    "async_save_pool_state",
-    "get_price_intervals_for_range",
-    "get_storage_key",
-]

custom_components/tibber_prices/interval_pool/cache.py

@@ -1,206 +0,0 @@
-"""Fetch group cache for price intervals."""
-
-from __future__ import annotations
-
-import logging
-from datetime import datetime, timedelta
-from typing import TYPE_CHECKING, Any
-
-from homeassistant.util import dt as dt_utils
-
-if TYPE_CHECKING:
-    from custom_components.tibber_prices.coordinator.time_service import (
-        TibberPricesTimeService,
-    )
-
-_LOGGER = logging.getLogger(__name__)
-_LOGGER_DETAILS = logging.getLogger(__name__ + ".details")
-
-# Protected date range: day-before-yesterday to tomorrow (4 days total)
-PROTECTED_DAYS_BEFORE = 2  # day-before-yesterday + yesterday
-PROTECTED_DAYS_AFTER = 1  # tomorrow
-
-
-class TibberPricesIntervalPoolFetchGroupCache:
-    """
-    Storage for fetch groups with protected range management.
-
-    A fetch group is a collection of intervals fetched at the same time,
-    stored together with their fetch timestamp for GC purposes.
-
-    Structure:
-        {
-            "fetched_at": datetime,  # When this group was fetched
-            "intervals": [dict, ...]  # List of interval dicts
-        }
-
-    Protected Range:
-        Intervals within day-before-yesterday to tomorrow are protected
-        and never evicted from cache. This range shifts daily automatically.
-
-    Example (today = 2025-11-25):
-        Protected: 2025-11-23 00:00 to 2025-11-27 00:00
-    """
-
-    def __init__(self, *, time_service: TibberPricesTimeService | None = None) -> None:
-        """Initialize empty fetch group cache with optional TimeService."""
-        self._fetch_groups: list[dict[str, Any]] = []
-        self._time_service = time_service
-
-        # Protected range cache (invalidated daily)
-        self._protected_range_cache: tuple[str, str] | None = None
-        self._protected_range_cache_date: str | None = None
-
-    def add_fetch_group(
-        self,
-        intervals: list[dict[str, Any]],
-        fetched_at: datetime,
-    ) -> int:
-        """
-        Add new fetch group to cache.
-
-        Args:
-            intervals: List of interval dicts (sorted by startsAt).
-            fetched_at: Timestamp when intervals were fetched.
-
-        Returns:
-            Index of the newly added fetch group.
-
-        """
-        fetch_group = {
-            "fetched_at": fetched_at,
-            "intervals": intervals,
-        }
-
-        fetch_group_index = len(self._fetch_groups)
-        self._fetch_groups.append(fetch_group)
-
-        _LOGGER_DETAILS.debug(
-            "Added fetch group %d: %d intervals (fetched at %s)",
-            fetch_group_index,
-            len(intervals),
-            fetched_at.isoformat(),
-        )
-
-        return fetch_group_index
-
-    def get_fetch_groups(self) -> list[dict[str, Any]]:
-        """Get all fetch groups (read-only access)."""
-        return self._fetch_groups
-
-    def set_fetch_groups(self, fetch_groups: list[dict[str, Any]]) -> None:
-        """Replace all fetch groups (used during GC)."""
-        self._fetch_groups = fetch_groups
-
-    def get_protected_range(self) -> tuple[str, str]:
-        """
-        Get protected date range as ISO strings.
-
-        Protected range: day-before-yesterday 00:00 to day-after-tomorrow 00:00.
-        This range shifts daily automatically.
-
-        Time Machine Support:
-            If time_service was provided at init, uses time_service.now() for
-            "today" calculation. This protects the correct date range when
-            simulating a different date.
-
-        Returns:
-            Tuple of (start_iso, end_iso) for protected range.
-            Start is inclusive, end is exclusive.
-
-        Example (today = 2025-11-25):
-            Returns: ("2025-11-23T00:00:00+01:00", "2025-11-27T00:00:00+01:00")
-            Protected days: 2025-11-23, 2025-11-24, 2025-11-25, 2025-11-26
-
-        """
-        # Use TimeService if available (Time Machine support), else real time
-        now = self._time_service.now() if self._time_service else dt_utils.now()
-        today_date_str = now.date().isoformat()
-
-        # Check cache validity (invalidate daily)
-        if self._protected_range_cache_date == today_date_str and self._protected_range_cache:
-            return self._protected_range_cache
-
-        # Calculate new protected range
-        today_midnight = now.replace(hour=0, minute=0, second=0, microsecond=0)
-
-        # Start: day-before-yesterday at 00:00
-        start_dt = today_midnight - timedelta(days=PROTECTED_DAYS_BEFORE)
-
-        # End: day after tomorrow at 00:00 (exclusive, so tomorrow is included)
-        end_dt = today_midnight + timedelta(days=PROTECTED_DAYS_AFTER + 1)
-
-        # Convert to ISO strings and cache
-        start_iso = start_dt.isoformat()
-        end_iso = end_dt.isoformat()
-
-        self._protected_range_cache = (start_iso, end_iso)
-        self._protected_range_cache_date = today_date_str
-
-        return start_iso, end_iso
-
-    def is_interval_protected(self, interval: dict[str, Any]) -> bool:
-        """
-        Check if interval is within protected date range.
-
-        Protected intervals are never evicted from cache.
-
-        Args:
-            interval: Interval dict with "startsAt" ISO timestamp.
-
-        Returns:
-            True if interval is protected (within protected range).
-
-        """
-        starts_at_iso = interval["startsAt"]
-        start_protected_iso, end_protected_iso = self.get_protected_range()
-
-        # Fast string comparison (ISO timestamps are lexicographically sortable)
-        return start_protected_iso <= starts_at_iso < end_protected_iso
-
-    def count_total_intervals(self) -> int:
-        """Count total intervals across all fetch groups."""
-        return sum(len(group["intervals"]) for group in self._fetch_groups)
-
-    def to_dict(self) -> dict[str, Any]:
-        """
-        Serialize fetch groups for storage.
-
-        Returns:
-            Dict with serializable fetch groups.
-
-        """
-        return {
-            "fetch_groups": [
-                {
-                    "fetched_at": group["fetched_at"].isoformat(),
-                    "intervals": group["intervals"],
-                }
-                for group in self._fetch_groups
-            ],
-        }
-
-    @classmethod
-    def from_dict(cls, data: dict[str, Any]) -> TibberPricesIntervalPoolFetchGroupCache:
-        """
-        Restore fetch groups from storage.
-
-        Args:
-            data: Dict with "fetch_groups" list.
-
-        Returns:
-            TibberPricesIntervalPoolFetchGroupCache instance with restored data.
-
-        """
-        cache = cls()
-
-        fetch_groups_data = data.get("fetch_groups", [])
-        cache._fetch_groups = [
-            {
-                "fetched_at": datetime.fromisoformat(group["fetched_at"]),
-                "intervals": group["intervals"],
-            }
-            for group in fetch_groups_data
-        ]
-
-        return cache

custom_components/tibber_prices/interval_pool/fetcher.py

@@ -1,321 +0,0 @@
-"""Interval fetcher - coverage check and API coordination for interval pool."""
-
-from __future__ import annotations
-
-import logging
-from datetime import UTC, datetime, timedelta
-from typing import TYPE_CHECKING, Any
-
-from homeassistant.util import dt as dt_utils
-
-if TYPE_CHECKING:
-    from collections.abc import Callable
-
-    from custom_components.tibber_prices.api import TibberPricesApiClient
-
-    from .cache import TibberPricesIntervalPoolFetchGroupCache
-    from .index import TibberPricesIntervalPoolTimestampIndex
-
-_LOGGER = logging.getLogger(__name__)
-_LOGGER_DETAILS = logging.getLogger(__name__ + ".details")
-
-# Resolution change date (hourly before, quarter-hourly after)
-# Use UTC for constant - timezone adjusted at runtime when comparing
-RESOLUTION_CHANGE_DATETIME = datetime(2025, 10, 1, tzinfo=UTC)
-RESOLUTION_CHANGE_ISO = "2025-10-01T00:00:00"
-
-# Interval lengths in minutes
-INTERVAL_HOURLY = 60
-INTERVAL_QUARTER_HOURLY = 15
-
-# Minimum gap sizes in seconds
-MIN_GAP_HOURLY = 3600  # 1 hour
-MIN_GAP_QUARTER_HOURLY = 900  # 15 minutes
-
-# Tolerance for time comparisons (±1 second for floating point/timezone issues)
-TIME_TOLERANCE_SECONDS = 1
-TIME_TOLERANCE_MINUTES = 1
-
-
-class TibberPricesIntervalPoolFetcher:
-    """Fetch missing intervals from API based on coverage check."""
-
-    def __init__(
-        self,
-        api: TibberPricesApiClient,
-        cache: TibberPricesIntervalPoolFetchGroupCache,
-        index: TibberPricesIntervalPoolTimestampIndex,
-        home_id: str,
-    ) -> None:
-        """
-        Initialize fetcher.
-
-        Args:
-            api: API client for Tibber GraphQL queries.
-            cache: Fetch group cache for storage operations.
-            index: Timestamp index for gap detection.
-            home_id: Tibber home ID for API calls.
-
-        """
-        self._api = api
-        self._cache = cache
-        self._index = index
-        self._home_id = home_id
-
-    def check_coverage(
-        self,
-        cached_intervals: list[dict[str, Any]],
-        start_time_iso: str,
-        end_time_iso: str,
-    ) -> list[tuple[str, str]]:
-        """
-        Check cache coverage and find missing time ranges.
-
-        This method minimizes API calls by:
-        1. Finding all gaps in cached intervals
-        2. Treating each cached interval as a discrete timestamp
-        3. Gaps are time ranges between consecutive cached timestamps
-
-        Handles both resolutions:
-        - Pre-2025-10-01: Hourly intervals (:00:00 only)
-        - Post-2025-10-01: Quarter-hourly intervals (:00:00, :15:00, :30:00, :45:00)
-        - DST transitions (23h/25h days)
-
-        The API requires an interval count (first: X parameter).
-        For historical data (pre-2025-10-01), Tibber only stored hourly prices.
-        The API returns whatever intervals exist for the requested period.
-
-        Args:
-            cached_intervals: List of cached intervals (may be empty).
-            start_time_iso: ISO timestamp string (inclusive).
-            end_time_iso: ISO timestamp string (exclusive).
-
-        Returns:
-            List of (start_iso, end_iso) tuples representing missing ranges.
-            Each tuple represents a continuous time span that needs fetching.
-            Ranges are automatically split at resolution change boundary.
-
-        Example:
-            Requested: 2025-11-13T00:00:00 to 2025-11-13T02:00:00
-            Cached: [00:00, 00:15, 01:30, 01:45]
-            Gaps: [(00:15, 01:30)] - missing intervals between groups
-
-        """
-        if not cached_intervals:
-            # No cache → fetch entire range
-            return [(start_time_iso, end_time_iso)]
-
-        # Filter and sort cached intervals within requested range
-        in_range_intervals = [
-            interval for interval in cached_intervals if start_time_iso <= interval["startsAt"] < end_time_iso
-        ]
-        sorted_intervals = sorted(in_range_intervals, key=lambda x: x["startsAt"])
-
-        if not sorted_intervals:
-            # All cached intervals are outside requested range
-            return [(start_time_iso, end_time_iso)]
-
-        missing_ranges = []
-
-        # Parse start/end times once
-        start_time_dt = datetime.fromisoformat(start_time_iso)
-        end_time_dt = datetime.fromisoformat(end_time_iso)
-
-        # Get first cached interval datetime for resolution logic
-        first_cached_dt = datetime.fromisoformat(sorted_intervals[0]["startsAt"])
-        resolution_change_dt = RESOLUTION_CHANGE_DATETIME.replace(tzinfo=first_cached_dt.tzinfo)
-
-        # Check gap before first cached interval
-        time_diff_before_first = (first_cached_dt - start_time_dt).total_seconds()
-        if time_diff_before_first > TIME_TOLERANCE_SECONDS:
-            missing_ranges.append((start_time_iso, sorted_intervals[0]["startsAt"]))
-            _LOGGER_DETAILS.debug(
-                "Missing range before first cached interval: %s to %s (%.1f seconds)",
-                start_time_iso,
-                sorted_intervals[0]["startsAt"],
-                time_diff_before_first,
-            )
-
-        # Check gaps between consecutive cached intervals
-        for i in range(len(sorted_intervals) - 1):
-            current_interval = sorted_intervals[i]
-            next_interval = sorted_intervals[i + 1]
-
-            current_start = current_interval["startsAt"]
-            next_start = next_interval["startsAt"]
-
-            # Parse to datetime for accurate time difference
-            current_dt = datetime.fromisoformat(current_start)
-            next_dt = datetime.fromisoformat(next_start)
-
-            # Calculate time difference in minutes
-            time_diff_minutes = (next_dt - current_dt).total_seconds() / 60
-
-            # Determine expected interval length based on date
-            expected_interval_minutes = (
-                INTERVAL_HOURLY if current_dt < resolution_change_dt else INTERVAL_QUARTER_HOURLY
-            )
-
-            # Only create gap if intervals are NOT consecutive
-            if time_diff_minutes > expected_interval_minutes + TIME_TOLERANCE_MINUTES:
-                # Gap exists - missing intervals between them
-                # Missing range starts AFTER current interval ends
-                current_interval_end = current_dt + timedelta(minutes=expected_interval_minutes)
-                missing_ranges.append((current_interval_end.isoformat(), next_start))
-                _LOGGER_DETAILS.debug(
-                    "Missing range between cached intervals: %s (ends at %s) to %s (%.1f min, expected %d min)",
-                    current_start,
-                    current_interval_end.isoformat(),
-                    next_start,
-                    time_diff_minutes,
-                    expected_interval_minutes,
-                )
-
-        # Check gap after last cached interval
-        # An interval's startsAt time represents the START of that interval.
-        # The interval covers [startsAt, startsAt + interval_length).
-        # So the last interval ENDS at (startsAt + interval_length), not at startsAt!
-        last_cached_dt = datetime.fromisoformat(sorted_intervals[-1]["startsAt"])
-
-        # Calculate when the last interval ENDS
-        interval_minutes = INTERVAL_QUARTER_HOURLY if last_cached_dt >= resolution_change_dt else INTERVAL_HOURLY
-        last_interval_end_dt = last_cached_dt + timedelta(minutes=interval_minutes)
-
-        # Only create gap if there's uncovered time AFTER the last interval ends
-        time_diff_after_last = (end_time_dt - last_interval_end_dt).total_seconds()
-
-        # Need at least one full interval of gap
-        min_gap_seconds = MIN_GAP_QUARTER_HOURLY if last_cached_dt >= resolution_change_dt else MIN_GAP_HOURLY
-        if time_diff_after_last >= min_gap_seconds:
-            # Missing range starts AFTER the last cached interval ends
-            missing_ranges.append((last_interval_end_dt.isoformat(), end_time_iso))
-            _LOGGER_DETAILS.debug(
-                "Missing range after last cached interval: %s (ends at %s) to %s (%.1f seconds, need >= %d)",
-                sorted_intervals[-1]["startsAt"],
-                last_interval_end_dt.isoformat(),
-                end_time_iso,
-                time_diff_after_last,
-                min_gap_seconds,
-            )
-
-        if not missing_ranges:
-            _LOGGER.debug(
-                "Full coverage - all intervals cached for range %s to %s",
-                start_time_iso,
-                end_time_iso,
-            )
-            return missing_ranges
-
-        # Split ranges at resolution change boundary (2025-10-01 00:00:00)
-        # This simplifies interval count calculation in API calls:
-        # - Pre-2025-10-01: Always hourly (1 interval/hour)
-        # - Post-2025-10-01: Always quarter-hourly (4 intervals/hour)
-        return self._split_at_resolution_boundary(missing_ranges)
-
-    def _split_at_resolution_boundary(self, ranges: list[tuple[str, str]]) -> list[tuple[str, str]]:
-        """
-        Split time ranges at resolution change boundary.
-
-        Args:
-            ranges: List of (start_iso, end_iso) tuples.
-
-        Returns:
-            List of ranges split at 2025-10-01T00:00:00 boundary.
-
-        """
-        split_ranges = []
-
-        for start_iso, end_iso in ranges:
-            # Check if range crosses the boundary
-            if start_iso < RESOLUTION_CHANGE_ISO < end_iso:
-                # Split into two ranges: before and after boundary
-                split_ranges.append((start_iso, RESOLUTION_CHANGE_ISO))
-                split_ranges.append((RESOLUTION_CHANGE_ISO, end_iso))
-                _LOGGER_DETAILS.debug(
-                    "Split range at resolution boundary: (%s, %s) → (%s, %s) + (%s, %s)",
-                    start_iso,
-                    end_iso,
-                    start_iso,
-                    RESOLUTION_CHANGE_ISO,
-                    RESOLUTION_CHANGE_ISO,
-                    end_iso,
-                )
-            else:
-                # Range doesn't cross boundary - keep as is
-                split_ranges.append((start_iso, end_iso))
-
-        return split_ranges
-
-    async def fetch_missing_ranges(
-        self,
-        api_client: TibberPricesApiClient,
-        user_data: dict[str, Any],
-        missing_ranges: list[tuple[str, str]],
-        *,
-        on_intervals_fetched: Callable[[list[dict[str, Any]], str], None] | None = None,
-    ) -> list[list[dict[str, Any]]]:
-        """
-        Fetch missing intervals from API.
-
-        Makes one API call per missing range. Uses routing logic to select
-        the optimal endpoint (PRICE_INFO vs PRICE_INFO_RANGE).
-
-        Args:
-            api_client: TibberPricesApiClient instance for API calls.
-            user_data: User data dict containing home metadata.
-            missing_ranges: List of (start_iso, end_iso) tuples to fetch.
-            on_intervals_fetched: Optional callback for each fetch result.
-                                Receives (intervals, fetch_time_iso).
-
-        Returns:
-            List of interval lists (one per missing range).
-            Each sublist contains intervals from one API call.
-
-        Raises:
-            TibberPricesApiClientError: If API calls fail.
-
-        """
-        # Import here to avoid circular dependency
-        from custom_components.tibber_prices.interval_pool.routing import (  # noqa: PLC0415
-            get_price_intervals_for_range,
-        )
-
-        fetch_time_iso = dt_utils.now().isoformat()
-        all_fetched_intervals = []
-
-        for idx, (missing_start_iso, missing_end_iso) in enumerate(missing_ranges, start=1):
-            _LOGGER_DETAILS.debug(
-                "Fetching from Tibber API (%d/%d) for home %s: range %s to %s",
-                idx,
-                len(missing_ranges),
-                self._home_id,
-                missing_start_iso,
-                missing_end_iso,
-            )
-
-            # Parse ISO strings back to datetime for API call
-            missing_start_dt = datetime.fromisoformat(missing_start_iso)
-            missing_end_dt = datetime.fromisoformat(missing_end_iso)
-
-            # Fetch intervals from API - routing returns ALL intervals (unfiltered)
-            fetched_intervals = await get_price_intervals_for_range(
-                api_client=api_client,
-                home_id=self._home_id,
-                user_data=user_data,
-                start_time=missing_start_dt,
-                end_time=missing_end_dt,
-            )
-
-            all_fetched_intervals.append(fetched_intervals)
-
-            _LOGGER_DETAILS.debug(
-                "Received %d intervals from Tibber API for home %s",
-                len(fetched_intervals),
-                self._home_id,
-            )
-
-            # Notify callback if provided (for immediate caching)
-            if on_intervals_fetched:
-                on_intervals_fetched(fetched_intervals, fetch_time_iso)
-
-        return all_fetched_intervals

custom_components/tibber_prices/interval_pool/garbage_collector.py

@@ -1,283 +0,0 @@
-"""Garbage collector for interval cache eviction."""
-
-from __future__ import annotations
-
-import logging
-from datetime import datetime
-from typing import TYPE_CHECKING, Any
-
-if TYPE_CHECKING:
-    from .cache import TibberPricesIntervalPoolFetchGroupCache
-    from .index import TibberPricesIntervalPoolTimestampIndex
-
-_LOGGER = logging.getLogger(__name__)
-_LOGGER_DETAILS = logging.getLogger(__name__ + ".details")
-
-# Maximum number of intervals to cache
-# 1 days @ 15min resolution = 10 * 96 = 960 intervals
-MAX_CACHE_SIZE = 960
-
-
-def _normalize_starts_at(starts_at: datetime | str) -> str:
-    """Normalize startsAt to consistent format (YYYY-MM-DDTHH:MM:SS)."""
-    if isinstance(starts_at, datetime):
-        return starts_at.strftime("%Y-%m-%dT%H:%M:%S")
-    return starts_at[:19]
-
-
-class TibberPricesIntervalPoolGarbageCollector:
-    """
-    Manages cache eviction and dead interval cleanup.
-
-    Eviction Strategy:
-        - Evict oldest fetch groups first (by fetched_at timestamp)
-        - Protected intervals (day-before-yesterday to tomorrow) are NEVER evicted
-        - Evict complete fetch groups, not individual intervals
-
-    Dead Interval Cleanup:
-        When intervals are "touched" (re-fetched), they move to a new fetch group
-        but remain in the old group. This creates "dead intervals" that occupy
-        memory but are no longer referenced by the index.
-    """
-
-    def __init__(
-        self,
-        cache: TibberPricesIntervalPoolFetchGroupCache,
-        index: TibberPricesIntervalPoolTimestampIndex,
-        home_id: str,
-    ) -> None:
-        """
-        Initialize garbage collector.
-
-        Args:
-            home_id: Home ID for logging purposes.
-            cache: Fetch group cache to manage.
-            index: Timestamp index for living interval detection.
-
-        """
-        self._home_id = home_id
-        self._cache = cache
-        self._index = index
-
-    def run_gc(self) -> bool:
-        """
-        Run garbage collection if needed.
-
-        Process:
-            1. Clean up dead intervals from all fetch groups
-            2. Count total intervals
-            3. If > MAX_CACHE_SIZE, evict oldest fetch groups
-            4. Rebuild index after eviction
-
-        Returns:
-            True if any cleanup or eviction happened, False otherwise.
-
-        """
-        fetch_groups = self._cache.get_fetch_groups()
-
-        # Phase 1: Clean up dead intervals
-        dead_count = self._cleanup_dead_intervals(fetch_groups)
-
-        if dead_count > 0:
-            _LOGGER_DETAILS.debug(
-                "GC cleaned %d dead intervals (home %s)",
-                dead_count,
-                self._home_id,
-            )
-
-        # Phase 1.5: Remove empty fetch groups (after dead interval cleanup)
-        empty_removed = self._remove_empty_groups(fetch_groups)
-        if empty_removed > 0:
-            _LOGGER_DETAILS.debug(
-                "GC removed %d empty fetch groups (home %s)",
-                empty_removed,
-                self._home_id,
-            )
-
-        # Phase 2: Count total intervals after cleanup
-        total_intervals = self._cache.count_total_intervals()
-
-        if total_intervals <= MAX_CACHE_SIZE:
-            _LOGGER_DETAILS.debug(
-                "GC cleanup only for home %s: %d intervals <= %d limit (no eviction needed)",
-                self._home_id,
-                total_intervals,
-                MAX_CACHE_SIZE,
-            )
-            return dead_count > 0
-
-        # Phase 3: Evict old fetch groups
-        evicted_indices = self._evict_old_groups(fetch_groups, total_intervals)
-
-        if not evicted_indices:
-            # All intervals are protected, cannot evict
-            return dead_count > 0 or empty_removed > 0
-
-        # Phase 4: Rebuild cache and index
-        new_fetch_groups = [group for idx, group in enumerate(fetch_groups) if idx not in evicted_indices]
-        self._cache.set_fetch_groups(new_fetch_groups)
-        self._index.rebuild(new_fetch_groups)
-
-        _LOGGER_DETAILS.debug(
-            "GC evicted %d fetch groups (home %s): %d intervals remaining",
-            len(evicted_indices),
-            self._home_id,
-            self._cache.count_total_intervals(),
-        )
-
-        return True
-
-    def _remove_empty_groups(self, fetch_groups: list[dict[str, Any]]) -> int:
-        """
-        Remove fetch groups with no intervals.
-
-        After dead interval cleanup, some groups may be completely empty.
-        These should be removed to prevent memory accumulation.
-
-        Note: This modifies the cache's internal list in-place and rebuilds
-        the index to maintain consistency.
-
-        Args:
-            fetch_groups: List of fetch groups (will be modified).
-
-        Returns:
-            Number of empty groups removed.
-
-        """
-        # Find non-empty groups
-        non_empty_groups = [group for group in fetch_groups if group["intervals"]]
-        removed_count = len(fetch_groups) - len(non_empty_groups)
-
-        if removed_count > 0:
-            # Update cache with filtered list
-            self._cache.set_fetch_groups(non_empty_groups)
-            # Rebuild index since group indices changed
-            self._index.rebuild(non_empty_groups)
-
-        return removed_count
-
-    def _cleanup_dead_intervals(self, fetch_groups: list[dict[str, Any]]) -> int:
-        """
-        Remove dead intervals from all fetch groups.
-
-        Dead intervals are no longer referenced by the index (they were touched
-        and moved to a newer fetch group).
-
-        Args:
-            fetch_groups: List of fetch groups to clean.
-
-        Returns:
-            Total number of dead intervals removed.
-
-        """
-        total_dead = 0
-
-        for group_idx, group in enumerate(fetch_groups):
-            old_intervals = group["intervals"]
-            if not old_intervals:
-                continue
-
-            # Find living intervals (still in index at correct position)
-            living_intervals = []
-
-            for interval_idx, interval in enumerate(old_intervals):
-                starts_at_normalized = _normalize_starts_at(interval["startsAt"])
-                index_entry = self._index.get(starts_at_normalized)
-
-                if index_entry is not None:
-                    # Check if index points to THIS position
-                    if index_entry["fetch_group_index"] == group_idx and index_entry["interval_index"] == interval_idx:
-                        living_intervals.append(interval)
-                    else:
-                        # Dead: index points elsewhere
-                        total_dead += 1
-                else:
-                    # Dead: not in index
-                    total_dead += 1
-
-            # Replace with cleaned list if any dead intervals found
-            if len(living_intervals) < len(old_intervals):
-                group["intervals"] = living_intervals
-                dead_count = len(old_intervals) - len(living_intervals)
-                _LOGGER_DETAILS.debug(
-                    "GC cleaned %d dead intervals from fetch group %d (home %s)",
-                    dead_count,
-                    group_idx,
-                    self._home_id,
-                )
-
-        return total_dead
-
-    def _evict_old_groups(
-        self,
-        fetch_groups: list[dict[str, Any]],
-        total_intervals: int,
-    ) -> set[int]:
-        """
-        Determine which fetch groups to evict to stay under MAX_CACHE_SIZE.
-
-        Only evicts groups without protected intervals.
-        Groups evicted oldest-first (by fetched_at).
-
-        Args:
-            fetch_groups: List of fetch groups.
-            total_intervals: Total interval count.
-
-        Returns:
-            Set of fetch group indices to evict.
-
-        """
-        start_protected_iso, end_protected_iso = self._cache.get_protected_range()
-
-        _LOGGER_DETAILS.debug(
-            "Protected range: %s to %s",
-            start_protected_iso[:10],
-            end_protected_iso[:10],
-        )
-
-        # Classify: protected vs evictable
-        evictable_groups = []
-
-        for idx, group in enumerate(fetch_groups):
-            has_protected = any(self._cache.is_interval_protected(interval) for interval in group["intervals"])
-
-            if not has_protected:
-                evictable_groups.append((idx, group))
-
-        # Sort by fetched_at (oldest first)
-        evictable_groups.sort(key=lambda x: x[1]["fetched_at"])
-
-        _LOGGER_DETAILS.debug(
-            "GC: %d protected groups, %d evictable groups",
-            len(fetch_groups) - len(evictable_groups),
-            len(evictable_groups),
-        )
-
-        # Evict until under limit
-        evicted_indices = set()
-        remaining = total_intervals
-
-        for idx, group in evictable_groups:
-            if remaining <= MAX_CACHE_SIZE:
-                break
-
-            group_count = len(group["intervals"])
-            evicted_indices.add(idx)
-            remaining -= group_count
-
-            _LOGGER_DETAILS.debug(
-                "GC evicting group %d (fetched %s): %d intervals, %d remaining",
-                idx,
-                group["fetched_at"].isoformat(),
-                group_count,
-                remaining,
-            )
-
-        if not evicted_indices:
-            _LOGGER.warning(
-                "GC cannot evict any groups (home %s): all %d intervals are protected",
-                self._home_id,
-                total_intervals,
-            )
-
-        return evicted_indices

custom_components/tibber_prices/interval_pool/index.py

@@ -1,173 +0,0 @@
-"""Timestamp index for O(1) interval lookups."""
-
-from __future__ import annotations
-
-import logging
-from typing import Any
-
-_LOGGER = logging.getLogger(__name__)
-_LOGGER_DETAILS = logging.getLogger(__name__ + ".details")
-
-
-class TibberPricesIntervalPoolTimestampIndex:
-    """
-    Fast O(1) timestamp-based interval lookup.
-
-    Maps normalized ISO timestamp strings to fetch group + interval indices.
-
-    Structure:
-        {
-            "2025-11-25T00:00:00": {
-                "fetch_group_index": 0,   # Index in fetch groups list
-                "interval_index": 2       # Index within that group's intervals
-            },
-            ...
-        }
-
-    Normalization:
-        Timestamps are normalized to 19 characters (YYYY-MM-DDTHH:MM:SS)
-        by truncating microseconds and timezone info for fast string comparison.
-    """
-
-    def __init__(self) -> None:
-        """Initialize empty timestamp index."""
-        self._index: dict[str, dict[str, int]] = {}
-
-    def add(
-        self,
-        interval: dict[str, Any],
-        fetch_group_index: int,
-        interval_index: int,
-    ) -> None:
-        """
-        Add interval to index.
-
-        Args:
-            interval: Interval dict with "startsAt" ISO timestamp.
-            fetch_group_index: Index of fetch group containing this interval.
-            interval_index: Index within that fetch group's intervals list.
-
-        """
-        starts_at_normalized = self._normalize_timestamp(interval["startsAt"])
-        self._index[starts_at_normalized] = {
-            "fetch_group_index": fetch_group_index,
-            "interval_index": interval_index,
-        }
-
-    def get(self, timestamp: str) -> dict[str, int] | None:
-        """
-        Look up interval location by timestamp.
-
-        Args:
-            timestamp: ISO timestamp string (will be normalized).
-
-        Returns:
-            Dict with fetch_group_index and interval_index, or None if not found.
-
-        """
-        starts_at_normalized = self._normalize_timestamp(timestamp)
-        return self._index.get(starts_at_normalized)
-
-    def contains(self, timestamp: str) -> bool:
-        """
-        Check if timestamp exists in index.
-
-        Args:
-            timestamp: ISO timestamp string (will be normalized).
-
-        Returns:
-            True if timestamp is in index.
-
-        """
-        starts_at_normalized = self._normalize_timestamp(timestamp)
-        return starts_at_normalized in self._index
-
-    def remove(self, timestamp: str) -> None:
-        """
-        Remove timestamp from index.
-
-        Args:
-            timestamp: ISO timestamp string (will be normalized).
-
-        """
-        starts_at_normalized = self._normalize_timestamp(timestamp)
-        self._index.pop(starts_at_normalized, None)
-
-    def update_batch(
-        self,
-        updates: list[tuple[str, int, int]],
-    ) -> None:
-        """
-        Update multiple index entries efficiently in a single operation.
-
-        More efficient than calling remove() + add() for each entry,
-        as it avoids repeated dict operations and normalization.
-
-        Args:
-            updates: List of (timestamp, fetch_group_index, interval_index) tuples.
-                    Timestamps will be normalized automatically.
-
-        """
-        for timestamp, fetch_group_index, interval_index in updates:
-            starts_at_normalized = self._normalize_timestamp(timestamp)
-            self._index[starts_at_normalized] = {
-                "fetch_group_index": fetch_group_index,
-                "interval_index": interval_index,
-            }
-
-    def clear(self) -> None:
-        """Clear entire index."""
-        self._index.clear()
-
-    def rebuild(self, fetch_groups: list[dict[str, Any]]) -> None:
-        """
-        Rebuild index from fetch groups.
-
-        Used after GC operations that modify fetch group structure.
-
-        Args:
-            fetch_groups: List of fetch group dicts.
-
-        """
-        self._index.clear()
-
-        for fetch_group_idx, group in enumerate(fetch_groups):
-            for interval_idx, interval in enumerate(group["intervals"]):
-                starts_at_normalized = self._normalize_timestamp(interval["startsAt"])
-                self._index[starts_at_normalized] = {
-                    "fetch_group_index": fetch_group_idx,
-                    "interval_index": interval_idx,
-                }
-
-        _LOGGER_DETAILS.debug(
-            "Rebuilt index: %d timestamps indexed",
-            len(self._index),
-        )
-
-    def get_raw_index(self) -> dict[str, dict[str, int]]:
-        """Get raw index dict (for serialization)."""
-        return self._index
-
-    def count(self) -> int:
-        """Count total indexed timestamps."""
-        return len(self._index)
-
-    @staticmethod
-    def _normalize_timestamp(timestamp: str) -> str:
-        """
-        Normalize ISO timestamp for indexing.
-
-        Truncates to 19 characters (YYYY-MM-DDTHH:MM:SS) to remove
-        microseconds and timezone info for consistent string comparison.
-
-        Args:
-            timestamp: Full ISO timestamp string.
-
-        Returns:
-            Normalized timestamp (19 chars).
-
-        Example:
-            "2025-11-25T00:00:00.000+01:00" → "2025-11-25T00:00:00"
-
-        """
-        return timestamp[:19]

custom_components/tibber_prices/interval_pool/manager.py

@@ -1,830 +0,0 @@
-"""Interval pool manager - main coordinator for interval caching."""
-
-from __future__ import annotations
-
-import asyncio
-import contextlib
-import logging
-from datetime import datetime, timedelta
-from typing import TYPE_CHECKING, Any
-from zoneinfo import ZoneInfo
-
-from custom_components.tibber_prices.api.exceptions import TibberPricesApiClientError
-from homeassistant.util import dt as dt_utils
-
-from .cache import TibberPricesIntervalPoolFetchGroupCache
-from .fetcher import TibberPricesIntervalPoolFetcher
-from .garbage_collector import MAX_CACHE_SIZE, TibberPricesIntervalPoolGarbageCollector
-from .index import TibberPricesIntervalPoolTimestampIndex
-from .storage import async_save_pool_state
-
-if TYPE_CHECKING:
-    from custom_components.tibber_prices.api.client import TibberPricesApiClient
-    from custom_components.tibber_prices.coordinator.time_service import (
-        TibberPricesTimeService,
-    )
-
-_LOGGER = logging.getLogger(__name__)
-_LOGGER_DETAILS = logging.getLogger(__name__ + ".details")
-
-# Interval lengths in minutes
-INTERVAL_HOURLY = 60
-INTERVAL_QUARTER_HOURLY = 15
-
-# Debounce delay for auto-save (seconds)
-DEBOUNCE_DELAY_SECONDS = 3.0
-
-
-def _normalize_starts_at(starts_at: datetime | str) -> str:
-    """Normalize startsAt to consistent format (YYYY-MM-DDTHH:MM:SS)."""
-    if isinstance(starts_at, datetime):
-        return starts_at.strftime("%Y-%m-%dT%H:%M:%S")
-    return starts_at[:19]
-
-
-class TibberPricesIntervalPool:
-    """
-    High-performance interval cache manager for a single Tibber home.
-
-    Coordinates all interval pool components:
-    - TibberPricesIntervalPoolFetchGroupCache: Stores fetch groups and manages protected ranges
-    - TibberPricesIntervalPoolTimestampIndex: Provides O(1) timestamp lookups
-    - TibberPricesIntervalPoolGarbageCollector: Evicts old fetch groups when cache exceeds limits
-    - TibberPricesIntervalPoolFetcher: Detects gaps and fetches missing intervals from API
-
-    Architecture:
-    - Each manager handles exactly ONE home (1:1 with config entry)
-    - home_id is immutable after initialization
-    - All operations are thread-safe via asyncio locks
-
-    Features:
-    - Fetch-time based eviction (oldest fetch groups removed first)
-    - Protected date range (day-before-yesterday to tomorrow never evicted)
-    - Fast O(1) lookups by timestamp
-    - Automatic gap detection and API fetching
-    - Debounced auto-save to prevent excessive I/O
-
-    Example:
-        manager = TibberPricesIntervalPool(home_id="abc123", hass=hass, entry_id=entry.entry_id)
-        intervals = await manager.get_intervals(
-            api_client=client,
-            user_data=data,
-            start_time=datetime(...),
-            end_time=datetime(...),
-        )
-
-    """
-
-    def __init__(
-        self,
-        *,
-        home_id: str,
-        api: TibberPricesApiClient,
-        hass: Any | None = None,
-        entry_id: str | None = None,
-        time_service: TibberPricesTimeService | None = None,
-    ) -> None:
-        """
-        Initialize interval pool manager.
-
-        Args:
-            home_id: Tibber home ID (required, immutable).
-            api: API client for fetching intervals.
-            hass: HomeAssistant instance for auto-save (optional).
-            entry_id: Config entry ID for auto-save (optional).
-            time_service: TimeService for time-travel support (optional).
-                         If None, uses real time (dt_utils.now()).
-
-        """
-        self._home_id = home_id
-        self._time_service = time_service
-
-        # Initialize components with dependency injection
-        self._cache = TibberPricesIntervalPoolFetchGroupCache(time_service=time_service)
-        self._index = TibberPricesIntervalPoolTimestampIndex()
-        self._gc = TibberPricesIntervalPoolGarbageCollector(self._cache, self._index, home_id)
-        self._fetcher = TibberPricesIntervalPoolFetcher(api, self._cache, self._index, home_id)
-
-        # Auto-save support
-        self._hass = hass
-        self._entry_id = entry_id
-        self._background_tasks: set[asyncio.Task] = set()
-        self._save_debounce_task: asyncio.Task | None = None
-        self._save_lock = asyncio.Lock()
-
-    async def get_intervals(
-        self,
-        api_client: TibberPricesApiClient,
-        user_data: dict[str, Any],
-        start_time: datetime,
-        end_time: datetime,
-    ) -> tuple[list[dict[str, Any]], bool]:
-        """
-        Get price intervals for time range (cached + fetch missing).
-
-        Main entry point for retrieving intervals. Coordinates:
-        1. Check cache for existing intervals
-        2. Detect missing time ranges
-        3. Fetch missing ranges from API
-        4. Add new intervals to cache (may trigger GC)
-        5. Return complete interval list
-
-        User receives ALL requested intervals even if cache exceeds limits.
-        Cache only keeps the most recent intervals (FIFO eviction).
-
-        Args:
-            api_client: TibberPricesApiClient instance for API calls.
-            user_data: User data dict containing home metadata.
-            start_time: Start of range (inclusive, timezone-aware).
-            end_time: End of range (exclusive, timezone-aware).
-
-        Returns:
-            Tuple of (intervals, api_called):
-            - intervals: List of price interval dicts, sorted by startsAt.
-                        Contains ALL intervals in requested range (cached + fetched).
-            - api_called: True if API was called to fetch missing data, False if all from cache.
-
-        Raises:
-            TibberPricesApiClientError: If API calls fail or validation errors.
-
-        """
-        # Validate inputs
-        if not user_data:
-            msg = "User data required for timezone-aware price fetching"
-            raise TibberPricesApiClientError(msg)
-
-        if start_time >= end_time:
-            msg = f"Invalid time range: start_time ({start_time}) must be before end_time ({end_time})"
-            raise TibberPricesApiClientError(msg)
-
-        # Convert to ISO strings for cache operations
-        start_time_iso = start_time.isoformat()
-        end_time_iso = end_time.isoformat()
-
-        _LOGGER_DETAILS.debug(
-            "Interval pool request for home %s: range %s to %s",
-            self._home_id,
-            start_time_iso,
-            end_time_iso,
-        )
-
-        # Get cached intervals using index
-        cached_intervals = self._get_cached_intervals(start_time_iso, end_time_iso)
-
-        # Check coverage - find ranges not in cache
-        missing_ranges = self._fetcher.check_coverage(cached_intervals, start_time_iso, end_time_iso)
-
-        if missing_ranges:
-            _LOGGER_DETAILS.debug(
-                "Coverage check for home %s: %d range(s) missing - will fetch from API",
-                self._home_id,
-                len(missing_ranges),
-            )
-        else:
-            _LOGGER_DETAILS.debug(
-                "Coverage check for home %s: full coverage in cache - no API calls needed",
-                self._home_id,
-            )
-
-        # Fetch missing ranges from API
-        if missing_ranges:
-            fetch_time_iso = dt_utils.now().isoformat()
-
-            # Fetch with callback for immediate caching
-            await self._fetcher.fetch_missing_ranges(
-                api_client=api_client,
-                user_data=user_data,
-                missing_ranges=missing_ranges,
-                on_intervals_fetched=lambda intervals, _: self._add_intervals(intervals, fetch_time_iso),
-            )
-
-        # After caching all API responses, read from cache again to get final result
-        # This ensures we return exactly what user requested, filtering out extra intervals
-        final_result = self._get_cached_intervals(start_time_iso, end_time_iso)
-
-        # Track if API was called (True if any missing ranges were fetched)
-        api_called = len(missing_ranges) > 0
-
-        _LOGGER_DETAILS.debug(
-            "Pool returning %d intervals for home %s (from cache: %d, fetched from API: %d ranges, api_called=%s)",
-            len(final_result),
-            self._home_id,
-            len(cached_intervals),
-            len(missing_ranges),
-            api_called,
-        )
-
-        return final_result, api_called
-
-    async def get_sensor_data(
-        self,
-        api_client: TibberPricesApiClient,
-        user_data: dict[str, Any],
-        home_timezone: str | None = None,
-        *,
-        include_tomorrow: bool = True,
-    ) -> tuple[list[dict[str, Any]], bool]:
-        """
-        Get price intervals for sensor data (day-before-yesterday to end-of-tomorrow).
-
-        Convenience method for coordinator/sensors that need the standard 4-day window:
-        - Day before yesterday (for trailing 24h averages at midnight)
-        - Yesterday (for trailing 24h averages)
-        - Today (current prices)
-        - Tomorrow (if available in cache)
-
-        IMPORTANT - Two distinct behaviors:
-        1. API FETCH: Controlled by include_tomorrow flag
-           - include_tomorrow=False → Only fetch up to end of today (prevents API spam before 13:00)
-           - include_tomorrow=True → Fetch including tomorrow data
-        2. RETURN DATA: Always returns full protected range (including tomorrow if cached)
-           - This ensures cached tomorrow data is used even if include_tomorrow=False
-
-        The separation prevents the following bug:
-        - If include_tomorrow affected both fetch AND return, cached tomorrow data
-          would be lost when include_tomorrow=False, causing infinite refresh loops.
-
-        Args:
-            api_client: TibberPricesApiClient instance for API calls.
-            user_data: User data dict containing home metadata.
-            home_timezone: Optional timezone string (e.g., "Europe/Berlin").
-            include_tomorrow: If True, fetch tomorrow's data from API. If False,
-                             only fetch up to end of today. Default True.
-                             DOES NOT affect returned data - always returns full range.
-
-        Returns:
-            Tuple of (intervals, api_called):
-            - intervals: List of price interval dicts for the 4-day window (including any cached
-                        tomorrow data), sorted by startsAt.
-            - api_called: True if API was called to fetch missing data, False if all from cache.
-
-        """
-        # Determine timezone
-        tz_str = home_timezone
-        if not tz_str:
-            tz_str = self._extract_timezone_from_user_data(user_data)
-
-        # Calculate range in home's timezone
-        tz = ZoneInfo(tz_str) if tz_str else None
-        now = self._time_service.now() if self._time_service else dt_utils.now()
-        now_local = now.astimezone(tz) if tz else now
-
-        # Day before yesterday 00:00 (start) - same for both fetch and return
-        day_before_yesterday = (now_local - timedelta(days=2)).replace(hour=0, minute=0, second=0, microsecond=0)
-
-        # End of tomorrow (full protected range) - used for RETURN data
-        end_of_tomorrow = (now_local + timedelta(days=2)).replace(hour=0, minute=0, second=0, microsecond=0)
-
-        # API fetch range depends on include_tomorrow flag
-        if include_tomorrow:
-            fetch_end_time = end_of_tomorrow
-            fetch_desc = "end-of-tomorrow"
-        else:
-            # Only fetch up to end of today (prevents API spam before 13:00)
-            fetch_end_time = (now_local + timedelta(days=1)).replace(hour=0, minute=0, second=0, microsecond=0)
-            fetch_desc = "end-of-today"
-
-        _LOGGER.debug(
-            "Sensor data request for home %s: fetch %s to %s (%s), return up to %s",
-            self._home_id,
-            day_before_yesterday.isoformat(),
-            fetch_end_time.isoformat(),
-            fetch_desc,
-            end_of_tomorrow.isoformat(),
-        )
-
-        # Fetch data (may be partial if include_tomorrow=False)
-        _intervals, api_called = await self.get_intervals(
-            api_client=api_client,
-            user_data=user_data,
-            start_time=day_before_yesterday,
-            end_time=fetch_end_time,
-        )
-
-        # Return FULL protected range (including any cached tomorrow data)
-        # This ensures cached tomorrow data is available even when include_tomorrow=False
-        final_intervals = self._get_cached_intervals(
-            day_before_yesterday.isoformat(),
-            end_of_tomorrow.isoformat(),
-        )
-
-        return final_intervals, api_called
-
-    def get_pool_stats(self) -> dict[str, Any]:
-        """
-        Get statistics about the interval pool.
-
-        Returns comprehensive statistics for diagnostic sensors, separated into:
-        - Sensor intervals (protected range: day-before-yesterday to tomorrow)
-        - Cache statistics (entire pool including service-requested data)
-
-        Protected Range:
-            The protected range covers 4 days at 15-min resolution = 384 intervals.
-            These intervals are never evicted by garbage collection.
-
-        Cache Fill Level:
-            Shows how full the cache is relative to MAX_CACHE_SIZE (960).
-            100% is not bad - just means we're using the available space.
-            GC will evict oldest non-protected intervals when limit is reached.
-
-        Returns:
-            Dict with sensor intervals, cache stats, and timestamps.
-
-        """
-        fetch_groups = self._cache.get_fetch_groups()
-
-        # === Sensor Intervals (Protected Range) ===
-        sensor_stats = self._get_sensor_interval_stats()
-
-        # === Cache Statistics (Entire Pool) ===
-        cache_total = self._index.count()
-        cache_limit = MAX_CACHE_SIZE
-        cache_fill_percent = round((cache_total / cache_limit) * 100, 1) if cache_limit > 0 else 0
-        cache_extra = max(0, cache_total - sensor_stats["count"])  # Intervals outside protected range
-
-        # === Timestamps ===
-        # Last sensor fetch (for protected range data)
-        last_sensor_fetch: str | None = None
-        oldest_interval: str | None = None
-        newest_interval: str | None = None
-
-        if fetch_groups:
-            # Find newest fetch group (most recent API call)
-            newest_group = max(fetch_groups, key=lambda g: g["fetched_at"])
-            last_sensor_fetch = newest_group["fetched_at"].isoformat()
-
-            # Find oldest and newest intervals across all fetch groups
-            all_timestamps = list(self._index.get_raw_index().keys())
-            if all_timestamps:
-                oldest_interval = min(all_timestamps)
-                newest_interval = max(all_timestamps)
-
-        return {
-            # Sensor intervals (protected range)
-            "sensor_intervals_count": sensor_stats["count"],
-            "sensor_intervals_expected": sensor_stats["expected"],
-            "sensor_intervals_has_gaps": sensor_stats["has_gaps"],
-            # Cache statistics
-            "cache_intervals_total": cache_total,
-            "cache_intervals_limit": cache_limit,
-            "cache_fill_percent": cache_fill_percent,
-            "cache_intervals_extra": cache_extra,
-            # Timestamps
-            "last_sensor_fetch": last_sensor_fetch,
-            "cache_oldest_interval": oldest_interval,
-            "cache_newest_interval": newest_interval,
-            # Fetch groups (API calls)
-            "fetch_groups_count": len(fetch_groups),
-        }
-
-    def _get_sensor_interval_stats(self) -> dict[str, Any]:
-        """
-        Get statistics for sensor intervals (protected range).
-
-        Protected range: day-before-yesterday 00:00 to day-after-tomorrow 00:00.
-        Expected: 4 days * 24 hours * 4 intervals = 384 intervals.
-
-        Returns:
-            Dict with count, expected, and has_gaps.
-
-        """
-        start_iso, end_iso = self._cache.get_protected_range()
-        start_dt = datetime.fromisoformat(start_iso)
-        end_dt = datetime.fromisoformat(end_iso)
-
-        # Count expected intervals (15-min resolution)
-        expected_count = int((end_dt - start_dt).total_seconds() / (15 * 60))
-
-        # Count actual intervals in range
-        actual_count = 0
-        current_dt = start_dt
-
-        while current_dt < end_dt:
-            current_key = current_dt.isoformat()[:19]
-            if self._index.contains(current_key):
-                actual_count += 1
-            current_dt += timedelta(minutes=15)
-
-        return {
-            "count": actual_count,
-            "expected": expected_count,
-            "has_gaps": actual_count < expected_count,
-        }
-
-    def _has_gaps_in_protected_range(self) -> bool:
-        """
-        Check if there are gaps in the protected date range.
-
-        Delegates to _get_sensor_interval_stats() for consistency.
-
-        Returns:
-            True if any gaps exist, False if protected range is complete.
-
-        """
-        return self._get_sensor_interval_stats()["has_gaps"]
-
-    def _extract_timezone_from_user_data(self, user_data: dict[str, Any]) -> str | None:
-        """Extract timezone for this home from user_data."""
-        if not user_data:
-            return None
-
-        viewer = user_data.get("viewer", {})
-        homes = viewer.get("homes", [])
-
-        for home in homes:
-            if home.get("id") == self._home_id:
-                return home.get("timeZone")
-
-        return None
-
-    def _get_cached_intervals(
-        self,
-        start_time_iso: str,
-        end_time_iso: str,
-    ) -> list[dict[str, Any]]:
-        """
-        Get cached intervals for time range using timestamp index.
-
-        Uses timestamp_index for O(1) lookups per timestamp.
-
-        IMPORTANT: Returns shallow copies of interval dicts to prevent external
-        mutations (e.g., by parse_all_timestamps()) from affecting cached data.
-        The Pool cache must remain immutable to ensure consistent behavior.
-
-        Args:
-            start_time_iso: ISO timestamp string (inclusive).
-            end_time_iso: ISO timestamp string (exclusive).
-
-        Returns:
-            List of cached interval dicts in time range (may be empty or incomplete).
-            Sorted by startsAt timestamp. Each dict is a shallow copy.
-
-        """
-        # Parse query range once
-        start_time_dt = datetime.fromisoformat(start_time_iso)
-        end_time_dt = datetime.fromisoformat(end_time_iso)
-
-        # CRITICAL: Use NAIVE local timestamps for iteration.
-        #
-        # Index keys are naive local timestamps (timezone stripped via [:19]).
-        # When start and end span a DST transition, they have different UTC offsets
-        # (e.g., start=+01:00 CET, end=+02:00 CEST). Using fixed-offset datetimes
-        # from fromisoformat() causes the loop to compare UTC values for the end
-        # boundary, ending 1 hour early on spring-forward days (or 1 hour late on
-        # fall-back days).
-        #
-        # By iterating in naive local time, we match the index key format exactly
-        # and the end boundary comparison works correctly regardless of DST.
-        current_naive = start_time_dt.replace(tzinfo=None)
-        end_naive = end_time_dt.replace(tzinfo=None)
-
-        # Use index to find intervals: iterate through expected timestamps
-        result = []
-
-        # Determine interval step (15 min post-2025-10-01, 60 min pre)
-        resolution_change_naive = datetime(2025, 10, 1)  # noqa: DTZ001
-        interval_minutes = INTERVAL_QUARTER_HOURLY if current_naive >= resolution_change_naive else INTERVAL_HOURLY
-
-        while current_naive < end_naive:
-            # Check if this timestamp exists in index (O(1) lookup)
-            current_dt_key = current_naive.isoformat()[:19]
-            location = self._index.get(current_dt_key)
-
-            if location is not None:
-                # Get interval from fetch group
-                fetch_groups = self._cache.get_fetch_groups()
-                fetch_group = fetch_groups[location["fetch_group_index"]]
-                interval = fetch_group["intervals"][location["interval_index"]]
-                # CRITICAL: Return shallow copy to prevent external mutations
-                # (e.g., parse_all_timestamps() converts startsAt to datetime in-place)
-                result.append(dict(interval))
-
-            # Move to next expected interval
-            current_naive += timedelta(minutes=interval_minutes)
-
-            # Handle resolution change boundary
-            if interval_minutes == INTERVAL_HOURLY and current_naive >= resolution_change_naive:
-                interval_minutes = INTERVAL_QUARTER_HOURLY
-
-        _LOGGER_DETAILS.debug(
-            "Retrieved %d intervals from cache for home %s (range %s to %s)",
-            len(result),
-            self._home_id,
-            start_time_iso,
-            end_time_iso,
-        )
-
-        return result
-
-    def _add_intervals(
-        self,
-        intervals: list[dict[str, Any]],
-        fetch_time_iso: str,
-    ) -> None:
-        """
-        Add intervals as new fetch group to cache with GC.
-
-        Strategy:
-        1. Filter out duplicates (intervals already in cache)
-        2. Handle "touch" (move cached intervals to new fetch group)
-        3. Add new fetch group to cache
-        4. Update timestamp index
-        5. Run GC if needed
-        6. Schedule debounced auto-save
-
-        Args:
-            intervals: List of interval dicts from API.
-            fetch_time_iso: ISO timestamp string when intervals were fetched.
-
-        """
-        if not intervals:
-            return
-
-        fetch_time_dt = datetime.fromisoformat(fetch_time_iso)
-
-        # Classify intervals: new vs already cached
-        new_intervals = []
-        intervals_to_touch = []
-
-        for interval in intervals:
-            starts_at_normalized = _normalize_starts_at(interval["startsAt"])
-            if not self._index.contains(starts_at_normalized):
-                new_intervals.append(interval)
-            else:
-                intervals_to_touch.append((starts_at_normalized, interval))
-                _LOGGER_DETAILS.debug(
-                    "Interval %s already cached for home %s, will touch (update fetch time)",
-                    interval["startsAt"],
-                    self._home_id,
-                )
-
-        # Handle touched intervals: move to new fetch group
-        if intervals_to_touch:
-            self._touch_intervals(intervals_to_touch, fetch_time_dt)
-
-        if not new_intervals:
-            if intervals_to_touch:
-                _LOGGER_DETAILS.debug(
-                    "All %d intervals already cached for home %s (touched only)",
-                    len(intervals),
-                    self._home_id,
-                )
-            return
-
-        # Sort new intervals by startsAt
-        new_intervals.sort(key=lambda x: x["startsAt"])
-
-        # Add new fetch group to cache
-        fetch_group_index = self._cache.add_fetch_group(new_intervals, fetch_time_dt)
-
-        # Update timestamp index for all new intervals
-        for interval_index, interval in enumerate(new_intervals):
-            starts_at_normalized = _normalize_starts_at(interval["startsAt"])
-            self._index.add(interval, fetch_group_index, interval_index)
-
-        _LOGGER_DETAILS.debug(
-            "Added fetch group %d to home %s cache: %d new intervals (fetched at %s)",
-            fetch_group_index,
-            self._home_id,
-            len(new_intervals),
-            fetch_time_iso,
-        )
-
-        # Run GC to evict old fetch groups if needed
-        gc_changed_data = self._gc.run_gc()
-
-        # Schedule debounced auto-save if data changed
-        data_changed = len(new_intervals) > 0 or len(intervals_to_touch) > 0 or gc_changed_data
-        if data_changed and self._hass is not None and self._entry_id is not None:
-            self._schedule_debounced_save()
-
-    def _touch_intervals(
-        self,
-        intervals_to_touch: list[tuple[str, dict[str, Any]]],
-        fetch_time_dt: datetime,
-    ) -> None:
-        """
-        Move cached intervals to new fetch group (update fetch time).
-
-        Creates a new fetch group containing references to existing intervals.
-        Updates the index to point to the new fetch group.
-
-        Args:
-            intervals_to_touch: List of (normalized_timestamp, interval_dict) tuples.
-            fetch_time_dt: Datetime when intervals were fetched.
-
-        """
-        fetch_groups = self._cache.get_fetch_groups()
-
-        # Create touch fetch group with existing interval references
-        touch_intervals = []
-        for starts_at_normalized, _interval in intervals_to_touch:
-            # Get existing interval from old fetch group
-            location = self._index.get(starts_at_normalized)
-            if location is None:
-                continue  # Should not happen, but be defensive
-
-            old_group = fetch_groups[location["fetch_group_index"]]
-            existing_interval = old_group["intervals"][location["interval_index"]]
-            touch_intervals.append(existing_interval)
-
-        # Add touch group to cache
-        touch_group_index = self._cache.add_fetch_group(touch_intervals, fetch_time_dt)
-
-        # Update index to point to new fetch group using batch operation
-        # This is more efficient than individual remove+add calls
-        index_updates = [
-            (starts_at_normalized, touch_group_index, interval_index)
-            for interval_index, (starts_at_normalized, _) in enumerate(intervals_to_touch)
-        ]
-        self._index.update_batch(index_updates)
-
-        _LOGGER.debug(
-            "Touched %d cached intervals for home %s (moved to fetch group %d, fetched at %s)",
-            len(intervals_to_touch),
-            self._home_id,
-            touch_group_index,
-            fetch_time_dt.isoformat(),
-        )
-
-    def _schedule_debounced_save(self) -> None:
-        """
-        Schedule debounced save with configurable delay.
-
-        Cancels existing timer and starts new one if already scheduled.
-        This prevents multiple saves during rapid successive changes.
-
-        """
-        # Cancel existing debounce timer if running
-        if self._save_debounce_task is not None and not self._save_debounce_task.done():
-            self._save_debounce_task.cancel()
-            _LOGGER.debug("Cancelled pending auto-save (new changes detected, resetting timer)")
-
-        # Schedule new debounced save
-        task = asyncio.create_task(
-            self._debounced_save_worker(),
-            name=f"interval_pool_debounce_{self._entry_id}",
-        )
-        self._save_debounce_task = task
-        self._background_tasks.add(task)
-        task.add_done_callback(self._background_tasks.discard)
-
-    async def _debounced_save_worker(self) -> None:
-        """Debounce worker: waits configured delay, then saves if not cancelled."""
-        try:
-            await asyncio.sleep(DEBOUNCE_DELAY_SECONDS)
-            await self._auto_save_pool_state()
-        except asyncio.CancelledError:
-            _LOGGER.debug("Auto-save timer cancelled (expected - new changes arrived)")
-            raise
-
-    async def async_shutdown(self) -> None:
-        """
-        Clean shutdown - cancel pending background tasks.
-
-        Should be called when the config entry is unloaded to prevent
-        orphaned tasks and ensure clean resource cleanup.
-
-        """
-        _LOGGER.debug("Shutting down interval pool for home %s", self._home_id)
-
-        # Cancel debounce task if running
-        if self._save_debounce_task is not None and not self._save_debounce_task.done():
-            self._save_debounce_task.cancel()
-            with contextlib.suppress(asyncio.CancelledError):
-                await self._save_debounce_task
-            _LOGGER.debug("Cancelled pending auto-save task")
-
-        # Cancel any other background tasks
-        if self._background_tasks:
-            for task in list(self._background_tasks):
-                if not task.done():
-                    task.cancel()
-            # Wait for all tasks to complete cancellation
-            if self._background_tasks:
-                await asyncio.gather(*self._background_tasks, return_exceptions=True)
-            _LOGGER.debug("Cancelled %d background tasks", len(self._background_tasks))
-            self._background_tasks.clear()
-
-        _LOGGER.debug("Interval pool shutdown complete for home %s", self._home_id)
-
-    async def _auto_save_pool_state(self) -> None:
-        """Auto-save pool state to storage with lock protection."""
-        if self._hass is None or self._entry_id is None:
-            return
-
-        async with self._save_lock:
-            try:
-                pool_state = self.to_dict()
-                await async_save_pool_state(self._hass, self._entry_id, pool_state)
-                _LOGGER.debug("Auto-saved interval pool for entry %s", self._entry_id)
-            except Exception:
-                _LOGGER.exception("Failed to auto-save interval pool for entry %s", self._entry_id)
-
-    def to_dict(self) -> dict[str, Any]:
-        """
-        Serialize interval pool state for storage.
-
-        Filters out dead intervals (no longer referenced by index).
-
-        Returns:
-            Dictionary containing serialized pool state (only living intervals).
-
-        """
-        fetch_groups = self._cache.get_fetch_groups()
-
-        # Serialize fetch groups (only living intervals)
-        serialized_fetch_groups = []
-
-        for group_idx, fetch_group in enumerate(fetch_groups):
-            living_intervals = []
-
-            for interval_idx, interval in enumerate(fetch_group["intervals"]):
-                starts_at_normalized = _normalize_starts_at(interval["startsAt"])
-
-                # Check if interval is still referenced in index
-                location = self._index.get(starts_at_normalized)
-                # Only keep if index points to THIS position in THIS group
-                if (
-                    location is not None
-                    and location["fetch_group_index"] == group_idx
-                    and location["interval_index"] == interval_idx
-                ):
-                    living_intervals.append(interval)
-
-            # Only serialize groups with living intervals
-            if living_intervals:
-                serialized_fetch_groups.append(
-                    {
-                        "fetched_at": fetch_group["fetched_at"].isoformat(),
-                        "intervals": living_intervals,
-                    }
-                )
-
-        return {
-            "version": 1,
-            "home_id": self._home_id,
-            "fetch_groups": serialized_fetch_groups,
-        }
-
-    @classmethod
-    def from_dict(
-        cls,
-        data: dict[str, Any],
-        *,
-        api: TibberPricesApiClient,
-        hass: Any | None = None,
-        entry_id: str | None = None,
-        time_service: TibberPricesTimeService | None = None,
-    ) -> TibberPricesIntervalPool | None:
-        """
-        Restore interval pool manager from storage.
-
-        Expects single-home format: {"version": 1, "home_id": "...", "fetch_groups": [...]}
-        Old multi-home format is treated as corrupted and returns None.
-
-        Args:
-            data: Dictionary containing serialized pool state.
-            api: API client for fetching intervals.
-            hass: HomeAssistant instance for auto-save (optional).
-            entry_id: Config entry ID for auto-save (optional).
-            time_service: TimeService for time-travel support (optional).
-
-        Returns:
-            Restored TibberPricesIntervalPool instance, or None if format unknown/corrupted.
-
-        """
-        # Validate format
-        if not data or "home_id" not in data or "fetch_groups" not in data:
-            if "homes" in data:
-                _LOGGER.info(
-                    "Interval pool storage uses old multi-home format (pre-2025-11-25). "
-                    "Treating as corrupted. Pool will rebuild from API."
-                )
-            else:
-                _LOGGER.warning("Interval pool storage format unknown or corrupted. Pool will rebuild from API.")
-            return None
-
-        home_id = data["home_id"]
-
-        # Create manager with home_id from storage
-        manager = cls(home_id=home_id, api=api, hass=hass, entry_id=entry_id, time_service=time_service)
-
-        # Restore fetch groups to cache
-        for serialized_group in data.get("fetch_groups", []):
-            fetched_at_dt = datetime.fromisoformat(serialized_group["fetched_at"])
-            intervals = serialized_group["intervals"]
-            fetch_group_index = manager._cache.add_fetch_group(intervals, fetched_at_dt)
-
-            # Rebuild index for this fetch group
-            for interval_index, interval in enumerate(intervals):
-                manager._index.add(interval, fetch_group_index, interval_index)
-
-        total_intervals = sum(len(group["intervals"]) for group in manager._cache.get_fetch_groups())
-        _LOGGER.debug(
-            "Interval pool restored from storage (home %s, %d intervals)",
-            home_id,
-            total_intervals,
-        )
-
-        return manager

custom_components/tibber_prices/interval_pool/routing.py

@@ -1,180 +0,0 @@
-"""
-Routing Module - API endpoint selection for price intervals.
-
-This module handles intelligent routing between different Tibber API endpoints:
-
-- PRICE_INFO: Recent data (from "day before yesterday midnight" onwards)
-- PRICE_INFO_RANGE: Historical data (before "day before yesterday midnight")
-- Automatic splitting and merging when range spans the boundary
-
-CRITICAL: Uses REAL TIME (dt_utils.now()) for API boundary calculation,
-NOT TimeService.now() which may be shifted for internal simulation.
-"""
-
-from __future__ import annotations
-
-import logging
-from typing import TYPE_CHECKING, Any
-
-from custom_components.tibber_prices.api.exceptions import TibberPricesApiClientError
-from homeassistant.util import dt as dt_utils
-
-if TYPE_CHECKING:
-    from datetime import datetime
-
-    from custom_components.tibber_prices.api.client import TibberPricesApiClient
-
-_LOGGER = logging.getLogger(__name__)
-_LOGGER_DETAILS = logging.getLogger(__name__ + ".details")
-
-
-async def get_price_intervals_for_range(
-    api_client: TibberPricesApiClient,
-    home_id: str,
-    user_data: dict[str, Any],
-    start_time: datetime,
-    end_time: datetime,
-) -> list[dict[str, Any]]:
-    """
-    Get price intervals for a specific time range with automatic routing.
-
-    Automatically routes to the correct API endpoint based on the time range:
-    - PRICE_INFO_RANGE: For intervals exclusively before "day before yesterday midnight" (real time)
-    - PRICE_INFO: For intervals from "day before yesterday midnight" onwards
-    - Both: If range spans across the boundary, splits the request
-
-    CRITICAL: Uses REAL TIME (dt_utils.now()) for API boundary calculation,
-    NOT TimeService.now() which may be shifted for internal simulation.
-    This ensures predictable API responses.
-
-    CACHING STRATEGY: Returns ALL intervals from API response, NOT filtered.
-    The caller (pool.py) will cache everything and then filter to user request.
-    This maximizes cache efficiency - one API call can populate cache for
-    multiple subsequent queries.
-
-    Args:
-        api_client: TibberPricesApiClient instance for API calls.
-        home_id: Home ID to fetch price data for.
-        user_data: User data dict containing home metadata (including timezone).
-        start_time: Start of the range (inclusive, timezone-aware).
-        end_time: End of the range (exclusive, timezone-aware).
-
-    Returns:
-        List of ALL price interval dicts from API (unfiltered).
-        - PRICE_INFO: Returns ~384 intervals (day-before-yesterday to tomorrow)
-        - PRICE_INFO_RANGE: Returns intervals for requested historical range
-        - Both: Returns all intervals from both endpoints
-
-    Raises:
-        TibberPricesApiClientError: If arguments invalid or requests fail.
-
-    """
-    if not user_data:
-        msg = "User data required for timezone-aware price fetching - fetch user data first"
-        raise TibberPricesApiClientError(msg)
-
-    if not home_id:
-        msg = "Home ID is required"
-        raise TibberPricesApiClientError(msg)
-
-    if start_time >= end_time:
-        msg = f"Invalid time range: start_time ({start_time}) must be before end_time ({end_time})"
-        raise TibberPricesApiClientError(msg)
-
-    # Calculate boundary: day before yesterday midnight (REAL TIME, not TimeService)
-    boundary = _calculate_boundary(api_client, user_data, home_id)
-
-    _LOGGER_DETAILS.debug(
-        "Routing price interval request for home %s: range %s to %s, boundary %s",
-        home_id,
-        start_time,
-        end_time,
-        boundary,
-    )
-
-    # Route based on time range
-    if end_time <= boundary:
-        # Entire range is historical (before day before yesterday) → use PRICE_INFO_RANGE
-        _LOGGER_DETAILS.debug("Range is fully historical, using PRICE_INFO_RANGE")
-        result = await api_client.async_get_price_info_range(
-            home_id=home_id,
-            user_data=user_data,
-            start_time=start_time,
-            end_time=end_time,
-        )
-        return result["price_info"]
-
-    if start_time >= boundary:
-        # Entire range is recent (from day before yesterday onwards) → use PRICE_INFO
-        _LOGGER_DETAILS.debug("Range is fully recent, using PRICE_INFO")
-        result = await api_client.async_get_price_info(home_id, user_data)
-
-        # Return ALL intervals (unfiltered) for maximum cache efficiency
-        # Pool will cache everything, then filter to user request
-        return result["price_info"]
-
-    # Range spans boundary → split request
-    _LOGGER_DETAILS.debug("Range spans boundary, splitting request")
-
-    # Fetch historical part (start_time to boundary)
-    historical_result = await api_client.async_get_price_info_range(
-        home_id=home_id,
-        user_data=user_data,
-        start_time=start_time,
-        end_time=boundary,
-    )
-
-    # Fetch recent part (boundary onwards)
-    recent_result = await api_client.async_get_price_info(home_id, user_data)
-
-    # Return ALL intervals (unfiltered) for maximum cache efficiency
-    # Pool will cache everything, then filter to user request
-    return historical_result["price_info"] + recent_result["price_info"]
-
-
-def _calculate_boundary(
-    api_client: TibberPricesApiClient,
-    user_data: dict[str, Any],
-    home_id: str,
-) -> datetime:
-    """
-    Calculate the API boundary (day before yesterday midnight).
-
-    Uses the API client's helper method to extract timezone and calculate boundary.
-
-    Args:
-        api_client: TibberPricesApiClient instance.
-        user_data: User data dict containing home metadata.
-        home_id: Home ID to get timezone for.
-
-    Returns:
-        Timezone-aware datetime for day before yesterday midnight.
-
-    """
-    # Extract timezone for this home
-    home_timezones = api_client._extract_home_timezones(user_data)  # noqa: SLF001
-    home_tz = home_timezones.get(home_id)
-
-    # Calculate boundary using API client's method
-    return api_client._calculate_day_before_yesterday_midnight(home_tz)  # noqa: SLF001
-
-
-def _parse_timestamp(timestamp_str: str) -> datetime:
-    """
-    Parse ISO timestamp string to timezone-aware datetime.
-
-    Args:
-        timestamp_str: ISO format timestamp string.
-
-    Returns:
-        Timezone-aware datetime object.
-
-    Raises:
-        ValueError: If timestamp string cannot be parsed.
-
-    """
-    result = dt_utils.parse_datetime(timestamp_str)
-    if result is None:
-        msg = f"Failed to parse timestamp: {timestamp_str}"
-        raise ValueError(msg)
-    return result

custom_components/tibber_prices/interval_pool/storage.py

@@ -1,165 +0,0 @@
-"""Storage management for interval pool."""
-
-from __future__ import annotations
-
-import errno
-import logging
-from typing import TYPE_CHECKING, Any
-
-from homeassistant.helpers.storage import Store
-
-if TYPE_CHECKING:
-    from homeassistant.core import HomeAssistant
-
-_LOGGER = logging.getLogger(__name__)
-_LOGGER_DETAILS = logging.getLogger(__name__ + ".details")
-
-# Storage version - increment when changing data structure
-INTERVAL_POOL_STORAGE_VERSION = 1
-
-
-def get_storage_key(entry_id: str) -> str:
-    """
-    Get storage key for interval pool based on config entry ID.
-
-    Args:
-        entry_id: Home Assistant config entry ID
-
-    Returns:
-        Storage key string
-
-    """
-    return f"tibber_prices.interval_pool.{entry_id}"
-
-
-async def async_load_pool_state(
-    hass: HomeAssistant,
-    entry_id: str,
-) -> dict[str, Any] | None:
-    """
-    Load interval pool state from storage.
-
-    Args:
-        hass: Home Assistant instance
-        entry_id: Config entry ID
-
-    Returns:
-        Pool state dict or None if no cache exists
-
-    """
-    storage_key = get_storage_key(entry_id)
-    store: Store = Store(hass, INTERVAL_POOL_STORAGE_VERSION, storage_key)
-
-    try:
-        stored = await store.async_load()
-    except Exception:
-        # Corrupted storage file, JSON parse error, or other exception
-        _LOGGER.exception(
-            "Failed to load interval pool storage for entry %s (corrupted file?), starting with empty pool",
-            entry_id,
-        )
-        return None
-
-    if stored is None:
-        _LOGGER.debug("No interval pool cache found for entry %s (first run)", entry_id)
-        return None
-
-    # Validate storage structure (single-home format)
-    if not isinstance(stored, dict):
-        _LOGGER.warning(
-            "Invalid interval pool storage structure for entry %s (not a dict), ignoring",
-            entry_id,
-        )
-        return None
-
-    # Check for new single-home format (version 1, home_id, fetch_groups)
-    if "home_id" in stored and "fetch_groups" in stored:
-        _LOGGER.debug(
-            "Interval pool state loaded for entry %s (single-home format, %d fetch groups)",
-            entry_id,
-            len(stored.get("fetch_groups", [])),
-        )
-        return stored
-
-    # Check for old multi-home format (homes dict) - treat as incompatible
-    if "homes" in stored:
-        _LOGGER.info(
-            "Interval pool storage for entry %s uses old multi-home format (pre-2025-11-25). "
-            "Treating as incompatible. Pool will rebuild from API.",
-            entry_id,
-        )
-        return None
-
-    # Unknown format
-    _LOGGER.warning(
-        "Invalid interval pool storage structure for entry %s (missing required keys), ignoring",
-        entry_id,
-    )
-    return None
-
-
-async def async_save_pool_state(
-    hass: HomeAssistant,
-    entry_id: str,
-    pool_state: dict[str, Any],
-) -> None:
-    """
-    Save interval pool state to storage.
-
-    Args:
-        hass: Home Assistant instance
-        entry_id: Config entry ID
-        pool_state: Pool state dict to save
-
-    """
-    storage_key = get_storage_key(entry_id)
-    store: Store = Store(hass, INTERVAL_POOL_STORAGE_VERSION, storage_key)
-
-    try:
-        await store.async_save(pool_state)
-        _LOGGER_DETAILS.debug(
-            "Interval pool state saved for entry %s (%d fetch groups)",
-            entry_id,
-            len(pool_state.get("fetch_groups", [])),
-        )
-    except OSError as err:
-        # Provide specific error messages based on errno
-        if err.errno == errno.ENOSPC:  # Disk full
-            _LOGGER.exception(
-                "Cannot save interval pool storage for entry %s: Disk full!",
-                entry_id,
-            )
-        elif err.errno == errno.EACCES:  # Permission denied
-            _LOGGER.exception(
-                "Cannot save interval pool storage for entry %s: Permission denied!",
-                entry_id,
-            )
-        else:
-            _LOGGER.exception(
-                "Failed to save interval pool storage for entry %s",
-                entry_id,
-            )
-
-
-async def async_remove_pool_storage(
-    hass: HomeAssistant,
-    entry_id: str,
-) -> None:
-    """
-    Remove interval pool storage file.
-
-    Used when config entry is removed.
-
-    Args:
-        hass: Home Assistant instance
-        entry_id: Config entry ID
-
-    """
-    storage_key = get_storage_key(entry_id)
-    store: Store = Store(hass, INTERVAL_POOL_STORAGE_VERSION, storage_key)
-
-    try:
-        await store.async_remove()
-        _LOGGER.debug("Interval pool storage removed for entry %s", entry_id)
-    except OSError as ex:
-        _LOGGER.warning("Failed to remove interval pool storage for entry %s: %s", entry_id, ex)

custom_components/tibber_prices/manifest.json

@@ -6,10 +6,11 @@
   ],
   "config_flow": true,
   "documentation": "https://github.com/jpawlowski/hass.tibber_prices",
+  "integration_type": "hub",
   "iot_class": "cloud_polling",
   "issue_tracker": "https://github.com/jpawlowski/hass.tibber_prices/issues",
   "requirements": [
     "aiofiles>=23.2.1"
   ],
-  "version": "0.27.0"
+  "version": "0.12.0"
 }

custom_components/tibber_prices/number/__init__.py

@@ -1,39 +0,0 @@
-"""
-Number platform for Tibber Prices integration.
-
-Provides configurable number entities for runtime overrides of Best Price
-and Peak Price period calculation settings. These entities allow automation
-of configuration parameters without using the options flow.
-
-When enabled, these entities take precedence over the options flow settings.
-When disabled (default), the options flow settings are used.
-"""
-
-from __future__ import annotations
-
-from typing import TYPE_CHECKING
-
-from .core import TibberPricesConfigNumber
-from .definitions import NUMBER_ENTITY_DESCRIPTIONS
-
-if TYPE_CHECKING:
-    from custom_components.tibber_prices.data import TibberPricesConfigEntry
-    from homeassistant.core import HomeAssistant
-    from homeassistant.helpers.entity_platform import AddEntitiesCallback
-
-
-async def async_setup_entry(
-    _hass: HomeAssistant,
-    entry: TibberPricesConfigEntry,
-    async_add_entities: AddEntitiesCallback,
-) -> None:
-    """Set up Tibber Prices number entities based on a config entry."""
-    coordinator = entry.runtime_data.coordinator
-
-    async_add_entities(
-        TibberPricesConfigNumber(
-            coordinator=coordinator,
-            entity_description=entity_description,
-        )
-        for entity_description in NUMBER_ENTITY_DESCRIPTIONS
-    )

custom_components/tibber_prices/number/core.py

@@ -1,242 +0,0 @@
-"""
-Number entity implementation for Tibber Prices configuration overrides.
-
-These entities allow runtime configuration of period calculation settings.
-When a config entity is enabled, its value takes precedence over the
-options flow setting for period calculations.
-"""
-
-from __future__ import annotations
-
-import logging
-from typing import TYPE_CHECKING, Any
-
-from custom_components.tibber_prices.const import (
-    DOMAIN,
-    get_home_type_translation,
-    get_translation,
-)
-from homeassistant.components.number import NumberEntity, RestoreNumber
-from homeassistant.core import callback
-from homeassistant.helpers.device_registry import DeviceEntryType, DeviceInfo
-
-if TYPE_CHECKING:
-    from custom_components.tibber_prices.coordinator import (
-        TibberPricesDataUpdateCoordinator,
-    )
-
-    from .definitions import TibberPricesNumberEntityDescription
-
-_LOGGER = logging.getLogger(__name__)
-
-
-class TibberPricesConfigNumber(RestoreNumber, NumberEntity):
-    """
-    A number entity for configuring period calculation settings at runtime.
-
-    When this entity is enabled, its value overrides the corresponding
-    options flow setting. When disabled (default), the options flow
-    setting is used for period calculations.
-
-    The entity restores its value after Home Assistant restart.
-    """
-
-    _attr_has_entity_name = True
-    entity_description: TibberPricesNumberEntityDescription
-
-    # Exclude all attributes from recorder history - config entities don't need history
-    _unrecorded_attributes = frozenset(
-        {
-            "description",
-            "long_description",
-            "usage_tips",
-            "friendly_name",
-            "icon",
-            "unit_of_measurement",
-            "mode",
-            "min",
-            "max",
-            "step",
-        }
-    )
-
-    def __init__(
-        self,
-        coordinator: TibberPricesDataUpdateCoordinator,
-        entity_description: TibberPricesNumberEntityDescription,
-    ) -> None:
-        """Initialize the config number entity."""
-        self.coordinator = coordinator
-        self.entity_description = entity_description
-
-        # Set unique ID
-        self._attr_unique_id = (
-            f"{coordinator.config_entry.unique_id or coordinator.config_entry.entry_id}_{entity_description.key}"
-        )
-
-        # Initialize with None - will be set in async_added_to_hass
-        self._attr_native_value: float | None = None
-
-        # Setup device info
-        self._setup_device_info()
-
-    def _setup_device_info(self) -> None:
-        """Set up device information."""
-        home_name, home_id, home_type = self._get_device_info()
-        language = self.coordinator.hass.config.language or "en"
-        translated_model = get_home_type_translation(home_type, language) if home_type else "Unknown"
-
-        self._attr_device_info = DeviceInfo(
-            entry_type=DeviceEntryType.SERVICE,
-            identifiers={
-                (
-                    DOMAIN,
-                    self.coordinator.config_entry.unique_id or self.coordinator.config_entry.entry_id,
-                )
-            },
-            name=home_name,
-            manufacturer="Tibber",
-            model=translated_model,
-            serial_number=home_id if home_id else None,
-            configuration_url="https://developer.tibber.com/explorer",
-        )
-
-    def _get_device_info(self) -> tuple[str, str | None, str | None]:
-        """Get device name, ID and type."""
-        user_profile = self.coordinator.get_user_profile()
-        is_subentry = bool(self.coordinator.config_entry.data.get("home_id"))
-        home_id = self.coordinator.config_entry.unique_id
-        home_type = None
-
-        if is_subentry:
-            home_data = self.coordinator.config_entry.data.get("home_data", {})
-            home_id = self.coordinator.config_entry.data.get("home_id")
-            address = home_data.get("address", {})
-            address1 = address.get("address1", "")
-            city = address.get("city", "")
-            app_nickname = home_data.get("appNickname", "")
-            home_type = home_data.get("type", "")
-
-            if app_nickname and app_nickname.strip():
-                home_name = app_nickname.strip()
-            elif address1:
-                home_name = address1
-                if city:
-                    home_name = f"{home_name}, {city}"
-            else:
-                home_name = f"Tibber Home {home_id[:8]}" if home_id else "Tibber Home"
-        elif user_profile:
-            home_name = user_profile.get("name") or "Tibber Home"
-        else:
-            home_name = "Tibber Home"
-
-        return home_name, home_id, home_type
-
-    async def async_added_to_hass(self) -> None:
-        """Handle entity which was added to Home Assistant."""
-        await super().async_added_to_hass()
-
-        # Try to restore previous state
-        last_number_data = await self.async_get_last_number_data()
-        if last_number_data is not None and last_number_data.native_value is not None:
-            self._attr_native_value = last_number_data.native_value
-            _LOGGER.debug(
-                "Restored %s value: %s",
-                self.entity_description.key,
-                self._attr_native_value,
-            )
-        else:
-            # Initialize with value from options flow (or default)
-            self._attr_native_value = self._get_value_from_options()
-            _LOGGER.debug(
-                "Initialized %s from options: %s",
-                self.entity_description.key,
-                self._attr_native_value,
-            )
-
-        # Register override with coordinator if entity is enabled
-        # This happens during add, so check entity registry
-        await self._sync_override_state()
-
-    async def async_will_remove_from_hass(self) -> None:
-        """Handle entity removal from Home Assistant."""
-        # Remove override when entity is removed
-        self.coordinator.remove_config_override(
-            self.entity_description.config_key,
-            self.entity_description.config_section,
-        )
-        await super().async_will_remove_from_hass()
-
-    def _get_value_from_options(self) -> float:
-        """Get the current value from options flow or default."""
-        options = self.coordinator.config_entry.options
-        section = options.get(self.entity_description.config_section, {})
-        value = section.get(
-            self.entity_description.config_key,
-            self.entity_description.default_value,
-        )
-        return float(value)
-
-    async def _sync_override_state(self) -> None:
-        """Sync the override state with the coordinator based on entity enabled state."""
-        # Check if entity is enabled in registry
-        if self.registry_entry is not None and not self.registry_entry.disabled:
-            # Entity is enabled - register the override
-            if self._attr_native_value is not None:
-                self.coordinator.set_config_override(
-                    self.entity_description.config_key,
-                    self.entity_description.config_section,
-                    self._attr_native_value,
-                )
-        else:
-            # Entity is disabled - remove override
-            self.coordinator.remove_config_override(
-                self.entity_description.config_key,
-                self.entity_description.config_section,
-            )
-
-    async def async_set_native_value(self, value: float) -> None:
-        """Update the current value and trigger recalculation."""
-        self._attr_native_value = value
-
-        # Update the coordinator's runtime override
-        self.coordinator.set_config_override(
-            self.entity_description.config_key,
-            self.entity_description.config_section,
-            value,
-        )
-
-        # Trigger period recalculation (same path as options update)
-        await self.coordinator.async_handle_config_override_update()
-
-        _LOGGER.debug(
-            "Updated %s to %s, triggered period recalculation",
-            self.entity_description.key,
-            value,
-        )
-
-    @property
-    def extra_state_attributes(self) -> dict[str, Any] | None:
-        """Return entity state attributes with description."""
-        language = self.coordinator.hass.config.language or "en"
-
-        # Try to get description from custom translations
-        # Custom translations use direct path: number.{key}.description
-        translation_path = [
-            "number",
-            self.entity_description.translation_key or self.entity_description.key,
-            "description",
-        ]
-        description = get_translation(translation_path, language)
-
-        attrs: dict[str, Any] = {}
-        if description:
-            attrs["description"] = description
-
-        return attrs if attrs else None
-
-    @callback
-    def async_registry_entry_updated(self) -> None:
-        """Handle entity registry update (enabled/disabled state change)."""
-        # This is called when the entity is enabled/disabled in the UI
-        self.hass.async_create_task(self._sync_override_state())

custom_components/tibber_prices/number/definitions.py

@@ -1,250 +0,0 @@
-"""
-Number entity definitions for Tibber Prices configuration overrides.
-
-These number entities allow runtime configuration of Best Price and Peak Price
-period calculation settings. They are disabled by default - users can enable
-individual entities to override specific settings at runtime.
-
-When enabled, the entity value takes precedence over the options flow setting.
-When disabled (default), the options flow setting is used.
-"""
-
-from __future__ import annotations
-
-from dataclasses import dataclass
-
-from homeassistant.components.number import (
-    NumberEntityDescription,
-    NumberMode,
-)
-from homeassistant.const import PERCENTAGE, EntityCategory
-
-
-@dataclass(frozen=True, kw_only=True)
-class TibberPricesNumberEntityDescription(NumberEntityDescription):
-    """Describes a Tibber Prices number entity for config overrides."""
-
-    # The config key this entity overrides (matches CONF_* constants)
-    config_key: str
-    # The section in options where this setting is stored (e.g., "flexibility_settings")
-    config_section: str
-    # Whether this is for best_price (False) or peak_price (True)
-    is_peak_price: bool = False
-    # Default value from const.py
-    default_value: float | int = 0
-
-
-# ============================================================================
-# BEST PRICE PERIOD CONFIGURATION OVERRIDES
-# ============================================================================
-
-BEST_PRICE_NUMBER_ENTITIES = (
-    TibberPricesNumberEntityDescription(
-        key="best_price_flex_override",
-        translation_key="best_price_flex_override",
-        name="Best Price: Flexibility",
-        icon="mdi:arrow-down-bold-circle",
-        entity_category=EntityCategory.CONFIG,
-        entity_registry_enabled_default=False,
-        native_min_value=0,
-        native_max_value=50,
-        native_step=1,
-        native_unit_of_measurement=PERCENTAGE,
-        mode=NumberMode.SLIDER,
-        config_key="best_price_flex",
-        config_section="flexibility_settings",
-        is_peak_price=False,
-        default_value=15,  # DEFAULT_BEST_PRICE_FLEX
-    ),
-    TibberPricesNumberEntityDescription(
-        key="best_price_min_distance_override",
-        translation_key="best_price_min_distance_override",
-        name="Best Price: Minimum Distance",
-        icon="mdi:arrow-down-bold-circle",
-        entity_category=EntityCategory.CONFIG,
-        entity_registry_enabled_default=False,
-        native_min_value=-50,
-        native_max_value=0,
-        native_step=1,
-        native_unit_of_measurement=PERCENTAGE,
-        mode=NumberMode.SLIDER,
-        config_key="best_price_min_distance_from_avg",
-        config_section="flexibility_settings",
-        is_peak_price=False,
-        default_value=-5,  # DEFAULT_BEST_PRICE_MIN_DISTANCE_FROM_AVG
-    ),
-    TibberPricesNumberEntityDescription(
-        key="best_price_min_period_length_override",
-        translation_key="best_price_min_period_length_override",
-        name="Best Price: Minimum Period Length",
-        icon="mdi:arrow-down-bold-circle",
-        entity_category=EntityCategory.CONFIG,
-        entity_registry_enabled_default=False,
-        native_min_value=15,
-        native_max_value=180,
-        native_step=15,
-        native_unit_of_measurement="min",
-        mode=NumberMode.SLIDER,
-        config_key="best_price_min_period_length",
-        config_section="period_settings",
-        is_peak_price=False,
-        default_value=60,  # DEFAULT_BEST_PRICE_MIN_PERIOD_LENGTH
-    ),
-    TibberPricesNumberEntityDescription(
-        key="best_price_min_periods_override",
-        translation_key="best_price_min_periods_override",
-        name="Best Price: Minimum Periods",
-        icon="mdi:arrow-down-bold-circle",
-        entity_category=EntityCategory.CONFIG,
-        entity_registry_enabled_default=False,
-        native_min_value=1,
-        native_max_value=10,
-        native_step=1,
-        mode=NumberMode.SLIDER,
-        config_key="min_periods_best",
-        config_section="relaxation_and_target_periods",
-        is_peak_price=False,
-        default_value=2,  # DEFAULT_MIN_PERIODS_BEST
-    ),
-    TibberPricesNumberEntityDescription(
-        key="best_price_relaxation_attempts_override",
-        translation_key="best_price_relaxation_attempts_override",
-        name="Best Price: Relaxation Attempts",
-        icon="mdi:arrow-down-bold-circle",
-        entity_category=EntityCategory.CONFIG,
-        entity_registry_enabled_default=False,
-        native_min_value=1,
-        native_max_value=12,
-        native_step=1,
-        mode=NumberMode.SLIDER,
-        config_key="relaxation_attempts_best",
-        config_section="relaxation_and_target_periods",
-        is_peak_price=False,
-        default_value=11,  # DEFAULT_RELAXATION_ATTEMPTS_BEST
-    ),
-    TibberPricesNumberEntityDescription(
-        key="best_price_gap_count_override",
-        translation_key="best_price_gap_count_override",
-        name="Best Price: Gap Tolerance",
-        icon="mdi:arrow-down-bold-circle",
-        entity_category=EntityCategory.CONFIG,
-        entity_registry_enabled_default=False,
-        native_min_value=0,
-        native_max_value=8,
-        native_step=1,
-        mode=NumberMode.SLIDER,
-        config_key="best_price_max_level_gap_count",
-        config_section="period_settings",
-        is_peak_price=False,
-        default_value=1,  # DEFAULT_BEST_PRICE_MAX_LEVEL_GAP_COUNT
-    ),
-)
-
-# ============================================================================
-# PEAK PRICE PERIOD CONFIGURATION OVERRIDES
-# ============================================================================
-
-PEAK_PRICE_NUMBER_ENTITIES = (
-    TibberPricesNumberEntityDescription(
-        key="peak_price_flex_override",
-        translation_key="peak_price_flex_override",
-        name="Peak Price: Flexibility",
-        icon="mdi:arrow-up-bold-circle",
-        entity_category=EntityCategory.CONFIG,
-        entity_registry_enabled_default=False,
-        native_min_value=-50,
-        native_max_value=0,
-        native_step=1,
-        native_unit_of_measurement=PERCENTAGE,
-        mode=NumberMode.SLIDER,
-        config_key="peak_price_flex",
-        config_section="flexibility_settings",
-        is_peak_price=True,
-        default_value=-20,  # DEFAULT_PEAK_PRICE_FLEX
-    ),
-    TibberPricesNumberEntityDescription(
-        key="peak_price_min_distance_override",
-        translation_key="peak_price_min_distance_override",
-        name="Peak Price: Minimum Distance",
-        icon="mdi:arrow-up-bold-circle",
-        entity_category=EntityCategory.CONFIG,
-        entity_registry_enabled_default=False,
-        native_min_value=0,
-        native_max_value=50,
-        native_step=1,
-        native_unit_of_measurement=PERCENTAGE,
-        mode=NumberMode.SLIDER,
-        config_key="peak_price_min_distance_from_avg",
-        config_section="flexibility_settings",
-        is_peak_price=True,
-        default_value=5,  # DEFAULT_PEAK_PRICE_MIN_DISTANCE_FROM_AVG
-    ),
-    TibberPricesNumberEntityDescription(
-        key="peak_price_min_period_length_override",
-        translation_key="peak_price_min_period_length_override",
-        name="Peak Price: Minimum Period Length",
-        icon="mdi:arrow-up-bold-circle",
-        entity_category=EntityCategory.CONFIG,
-        entity_registry_enabled_default=False,
-        native_min_value=15,
-        native_max_value=180,
-        native_step=15,
-        native_unit_of_measurement="min",
-        mode=NumberMode.SLIDER,
-        config_key="peak_price_min_period_length",
-        config_section="period_settings",
-        is_peak_price=True,
-        default_value=30,  # DEFAULT_PEAK_PRICE_MIN_PERIOD_LENGTH
-    ),
-    TibberPricesNumberEntityDescription(
-        key="peak_price_min_periods_override",
-        translation_key="peak_price_min_periods_override",
-        name="Peak Price: Minimum Periods",
-        icon="mdi:arrow-up-bold-circle",
-        entity_category=EntityCategory.CONFIG,
-        entity_registry_enabled_default=False,
-        native_min_value=1,
-        native_max_value=10,
-        native_step=1,
-        mode=NumberMode.SLIDER,
-        config_key="min_periods_peak",
-        config_section="relaxation_and_target_periods",
-        is_peak_price=True,
-        default_value=2,  # DEFAULT_MIN_PERIODS_PEAK
-    ),
-    TibberPricesNumberEntityDescription(
-        key="peak_price_relaxation_attempts_override",
-        translation_key="peak_price_relaxation_attempts_override",
-        name="Peak Price: Relaxation Attempts",
-        icon="mdi:arrow-up-bold-circle",
-        entity_category=EntityCategory.CONFIG,
-        entity_registry_enabled_default=False,
-        native_min_value=1,
-        native_max_value=12,
-        native_step=1,
-        mode=NumberMode.SLIDER,
-        config_key="relaxation_attempts_peak",
-        config_section="relaxation_and_target_periods",
-        is_peak_price=True,
-        default_value=11,  # DEFAULT_RELAXATION_ATTEMPTS_PEAK
-    ),
-    TibberPricesNumberEntityDescription(
-        key="peak_price_gap_count_override",
-        translation_key="peak_price_gap_count_override",
-        name="Peak Price: Gap Tolerance",
-        icon="mdi:arrow-up-bold-circle",
-        entity_category=EntityCategory.CONFIG,
-        entity_registry_enabled_default=False,
-        native_min_value=0,
-        native_max_value=8,
-        native_step=1,
-        mode=NumberMode.SLIDER,
-        config_key="peak_price_max_level_gap_count",
-        config_section="period_settings",
-        is_peak_price=True,
-        default_value=1,  # DEFAULT_PEAK_PRICE_MAX_LEVEL_GAP_COUNT
-    ),
-)
-
-# All number entity descriptions combined
-NUMBER_ENTITY_DESCRIPTIONS = BEST_PRICE_NUMBER_ENTITIES + PEAK_PRICE_NUMBER_ENTITIES

custom_components/tibber_prices/sensor/__init__.py

@@ -17,11 +17,6 @@ from __future__ import annotations
 
 from typing import TYPE_CHECKING
 
-from custom_components.tibber_prices.const import (
-    CONF_CURRENCY_DISPLAY_MODE,
-    DISPLAY_MODE_BASE,
-)
-
 from .core import TibberPricesSensor
 from .definitions import ENTITY_DESCRIPTIONS
 
@@ -39,22 +34,10 @@ async def async_setup_entry(
     """Set up Tibber Prices sensor based on a config entry."""
     coordinator = entry.runtime_data.coordinator
 
-    # Get display mode from config
-    display_mode = entry.options.get(CONF_CURRENCY_DISPLAY_MODE, DISPLAY_MODE_BASE)
-
-    # Filter entity descriptions based on display mode
-    # Skip current_interval_price_base if user configured major display
-    # (regular current_interval_price already shows major units)
-    entities_to_create = [
-        entity_description
-        for entity_description in ENTITY_DESCRIPTIONS
-        if not (entity_description.key == "current_interval_price_base" and display_mode == DISPLAY_MODE_BASE)
-    ]
-
     async_add_entities(
         TibberPricesSensor(
             coordinator=coordinator,
             entity_description=entity_description,
         )
-        for entity_description in entities_to_create
+        for entity_description in ENTITY_DESCRIPTIONS
     )

custom_components/tibber_prices/sensor/attributes/__init__.py

@@ -14,22 +14,6 @@ from custom_components.tibber_prices.entity_utils import (
     add_description_attributes,
     add_icon_color_attribute,
 )
-from custom_components.tibber_prices.sensor.types import (
-    DailyStatPriceAttributes,
-    DailyStatRatingAttributes,
-    FutureAttributes,
-    IntervalLevelAttributes,
-    # Import all types for re-export
-    IntervalPriceAttributes,
-    IntervalRatingAttributes,
-    LifecycleAttributes,
-    MetadataAttributes,
-    SensorAttributes,
-    TimingAttributes,
-    TrendAttributes,
-    VolatilityAttributes,
-    Window24hAttributes,
-)
 
 if TYPE_CHECKING:
     from custom_components.tibber_prices.coordinator.core import (
@@ -50,20 +34,6 @@ from .volatility import add_volatility_type_attributes, get_prices_for_volatilit
 from .window_24h import add_average_price_attributes
 
 __all__ = [
-    "DailyStatPriceAttributes",
-    "DailyStatRatingAttributes",
-    "FutureAttributes",
-    "IntervalLevelAttributes",
-    "IntervalPriceAttributes",
-    "IntervalRatingAttributes",
-    "LifecycleAttributes",
-    "MetadataAttributes",
-    # Type exports
-    "SensorAttributes",
-    "TimingAttributes",
-    "TrendAttributes",
-    "VolatilityAttributes",
-    "Window24hAttributes",
     "add_volatility_type_attributes",
     "build_extra_state_attributes",
     "build_sensor_attributes",
@@ -77,9 +47,7 @@ def build_sensor_attributes(
     coordinator: TibberPricesDataUpdateCoordinator,
     native_value: Any,
     cached_data: dict,
-    *,
+) -> dict | None:
-    config_entry: TibberPricesConfigEntry,
-) -> dict[str, Any] | None:
     """
     Build attributes for a sensor based on its key.
 
@@ -90,7 +58,6 @@ def build_sensor_attributes(
         coordinator: The data update coordinator
         native_value: The current native value of the sensor
         cached_data: Dictionary containing cached sensor data
-        config_entry: Config entry for user preferences
 
     Returns:
         Dictionary of attributes or None if no attributes should be added
@@ -130,7 +97,6 @@ def build_sensor_attributes(
                 native_value=native_value,
                 cached_data=cached_data,
                 time=time,
-                config_entry=config_entry,
             )
         elif key in [
             "trailing_price_average",
@@ -140,23 +106,9 @@ def build_sensor_attributes(
             "leading_price_min",
             "leading_price_max",
         ]:
-            add_average_price_attributes(
+            add_average_price_attributes(attributes=attributes, key=key, coordinator=coordinator, time=time)
-                attributes=attributes,
-                key=key,
-                coordinator=coordinator,
-                time=time,
-                cached_data=cached_data,
-                config_entry=config_entry,
-            )
         elif key.startswith("next_avg_"):
-            add_next_avg_attributes(
+            add_next_avg_attributes(attributes=attributes, key=key, coordinator=coordinator, time=time)
-                attributes=attributes,
-                key=key,
-                coordinator=coordinator,
-                time=time,
-                cached_data=cached_data,
-                config_entry=config_entry,
-            )
         elif any(
             pattern in key
             for pattern in [
@@ -178,7 +130,6 @@ def build_sensor_attributes(
                 key=key,
                 cached_data=cached_data,
                 time=time,
-                config_entry=config_entry,
             )
         elif key == "data_lifecycle_status":
             # Lifecycle sensor uses dedicated builder with calculator
@@ -224,7 +175,7 @@ def build_extra_state_attributes(  # noqa: PLR0913
     *,
     config_entry: TibberPricesConfigEntry,
     coordinator_data: dict,
-    sensor_attrs: dict[str, Any] | None = None,
+    sensor_attrs: dict | None = None,
     time: TibberPricesTimeService,
 ) -> dict[str, Any] | None:
     """

custom_components/tibber_prices/sensor/attributes/daily_stat.py

@@ -5,18 +5,12 @@ from __future__ import annotations
 from typing import TYPE_CHECKING
 
 from custom_components.tibber_prices.const import PRICE_RATING_MAPPING
-from custom_components.tibber_prices.coordinator.helpers import (
-    get_intervals_for_day_offsets,
-)
 from homeassistant.const import PERCENTAGE
 
 if TYPE_CHECKING:
     from datetime import datetime
 
     from custom_components.tibber_prices.coordinator.time_service import TibberPricesTimeService
-    from custom_components.tibber_prices.data import TibberPricesConfigEntry
-
-from .helpers import add_alternate_average_attribute
 
 
 def _get_day_midnight_timestamp(key: str, *, time: TibberPricesTimeService) -> datetime:
@@ -52,32 +46,20 @@ def _get_day_key_from_sensor_key(key: str) -> str:
     return "today"
 
 
-def _add_fallback_timestamp(
+def _add_fallback_timestamp(attributes: dict, key: str, price_info: dict) -> None:
-    attributes: dict,
-    key: str,
-    price_info: dict,
-) -> None:
     """
     Add fallback timestamp to attributes based on the day in the sensor key.
 
     Args:
         attributes: Dictionary to add timestamp to
         key: The sensor entity key
-        price_info: Price info dictionary from coordinator data (flat structure)
+        price_info: Price info dictionary from coordinator data
 
     """
     day_key = _get_day_key_from_sensor_key(key)
-
+    day_data = price_info.get(day_key, [])
-    # Use helper to get intervals for this day
+    if day_data:
-    # Build minimal coordinator_data structure for helper
+        attributes["timestamp"] = day_data[0].get("startsAt")
-    coordinator_data = {"priceInfo": price_info}
-    # Map day key to offset: yesterday=-1, today=0, tomorrow=1
-    day_offset = {"yesterday": -1, "today": 0, "tomorrow": 1}[day_key]
-    day_intervals = get_intervals_for_day_offsets(coordinator_data, [day_offset])
-
-    # Use first interval's timestamp if available
-    if day_intervals:
-        attributes["timestamp"] = day_intervals[0].get("startsAt")
 
 
 def add_statistics_attributes(
@@ -86,7 +68,6 @@ def add_statistics_attributes(
     cached_data: dict,
     *,
     time: TibberPricesTimeService,
-    config_entry: TibberPricesConfigEntry,
 ) -> None:
     """
     Add attributes for statistics and rating sensors.
@@ -96,7 +77,6 @@ def add_statistics_attributes(
         key: The sensor entity key
         cached_data: Dictionary containing cached sensor data
         time: TibberPricesTimeService instance (required)
-        config_entry: Config entry for user preferences
 
     """
     # Data timestamp sensor - shows API fetch time
@@ -131,17 +111,10 @@ def add_statistics_attributes(
                 attributes["timestamp"] = extreme_starts_at
         return
 
-    # Daily average sensors - show midnight to indicate whole day + add alternate value
+    # Daily average sensors - show midnight to indicate whole day
     daily_avg_sensors = {"average_price_today", "average_price_tomorrow"}
     if key in daily_avg_sensors:
         attributes["timestamp"] = _get_day_midnight_timestamp(key, time=time)
-        # Add alternate average attribute
-        add_alternate_average_attribute(
-            attributes,
-            cached_data,
-            key,  # base_key = key itself ("average_price_today" or "average_price_tomorrow")
-            config_entry=config_entry,
-        )
         return
 
     # Daily aggregated level/rating sensors - show midnight to indicate whole day

custom_components/tibber_prices/sensor/attributes/future.py

@@ -4,30 +4,22 @@ from __future__ import annotations
 
 from typing import TYPE_CHECKING
 
-from custom_components.tibber_prices.const import get_display_unit_factor
-from custom_components.tibber_prices.coordinator.helpers import get_intervals_for_day_offsets
-
 if TYPE_CHECKING:
     from custom_components.tibber_prices.coordinator.core import (
         TibberPricesDataUpdateCoordinator,
     )
     from custom_components.tibber_prices.coordinator.time_service import TibberPricesTimeService
-    from custom_components.tibber_prices.data import TibberPricesConfigEntry
-
-from .helpers import add_alternate_average_attribute
 
 # Constants
 MAX_FORECAST_INTERVALS = 8  # Show up to 8 future intervals (2 hours with 15-min intervals)
 
 
-def add_next_avg_attributes(  # noqa: PLR0913
+def add_next_avg_attributes(
     attributes: dict,
     key: str,
     coordinator: TibberPricesDataUpdateCoordinator,
     *,
     time: TibberPricesTimeService,
-    cached_data: dict | None = None,
-    config_entry: TibberPricesConfigEntry | None = None,
 ) -> None:
     """
     Add attributes for next N hours average price sensors.
@@ -37,8 +29,6 @@ def add_next_avg_attributes(  # noqa: PLR0913
         key: The sensor entity key
         coordinator: The data update coordinator
         time: TibberPricesTimeService instance (required)
-        cached_data: Optional cached data dictionary for median values
-        config_entry: Optional config entry for user preferences
 
     """
     # Extract hours from sensor key (e.g., "next_avg_3h" -> 3)
@@ -50,11 +40,15 @@ def add_next_avg_attributes(  # noqa: PLR0913
     # Use TimeService to get the N-hour window starting from next interval
     next_interval_start, window_end = time.get_next_n_hours_window(hours)
 
-    # Get all intervals (yesterday, today, tomorrow) via helper
+    # Get all price intervals
-    all_prices = get_intervals_for_day_offsets(coordinator.data, [-1, 0, 1])
+    price_info = coordinator.data.get("priceInfo", {})
+    today_prices = price_info.get("today", [])
+    tomorrow_prices = price_info.get("tomorrow", [])
+    all_prices = today_prices + tomorrow_prices
 
     if not all_prices:
         return
+
     # Find all intervals in the window
     intervals_in_window = []
     for price_data in all_prices:
@@ -70,42 +64,33 @@ def add_next_avg_attributes(  # noqa: PLR0913
         attributes["interval_count"] = len(intervals_in_window)
         attributes["hours"] = hours
 
-        # Add alternate average attribute if available in cached_data
-        if cached_data and config_entry:
-            base_key = f"next_avg_{hours}h"
-            add_alternate_average_attribute(
-                attributes,
-                cached_data,
-                base_key,
-                config_entry=config_entry,
-            )
-
 
 def get_future_prices(
     coordinator: TibberPricesDataUpdateCoordinator,
     max_intervals: int | None = None,
     *,
     time: TibberPricesTimeService,
-    config_entry: TibberPricesConfigEntry,
 ) -> list[dict] | None:
     """
     Get future price data for multiple upcoming intervals.
 
     Args:
-        coordinator: The data update coordinator.
+        coordinator: The data update coordinator
-        max_intervals: Maximum number of future intervals to return.
+        max_intervals: Maximum number of future intervals to return
-        time: TibberPricesTimeService instance (required).
+        time: TibberPricesTimeService instance (required)
-        config_entry: Config entry to get display unit configuration.
 
     Returns:
-        List of upcoming price intervals with timestamps and prices.
+        List of upcoming price intervals with timestamps and prices
 
     """
     if not coordinator.data:
         return None
 
-    # Get all intervals (yesterday, today, tomorrow) via helper
+    price_info = coordinator.data.get("priceInfo", {})
-    all_prices = get_intervals_for_day_offsets(coordinator.data, [-1, 0, 1])
+
+    today_prices = price_info.get("today", [])
+    tomorrow_prices = price_info.get("tomorrow", [])
+    all_prices = today_prices + tomorrow_prices
 
     if not all_prices:
         return None
@@ -116,46 +101,28 @@ def get_future_prices(
     # Track the maximum intervals to return
     intervals_to_return = MAX_FORECAST_INTERVALS if max_intervals is None else max_intervals
 
-    # Get current date for day key determination
+    for day_key in ["today", "tomorrow"]:
-    now = time.now()
+        for price_data in price_info.get(day_key, []):
-    today_date = now.date()
+            starts_at = time.get_interval_time(price_data)
-    tomorrow_date = time.get_local_date(offset_days=1)
+            if starts_at is None:
+                continue
 
-    for price_data in all_prices:
+            interval_end = starts_at + time.get_interval_duration()
-        starts_at = time.get_interval_time(price_data)
-        if starts_at is None:
-            continue
 
-        interval_end = starts_at + time.get_interval_duration()
+            # Use TimeService to check if interval is in future
-
+            if time.is_in_future(starts_at):
-        # Use TimeService to check if interval is in future
+                future_prices.append(
-        if time.is_in_future(starts_at):
+                    {
-            # Determine which day this interval belongs to
+                        "interval_start": starts_at,
-            interval_date = starts_at.date()
+                        "interval_end": interval_end,
-            if interval_date == today_date:
+                        "price": float(price_data["total"]),
-                day_key = "today"
+                        "price_minor": round(float(price_data["total"]) * 100, 2),
-            elif interval_date == tomorrow_date:
+                        "level": price_data.get("level", "NORMAL"),
-                day_key = "tomorrow"
+                        "rating": price_data.get("difference", None),
-            else:
+                        "rating_level": price_data.get("rating_level"),
-                day_key = "unknown"
+                        "day": day_key,
-
+                    }
-            # Convert to display currency unit based on configuration
+                )
-            price_major = float(price_data["total"])
-            factor = get_display_unit_factor(config_entry)
-            price_display = round(price_major * factor, 2)
-
-            future_prices.append(
-                {
-                    "interval_start": starts_at,
-                    "interval_end": interval_end,
-                    "price": price_major,
-                    "price_minor": price_display,
-                    "level": price_data.get("level", "NORMAL"),
-                    "rating": price_data.get("difference", None),
-                    "rating_level": price_data.get("rating_level"),
-                    "day": day_key,
-                }
-            )
 
     # Sort by start time
     future_prices.sort(key=lambda x: x["interval_start"])

custom_components/tibber_prices/sensor/attributes/helpers.py

@@ -1,41 +0,0 @@
-"""Helper functions for sensor attributes."""
-
-from __future__ import annotations
-
-from typing import TYPE_CHECKING
-
-if TYPE_CHECKING:
-    from custom_components.tibber_prices.data import TibberPricesConfigEntry
-
-
-def add_alternate_average_attribute(
-    attributes: dict,
-    cached_data: dict,
-    base_key: str,
-    *,
-    config_entry: TibberPricesConfigEntry,  # noqa: ARG001
-) -> None:
-    """
-    Add both average values (mean and median) as attributes.
-
-    This ensures automations work consistently regardless of which value
-    is displayed in the state. Both values are always available as attributes.
-
-    Note: To avoid duplicate recording, the value used as state should be
-    excluded from recorder via dynamic _unrecorded_attributes in sensor core.
-
-    Args:
-        attributes: Dictionary to add attribute to
-        cached_data: Cached calculation data containing mean/median values
-        base_key: Base key for cached values (e.g., "average_price_today", "rolling_hour_0")
-        config_entry: Config entry for user preferences (used to determine which value is in state)
-
-    """
-    # Always add both mean and median values as attributes
-    mean_value = cached_data.get(f"{base_key}_mean")
-    if mean_value is not None:
-        attributes["price_mean"] = mean_value
-
-    median_value = cached_data.get(f"{base_key}_median")
-    if median_value is not None:
-        attributes["price_median"] = median_value

custom_components/tibber_prices/sensor/attributes/interval.py

@@ -17,78 +17,10 @@ if TYPE_CHECKING:
         TibberPricesDataUpdateCoordinator,
     )
     from custom_components.tibber_prices.coordinator.time_service import TibberPricesTimeService
-    from custom_components.tibber_prices.data import TibberPricesConfigEntry
 
-from .helpers import add_alternate_average_attribute
 from .metadata import get_current_interval_data
 
 
-def _get_interval_data_for_attributes(
-    key: str,
-    coordinator: TibberPricesDataUpdateCoordinator,
-    attributes: dict,
-    *,
-    time: TibberPricesTimeService,
-) -> dict | None:
-    """
-    Get interval data and set timestamp based on sensor type.
-
-    Refactored to reduce branch complexity in main function.
-
-    Args:
-        key: The sensor entity key
-        coordinator: The data update coordinator
-        attributes: Attributes dict to update with timestamp if needed
-        time: TibberPricesTimeService instance
-
-    Returns:
-        Interval data if found, None otherwise
-
-    """
-    now = time.now()
-
-    # Current/next price sensors - override timestamp with interval's startsAt
-    next_sensors = ["next_interval_price", "next_interval_price_level", "next_interval_price_rating"]
-    prev_sensors = ["previous_interval_price", "previous_interval_price_level", "previous_interval_price_rating"]
-    next_hour = ["next_hour_average_price", "next_hour_price_level", "next_hour_price_rating"]
-    curr_interval = ["current_interval_price", "current_interval_price_base"]
-    curr_hour = ["current_hour_average_price", "current_hour_price_level", "current_hour_price_rating"]
-
-    if key in next_sensors:
-        target_time = time.get_next_interval_start()
-        interval_data = find_price_data_for_interval(coordinator.data, target_time, time=time)
-        if interval_data:
-            attributes["timestamp"] = interval_data["startsAt"]
-        return interval_data
-
-    if key in prev_sensors:
-        target_time = time.get_interval_offset_time(-1)
-        interval_data = find_price_data_for_interval(coordinator.data, target_time, time=time)
-        if interval_data:
-            attributes["timestamp"] = interval_data["startsAt"]
-        return interval_data
-
-    if key in next_hour:
-        target_time = now + timedelta(hours=1)
-        interval_data = find_price_data_for_interval(coordinator.data, target_time, time=time)
-        if interval_data:
-            attributes["timestamp"] = interval_data["startsAt"]
-        return interval_data
-
-    # Current interval sensors (both variants)
-    if key in curr_interval:
-        interval_data = get_current_interval_data(coordinator, time=time)
-        if interval_data and "startsAt" in interval_data:
-            attributes["timestamp"] = interval_data["startsAt"]
-        return interval_data
-
-    # Current hour sensors - keep default timestamp
-    if key in curr_hour:
-        return get_current_interval_data(coordinator, time=time)
-
-    return None
-
-
 def add_current_interval_price_attributes(  # noqa: PLR0913
     attributes: dict,
     key: str,
@@ -97,7 +29,6 @@ def add_current_interval_price_attributes(  # noqa: PLR0913
     cached_data: dict,
     *,
     time: TibberPricesTimeService,
-    config_entry: TibberPricesConfigEntry,
 ) -> None:
     """
     Add attributes for current interval price sensors.
@@ -109,19 +40,65 @@ def add_current_interval_price_attributes(  # noqa: PLR0913
         native_value: The current native value of the sensor
         cached_data: Dictionary containing cached sensor data
         time: TibberPricesTimeService instance (required)
-        config_entry: Config entry for user preferences
 
     """
-    # Get interval data and handle timestamp overrides
+    price_info = coordinator.data.get("priceInfo", {}) if coordinator.data else {}
-    interval_data = _get_interval_data_for_attributes(key, coordinator, attributes, time=time)
+    now = time.now()
+
+    # Determine which interval to use based on sensor type
+    next_interval_sensors = [
+        "next_interval_price",
+        "next_interval_price_level",
+        "next_interval_price_rating",
+    ]
+    previous_interval_sensors = [
+        "previous_interval_price",
+        "previous_interval_price_level",
+        "previous_interval_price_rating",
+    ]
+    next_hour_sensors = [
+        "next_hour_average_price",
+        "next_hour_price_level",
+        "next_hour_price_rating",
+    ]
+    current_hour_sensors = [
+        "current_hour_average_price",
+        "current_hour_price_level",
+        "current_hour_price_rating",
+    ]
+
+    # Set interval data based on sensor type
+    # For sensors showing data from OTHER intervals (next/previous), override timestamp with that interval's startsAt
+    # For current interval sensors, keep the default platform timestamp (calculation time)
+    interval_data = None
+    if key in next_interval_sensors:
+        target_time = time.get_next_interval_start()
+        interval_data = find_price_data_for_interval(price_info, target_time, time=time)
+        # Override timestamp with the NEXT interval's startsAt (when that interval starts)
+        if interval_data:
+            attributes["timestamp"] = interval_data["startsAt"]
+    elif key in previous_interval_sensors:
+        target_time = time.get_interval_offset_time(-1)
+        interval_data = find_price_data_for_interval(price_info, target_time, time=time)
+        # Override timestamp with the PREVIOUS interval's startsAt
+        if interval_data:
+            attributes["timestamp"] = interval_data["startsAt"]
+    elif key in next_hour_sensors:
+        target_time = now + timedelta(hours=1)
+        interval_data = find_price_data_for_interval(price_info, target_time, time=time)
+        # Override timestamp with the center of the next rolling hour window
+        if interval_data:
+            attributes["timestamp"] = interval_data["startsAt"]
+    elif key in current_hour_sensors:
+        current_interval_data = get_current_interval_data(coordinator, time=time)
+        # Keep default timestamp (when calculation was made) for current hour sensors
+    else:
+        current_interval_data = get_current_interval_data(coordinator, time=time)
+        interval_data = current_interval_data  # Use current_interval_data as interval_data for current_interval_price
+        # Keep default timestamp (current calculation time) for current interval sensors
 
     # Add icon_color for price sensors (based on their price level)
-    if key in [
+    if key in ["current_interval_price", "next_interval_price", "previous_interval_price"]:
-        "current_interval_price",
-        "current_interval_price_base",
-        "next_interval_price",
-        "previous_interval_price",
-    ]:
         # For interval-based price sensors, get level from interval_data
         if interval_data and "level" in interval_data:
             level = interval_data["level"]
@@ -132,15 +109,6 @@ def add_current_interval_price_attributes(  # noqa: PLR0913
         if level:
             add_icon_color_attribute(attributes, key="price_level", state_value=level)
 
-        # Add alternate average attribute for rolling hour average price sensors
-        base_key = "rolling_hour_0" if key == "current_hour_average_price" else "rolling_hour_1"
-        add_alternate_average_attribute(
-            attributes,
-            cached_data,
-            base_key,
-            config_entry=config_entry,
-        )
-
     # Add price level attributes for all level sensors
     add_level_attributes_for_sensor(
         attributes=attributes,

custom_components/tibber_prices/sensor/attributes/lifecycle.py

@@ -1,24 +1,4 @@
-"""
+"""Attribute builders for lifecycle diagnostic sensor."""
-Attribute builders for lifecycle diagnostic sensor.
-
-This sensor uses event-based updates with state-change filtering to minimize
-recorder entries. Only attributes that are relevant to the lifecycle STATE
-are included here - attributes that change independently of state belong
-in a separate sensor or diagnostics.
-
-Included attributes (update only on state change):
-- tomorrow_available: Whether tomorrow's price data is available
-- next_api_poll: When the next API poll will occur (builds user trust)
-- updates_today: Number of API calls made today
-- last_turnover: When the last midnight turnover occurred
-- last_error: Details of the last error (if any)
-
-Pool statistics (sensor_intervals_count, cache_fill_percent, etc.) are
-intentionally NOT included here because they change independently of
-the lifecycle state. With state-change filtering, these would become
-stale. Pool statistics are available via diagnostics or could be
-exposed as a separate sensor if needed.
-"""
 
 from __future__ import annotations
 
@@ -33,6 +13,11 @@ if TYPE_CHECKING:
     )
 
 
+# Constants for cache age formatting
+MINUTES_PER_HOUR = 60
+MINUTES_PER_DAY = 1440  # 24 * 60
+
+
 def build_lifecycle_attributes(
     coordinator: TibberPricesDataUpdateCoordinator,
     lifecycle_calculator: TibberPricesLifecycleCalculator,
@@ -40,11 +25,7 @@ def build_lifecycle_attributes(
     """
     Build attributes for data_lifecycle_status sensor.
 
-    Event-based updates with state-change filtering - attributes only update
+    Shows comprehensive cache status, data availability, and update timing.
-    when the lifecycle STATE changes (fresh→cached, cached→turnover_pending, etc.).
-
-    Only includes attributes that are directly relevant to the lifecycle state.
-    Pool statistics are intentionally excluded to avoid stale data.
 
     Returns:
         Dict with lifecycle attributes
@@ -52,31 +33,60 @@ def build_lifecycle_attributes(
     """
     attributes: dict[str, Any] = {}
 
-    # === Tomorrow Data Status ===
+    # Cache Status (formatted for readability)
-    # Critical for understanding lifecycle state transitions
+    cache_age = lifecycle_calculator.get_cache_age_minutes()
-    attributes["tomorrow_available"] = lifecycle_calculator.has_tomorrow_data()
+    if cache_age is not None:
+        # Format cache age with units for better readability
+        if cache_age < MINUTES_PER_HOUR:
+            attributes["cache_age"] = f"{cache_age} min"
+        elif cache_age < MINUTES_PER_DAY:  # Less than 24 hours
+            hours = cache_age // MINUTES_PER_HOUR
+            minutes = cache_age % MINUTES_PER_HOUR
+            attributes["cache_age"] = f"{hours}h {minutes}min" if minutes > 0 else f"{hours}h"
+        else:  # 24+ hours
+            days = cache_age // MINUTES_PER_DAY
+            hours = (cache_age % MINUTES_PER_DAY) // MINUTES_PER_HOUR
+            attributes["cache_age"] = f"{days}d {hours}h" if hours > 0 else f"{days}d"
 
-    # === Next API Poll Time ===
+        # Keep raw value for automations
-    # Builds user trust: shows when the integration will check for tomorrow data
+        attributes["cache_age_minutes"] = cache_age
-    # - Before 13:00: Shows today 13:00 (when tomorrow-search begins)
+
-    # - After 13:00 without tomorrow data: Shows next Timer #1 execution (active polling)
+    cache_validity = lifecycle_calculator.get_cache_validity_status()
-    # - After 13:00 with tomorrow data: Shows tomorrow 13:00 (predictive)
+    attributes["cache_validity"] = cache_validity
+
+    if coordinator._last_price_update:  # noqa: SLF001 - Internal state access for diagnostic display
+        attributes["last_api_fetch"] = coordinator._last_price_update.isoformat()  # noqa: SLF001
+        attributes["last_cache_update"] = coordinator._last_price_update.isoformat()  # noqa: SLF001
+
+    # Data Availability & Completeness
+    data_completeness = lifecycle_calculator.get_data_completeness_status()
+    attributes["data_completeness"] = data_completeness
+
+    attributes["yesterday_available"] = lifecycle_calculator.is_data_available("yesterday")
+    attributes["today_available"] = lifecycle_calculator.is_data_available("today")
+    attributes["tomorrow_available"] = lifecycle_calculator.is_data_available("tomorrow")
+    attributes["tomorrow_expected_after"] = "13:00"
+
+    # Next Actions (only show if meaningful)
     next_poll = lifecycle_calculator.get_next_api_poll_time()
-    if next_poll:
+    if next_poll:  # None means data is complete, no more polls needed
         attributes["next_api_poll"] = next_poll.isoformat()
 
-    # === Update Statistics ===
+    next_tomorrow_check = lifecycle_calculator.get_next_tomorrow_check_time()
-    # Shows API activity - resets at midnight with turnover
+    if next_tomorrow_check:
+        attributes["next_tomorrow_check"] = next_tomorrow_check.isoformat()
+
+    next_midnight = lifecycle_calculator.get_next_midnight_turnover_time()
+    attributes["next_midnight_turnover"] = next_midnight.isoformat()
+
+    # Update Statistics
     api_calls = lifecycle_calculator.get_api_calls_today()
     attributes["updates_today"] = api_calls
 
-    # === Midnight Turnover Info ===
+    if coordinator._last_actual_turnover:  # noqa: SLF001 - Internal state access for diagnostic display
-    # When was the last successful data rotation
+        attributes["last_turnover"] = coordinator._last_actual_turnover.isoformat()  # noqa: SLF001
-    if coordinator._midnight_handler.last_turnover_time:  # noqa: SLF001
-        attributes["last_turnover"] = coordinator._midnight_handler.last_turnover_time.isoformat()  # noqa: SLF001
 
-    # === Error Status ===
+    # Last Error (if any)
-    # Present only when there's an active error
     if coordinator.last_exception:
         attributes["last_error"] = str(coordinator.last_exception)
 

custom_components/tibber_prices/sensor/attributes/metadata.py

@@ -32,6 +32,7 @@ def get_current_interval_data(
     if not coordinator.data:
         return None
 
+    price_info = coordinator.data.get("priceInfo", {})
     now = time.now()
 
-    return find_price_data_for_interval(coordinator.data, now, time=time)
+    return find_price_data_for_interval(price_info, now, time=time)

custom_components/tibber_prices/sensor/attributes/timing.py

@@ -13,17 +13,6 @@ if TYPE_CHECKING:
 TIMER_30_SEC_BOUNDARY = 30
 
 
-def _hours_to_minutes(state_value: Any) -> int | None:
-    """Convert hour-based state back to rounded minutes for attributes."""
-    if state_value is None:
-        return None
-
-    try:
-        return round(float(state_value) * 60)
-    except (TypeError, ValueError):
-        return None
-
-
 def _is_timing_or_volatility_sensor(key: str) -> bool:
     """Check if sensor is a timing or volatility sensor."""
     return key.endswith("_volatility") or (
@@ -80,16 +69,5 @@ def add_period_timing_attributes(
 
     attributes["timestamp"] = timestamp
 
-    # Add minute-precision attributes for hour-based states to keep automation-friendly values
-    minute_value = _hours_to_minutes(state_value)
-
-    if minute_value is not None:
-        if key.endswith("period_duration"):
-            attributes["period_duration_minutes"] = minute_value
-        elif key.endswith("remaining_minutes"):
-            attributes["remaining_minutes"] = minute_value
-        elif key.endswith("next_in_minutes"):
-            attributes["next_in_minutes"] = minute_value
-
     # Add icon_color for dynamic styling
     add_icon_color_attribute(attributes, key=key, state_value=state_value)

custom_components/tibber_prices/sensor/attributes/volatility.py

@@ -5,7 +5,6 @@ from __future__ import annotations
 from datetime import timedelta
 from typing import TYPE_CHECKING
 
-from custom_components.tibber_prices.coordinator.helpers import get_intervals_for_day_offsets
 from custom_components.tibber_prices.utils.price import calculate_volatility_level
 
 if TYPE_CHECKING:
@@ -33,7 +32,7 @@ def add_volatility_attributes(
 
 def get_prices_for_volatility(
     volatility_type: str,
-    coordinator_data: dict,
+    price_info: dict,
     *,
     time: TibberPricesTimeService,
 ) -> list[float]:
@@ -42,33 +41,18 @@ def get_prices_for_volatility(
 
     Args:
         volatility_type: One of "today", "tomorrow", "next_24h", "today_tomorrow"
-        coordinator_data: Coordinator data dict
+        price_info: Price information dictionary from coordinator data
         time: TibberPricesTimeService instance (required)
 
     Returns:
         List of prices to analyze
 
     """
-    # Get all intervals (yesterday, today, tomorrow) via helper
-    all_intervals = get_intervals_for_day_offsets(coordinator_data, [-1, 0, 1])
-
     if volatility_type == "today":
-        # Filter for today's intervals
+        return [float(p["total"]) for p in price_info.get("today", []) if "total" in p]
-        today_date = time.now().date()
-        return [
-            float(p["total"])
-            for p in all_intervals
-            if "total" in p and p.get("startsAt") and p["startsAt"].date() == today_date
-        ]
 
     if volatility_type == "tomorrow":
-        # Filter for tomorrow's intervals
+        return [float(p["total"]) for p in price_info.get("tomorrow", []) if "total" in p]
-        tomorrow_date = (time.now() + timedelta(days=1)).date()
-        return [
-            float(p["total"])
-            for p in all_intervals
-            if "total" in p and p.get("startsAt") and p["startsAt"].date() == tomorrow_date
-        ]
 
     if volatility_type == "next_24h":
         # Rolling 24h from now
@@ -76,24 +60,23 @@ def get_prices_for_volatility(
         end_time = now + timedelta(hours=24)
         prices = []
 
-        for price_data in all_intervals:
+        for day_key in ["today", "tomorrow"]:
-            starts_at = price_data.get("startsAt")  # Already datetime in local timezone
+            for price_data in price_info.get(day_key, []):
-            if starts_at is None:
+                starts_at = price_data.get("startsAt")  # Already datetime in local timezone
-                continue
+                if starts_at is None:
+                    continue
 
-            if time.is_in_future(starts_at) and starts_at < end_time and "total" in price_data:
+                if time.is_in_future(starts_at) and starts_at < end_time and "total" in price_data:
-                prices.append(float(price_data["total"]))
+                    prices.append(float(price_data["total"]))
         return prices
 
     if volatility_type == "today_tomorrow":
         # Combined today + tomorrow
-        today_date = time.now().date()
-        tomorrow_date = (time.now() + timedelta(days=1)).date()
         prices = []
-        for price_data in all_intervals:
+        for day_key in ["today", "tomorrow"]:
-            starts_at = price_data.get("startsAt")
+            for price_data in price_info.get(day_key, []):
-            if starts_at and starts_at.date() in [today_date, tomorrow_date] and "total" in price_data:
+                if "total" in price_data:
-                prices.append(float(price_data["total"]))
+                    prices.append(float(price_data["total"]))
         return prices
 
     return []
@@ -102,7 +85,7 @@ def get_prices_for_volatility(
 def add_volatility_type_attributes(
     volatility_attributes: dict,
     volatility_type: str,
-    coordinator_data: dict,
+    price_info: dict,
     thresholds: dict,
     *,
     time: TibberPricesTimeService,
@@ -113,51 +96,41 @@ def add_volatility_type_attributes(
     Args:
         volatility_attributes: Dictionary to add type-specific attributes to
         volatility_type: Type of volatility calculation
-        coordinator_data: Coordinator data dict
+        price_info: Price information dictionary from coordinator data
         thresholds: Volatility thresholds configuration
         time: TibberPricesTimeService instance (required)
 
     """
-    # Get all intervals (yesterday, today, tomorrow) via helper
-    all_intervals = get_intervals_for_day_offsets(coordinator_data, [-1, 0, 1])
-    now = time.now()
-    today_date = now.date()
-    tomorrow_date = (now + timedelta(days=1)).date()
-
     # Add timestamp for calendar day volatility sensors (midnight of the day)
     if volatility_type == "today":
-        today_data = [p for p in all_intervals if p.get("startsAt") and p["startsAt"].date() == today_date]
+        today_data = price_info.get("today", [])
         if today_data:
             volatility_attributes["timestamp"] = today_data[0].get("startsAt")
     elif volatility_type == "tomorrow":
-        tomorrow_data = [p for p in all_intervals if p.get("startsAt") and p["startsAt"].date() == tomorrow_date]
+        tomorrow_data = price_info.get("tomorrow", [])
         if tomorrow_data:
             volatility_attributes["timestamp"] = tomorrow_data[0].get("startsAt")
     elif volatility_type == "today_tomorrow":
         # For combined today+tomorrow, use today's midnight
-        today_data = [p for p in all_intervals if p.get("startsAt") and p["startsAt"].date() == today_date]
+        today_data = price_info.get("today", [])
         if today_data:
             volatility_attributes["timestamp"] = today_data[0].get("startsAt")
 
         # Add breakdown for today vs tomorrow
-        today_prices = [
+        today_prices = [float(p["total"]) for p in price_info.get("today", []) if "total" in p]
-            float(p["total"])
+        tomorrow_prices = [float(p["total"]) for p in price_info.get("tomorrow", []) if "total" in p]
-            for p in all_intervals
-            if "total" in p and p.get("startsAt") and p["startsAt"].date() == today_date
-        ]
-        tomorrow_prices = [
-            float(p["total"])
-            for p in all_intervals
-            if "total" in p and p.get("startsAt") and p["startsAt"].date() == tomorrow_date
-        ]
 
         if today_prices:
             today_vol = calculate_volatility_level(today_prices, **thresholds)
+            today_spread = (max(today_prices) - min(today_prices)) * 100
+            volatility_attributes["today_spread"] = round(today_spread, 2)
             volatility_attributes["today_volatility"] = today_vol
             volatility_attributes["interval_count_today"] = len(today_prices)
 
         if tomorrow_prices:
             tomorrow_vol = calculate_volatility_level(tomorrow_prices, **thresholds)
+            tomorrow_spread = (max(tomorrow_prices) - min(tomorrow_prices)) * 100
+            volatility_attributes["tomorrow_spread"] = round(tomorrow_spread, 2)
             volatility_attributes["tomorrow_volatility"] = tomorrow_vol
             volatility_attributes["interval_count_tomorrow"] = len(tomorrow_prices)
     elif volatility_type == "next_24h":

custom_components/tibber_prices/sensor/attributes/window_24h.py

@@ -4,16 +4,11 @@ from __future__ import annotations
 
 from typing import TYPE_CHECKING
 
-from custom_components.tibber_prices.coordinator.helpers import get_intervals_for_day_offsets
-
 if TYPE_CHECKING:
     from custom_components.tibber_prices.coordinator.core import (
         TibberPricesDataUpdateCoordinator,
     )
     from custom_components.tibber_prices.coordinator.time_service import TibberPricesTimeService
-    from custom_components.tibber_prices.data import TibberPricesConfigEntry
-
-from .helpers import add_alternate_average_attribute
 
 
 def _update_extreme_interval(extreme_interval: dict | None, price_data: dict, key: str) -> dict:
@@ -43,14 +38,12 @@ def _update_extreme_interval(extreme_interval: dict | None, price_data: dict, ke
     return price_data if is_new_extreme else extreme_interval
 
 
-def add_average_price_attributes(  # noqa: PLR0913
+def add_average_price_attributes(
     attributes: dict,
     key: str,
     coordinator: TibberPricesDataUpdateCoordinator,
     *,
     time: TibberPricesTimeService,
-    cached_data: dict | None = None,
-    config_entry: TibberPricesConfigEntry | None = None,
 ) -> None:
     """
     Add attributes for trailing and leading average/min/max price sensors.
@@ -60,15 +53,17 @@ def add_average_price_attributes(  # noqa: PLR0913
         key: The sensor entity key
         coordinator: The data update coordinator
         time: TibberPricesTimeService instance (required)
-        cached_data: Optional cached data dictionary for median values
-        config_entry: Optional config entry for user preferences
 
     """
     # Determine if this is trailing or leading
     is_trailing = "trailing" in key
 
-    # Get all intervals (yesterday, today, tomorrow) via helper
+    # Get all price intervals
-    all_prices = get_intervals_for_day_offsets(coordinator.data, [-1, 0, 1])
+    price_info = coordinator.data.get("priceInfo", {})
+    yesterday_prices = price_info.get("yesterday", [])
+    today_prices = price_info.get("today", [])
+    tomorrow_prices = price_info.get("tomorrow", [])
+    all_prices = yesterday_prices + today_prices + tomorrow_prices
 
     if not all_prices:
         return
@@ -105,13 +100,3 @@ def add_average_price_attributes(  # noqa: PLR0913
             attributes["timestamp"] = intervals_in_window[0].get("startsAt")
 
         attributes["interval_count"] = len(intervals_in_window)
-
-        # Add alternate average attribute for average sensors if available in cached_data
-        if cached_data and config_entry and "average" in key:
-            base_key = key.replace("_average", "")
-            add_alternate_average_attribute(
-                attributes,
-                cached_data,
-                base_key,
-                config_entry=config_entry,
-            )

custom_components/tibber_prices/sensor/calculators/base.py

@@ -4,10 +4,6 @@ from __future__ import annotations
 
 from typing import TYPE_CHECKING, Any
 
-from custom_components.tibber_prices.coordinator.helpers import (
-    get_intervals_for_day_offsets,
-)
-
 if TYPE_CHECKING:
     from custom_components.tibber_prices.coordinator import (
         TibberPricesDataUpdateCoordinator,
@@ -60,9 +56,9 @@ class TibberPricesBaseCalculator:
         return self._coordinator.data
 
     @property
-    def price_info(self) -> list[dict[str, Any]]:
+    def price_info(self) -> dict[str, Any]:
-        """Get price info (intervals list) from coordinator data."""
+        """Get price information from coordinator data."""
-        return self.coordinator_data.get("priceInfo", [])
+        return self.coordinator_data.get("priceInfo", {})
 
     @property
     def user_data(self) -> dict[str, Any]:
@@ -71,116 +67,5 @@ class TibberPricesBaseCalculator:
 
     @property
     def currency(self) -> str:
-        """Get currency code from coordinator data."""
+        """Get currency code from price info."""
-        return self.coordinator_data.get("currency", "EUR")
+        return self.price_info.get("currency", "EUR")
-
-    # Smart data access methods with built-in None-safety
-
-    def get_intervals(self, day_offset: int) -> list[dict]:
-        """
-        Get price intervals for a specific day with None-safety.
-
-        Uses get_intervals_for_day_offsets() to abstract data structure access.
-
-        Args:
-            day_offset: Day offset (-1=yesterday, 0=today, 1=tomorrow).
-
-        Returns:
-            List of interval dictionaries, empty list if unavailable.
-
-        """
-        if not self.coordinator_data:
-            return []
-        return get_intervals_for_day_offsets(self.coordinator_data, [day_offset])
-
-    @property
-    def intervals_today(self) -> list[dict]:
-        """Get today's intervals with None-safety."""
-        return self.get_intervals(0)
-
-    @property
-    def intervals_tomorrow(self) -> list[dict]:
-        """Get tomorrow's intervals with None-safety."""
-        return self.get_intervals(1)
-
-    @property
-    def intervals_yesterday(self) -> list[dict]:
-        """Get yesterday's intervals with None-safety."""
-        return self.get_intervals(-1)
-
-    def find_interval_at_offset(self, offset: int) -> dict | None:
-        """
-        Find interval at given offset from current time with bounds checking.
-
-        Args:
-            offset: Offset from current interval (0=current, 1=next, -1=previous).
-
-        Returns:
-            Interval dictionary or None if out of bounds or unavailable.
-
-        """
-        if not self.coordinator_data:
-            return None
-
-        from custom_components.tibber_prices.utils.price import (  # noqa: PLC0415 - avoid circular import
-            find_price_data_for_interval,
-        )
-
-        time = self.coordinator.time
-        target_time = time.get_interval_offset_time(offset)
-        return find_price_data_for_interval(self.coordinator.data, target_time, time=time)
-
-    def safe_get_from_interval(
-        self,
-        interval: dict[str, Any],
-        key: str,
-        default: Any = None,
-    ) -> Any:
-        """
-        Safely get a value from an interval dictionary.
-
-        Args:
-            interval: Interval dictionary.
-            key: Key to retrieve.
-            default: Default value if key not found.
-
-        Returns:
-            Value from interval or default.
-
-        """
-        return interval.get(key, default) if interval else default
-
-    def has_data(self) -> bool:
-        """
-        Check if coordinator has any data available.
-
-        Returns:
-            True if data is available, False otherwise.
-
-        """
-        return bool(self.coordinator_data)
-
-    def has_price_info(self) -> bool:
-        """
-        Check if price info is available in coordinator data.
-
-        Returns:
-            True if price info exists, False otherwise.
-
-        """
-        return bool(self.price_info)
-
-    def get_day_intervals(self, day_offset: int) -> list[dict]:
-        """
-        Get intervals for a specific day from coordinator data.
-
-        This is an alias for get_intervals() with consistent naming.
-
-        Args:
-            day_offset: Day offset (-1=yesterday, 0=today, 1=tomorrow).
-
-        Returns:
-            List of interval dictionaries, empty list if unavailable.
-
-        """
-        return self.get_intervals(day_offset)

custom_components/tibber_prices/sensor/calculators/daily_stat.py

@@ -49,8 +49,8 @@ class TibberPricesDailyStatCalculator(TibberPricesBaseCalculator):
         self,
         *,
         day: str = "today",
-        stat_func: Callable[[list[float]], float] | Callable[[list[float]], tuple[float, float | None]],
+        stat_func: Callable[[list[float]], float],
-    ) -> float | tuple[float, float | None] | None:
+    ) -> float | None:
         """
         Unified method for daily statistics (min/max/avg within calendar day).
 
@@ -59,17 +59,17 @@ class TibberPricesDailyStatCalculator(TibberPricesBaseCalculator):
 
         Args:
             day: "today" or "tomorrow" - which calendar day to calculate for.
-            stat_func: Statistical function (min, max, or lambda for avg/median).
+            stat_func: Statistical function (min, max, or lambda for avg).
 
         Returns:
-            Price value in subunit currency units (cents/øre), or None if unavailable.
+            Price value in minor currency units (cents/øre), or None if unavailable.
-            For average functions: tuple of (avg, median) where median may be None.
-            For min/max functions: single float value.
 
         """
-        if not self.has_data():
+        if not self.coordinator_data:
             return None
 
+        price_info = self.price_info
+
         # Get local midnight boundaries based on the requested day using TimeService
         time = self.coordinator.time
         local_midnight, local_midnight_next_day = time.get_day_boundaries(day)
@@ -77,8 +77,8 @@ class TibberPricesDailyStatCalculator(TibberPricesBaseCalculator):
         # Collect all prices and their intervals from both today and tomorrow data
         # that fall within the target day's local date boundaries
         price_intervals = []
-        for day_offset in [0, 1]:  # today=0, tomorrow=1
+        for day_key in ["today", "tomorrow"]:
-            for price_data in self.get_intervals(day_offset):
+            for price_data in price_info.get(day_key, []):
                 starts_at = price_data.get("startsAt")  # Already datetime in local timezone
                 if not starts_at:
                     continue
@@ -99,25 +99,7 @@ class TibberPricesDailyStatCalculator(TibberPricesBaseCalculator):
 
         # Find the extreme value and store its interval for later use in attributes
         prices = [pi["price"] for pi in price_intervals]
-        result = stat_func(prices)
+        value = stat_func(prices)
-
-        # Check if result is a tuple (avg, median) from average functions
-        if isinstance(result, tuple):
-            value, median = result
-            # Store the interval (for avg, use first interval as reference)
-            if price_intervals:
-                self._last_extreme_interval = price_intervals[0]["interval"]
-            # Convert to display currency units based on config
-            avg_result = round(get_price_value(value, config_entry=self.coordinator.config_entry), 2)
-            median_result = (
-                round(get_price_value(median, config_entry=self.coordinator.config_entry), 2)
-                if median is not None
-                else None
-            )
-            return avg_result, median_result
-
-        # Single value result (min/max functions)
-        value = result
 
         # Store the interval with the extreme price for use in attributes
         for pi in price_intervals:
@@ -125,8 +107,8 @@ class TibberPricesDailyStatCalculator(TibberPricesBaseCalculator):
                 self._last_extreme_interval = pi["interval"]
                 break
 
-        # Return in configured display currency units with 2 decimals
+        # Always return in minor currency units (cents/øre) with 2 decimals
-        result = get_price_value(value, config_entry=self.coordinator.config_entry)
+        result = get_price_value(value, in_euro=False)
         return round(result, 2)
 
     def get_daily_aggregated_value(
@@ -149,9 +131,11 @@ class TibberPricesDailyStatCalculator(TibberPricesBaseCalculator):
             Aggregated level/rating value (lowercase), or None if unavailable.
 
         """
-        if not self.has_data():
+        if not self.coordinator_data:
             return None
 
+        price_info = self.price_info
+
         # Get local midnight boundaries based on the requested day using TimeService
         time = self.coordinator.time
         local_midnight, local_midnight_next_day = time.get_day_boundaries(day)
@@ -159,8 +143,8 @@ class TibberPricesDailyStatCalculator(TibberPricesBaseCalculator):
         # Collect all intervals from both today and tomorrow data
         # that fall within the target day's local date boundaries
         day_intervals = []
-        for day_offset in [-1, 0, 1]:  # yesterday=-1, today=0, tomorrow=1
+        for day_key in ["yesterday", "today", "tomorrow"]:
-            for price_data in self.get_intervals(day_offset):
+            for price_data in price_info.get(day_key, []):
                 starts_at = price_data.get("startsAt")  # Already datetime in local timezone
                 if not starts_at:
                     continue

custom_components/tibber_prices/sensor/calculators/interval.py

@@ -4,7 +4,7 @@ from __future__ import annotations
 
 from typing import TYPE_CHECKING
 
-from custom_components.tibber_prices.const import get_display_unit_factor
+from custom_components.tibber_prices.utils.price import find_price_data_for_interval
 
 from .base import TibberPricesBaseCalculator
 
@@ -36,7 +36,7 @@ class TibberPricesIntervalCalculator(TibberPricesBaseCalculator):
         self._last_rating_level: str | None = None
         self._last_rating_difference: float | None = None
 
-    def get_interval_value(  # noqa: PLR0911
+    def get_interval_value(
         self,
         *,
         interval_offset: int,
@@ -57,31 +57,32 @@ class TibberPricesIntervalCalculator(TibberPricesBaseCalculator):
             None if data unavailable.
 
         """
-        if not self.has_data():
+        if not self.coordinator_data:
             return None
 
-        interval_data = self.find_interval_at_offset(interval_offset)
+        price_info = self.price_info
+        time = self.coordinator.time
+        # Use TimeService to get interval offset time
+        target_time = time.get_interval_offset_time(interval_offset)
+
+        interval_data = find_price_data_for_interval(price_info, target_time, time=time)
         if not interval_data:
             return None
 
         # Extract value based on type
         if value_type == "price":
-            price = self.safe_get_from_interval(interval_data, "total")
+            price = interval_data.get("total")
             if price is None:
                 return None
             price = float(price)
-            # Return in base currency if in_euro=True, otherwise in display unit
+            return price if in_euro else round(price * 100, 2)
-            if in_euro:
-                return price
-            factor = get_display_unit_factor(self.config_entry)
-            return round(price * factor, 2)
 
         if value_type == "level":
-            level = self.safe_get_from_interval(interval_data, "level")
+            level = interval_data.get("level")
             return level.lower() if level else None
 
         # For rating: extract rating_level
-        rating = self.safe_get_from_interval(interval_data, "rating_level")
+        rating = interval_data.get("rating_level")
         return rating.lower() if rating else None
 
     def get_price_level_value(self) -> str | None:
@@ -116,16 +117,19 @@ class TibberPricesIntervalCalculator(TibberPricesBaseCalculator):
             Rating level (lowercase), or None if unavailable.
 
         """
-        if not self.has_data() or rating_type != "current":
+        if not self.coordinator_data or rating_type != "current":
             self._last_rating_difference = None
             self._last_rating_level = None
             return None
 
-        current_interval = self.find_interval_at_offset(0)
+        time = self.coordinator.time
+        now = time.now()
+        price_info = self.price_info
+        current_interval = find_price_data_for_interval(price_info, now, time=time)
 
         if current_interval:
-            rating_level = self.safe_get_from_interval(current_interval, "rating_level")
+            rating_level = current_interval.get("rating_level")
-            difference = self.safe_get_from_interval(current_interval, "difference")
+            difference = current_interval.get("difference")
             if rating_level is not None:
                 self._last_rating_difference = float(difference) if difference is not None else None
                 self._last_rating_level = rating_level

custom_components/tibber_prices/sensor/calculators/lifecycle.py

@@ -2,7 +2,11 @@
 
 from __future__ import annotations
 
-from datetime import datetime, timedelta
+from datetime import timedelta
+from typing import TYPE_CHECKING
+
+if TYPE_CHECKING:
+    from datetime import datetime
 
 from custom_components.tibber_prices.coordinator.constants import UPDATE_INTERVAL
 
@@ -11,7 +15,11 @@ from .base import TibberPricesBaseCalculator
 # Constants for lifecycle state determination
 FRESH_DATA_THRESHOLD_MINUTES = 5  # Data is "fresh" within 5 minutes of API fetch
 TOMORROW_CHECK_HOUR = 13  # After 13:00, we actively check for tomorrow data
-TURNOVER_WARNING_SECONDS = 900  # Warn 15 minutes before midnight (last quarter-hour: 23:45-00:00)
+TURNOVER_WARNING_SECONDS = 300  # Warn 5 minutes before midnight
+
+# Constants for 15-minute update boundaries (Timer #1)
+QUARTER_HOUR_BOUNDARIES = [0, 15, 30, 45]  # Minutes when Timer #1 can trigger
+LAST_HOUR_OF_DAY = 23
 
 
 class TibberPricesLifecycleCalculator(TibberPricesBaseCalculator):
@@ -26,31 +34,29 @@ class TibberPricesLifecycleCalculator(TibberPricesBaseCalculator):
         - "fresh": Just fetched from API (within 5 minutes)
         - "refreshing": Currently fetching data from API
         - "searching_tomorrow": After 13:00, actively looking for tomorrow data
-        - "turnover_pending": Last interval of day (23:45-00:00, midnight approaching)
+        - "turnover_pending": Midnight is approaching (within 5 minutes)
         - "error": Last API call failed
 
-        Priority order (highest to lowest):
-        1. refreshing - Active operation has highest priority
-        2. error - Errors must be immediately visible
-        3. turnover_pending - Important event at 23:45, should stay visible
-        4. searching_tomorrow - Stable during search phase (13:00-~15:00)
-        5. fresh - Informational only, lowest priority among active states
-        6. cached - Default fallback
-
         """
         coordinator = self.coordinator
         current_time = coordinator.time.now()
 
-        # Priority 1: Check if actively fetching (highest priority)
+        # Check if actively fetching
         if coordinator._is_fetching:  # noqa: SLF001 - Internal state access for lifecycle tracking
             return "refreshing"
 
-        # Priority 2: Check if last update failed
+        # Check if last update failed
         # If coordinator has last_exception set, the last fetch failed
         if coordinator.last_exception is not None:
             return "error"
 
-        # Priority 3: Check if midnight turnover is pending (last quarter of day: 23:45-00:00)
+        # Check if data is fresh (within 5 minutes of last API fetch)
+        if coordinator._last_price_update:  # noqa: SLF001 - Internal state access for lifecycle tracking
+            age = current_time - coordinator._last_price_update  # noqa: SLF001
+            if age <= timedelta(minutes=FRESH_DATA_THRESHOLD_MINUTES):
+                return "fresh"
+
+        # Check if midnight turnover is pending (within 15 minutes)
         midnight = coordinator.time.as_local(current_time).replace(
             hour=0, minute=0, second=0, microsecond=0
         ) + timedelta(days=1)
@@ -58,22 +64,26 @@ class TibberPricesLifecycleCalculator(TibberPricesBaseCalculator):
         if 0 < time_to_midnight <= TURNOVER_WARNING_SECONDS:  # Within 15 minutes of midnight (23:45-00:00)
             return "turnover_pending"
 
-        # Priority 4: Check if we're in tomorrow data search mode (after 13:00 and tomorrow missing)
+        # Check if we're in tomorrow data search mode (after 13:00 and tomorrow missing)
-        # This should remain stable during the search phase, not flicker with "fresh" every 15 minutes
         now_local = coordinator.time.as_local(current_time)
-        if now_local.hour >= TOMORROW_CHECK_HOUR and coordinator._needs_tomorrow_data():  # noqa: SLF001 - Internal state access
+        if now_local.hour >= TOMORROW_CHECK_HOUR:
-            return "searching_tomorrow"
+            _, tomorrow_midnight = coordinator.time.get_day_boundaries("today")
+            tomorrow_date = tomorrow_midnight.date()
+            if coordinator._needs_tomorrow_data(tomorrow_date):  # noqa: SLF001 - Internal state access
+                return "searching_tomorrow"
 
-        # Priority 5: Check if data is fresh (within 5 minutes of last API fetch)
+        # Default: using cached data
-        # Lower priority than searching_tomorrow to avoid state flickering during search phase
-        if coordinator._last_price_update:  # noqa: SLF001 - Internal state access for lifecycle tracking
-            age = current_time - coordinator._last_price_update  # noqa: SLF001
-            if age <= timedelta(minutes=FRESH_DATA_THRESHOLD_MINUTES):
-                return "fresh"
-
-        # Priority 6: Default - using cached data
         return "cached"
 
+    def get_cache_age_minutes(self) -> int | None:
+        """Calculate how many minutes old the cached data is."""
+        coordinator = self.coordinator
+        if not coordinator._last_price_update:  # noqa: SLF001 - Internal state access for lifecycle tracking
+            return None
+
+        age = coordinator.time.now() - coordinator._last_price_update  # noqa: SLF001
+        return int(age.total_seconds() / 60)
+
     def get_next_api_poll_time(self) -> datetime | None:
         """
         Calculate when the next API poll attempt will occur.
@@ -95,31 +105,12 @@ class TibberPricesLifecycleCalculator(TibberPricesBaseCalculator):
         now_local = coordinator.time.as_local(current_time)
 
         # Check if tomorrow data is missing
-        tomorrow_missing = coordinator._needs_tomorrow_data()  # noqa: SLF001
-
-        # Get tomorrow date for time calculations
         _, tomorrow_midnight = coordinator.time.get_day_boundaries("today")
+        tomorrow_date = tomorrow_midnight.date()
+        tomorrow_missing = coordinator._needs_tomorrow_data(tomorrow_date)  # noqa: SLF001
 
-        # Case 1: Before 13:00 today - next poll is today at 13:xx:xx (when tomorrow-search begins)
+        # Case 1: Before 13:00 today - next poll is today at 13:00 (when tomorrow-search begins)
         if now_local.hour < TOMORROW_CHECK_HOUR:
-            # Calculate exact time based on Timer #1 offset (minute and second precision)
-            if coordinator._last_coordinator_update is not None:  # noqa: SLF001
-                last_update_local = coordinator.time.as_local(coordinator._last_coordinator_update)  # noqa: SLF001
-                # Timer offset: minutes + seconds past the quarter-hour
-                minutes_past_quarter = last_update_local.minute % 15
-                seconds_offset = last_update_local.second
-
-                # Calculate first timer execution at or after 13:00 today
-                # Just apply timer offset to 13:00 (first quarter-hour mark >= 13:00)
-                # Timer runs at X:04:37 → Next poll at 13:04:37
-                return now_local.replace(
-                    hour=TOMORROW_CHECK_HOUR,
-                    minute=minutes_past_quarter,
-                    second=seconds_offset,
-                    microsecond=0,
-                )
-
-            # Fallback: No timer history yet
             return now_local.replace(hour=TOMORROW_CHECK_HOUR, minute=0, second=0, microsecond=0)
 
         # Case 2: After 13:00 today AND tomorrow data missing - actively polling now
@@ -162,6 +153,118 @@ class TibberPricesLifecycleCalculator(TibberPricesBaseCalculator):
         # Fallback: If we don't know timer offset yet, assume 13:00:00
         return tomorrow_13
 
+    def get_next_tomorrow_check_time(self) -> datetime | None:
+        """
+        Calculate when the next tomorrow data check will occur.
+
+        Returns None if not applicable (before 13:00 or tomorrow already available).
+        """
+        coordinator = self.coordinator
+        current_time = coordinator.time.now()
+        now_local = coordinator.time.as_local(current_time)
+
+        # Only relevant after 13:00
+        if now_local.hour < TOMORROW_CHECK_HOUR:
+            return None
+
+        # Only relevant if tomorrow data is missing
+        _, tomorrow_midnight = coordinator.time.get_day_boundaries("today")
+        tomorrow_date = tomorrow_midnight.date()
+        if not coordinator._needs_tomorrow_data(tomorrow_date):  # noqa: SLF001 - Internal state access
+            return None
+
+        # Next check = next regular API poll (same as get_next_api_poll_time)
+        return self.get_next_api_poll_time()
+
+    def get_next_midnight_turnover_time(self) -> datetime:
+        """Calculate when the next midnight turnover will occur."""
+        coordinator = self.coordinator
+        current_time = coordinator.time.now()
+        now_local = coordinator.time.as_local(current_time)
+
+        # Next midnight
+        return now_local.replace(hour=0, minute=0, second=0, microsecond=0) + timedelta(days=1)
+
+    def is_data_available(self, day: str) -> bool:
+        """
+        Check if data is available for a specific day.
+
+        Args:
+            day: "yesterday", "today", or "tomorrow"
+
+        Returns:
+            True if data exists and is not empty
+
+        """
+        coordinator = self.coordinator
+        if not coordinator.data:
+            return False
+
+        price_info = coordinator.data.get("priceInfo", {})
+        day_data = price_info.get(day, [])
+        return bool(day_data)
+
+    def get_data_completeness_status(self) -> str:
+        """
+        Get human-readable data completeness status.
+
+        Returns:
+            'complete': All data (yesterday/today/tomorrow) available
+            'missing_tomorrow': Only yesterday and today available
+            'missing_yesterday': Only today and tomorrow available
+            'partial': Only today or some other partial combination
+            'no_data': No data available at all
+
+        """
+        yesterday_available = self.is_data_available("yesterday")
+        today_available = self.is_data_available("today")
+        tomorrow_available = self.is_data_available("tomorrow")
+
+        if yesterday_available and today_available and tomorrow_available:
+            return "complete"
+        if yesterday_available and today_available and not tomorrow_available:
+            return "missing_tomorrow"
+        if not yesterday_available and today_available and tomorrow_available:
+            return "missing_yesterday"
+        if today_available:
+            return "partial"
+        return "no_data"
+
+    def get_cache_validity_status(self) -> str:
+        """
+        Get cache validity status.
+
+        Returns:
+            "valid": Cache is current and matches today's date
+            "stale": Cache exists but is outdated
+            "date_mismatch": Cache is from a different day
+            "empty": No cache data
+
+        """
+        coordinator = self.coordinator
+        # Check if coordinator has data (transformed, ready for entities)
+        if not coordinator.data:
+            return "empty"
+
+        # Check if we have price update timestamp
+        if not coordinator._last_price_update:  # noqa: SLF001 - Internal state access for lifecycle tracking
+            return "empty"
+
+        current_time = coordinator.time.now()
+        current_local_date = coordinator.time.as_local(current_time).date()
+        last_update_local_date = coordinator.time.as_local(coordinator._last_price_update).date()  # noqa: SLF001
+
+        if current_local_date != last_update_local_date:
+            return "date_mismatch"
+
+        # Check if cache is stale (older than expected)
+        age = current_time - coordinator._last_price_update  # noqa: SLF001
+        # Consider stale if older than 2 hours (8 * 15-minute intervals)
+        if age > timedelta(hours=2):
+            return "stale"
+
+        return "valid"
+
     def get_api_calls_today(self) -> int:
         """Get the number of API calls made today."""
         coordinator = self.coordinator
@@ -172,13 +275,3 @@ class TibberPricesLifecycleCalculator(TibberPricesBaseCalculator):
             return 0
 
         return coordinator._api_calls_today  # noqa: SLF001
-
-    def has_tomorrow_data(self) -> bool:
-        """
-        Check if tomorrow's price data is available.
-
-        Returns:
-            True if tomorrow data exists in the pool.
-
-        """
-        return not self.coordinator._needs_tomorrow_data()  # noqa: SLF001

custom_components/tibber_prices/sensor/calculators/rolling_hour.py

@@ -8,11 +8,10 @@ from custom_components.tibber_prices.const import (
     DEFAULT_PRICE_RATING_THRESHOLD_HIGH,
     DEFAULT_PRICE_RATING_THRESHOLD_LOW,
 )
-from custom_components.tibber_prices.coordinator.helpers import get_intervals_for_day_offsets
 from custom_components.tibber_prices.entity_utils import find_rolling_hour_center_index
 from custom_components.tibber_prices.sensor.helpers import (
-    aggregate_average_data,
     aggregate_level_data,
+    aggregate_price_data,
     aggregate_rating_data,
 )
 
@@ -32,7 +31,7 @@ class TibberPricesRollingHourCalculator(TibberPricesBaseCalculator):
         *,
         hour_offset: int = 0,
         value_type: str = "price",
-    ) -> str | float | tuple[float | None, float | None] | None:
+    ) -> str | float | None:
         """
         Unified method to get aggregated values from 5-interval rolling window.
 
@@ -44,16 +43,17 @@ class TibberPricesRollingHourCalculator(TibberPricesBaseCalculator):
 
         Returns:
             Aggregated value based on type:
-            - "price": float or tuple[float, float | None] (avg, median)
+            - "price": float (average price in minor currency units)
             - "level": str (aggregated level: "very_cheap", "cheap", etc.)
             - "rating": str (aggregated rating: "low", "normal", "high")
 
         """
-        if not self.has_data():
+        if not self.coordinator_data:
             return None
 
-        # Get all available price data (yesterday, today, tomorrow)
+        # Get all available price data
-        all_prices = get_intervals_for_day_offsets(self.coordinator_data, [-1, 0, 1])
+        price_info = self.price_info
+        all_prices = price_info.get("yesterday", []) + price_info.get("today", []) + price_info.get("tomorrow", [])
 
         if not all_prices:
             return None
@@ -81,7 +81,7 @@ class TibberPricesRollingHourCalculator(TibberPricesBaseCalculator):
         self,
         window_data: list[dict],
         value_type: str,
-    ) -> str | float | tuple[float | None, float | None] | None:
+    ) -> str | float | None:
         """
         Aggregate data from multiple intervals based on value type.
 
@@ -90,10 +90,7 @@ class TibberPricesRollingHourCalculator(TibberPricesBaseCalculator):
             value_type: "price" | "level" | "rating".
 
         Returns:
-            Aggregated value based on type:
+            Aggregated value based on type.
-            - "price": tuple[float, float | None] (avg, median)
-            - "level": str
-            - "rating": str
 
         """
         # Get thresholds from config for rating aggregation
@@ -106,12 +103,9 @@ class TibberPricesRollingHourCalculator(TibberPricesBaseCalculator):
             DEFAULT_PRICE_RATING_THRESHOLD_HIGH,
         )
 
-        # Handle price aggregation - return tuple directly
+        # Map value types to aggregation functions
-        if value_type == "price":
-            return aggregate_average_data(window_data, self.config_entry)
-
-        # Map other value types to aggregation functions
         aggregators = {
+            "price": lambda data: aggregate_price_data(data),
             "level": lambda data: aggregate_level_data(data),
             "rating": lambda data: aggregate_rating_data(data, threshold_low, threshold_high),
         }

custom_components/tibber_prices/sensor/calculators/timing.py

@@ -65,11 +65,11 @@ class TibberPricesTimingCalculator(TibberPricesBaseCalculator):
             - None if no relevant period data available
 
         """
-        if not self.has_data():
+        if not self.coordinator.data:
             return None
 
         # Get period data from coordinator
-        periods_data = self.coordinator_data.get("pricePeriods", {})
+        periods_data = self.coordinator.data.get("periods", {})
         period_data = periods_data.get(period_type)
 
         if not period_data or not period_data.get("periods"):
@@ -158,8 +158,7 @@ class TibberPricesTimingCalculator(TibberPricesBaseCalculator):
 
         Args:
             periods: List of period dictionaries
-            skip_current: If True, try to skip the first future period (to get next-next)
+            skip_current: If True, skip the first future period (to get next-next)
-                         If only one future period exists, return it anyway (pragmatic fallback)
 
         Returns:
             Next period dict or None if no future periods
@@ -174,13 +173,11 @@ class TibberPricesTimingCalculator(TibberPricesBaseCalculator):
         # Sort by start time to ensure correct order
         future_periods.sort(key=lambda p: p["start"])
 
-        # If skip_current requested and we have multiple periods, return second
+        # Return second period if skip_current=True (next-next), otherwise first (next)
-        # If only one period left, return it anyway (pragmatic: better than showing unknown)
         if skip_current and len(future_periods) > 1:
             return future_periods[1]
-
+        if not skip_current and future_periods:
-        # Default: return first future period
+            return future_periods[0]
-        return future_periods[0] if future_periods else None
 
         return None
 

custom_components/tibber_prices/sensor/calculators/trend.py

@@ -15,9 +15,7 @@ Caching strategy:
 from datetime import datetime
 from typing import TYPE_CHECKING, Any
 
-from custom_components.tibber_prices.const import get_display_unit_factor
+from custom_components.tibber_prices.utils.average import calculate_next_n_hours_avg
-from custom_components.tibber_prices.coordinator.helpers import get_intervals_for_day_offsets
-from custom_components.tibber_prices.utils.average import calculate_mean, calculate_next_n_hours_mean
 from custom_components.tibber_prices.utils.price import (
     calculate_price_trend,
     find_price_data_for_interval,
@@ -80,7 +78,7 @@ class TibberPricesTrendCalculator(TibberPricesBaseCalculator):
         if self._cached_trend_value is not None and self._trend_attributes:
             return self._cached_trend_value
 
-        if not self.has_data():
+        if not self.coordinator.data:
             return None
 
         # Get current interval price and timestamp
@@ -97,33 +95,30 @@ class TibberPricesTrendCalculator(TibberPricesBaseCalculator):
         # Get next interval timestamp (basis for calculation)
         next_interval_start = time.get_next_interval_start()
 
-        # Get future mean price (ignore median for trend calculation)
+        # Get future average price
-        future_mean, _ = calculate_next_n_hours_mean(self.coordinator.data, hours, time=self.coordinator.time)
+        future_avg = calculate_next_n_hours_avg(self.coordinator.data, hours, time=self.coordinator.time)
-        if future_mean is None:
+        if future_avg is None:
             return None
 
         # Get configured thresholds from options
         threshold_rising = self.config.get("price_trend_threshold_rising", 5.0)
         threshold_falling = self.config.get("price_trend_threshold_falling", -5.0)
-        threshold_strongly_rising = self.config.get("price_trend_threshold_strongly_rising", 6.0)
-        threshold_strongly_falling = self.config.get("price_trend_threshold_strongly_falling", -6.0)
         volatility_threshold_moderate = self.config.get("volatility_threshold_moderate", 15.0)
         volatility_threshold_high = self.config.get("volatility_threshold_high", 30.0)
 
         # Prepare data for volatility-adaptive thresholds
-        today_prices = self.intervals_today
+        price_info = self.coordinator.data.get("priceInfo", {})
-        tomorrow_prices = self.intervals_tomorrow
+        today_prices = price_info.get("today", [])
+        tomorrow_prices = price_info.get("tomorrow", [])
         all_intervals = today_prices + tomorrow_prices
         lookahead_intervals = self.coordinator.time.minutes_to_intervals(hours * 60)
 
         # Calculate trend with volatility-adaptive thresholds
-        trend_state, diff_pct, trend_value = calculate_price_trend(
+        trend_state, diff_pct = calculate_price_trend(
             current_interval_price,
-            future_mean,
+            future_avg,
             threshold_rising=threshold_rising,
             threshold_falling=threshold_falling,
-            threshold_strongly_rising=threshold_strongly_rising,
-            threshold_strongly_falling=threshold_strongly_falling,
             volatility_adjustment=True,  # Always enabled
             lookahead_intervals=lookahead_intervals,
             all_intervals=all_intervals,
@@ -131,25 +126,18 @@ class TibberPricesTrendCalculator(TibberPricesBaseCalculator):
             volatility_threshold_high=volatility_threshold_high,
         )
 
-        # Determine icon color based on trend state (5-level scale)
+        # Determine icon color based on trend state
-        # Strongly rising/falling uses more intense colors
         icon_color = {
-            "strongly_rising": "var(--error-color)",  # Red for strongly rising (very expensive)
+            "rising": "var(--error-color)",  # Red/Orange for rising prices (expensive)
-            "rising": "var(--warning-color)",  # Orange/Yellow for rising prices
-            "stable": "var(--state-icon-color)",  # Default gray for stable prices
             "falling": "var(--success-color)",  # Green for falling prices (cheaper)
-            "strongly_falling": "var(--success-color)",  # Green for strongly falling (great deal)
+            "stable": "var(--state-icon-color)",  # Default gray for stable prices
         }.get(trend_state, "var(--state-icon-color)")
 
-        # Convert prices to display currency unit based on configuration
-        factor = get_display_unit_factor(self.config_entry)
-
         # Store attributes in sensor-specific dictionary AND cache the trend value
         self._trend_attributes = {
             "timestamp": next_interval_start,
-            "trend_value": trend_value,
             f"trend_{hours}h_%": round(diff_pct, 1),
-            f"next_{hours}h_avg": round(future_mean * factor, 2),
+            f"next_{hours}h_avg": round(future_avg * 100, 2),
             "interval_count": lookahead_intervals,
             "threshold_rising": threshold_rising,
             "threshold_falling": threshold_falling,
@@ -161,13 +149,11 @@ class TibberPricesTrendCalculator(TibberPricesBaseCalculator):
             # Get second half average for longer periods
             later_half_avg = self._calculate_later_half_average(hours, next_interval_start)
             if later_half_avg is not None:
-                self._trend_attributes[f"second_half_{hours}h_avg"] = round(later_half_avg * factor, 2)
+                self._trend_attributes[f"second_half_{hours}h_avg"] = round(later_half_avg * 100, 2)
 
                 # Calculate incremental change: how much does the later half differ from current?
-                # CRITICAL: Use abs() for negative prices and allow calculation for all non-zero prices
+                if current_interval_price > 0:
-                # Example: current=-10, later=-5 → diff=5, pct=5/abs(-10)*100=+50% (correctly shows increase)
+                    later_half_diff = ((later_half_avg - current_interval_price) / current_interval_price) * 100
-                if current_interval_price != 0:
-                    later_half_diff = ((later_half_avg - current_interval_price) / abs(current_interval_price)) * 100
                     self._trend_attributes[f"second_half_{hours}h_diff_from_current_%"] = round(later_half_diff, 1)
 
         # Cache the trend value for consistency
@@ -259,11 +245,12 @@ class TibberPricesTrendCalculator(TibberPricesBaseCalculator):
             Average price for the later half intervals, or None if insufficient data
 
         """
-        if not self.has_data():
+        if not self.coordinator.data:
             return None
 
-        today_prices = self.intervals_today
+        price_info = self.coordinator.data.get("priceInfo", {})
-        tomorrow_prices = self.intervals_tomorrow
+        today_prices = price_info.get("today", [])
+        tomorrow_prices = price_info.get("tomorrow", [])
         all_prices = today_prices + tomorrow_prices
 
         if not all_prices:
@@ -290,7 +277,7 @@ class TibberPricesTrendCalculator(TibberPricesBaseCalculator):
                     later_prices.append(float(price))
 
         if later_prices:
-            return calculate_mean(later_prices)
+            return sum(later_prices) / len(later_prices)
 
         return None
 
@@ -318,11 +305,12 @@ class TibberPricesTrendCalculator(TibberPricesBaseCalculator):
             return self._trend_calculation_cache
 
         # Validate coordinator data
-        if not self.has_data():
+        if not self.coordinator.data:
             return None
 
-        all_intervals = get_intervals_for_day_offsets(self.coordinator_data, [-1, 0, 1])
+        price_info = self.coordinator.data.get("priceInfo", {})
-        current_interval = find_price_data_for_interval(self.coordinator.data, now, time=time)
+        all_intervals = price_info.get("today", []) + price_info.get("tomorrow", [])
+        current_interval = find_price_data_for_interval(price_info, now, time=time)
 
         if not all_intervals or not current_interval:
             return None
@@ -357,11 +345,11 @@ class TibberPricesTrendCalculator(TibberPricesBaseCalculator):
 
         # Combine momentum + future outlook to get ACTUAL current trend
         if len(future_intervals) >= min_intervals_for_trend and future_prices:
-            future_mean = calculate_mean(future_prices)
+            future_avg = sum(future_prices) / len(future_prices)
             current_trend_state = self._combine_momentum_with_future(
                 current_momentum=current_momentum,
                 current_price=current_price,
-                future_mean=future_mean,
+                future_avg=future_avg,
                 context={
                     "all_intervals": all_intervals,
                     "current_index": current_index,
@@ -422,8 +410,6 @@ class TibberPricesTrendCalculator(TibberPricesBaseCalculator):
         return {
             "rising": self.config.get("price_trend_threshold_rising", 5.0),
             "falling": self.config.get("price_trend_threshold_falling", -5.0),
-            "strongly_rising": self.config.get("price_trend_threshold_strongly_rising", 6.0),
-            "strongly_falling": self.config.get("price_trend_threshold_strongly_falling", -6.0),
             "moderate": self.config.get("volatility_threshold_moderate", 15.0),
             "high": self.config.get("volatility_threshold_high", 30.0),
         }
@@ -438,7 +424,7 @@ class TibberPricesTrendCalculator(TibberPricesBaseCalculator):
             current_index: Index of current interval
 
         Returns:
-            Momentum direction: "strongly_rising", "rising", "stable", "falling", or "strongly_falling"
+            Momentum direction: "rising", "falling", or "stable"
 
         """
         # Look back 1 hour (4 intervals) for quick reaction
@@ -461,91 +447,64 @@ class TibberPricesTrendCalculator(TibberPricesBaseCalculator):
         weighted_sum = sum(price * weight for price, weight in zip(trailing_prices, weights, strict=True))
         weighted_avg = weighted_sum / sum(weights)
 
-        # Calculate momentum with thresholds
+        # Calculate momentum with 3% threshold
-        # Using same logic as 5-level trend: 3% for normal, 6% (2x) for strong
         momentum_threshold = 0.03
-        strong_momentum_threshold = 0.06
+        diff = (current_price - weighted_avg) / weighted_avg
-        diff = (current_price - weighted_avg) / abs(weighted_avg) if weighted_avg != 0 else 0
 
-        # Determine momentum level based on thresholds
+        if diff > momentum_threshold:
-        if diff >= strong_momentum_threshold:
+            return "rising"
-            momentum = "strongly_rising"
+        if diff < -momentum_threshold:
-        elif diff > momentum_threshold:
+            return "falling"
-            momentum = "rising"
+        return "stable"
-        elif diff <= -strong_momentum_threshold:
-            momentum = "strongly_falling"
-        elif diff < -momentum_threshold:
-            momentum = "falling"
-        else:
-            momentum = "stable"
-
-        return momentum
 
     def _combine_momentum_with_future(
         self,
         *,
         current_momentum: str,
         current_price: float,
-        future_mean: float,
+        future_avg: float,
         context: dict,
     ) -> str:
         """
         Combine momentum analysis with future outlook to determine final trend.
 
-        Uses 5-level scale: strongly_rising, rising, stable, falling, strongly_falling.
-        Momentum intensity is preserved when future confirms the trend direction.
-
         Args:
-            current_momentum: Current momentum direction (5-level scale)
+            current_momentum: Current momentum direction (rising/falling/stable)
             current_price: Current interval price
-            future_mean: Average price in future window
+            future_avg: Average price in future window
             context: Dict with all_intervals, current_index, lookahead_intervals, thresholds
 
         Returns:
-            Final trend direction (5-level scale)
+            Final trend direction: "rising", "falling", or "stable"
 
         """
-        # Use calculate_price_trend for consistency with 5-level logic
+        if current_momentum == "rising":
+            # We're in uptrend - does it continue?
+            return "rising" if future_avg >= current_price * 0.98 else "falling"
+
+        if current_momentum == "falling":
+            # We're in downtrend - does it continue?
+            return "falling" if future_avg <= current_price * 1.02 else "rising"
+
+        # current_momentum == "stable" - what's coming?
         all_intervals = context["all_intervals"]
         current_index = context["current_index"]
         lookahead_intervals = context["lookahead_intervals"]
         thresholds = context["thresholds"]
 
         lookahead_for_volatility = all_intervals[current_index : current_index + lookahead_intervals]
-        future_trend, _, _ = calculate_price_trend(
+        trend_state, _ = calculate_price_trend(
             current_price,
-            future_mean,
+            future_avg,
             threshold_rising=thresholds["rising"],
             threshold_falling=thresholds["falling"],
-            threshold_strongly_rising=thresholds["strongly_rising"],
-            threshold_strongly_falling=thresholds["strongly_falling"],
             volatility_adjustment=True,
             lookahead_intervals=lookahead_intervals,
             all_intervals=lookahead_for_volatility,
             volatility_threshold_moderate=thresholds["moderate"],
             volatility_threshold_high=thresholds["high"],
         )
-
+        return trend_state
-        # Check if momentum and future trend are aligned (same direction)
-        momentum_rising = current_momentum in ("rising", "strongly_rising")
-        momentum_falling = current_momentum in ("falling", "strongly_falling")
-        future_rising = future_trend in ("rising", "strongly_rising")
-        future_falling = future_trend in ("falling", "strongly_falling")
-
-        if momentum_rising and future_rising:
-            # Both indicate rising - use the stronger signal
-            if current_momentum == "strongly_rising" or future_trend == "strongly_rising":
-                return "strongly_rising"
-            return "rising"
-
-        if momentum_falling and future_falling:
-            # Both indicate falling - use the stronger signal
-            if current_momentum == "strongly_falling" or future_trend == "strongly_falling":
-                return "strongly_falling"
-            return "falling"
-
-        # Conflicting signals or stable momentum - trust future trend calculation
-        return future_trend
 
     def _calculate_standard_trend(
         self,
@@ -567,17 +526,15 @@ class TibberPricesTrendCalculator(TibberPricesBaseCalculator):
         if not standard_future_prices:
             return "stable"
 
-        standard_future_mean = calculate_mean(standard_future_prices)
+        standard_future_avg = sum(standard_future_prices) / len(standard_future_prices)
         current_price = float(current_interval["total"])
 
         standard_lookahead_volatility = all_intervals[current_index : current_index + standard_lookahead]
-        current_trend_3h, _, _ = calculate_price_trend(
+        current_trend_3h, _ = calculate_price_trend(
             current_price,
-            standard_future_mean,
+            standard_future_avg,
             threshold_rising=thresholds["rising"],
             threshold_falling=thresholds["falling"],
-            threshold_strongly_rising=thresholds["strongly_rising"],
-            threshold_strongly_falling=thresholds["strongly_falling"],
             volatility_adjustment=True,
             lookahead_intervals=standard_lookahead,
             all_intervals=standard_lookahead_volatility,
@@ -640,18 +597,16 @@ class TibberPricesTrendCalculator(TibberPricesBaseCalculator):
             if not future_prices:
                 continue
 
-            future_mean = calculate_mean(future_prices)
+            future_avg = sum(future_prices) / len(future_prices)
             price = float(interval["total"])
 
             # Calculate trend at this past point
             lookahead_for_volatility = all_intervals[i : i + intervals_in_3h]
-            trend_state, _, _ = calculate_price_trend(
+            trend_state, _ = calculate_price_trend(
                 price,
-                future_mean,
+                future_avg,
                 threshold_rising=thresholds["rising"],
                 threshold_falling=thresholds["falling"],
-                threshold_strongly_rising=thresholds["strongly_rising"],
-                threshold_strongly_falling=thresholds["strongly_falling"],
                 volatility_adjustment=True,
                 lookahead_intervals=intervals_in_3h,
                 all_intervals=lookahead_for_volatility,
@@ -714,18 +669,16 @@ class TibberPricesTrendCalculator(TibberPricesBaseCalculator):
             if not future_prices:
                 continue
 
-            future_mean = calculate_mean(future_prices)
+            future_avg = sum(future_prices) / len(future_prices)
             current_price = float(interval["total"])
 
             # Calculate trend at this future point
             lookahead_for_volatility = all_intervals[i : i + intervals_in_3h]
-            trend_state, _, _ = calculate_price_trend(
+            trend_state, _ = calculate_price_trend(
                 current_price,
-                future_mean,
+                future_avg,
                 threshold_rising=thresholds["rising"],
                 threshold_falling=thresholds["falling"],
-                threshold_strongly_rising=thresholds["strongly_rising"],
-                threshold_strongly_falling=thresholds["strongly_falling"],
                 volatility_adjustment=True,
                 lookahead_intervals=intervals_in_3h,
                 all_intervals=lookahead_for_volatility,
@@ -740,17 +693,14 @@ class TibberPricesTrendCalculator(TibberPricesBaseCalculator):
                 time = self.coordinator.time
                 minutes_until = int(time.minutes_until(interval_start))
 
-                # Convert prices to display currency unit
-                factor = get_display_unit_factor(self.config_entry)
-
                 self._trend_change_attributes = {
                     "direction": trend_state,
                     "from_direction": current_trend_state,
                     "minutes_until_change": minutes_until,
-                    "current_price_now": round(float(current_interval["total"]) * factor, 2),
+                    "current_price_now": round(float(current_interval["total"]) * 100, 2),
-                    "price_at_change": round(current_price * factor, 2),
+                    "price_at_change": round(current_price * 100, 2),
-                    "avg_after_change": round(future_mean * factor, 2),
+                    "avg_after_change": round(future_avg * 100, 2),
-                    "trend_diff_%": round((future_mean - current_price) / current_price * 100, 1),
+                    "trend_diff_%": round((future_avg - current_price) / current_price * 100, 1),
                 }
                 return interval_start
 

custom_components/tibber_prices/sensor/calculators/volatility.py

@@ -4,22 +4,12 @@ from __future__ import annotations
 
 from typing import TYPE_CHECKING
 
-from custom_components.tibber_prices.const import (
-    CONF_VOLATILITY_THRESHOLD_HIGH,
-    CONF_VOLATILITY_THRESHOLD_MODERATE,
-    CONF_VOLATILITY_THRESHOLD_VERY_HIGH,
-    DEFAULT_VOLATILITY_THRESHOLD_HIGH,
-    DEFAULT_VOLATILITY_THRESHOLD_MODERATE,
-    DEFAULT_VOLATILITY_THRESHOLD_VERY_HIGH,
-    get_display_unit_factor,
-)
 from custom_components.tibber_prices.entity_utils import add_icon_color_attribute
 from custom_components.tibber_prices.sensor.attributes import (
     add_volatility_type_attributes,
     get_prices_for_volatility,
 )
-from custom_components.tibber_prices.utils.average import calculate_mean
+from custom_components.tibber_prices.utils.price import calculate_volatility_level
-from custom_components.tibber_prices.utils.price import calculate_volatility_with_cv
 
 from .base import TibberPricesBaseCalculator
 
@@ -61,28 +51,20 @@ class TibberPricesVolatilityCalculator(TibberPricesBaseCalculator):
             Volatility level: "low", "moderate", "high", "very_high", or None if unavailable.
 
         """
-        if not self.has_data():
+        if not self.coordinator_data:
             return None
 
+        price_info = self.price_info
+
         # Get volatility thresholds from config
         thresholds = {
-            "threshold_moderate": self.config.get(
+            "threshold_moderate": self.config.get("volatility_threshold_moderate", 5.0),
-                CONF_VOLATILITY_THRESHOLD_MODERATE,
+            "threshold_high": self.config.get("volatility_threshold_high", 15.0),
-                DEFAULT_VOLATILITY_THRESHOLD_MODERATE,
+            "threshold_very_high": self.config.get("volatility_threshold_very_high", 30.0),
-            ),
-            "threshold_high": self.config.get(CONF_VOLATILITY_THRESHOLD_HIGH, DEFAULT_VOLATILITY_THRESHOLD_HIGH),
-            "threshold_very_high": self.config.get(
-                CONF_VOLATILITY_THRESHOLD_VERY_HIGH,
-                DEFAULT_VOLATILITY_THRESHOLD_VERY_HIGH,
-            ),
         }
 
         # Get prices based on volatility type
-        prices_to_analyze = get_prices_for_volatility(
+        prices_to_analyze = get_prices_for_volatility(volatility_type, price_info, time=self.coordinator.time)
-            volatility_type,
-            self.coordinator.data,
-            time=self.coordinator.time,
-        )
 
         if not prices_to_analyze:
             return None
@@ -91,24 +73,21 @@ class TibberPricesVolatilityCalculator(TibberPricesBaseCalculator):
         price_min = min(prices_to_analyze)
         price_max = max(prices_to_analyze)
         spread = price_max - price_min
-        # Use arithmetic mean for volatility calculation (required for coefficient of variation)
+        price_avg = sum(prices_to_analyze) / len(prices_to_analyze)
-        price_mean = calculate_mean(prices_to_analyze)
 
-        # Convert to display currency unit based on configuration
+        # Convert to minor currency units (ct/øre) for display
-        factor = get_display_unit_factor(self.config_entry)
+        spread_minor = spread * 100
-        spread_display = spread * factor
 
-        # Calculate volatility level AND coefficient of variation
+        # Calculate volatility level with custom thresholds (pass price list, not spread)
-        volatility, cv = calculate_volatility_with_cv(prices_to_analyze, **thresholds)
+        volatility = calculate_volatility_level(prices_to_analyze, **thresholds)
 
         # Store attributes for this sensor
         self._last_volatility_attributes = {
-            "price_spread": round(spread_display, 2),
+            "price_spread": round(spread_minor, 2),
-            "price_coefficient_variation_%": round(cv, 2) if cv is not None else None,
+            "price_volatility": volatility,
-            "price_volatility": volatility.lower(),
+            "price_min": round(price_min * 100, 2),
-            "price_min": round(price_min * factor, 2),
+            "price_max": round(price_max * 100, 2),
-            "price_max": round(price_max * factor, 2),
+            "price_avg": round(price_avg * 100, 2),
-            "price_mean": round(price_mean * factor, 2),
             "interval_count": len(prices_to_analyze),
         }
 
@@ -117,11 +96,7 @@ class TibberPricesVolatilityCalculator(TibberPricesBaseCalculator):
 
         # Add type-specific attributes
         add_volatility_type_attributes(
-            self._last_volatility_attributes,
+            self._last_volatility_attributes, volatility_type, price_info, thresholds, time=self.coordinator.time
-            volatility_type,
-            self.coordinator.data,
-            thresholds,
-            time=self.coordinator.time,
         )
 
         # Return lowercase for ENUM device class

custom_components/tibber_prices/sensor/calculators/window_24h.py

@@ -24,7 +24,7 @@ class TibberPricesWindow24hCalculator(TibberPricesBaseCalculator):
         self,
         *,
         stat_func: Callable,
-    ) -> float | tuple[float, float | None] | None:
+    ) -> float | None:
         """
         Unified method for 24-hour sliding window statistics.
 
@@ -33,38 +33,20 @@ class TibberPricesWindow24hCalculator(TibberPricesBaseCalculator):
         - "leading": Next 24 hours (96 intervals after current)
 
         Args:
-            stat_func: Function from average_utils (e.g., calculate_current_trailing_mean).
+            stat_func: Function from average_utils (e.g., calculate_current_trailing_avg).
 
         Returns:
-            Price value in subunit currency units (cents/øre), or None if unavailable.
+            Price value in minor currency units (cents/øre), or None if unavailable.
-            For mean functions: tuple of (mean, median) where median may be None.
-            For min/max functions: single float value.
 
         """
-        if not self.has_data():
+        if not self.coordinator_data:
             return None
 
-        result = stat_func(self.coordinator_data, time=self.coordinator.time)
+        value = stat_func(self.coordinator_data, time=self.coordinator.time)
 
-        # Check if result is a tuple (mean, median) from mean functions
-        if isinstance(result, tuple):
-            value, median = result
-            if value is None:
-                return None
-            # Convert to display currency units based on config
-            mean_result = round(get_price_value(value, config_entry=self.coordinator.config_entry), 2)
-            median_result = (
-                round(get_price_value(median, config_entry=self.coordinator.config_entry), 2)
-                if median is not None
-                else None
-            )
-            return mean_result, median_result
-
-        # Single value result (min/max functions)
-        value = result
         if value is None:
             return None
 
-        # Return in configured display currency units with 2 decimals
+        # Always return in minor currency units (cents/øre) with 2 decimals
-        result = get_price_value(value, config_entry=self.coordinator.config_entry)
+        result = get_price_value(value, in_euro=False)
         return round(result, 2)

custom_components/tibber_prices/sensor/chart_data.py

@@ -38,9 +38,6 @@ async def call_chartdata_service_async(
     # Add required entry_id parameter
     service_params["entry_id"] = config_entry.entry_id
 
-    # Make sure metadata is never requested for this sensor
-    service_params["metadata"] = "none"
-
     # Call get_chartdata service using official HA service system
     try:
         response = await hass.services.async_call(

custom_components/tibber_prices/sensor/chart_metadata.py

@@ -1,149 +0,0 @@
-"""Chart metadata export functionality for Tibber Prices sensors."""
-
-from __future__ import annotations
-
-from typing import TYPE_CHECKING
-
-from custom_components.tibber_prices.const import (
-    CONF_CURRENCY_DISPLAY_MODE,
-    DATA_CHART_METADATA_CONFIG,
-    DISPLAY_MODE_SUBUNIT,
-    DOMAIN,
-)
-
-if TYPE_CHECKING:
-    from datetime import datetime
-
-    from custom_components.tibber_prices.coordinator import TibberPricesDataUpdateCoordinator
-    from custom_components.tibber_prices.data import TibberPricesConfigEntry
-    from homeassistant.core import HomeAssistant
-
-
-async def call_chartdata_service_for_metadata_async(
-    hass: HomeAssistant,
-    coordinator: TibberPricesDataUpdateCoordinator,
-    config_entry: TibberPricesConfigEntry,
-) -> tuple[dict | None, str | None]:
-    """
-    Call get_chartdata service with configuration from configuration.yaml for metadata (async).
-
-    Returns:
-        Tuple of (response, error_message).
-        If successful: (response_dict, None)
-        If failed: (None, error_string)
-
-    """
-    # Get configuration from hass.data (loaded from configuration.yaml)
-    domain_data = hass.data.get(DOMAIN, {})
-    chart_metadata_config = domain_data.get(DATA_CHART_METADATA_CONFIG, {})
-
-    # Use chart_metadata_config directly (already a dict from async_setup)
-    service_params = dict(chart_metadata_config) if chart_metadata_config else {}
-
-    # Add required entry_id parameter
-    service_params["entry_id"] = config_entry.entry_id
-
-    # Force metadata to "only" - this sensor ONLY provides metadata
-    service_params["metadata"] = "only"
-
-    # Use user's display unit preference from config_entry
-    # This ensures chart_metadata yaxis values match the user's configured currency display mode
-    if "subunit_currency" not in service_params:
-        display_mode = config_entry.options.get(CONF_CURRENCY_DISPLAY_MODE, DISPLAY_MODE_SUBUNIT)
-        service_params["subunit_currency"] = display_mode == DISPLAY_MODE_SUBUNIT
-
-    # Call get_chartdata service using official HA service system
-    try:
-        response = await hass.services.async_call(
-            DOMAIN,
-            "get_chartdata",
-            service_params,
-            blocking=True,
-            return_response=True,
-        )
-    except Exception as ex:
-        coordinator.logger.exception("Chart metadata service call failed")
-        return None, str(ex)
-    else:
-        return response, None
-
-
-def get_chart_metadata_state(
-    chart_metadata_response: dict | None,
-    chart_metadata_error: str | None,
-) -> str | None:
-    """
-    Return state for chart_metadata sensor.
-
-    Args:
-        chart_metadata_response: Last service response (or None)
-        chart_metadata_error: Last error message (or None)
-
-    Returns:
-        "error" if error occurred
-        "ready" if metadata available
-        "pending" if no data yet
-
-    """
-    if chart_metadata_error:
-        return "error"
-    if chart_metadata_response:
-        return "ready"
-    return "pending"
-
-
-def build_chart_metadata_attributes(
-    chart_metadata_response: dict | None,
-    chart_metadata_last_update: datetime | None,
-    chart_metadata_error: str | None,
-) -> dict[str, object] | None:
-    """
-    Return chart metadata from last service call as attributes.
-
-    Attribute order: timestamp, error (if any), metadata fields (at the end).
-
-    Args:
-        chart_metadata_response: Last service response (should contain "metadata" key)
-        chart_metadata_last_update: Timestamp of last update
-        chart_metadata_error: Error message if service call failed
-
-    Returns:
-        Dict with timestamp, optional error, and metadata fields.
-
-    """
-    # Build base attributes with timestamp FIRST
-    attributes: dict[str, object] = {
-        "timestamp": chart_metadata_last_update,
-    }
-
-    # Add error message if service call failed
-    if chart_metadata_error:
-        attributes["error"] = chart_metadata_error
-
-    if not chart_metadata_response:
-        # No data - only timestamp (and error if present)
-        return attributes
-
-    # Extract metadata from response (get_chartdata returns {"metadata": {...}})
-    metadata = chart_metadata_response.get("metadata", {})
-
-    # Extract the fields we care about for charts
-    # These are the universal chart metadata fields useful for any chart card
-    if metadata:
-        yaxis_suggested = metadata.get("yaxis_suggested", {})
-
-        # Add yaxis bounds (useful for all chart cards)
-        if "min" in yaxis_suggested:
-            attributes["yaxis_min"] = yaxis_suggested["min"]
-        if "max" in yaxis_suggested:
-            attributes["yaxis_max"] = yaxis_suggested["max"]
-
-        # Add currency info (useful for labeling)
-        if "currency" in metadata:
-            attributes["currency"] = metadata["currency"]
-
-        # Add resolution info (interval duration in minutes)
-        if "resolution" in metadata:
-            attributes["resolution"] = metadata["resolution"]
-
-    return attributes

custom_components/tibber_prices/sensor/core.py

@@ -9,26 +9,18 @@ from custom_components.tibber_prices.binary_sensor.attributes import (
     get_price_intervals_attributes,
 )
 from custom_components.tibber_prices.const import (
-    CONF_AVERAGE_SENSOR_DISPLAY,
-    CONF_CURRENCY_DISPLAY_MODE,
     CONF_PRICE_RATING_THRESHOLD_HIGH,
     CONF_PRICE_RATING_THRESHOLD_LOW,
-    DEFAULT_AVERAGE_SENSOR_DISPLAY,
     DEFAULT_PRICE_RATING_THRESHOLD_HIGH,
     DEFAULT_PRICE_RATING_THRESHOLD_LOW,
-    DISPLAY_MODE_BASE,
     DOMAIN,
-    format_price_unit_base,
+    format_price_unit_major,
-    get_display_unit_factor,
+    format_price_unit_minor,
-    get_display_unit_string,
 )
 from custom_components.tibber_prices.coordinator import (
     MINUTE_UPDATE_ENTITY_KEYS,
     TIME_SENSITIVE_ENTITY_KEYS,
 )
-from custom_components.tibber_prices.coordinator.helpers import (
-    get_intervals_for_day_offsets,
-)
 from custom_components.tibber_prices.entity import TibberPricesEntity
 from custom_components.tibber_prices.entity_utils import (
     add_icon_color_attribute,
@@ -40,14 +32,14 @@ from custom_components.tibber_prices.entity_utils.icons import (
     get_dynamic_icon,
 )
 from custom_components.tibber_prices.utils.average import (
-    calculate_next_n_hours_mean,
+    calculate_next_n_hours_avg,
 )
 from custom_components.tibber_prices.utils.price import (
     calculate_volatility_level,
 )
 from homeassistant.components.sensor import (
-    RestoreSensor,
     SensorDeviceClass,
+    SensorEntity,
     SensorEntityDescription,
 )
 from homeassistant.const import EntityCategory
@@ -75,11 +67,6 @@ from .chart_data import (
     call_chartdata_service_async,
     get_chart_data_state,
 )
-from .chart_metadata import (
-    build_chart_metadata_attributes,
-    call_chartdata_service_for_metadata_async,
-    get_chart_metadata_state,
-)
 from .helpers import aggregate_level_data, aggregate_rating_data
 from .value_getters import get_value_getter_mapping
 
@@ -97,60 +84,8 @@ MAX_FORECAST_INTERVALS = 8  # Show up to 8 future intervals (2 hours with 15-min
 MIN_HOURS_FOR_LATER_HALF = 3  # Minimum hours needed to calculate later half average
 
 
-class TibberPricesSensor(TibberPricesEntity, RestoreSensor):
+class TibberPricesSensor(TibberPricesEntity, SensorEntity):
-    """tibber_prices Sensor class with state restoration."""
+    """tibber_prices Sensor class."""
-
-    # Base attributes excluded from recorder history (shared across all sensors)
-    # See: https://developers.home-assistant.io/docs/core/entity/#excluding-state-attributes-from-recorder-history
-    _unrecorded_attributes = frozenset(
-        {
-            "timestamp",
-            # Descriptions/Help Text (static, large)
-            "description",
-            "usage_tips",
-            # Large Nested Structures
-            "trend_attributes",
-            "current_trend_attributes",
-            "trend_change_attributes",
-            "volatility_attributes",
-            "data",  # chart_data_export large nested data
-            # Frequently Changing Diagnostics
-            "icon_color",
-            "cache_age",
-            "cache_validity",
-            "data_completeness",
-            "data_status",
-            # Static/Rarely Changing
-            "tomorrow_expected_after",
-            "level_value",
-            "rating_value",
-            "level_id",
-            "rating_id",
-            "currency",
-            "resolution",
-            "yaxis_min",
-            "yaxis_max",
-            # Temporary/Time-Bound
-            "next_api_poll",
-            "next_midnight_turnover",
-            "last_update",  # Lifecycle sensor last update timestamp
-            "last_turnover",
-            "last_error",
-            "error",
-            # Relaxation Details
-            "relaxation_level",
-            "relaxation_threshold_original_%",
-            "relaxation_threshold_applied_%",
-            # Redundant/Derived (removed from attributes, kept here for safety)
-            "volatility",
-            "diff_%",
-            "rating_difference_%",
-            "period_price_diff_from_daily_min",
-            "period_price_diff_from_daily_min_%",
-            "periods_total",
-            "periods_remaining",
-        }
-    )
 
     def __init__(
         self,
@@ -162,8 +97,6 @@ class TibberPricesSensor(TibberPricesEntity, RestoreSensor):
         self.entity_description = entity_description
         self._attr_unique_id = f"{coordinator.config_entry.entry_id}_{entity_description.key}"
         self._attr_has_entity_name = True
-        # Cached data for attributes (e.g., median values)
-        self.cached_data: dict[str, Any] = {}
         # Instantiate calculators
         self._metadata_calculator = TibberPricesMetadataCalculator(coordinator)
         self._volatility_calculator = TibberPricesVolatilityCalculator(coordinator)
@@ -177,88 +110,19 @@ class TibberPricesSensor(TibberPricesEntity, RestoreSensor):
         self._value_getter: Callable | None = self._get_value_getter()
         self._time_sensitive_remove_listener: Callable | None = None
         self._minute_update_remove_listener: Callable | None = None
-        # Lifecycle sensor state change detection (for recorder optimization)
-        # Store as Any because native_value can be str/float/datetime depending on sensor type
-        self._last_lifecycle_state: Any = None
         # Chart data export (for chart_data_export sensor) - from binary_sensor
         self._chart_data_last_update = None  # Track last service call timestamp
         self._chart_data_error = None  # Track last service call error
         self._chart_data_response = None  # Store service response for attributes
-        # Chart metadata (for chart_metadata sensor)
+
-        self._chart_metadata_last_update = None  # Track last service call timestamp
+        # Register for push updates if this is the lifecycle sensor
-        self._chart_metadata_error = None  # Track last service call error
+        if entity_description.key == "data_lifecycle_status":
-        self._chart_metadata_response = None  # Store service response for attributes
+            coordinator.register_lifecycle_callback(self.async_write_ha_state)
 
     async def async_added_to_hass(self) -> None:
         """When entity is added to hass."""
         await super().async_added_to_hass()
 
-        # Configure dynamic attribute exclusion for average sensors
-        self._configure_average_sensor_exclusions()
-
-        # Restore last state if available
-        await self._restore_last_state()
-
-        # Register listeners for time-sensitive updates
-        self._register_update_listeners()
-
-        # Trigger initial chart data loads as background tasks
-        self._trigger_chart_data_loads()
-
-    def _configure_average_sensor_exclusions(self) -> None:
-        """Configure dynamic attribute exclusions for average sensors."""
-        # Dynamically exclude average attribute that matches state value
-        # (to avoid recording the same value twice: once as state, once as attribute)
-        key = self.entity_description.key
-        if key in (
-            "average_price_today",
-            "average_price_tomorrow",
-            "trailing_price_average",
-            "leading_price_average",
-            "current_hour_average_price",
-            "next_hour_average_price",
-        ) or key.startswith("next_avg_"):  # Future average sensors
-            display_mode = self.coordinator.config_entry.options.get(
-                CONF_AVERAGE_SENSOR_DISPLAY,
-                DEFAULT_AVERAGE_SENSOR_DISPLAY,
-            )
-            # Modify _state_info to add dynamic exclusion
-            if self._state_info is None:
-                self._state_info = {"unrecorded_attributes": frozenset()}
-            current_unrecorded = self._state_info.get("unrecorded_attributes", frozenset())
-            # State shows median → exclude price_median from attributes
-            # State shows mean → exclude price_mean from attributes
-            if display_mode == "median":
-                self._state_info["unrecorded_attributes"] = current_unrecorded | {"price_median"}
-            else:
-                self._state_info["unrecorded_attributes"] = current_unrecorded | {"price_mean"}
-
-    async def _restore_last_state(self) -> None:
-        """Restore last state if available."""
-        if (
-            (last_state := await self.async_get_last_state()) is not None
-            and last_state.state not in (None, "unknown", "unavailable", "")
-            and (last_sensor_data := await self.async_get_last_sensor_data()) is not None
-        ):
-            # Restore native_value from extra data (more reliable than state)
-            self._attr_native_value = last_sensor_data.native_value
-
-            # For chart sensors, restore response data from attributes
-            if self.entity_description.key == "chart_data_export":
-                self._chart_data_response = last_state.attributes.get("data")
-                self._chart_data_last_update = last_state.attributes.get("last_update")
-            elif self.entity_description.key == "chart_metadata":
-                # Restore metadata response from attributes
-                metadata_attrs = {}
-                for key in ["title", "yaxis_min", "yaxis_max", "currency", "resolution"]:
-                    if key in last_state.attributes:
-                        metadata_attrs[key] = last_state.attributes[key]
-                if metadata_attrs:
-                    self._chart_metadata_response = metadata_attrs
-                self._chart_metadata_last_update = last_state.attributes.get("last_update")
-
-    def _register_update_listeners(self) -> None:
-        """Register listeners for time-sensitive updates."""
         # Register with coordinator for time-sensitive updates if applicable
         if self.entity_description.key in TIME_SENSITIVE_ENTITY_KEYS:
             self._time_sensitive_remove_listener = self.coordinator.async_add_time_sensitive_listener(
@@ -271,17 +135,9 @@ class TibberPricesSensor(TibberPricesEntity, RestoreSensor):
                 self._handle_minute_update
             )
 
-    def _trigger_chart_data_loads(self) -> None:
+        # For chart_data_export, trigger initial service call
-        """Trigger initial chart data loads as background tasks."""
-        # For chart_data_export, trigger initial service call as background task
-        # (non-blocking to avoid delaying entity setup)
         if self.entity_description.key == "chart_data_export":
-            self.hass.async_create_task(self._refresh_chart_data())
+            await self._refresh_chart_data()
-
-        # For chart_metadata, trigger initial service call as background task
-        # (non-blocking to avoid delaying entity setup)
-        if self.entity_description.key == "chart_metadata":
-            self.hass.async_create_task(self._refresh_chart_metadata())
 
     async def async_will_remove_from_hass(self) -> None:
         """When entity will be removed from hass."""
@@ -315,18 +171,7 @@ class TibberPricesSensor(TibberPricesEntity, RestoreSensor):
         # Clear trend calculation cache for trend sensors
         elif self.entity_description.key in ("current_price_trend", "next_price_trend_change"):
             self._trend_calculator.clear_calculation_cache()
-
+        self.async_write_ha_state()
-        # For lifecycle sensor: Only write state if it actually changed (state-change filter)
-        # This enables precise detection at quarter-hour boundaries (23:45 turnover_pending,
-        # 13:00 searching_tomorrow, 00:00 turnover complete) without recorder spam
-        if self.entity_description.key == "data_lifecycle_status":
-            current_state = self.native_value
-            if current_state != self._last_lifecycle_state:
-                self._last_lifecycle_state = current_state
-                self.async_write_ha_state()
-            # If state didn't change, skip write to recorder
-        else:
-            self.async_write_ha_state()
 
     @callback
     def _handle_minute_update(self, time_service: TibberPricesTimeService) -> None:
@@ -348,29 +193,13 @@ class TibberPricesSensor(TibberPricesEntity, RestoreSensor):
         # Clear cached trend values when coordinator data changes
         if self.entity_description.key.startswith("price_trend_"):
             self._trend_calculator.clear_trend_cache()
-            # Also clear calculation cache (e.g., when threshold config changes)
-            self._trend_calculator.clear_calculation_cache()
 
         # Refresh chart data when coordinator updates (new price data or user data)
         if self.entity_description.key == "chart_data_export":
             # Schedule async refresh as a task (we're in a callback)
             self.hass.async_create_task(self._refresh_chart_data())
 
-        # Refresh chart metadata when coordinator updates (new price data or user data)
+        super()._handle_coordinator_update()
-        if self.entity_description.key == "chart_metadata":
-            # Schedule async refresh as a task (we're in a callback)
-            self.hass.async_create_task(self._refresh_chart_metadata())
-
-        # For lifecycle sensor: Only write state if it actually changed (event-based filter)
-        # Prevents excessive recorder entries while keeping quarter-hour update capability
-        if self.entity_description.key == "data_lifecycle_status":
-            current_state = self.native_value
-            if current_state != self._last_lifecycle_state:
-                self._last_lifecycle_state = current_state
-                super()._handle_coordinator_update()
-            # If state didn't change, skip write to recorder
-        else:
-            super()._handle_coordinator_update()
 
     def _get_value_getter(self) -> Callable | None:
         """Return the appropriate value getter method based on the sensor type."""
@@ -388,7 +217,6 @@ class TibberPricesSensor(TibberPricesEntity, RestoreSensor):
             get_next_avg_n_hours_value=self._get_next_avg_n_hours_value,
             get_data_timestamp=self._get_data_timestamp,
             get_chart_data_export_value=self._get_chart_data_export_value,
-            get_chart_metadata_value=self._get_chart_metadata_value,
         )
         return handlers.get(self.entity_description.key)
 
@@ -421,7 +249,7 @@ class TibberPricesSensor(TibberPricesEntity, RestoreSensor):
 
         Returns:
             Aggregated value based on type:
-            - "price": float (average price in subunit currency units)
+            - "price": float (average price in minor currency units)
             - "level": str (aggregated level: "very_cheap", "cheap", etc.)
             - "rating": str (aggregated rating: "low", "normal", "high")
 
@@ -429,8 +257,9 @@ class TibberPricesSensor(TibberPricesEntity, RestoreSensor):
         if not self.coordinator.data:
             return None
 
-        # Get all available price data (yesterday, today, tomorrow) via helper
+        # Get all available price data
-        all_prices = get_intervals_for_day_offsets(self.coordinator.data, [-1, 0, 1])
+        price_info = self.coordinator.data.get("priceInfo", {})
+        all_prices = price_info.get("yesterday", []) + price_info.get("today", []) + price_info.get("tomorrow", [])
 
         if not all_prices:
             return None
@@ -452,15 +281,7 @@ class TibberPricesSensor(TibberPricesEntity, RestoreSensor):
         if not window_data:
             return None
 
-        result = self._rolling_hour_calculator.aggregate_window_data(window_data, value_type)
+        return self._rolling_hour_calculator.aggregate_window_data(window_data, value_type)
-        # For price type, aggregate_window_data returns (avg, median)
-        if isinstance(result, tuple):
-            avg, median = result
-            # Cache median for attributes
-            if median is not None:
-                self.cached_data[f"{self.entity_description.key}_median"] = median
-            return avg
-        return result
 
     # ========================================================================
     # INTERVAL-BASED VALUE METHODS
@@ -490,27 +311,39 @@ class TibberPricesSensor(TibberPricesEntity, RestoreSensor):
             stat_func: Statistical function (min, max, or lambda for avg)
 
         Returns:
-            Price value in subunit currency units (cents/øre), or None if unavailable
+            Price value in minor currency units (cents/øre), or None if unavailable
 
         """
         if not self.coordinator.data:
             return None
 
-        # Map day key to offset: yesterday=-1, today=0, tomorrow=1
+        price_info = self.coordinator.data.get("priceInfo", {})
-        day_offset = {"yesterday": -1, "today": 0, "tomorrow": 1}[day]
-        day_intervals = get_intervals_for_day_offsets(self.coordinator.data, [day_offset])
 
-        # Collect all prices and their intervals
+        # Get TimeService from coordinator
+        time = self.coordinator.time
+
+        # Get local midnight boundaries based on the requested day using TimeService
+        local_midnight, local_midnight_next_day = time.get_day_boundaries(day)
+
+        # Collect all prices and their intervals from both today and tomorrow data
+        # that fall within the target day's local date boundaries
         price_intervals = []
-        for price_data in day_intervals:
+        for day_key in ["today", "tomorrow"]:
-            total_price = price_data.get("total")
+            for price_data in price_info.get(day_key, []):
-            if total_price is not None:
+                starts_at = price_data.get("startsAt")  # Already datetime in local timezone
-                price_intervals.append(
+                if not starts_at:
-                    {
+                    continue
-                        "price": float(total_price),
+
-                        "interval": price_data,
+                # Include price if it starts within the target day's local date boundaries
-                    }
+                if local_midnight <= starts_at < local_midnight_next_day:
-                )
+                    total_price = price_data.get("total")
+                    if total_price is not None:
+                        price_intervals.append(
+                            {
+                                "price": float(total_price),
+                                "interval": price_data,
+                            }
+                        )
 
         if not price_intervals:
             return None
@@ -525,8 +358,8 @@ class TibberPricesSensor(TibberPricesEntity, RestoreSensor):
                 self._last_extreme_interval = pi["interval"]
                 break
 
-        # Return in configured display currency units with 2 decimals
+        # Always return in minor currency units (cents/øre) with 2 decimals
-        result = get_price_value(value, config_entry=self.coordinator.config_entry)
+        result = get_price_value(value, in_euro=False)
         return round(result, 2)
 
     def _get_daily_aggregated_value(
@@ -552,9 +385,24 @@ class TibberPricesSensor(TibberPricesEntity, RestoreSensor):
         if not self.coordinator.data:
             return None
 
-        # Map day key to offset: yesterday=-1, today=0, tomorrow=1
+        price_info = self.coordinator.data.get("priceInfo", {})
-        day_offset = {"yesterday": -1, "today": 0, "tomorrow": 1}[day]
+
-        day_intervals = get_intervals_for_day_offsets(self.coordinator.data, [day_offset])
+        # Get local midnight boundaries based on the requested day using TimeService
+        time = self.coordinator.time
+        local_midnight, local_midnight_next_day = time.get_day_boundaries(day)
+
+        # Collect all intervals from both today and tomorrow data
+        # that fall within the target day's local date boundaries
+        day_intervals = []
+        for day_key in ["yesterday", "today", "tomorrow"]:
+            for price_data in price_info.get(day_key, []):
+                starts_at = price_data.get("startsAt")  # Already datetime in local timezone
+                if not starts_at:
+                    continue
+
+                # Include interval if it starts within the target day's local date boundaries
+                if local_midnight <= starts_at < local_midnight_next_day:
+                    day_intervals.append(price_data)
 
         if not day_intervals:
             return None
@@ -589,10 +437,10 @@ class TibberPricesSensor(TibberPricesEntity, RestoreSensor):
         - "leading": Next 24 hours (96 intervals after current)
 
         Args:
-            stat_func: Function from average_utils (e.g., calculate_current_trailing_mean)
+            stat_func: Function from average_utils (e.g., calculate_current_trailing_avg)
 
         Returns:
-            Price value in subunit currency units (cents/øre), or None if unavailable
+            Price value in minor currency units (cents/øre), or None if unavailable
 
         """
         if not self.coordinator.data:
@@ -603,8 +451,8 @@ class TibberPricesSensor(TibberPricesEntity, RestoreSensor):
         if value is None:
             return None
 
-        # Return in configured display currency units with 2 decimals
+        # Always return in minor currency units (cents/øre) with 2 decimals
-        result = get_price_value(value, config_entry=self.coordinator.config_entry)
+        result = get_price_value(value, in_euro=False)
         return round(result, 2)
 
     def _translate_rating_level(self, level: str) -> str:
@@ -638,37 +486,21 @@ class TibberPricesSensor(TibberPricesEntity, RestoreSensor):
 
     def _get_next_avg_n_hours_value(self, hours: int) -> float | None:
         """
-        Get mean price for next N hours starting from next interval.
+        Get average price for next N hours starting from next interval.
 
         Args:
             hours: Number of hours to look ahead (1, 2, 3, 4, 5, 6, 8, 12)
 
         Returns:
-            Mean or median price (based on config) in subunit currency units (e.g., cents),
+            Average price in minor currency units (e.g., cents), or None if unavailable
-            or None if unavailable
 
         """
-        mean_price, median_price = calculate_next_n_hours_mean(self.coordinator.data, hours, time=self.coordinator.time)
+        avg_price = calculate_next_n_hours_avg(self.coordinator.data, hours, time=self.coordinator.time)
-        if mean_price is None:
+        if avg_price is None:
             return None
 
-        # Get display unit factor (100 for minor, 1 for major)
+        # Convert from major to minor currency units (e.g., EUR to cents)
-        factor = get_display_unit_factor(self.coordinator.config_entry)
+        return round(avg_price * 100, 2)
-
-        # Get user preference for display (mean or median)
-        display_pref = self.coordinator.config_entry.options.get(
-            CONF_AVERAGE_SENSOR_DISPLAY, DEFAULT_AVERAGE_SENSOR_DISPLAY
-        )
-
-        # Store both values for attributes
-        self.cached_data[f"next_avg_{hours}h_mean"] = round(mean_price * factor, 2)
-        if median_price is not None:
-            self.cached_data[f"next_avg_{hours}h_median"] = round(median_price * factor, 2)
-
-        # Return the value chosen for state display
-        if display_pref == "median" and median_price is not None:
-            return round(median_price * factor, 2)
-        return round(mean_price * factor, 2)  # "mean"
 
     def _get_data_timestamp(self) -> datetime | None:
         """
@@ -684,17 +516,16 @@ class TibberPricesSensor(TibberPricesEntity, RestoreSensor):
         if not self.coordinator.data:
             return None
 
-        # Use helper to get all intervals (today and tomorrow)
+        price_info = self.coordinator.data.get("priceInfo", {})
-        all_intervals = get_intervals_for_day_offsets(self.coordinator.data, [0, 1])
         latest_timestamp = None
 
-        # Search through intervals to find latest timestamp
+        for day in ["today", "tomorrow"]:
-        for price_data in all_intervals:
+            for price_data in price_info.get(day, []):
-            starts_at = price_data.get("startsAt")  # Already datetime in local timezone
+                starts_at = price_data.get("startsAt")  # Already datetime in local timezone
-            if not starts_at:
+                if not starts_at:
-                continue
+                    continue
-            if not latest_timestamp or starts_at > latest_timestamp:
+                if not latest_timestamp or starts_at > latest_timestamp:
-                latest_timestamp = starts_at
+                    latest_timestamp = starts_at
 
         # Return timezone-aware datetime (HA handles timezone display automatically)
         return latest_timestamp
@@ -713,6 +544,8 @@ class TibberPricesSensor(TibberPricesEntity, RestoreSensor):
         if not self.coordinator.data:
             return None
 
+        price_info = self.coordinator.data.get("priceInfo", {})
+
         # Get volatility thresholds from config
         thresholds = {
             "threshold_moderate": self.coordinator.config_entry.options.get("volatility_threshold_moderate", 5.0),
@@ -721,19 +554,30 @@ class TibberPricesSensor(TibberPricesEntity, RestoreSensor):
         }
 
         # Get prices based on volatility type
-        prices_to_analyze = get_prices_for_volatility(
+        prices_to_analyze = get_prices_for_volatility(volatility_type, price_info, time=self.coordinator.time)
-            volatility_type, self.coordinator.data, time=self.coordinator.time
-        )
 
         if not prices_to_analyze:
             return None
 
-        # Calculate volatility level with custom thresholds
+        # Calculate spread and basic statistics
-        # Note: Volatility calculation (coefficient of variation) uses mean internally
+        price_min = min(prices_to_analyze)
+        price_max = max(prices_to_analyze)
+        spread = price_max - price_min
+        price_avg = sum(prices_to_analyze) / len(prices_to_analyze)
+
+        # Convert to minor currency units (ct/øre) for display
+        spread_minor = spread * 100
+
+        # Calculate volatility level with custom thresholds (pass price list, not spread)
         volatility = calculate_volatility_level(prices_to_analyze, **thresholds)
 
-        # Store minimal attributes (only unique info not available in other sensors)
+        # Store attributes for this sensor
         self._last_volatility_attributes = {
+            "price_spread": round(spread_minor, 2),
+            "price_volatility": volatility,
+            "price_min": round(price_min * 100, 2),
+            "price_max": round(price_max * 100, 2),
+            "price_avg": round(price_avg * 100, 2),
             "interval_count": len(prices_to_analyze),
         }
 
@@ -742,11 +586,7 @@ class TibberPricesSensor(TibberPricesEntity, RestoreSensor):
 
         # Add type-specific attributes
         add_volatility_type_attributes(
-            self._last_volatility_attributes,
+            self._last_volatility_attributes, volatility_type, price_info, thresholds, time=self.coordinator.time
-            volatility_type,
-            self.coordinator.data,
-            thresholds,
-            time=self.coordinator.time,
         )
 
         # Return lowercase for ENUM device class
@@ -860,7 +700,7 @@ class TibberPricesSensor(TibberPricesEntity, RestoreSensor):
         return True
 
     @property
-    def native_value(self) -> float | str | datetime | None:  # noqa: PLR0912
+    def native_value(self) -> float | str | datetime | None:
         """Return the native value of the sensor."""
         try:
             if not self.coordinator.data or not self._value_getter:
@@ -868,8 +708,7 @@ class TibberPricesSensor(TibberPricesEntity, RestoreSensor):
             # For price_level, ensure we return the translated value as state
             if self.entity_description.key == "current_interval_price_level":
                 return self._interval_calculator.get_price_level_value()
-
+            return self._value_getter()
-            result = self._value_getter()
         except (KeyError, ValueError, TypeError) as ex:
             self.coordinator.logger.exception(
                 "Error getting sensor value",
@@ -879,48 +718,6 @@ class TibberPricesSensor(TibberPricesEntity, RestoreSensor):
                 },
             )
             return None
-        else:
-            # Handle tuple results (average + median) from calculators
-            if isinstance(result, tuple):
-                avg, median = result
-                # Get user preference for state display
-                display_pref = self.coordinator.config_entry.options.get(
-                    CONF_AVERAGE_SENSOR_DISPLAY,
-                    DEFAULT_AVERAGE_SENSOR_DISPLAY,
-                )
-
-                # Cache BOTH values for attribute builders to use
-                key = self.entity_description.key
-                if "average_price_today" in key:
-                    self.cached_data["average_price_today_mean"] = avg
-                    self.cached_data["average_price_today_median"] = median
-                elif "average_price_tomorrow" in key:
-                    self.cached_data["average_price_tomorrow_mean"] = avg
-                    self.cached_data["average_price_tomorrow_median"] = median
-                elif "trailing_price_average" in key:
-                    self.cached_data["trailing_price_mean"] = avg
-                    self.cached_data["trailing_price_median"] = median
-                elif "leading_price_average" in key:
-                    self.cached_data["leading_price_mean"] = avg
-                    self.cached_data["leading_price_median"] = median
-                elif "current_hour_average_price" in key:
-                    self.cached_data["rolling_hour_0_mean"] = avg
-                    self.cached_data["rolling_hour_0_median"] = median
-                elif "next_hour_average_price" in key:
-                    self.cached_data["rolling_hour_1_mean"] = avg
-                    self.cached_data["rolling_hour_1_median"] = median
-                elif key.startswith("next_avg_"):
-                    # Extract hours from key (e.g., "next_avg_3h" -> "3")
-                    hours = key.split("_")[-1].replace("h", "")
-                    self.cached_data[f"next_avg_{hours}h_mean"] = avg
-                    self.cached_data[f"next_avg_{hours}h_median"] = median
-
-                # Return the value chosen for state display
-                if display_pref == "median":
-                    return median
-                return avg  # "mean"
-
-            return result
 
     @property
     def native_unit_of_measurement(self) -> str | None:
@@ -929,15 +726,15 @@ class TibberPricesSensor(TibberPricesEntity, RestoreSensor):
         if self.entity_description.device_class == SensorDeviceClass.MONETARY:
             currency = None
             if self.coordinator.data:
-                currency = self.coordinator.data.get("currency")
+                price_info = self.coordinator.data.get("priceInfo", {})
+                currency = price_info.get("currency")
 
-            # Special case: Energy Dashboard sensor always uses base currency
+            # Use major currency unit for Energy Dashboard sensor
-            # regardless of user display mode configuration
+            if self.entity_description.key == "current_interval_price_major":
-            if self.entity_description.key == "current_interval_price_base":
+                return format_price_unit_major(currency)
-                return format_price_unit_base(currency)
 
-            # Get unit based on user configuration (major or minor)
+            # Use minor currency unit for all other price sensors
-            return get_display_unit_string(self.coordinator.config_entry, currency)
+            return format_price_unit_minor(currency)
 
         # For all other sensors, use unit from entity description
         return self.entity_description.native_unit_of_measurement
@@ -946,12 +743,7 @@ class TibberPricesSensor(TibberPricesEntity, RestoreSensor):
         """Check if the current time is within a best price period."""
         if not self.coordinator.data:
             return False
-        attrs = get_price_intervals_attributes(
+        attrs = get_price_intervals_attributes(self.coordinator.data, reverse_sort=False, time=self.coordinator.time)
-            self.coordinator.data,
-            reverse_sort=False,
-            time=self.coordinator.time,
-            config_entry=self.coordinator.config_entry,
-        )
         if not attrs:
             return False
         start = attrs.get("start")
@@ -966,12 +758,7 @@ class TibberPricesSensor(TibberPricesEntity, RestoreSensor):
         """Check if the current time is within a peak price period."""
         if not self.coordinator.data:
             return False
-        attrs = get_price_intervals_attributes(
+        attrs = get_price_intervals_attributes(self.coordinator.data, reverse_sort=True, time=self.coordinator.time)
-            self.coordinator.data,
-            reverse_sort=True,
-            time=self.coordinator.time,
-            config_entry=self.coordinator.config_entry,
-        )
         if not attrs:
             return False
         start = attrs.get("start")
@@ -987,13 +774,11 @@ class TibberPricesSensor(TibberPricesEntity, RestoreSensor):
         key = self.entity_description.key
         value = self.native_value
 
-        # Icon mapping for trend directions (5-level scale)
+        # Icon mapping for trend directions
         trend_icons = {
-            "strongly_rising": "mdi:chevron-double-up",
             "rising": "mdi:trending-up",
-            "stable": "mdi:trending-neutral",
             "falling": "mdi:trending-down",
-            "strongly_falling": "mdi:chevron-double-down",
+            "stable": "mdi:trending-neutral",
         }
 
         # Special handling for next_price_trend_change: Icon based on direction attribute
@@ -1030,43 +815,6 @@ class TibberPricesSensor(TibberPricesEntity, RestoreSensor):
         # Fall back to static icon from entity description
         return icon or self.entity_description.icon
 
-    @property
-    def suggested_display_precision(self) -> int | None:
-        """
-        Return suggested display precision based on currency display mode.
-
-        For MONETARY sensors:
-        - Current/Next Interval Price: Show exact price with higher precision
-          - Base currency (€/kr): 4 decimals (e.g., 0.1234 €)
-          - Subunit currency (ct/øre): 2 decimals (e.g., 12.34 ct)
-        - All other price sensors:
-          - Base currency (€/kr): 2 decimals (e.g., 0.12 €)
-          - Subunit currency (ct/øre): 1 decimal (e.g., 12.5 ct)
-
-        For non-MONETARY sensors, use static value from entity description.
-        """
-        # Only apply dynamic precision to MONETARY sensors
-        if self.entity_description.device_class != SensorDeviceClass.MONETARY:
-            return self.entity_description.suggested_display_precision
-
-        # Check display mode configuration
-        display_mode = self.coordinator.config_entry.options.get(CONF_CURRENCY_DISPLAY_MODE, DISPLAY_MODE_BASE)
-
-        # Special case: Energy Dashboard sensor always shows base currency with 4 decimals
-        # regardless of display mode (it's always in base currency by design)
-        if self.entity_description.key == "current_interval_price_base":
-            return 4
-
-        # Special case: Current and Next interval price sensors get higher precision
-        # to show exact prices as received from API
-        if self.entity_description.key in ("current_interval_price", "next_interval_price"):
-            # Major: 4 decimals (0.1234 €), Minor: 2 decimals (12.34 ct)
-            return 4 if display_mode == DISPLAY_MODE_BASE else 2
-
-        # All other sensors: Standard precision
-        # Major: 2 decimals (0.12 €), Minor: 1 decimal (12.5 ct)
-        return 2 if display_mode == DISPLAY_MODE_BASE else 1
-
     @property
     def extra_state_attributes(self) -> dict[str, Any] | None:
         """Return additional state attributes."""
@@ -1108,30 +856,20 @@ class TibberPricesSensor(TibberPricesEntity, RestoreSensor):
         if key == "chart_data_export":
             return self._get_chart_data_export_attributes()
 
-        # Special handling for chart_metadata - returns metadata in attributes
-        if key == "chart_metadata":
-            return self._get_chart_metadata_attributes()
-
         # Prepare cached data that attribute builders might need
-        # Start with all mean/median values from self.cached_data
+        cached_data = {
-        cached_data = {k: v for k, v in self.cached_data.items() if "_mean" in k or "_median" in k}
+            "trend_attributes": self._trend_calculator.get_trend_attributes(),
-
+            "current_trend_attributes": self._trend_calculator.get_current_trend_attributes(),
-        # Add special calculator results
+            "trend_change_attributes": self._trend_calculator.get_trend_change_attributes(),
-        cached_data.update(
+            "volatility_attributes": self._volatility_calculator.get_volatility_attributes(),
-            {
+            "last_extreme_interval": self._daily_stat_calculator.get_last_extreme_interval(),
-                "trend_attributes": self._trend_calculator.get_trend_attributes(),
+            "last_price_level": self._interval_calculator.get_last_price_level(),
-                "current_trend_attributes": self._trend_calculator.get_current_trend_attributes(),
+            "last_rating_difference": self._interval_calculator.get_last_rating_difference(),
-                "trend_change_attributes": self._trend_calculator.get_trend_change_attributes(),
+            "last_rating_level": self._interval_calculator.get_last_rating_level(),
-                "volatility_attributes": self._volatility_calculator.get_volatility_attributes(),
+            "data_timestamp": getattr(self, "_data_timestamp", None),
-                "last_extreme_interval": self._daily_stat_calculator.get_last_extreme_interval(),
+            "rolling_hour_level": self._get_rolling_hour_level_for_cached_data(key),
-                "last_price_level": self._interval_calculator.get_last_price_level(),
+            "lifecycle_calculator": self._lifecycle_calculator,  # For lifecycle sensor attributes
-                "last_rating_difference": self._interval_calculator.get_last_rating_difference(),
+        }
-                "last_rating_level": self._interval_calculator.get_last_rating_level(),
-                "data_timestamp": getattr(self, "_data_timestamp", None),
-                "rolling_hour_level": self._get_rolling_hour_level_for_cached_data(key),
-                "lifecycle_calculator": self._lifecycle_calculator,  # For lifecycle sensor attributes
-            }
-        )
 
         # Use the centralized attribute builder
         return build_sensor_attributes(
@@ -1139,7 +877,6 @@ class TibberPricesSensor(TibberPricesEntity, RestoreSensor):
             coordinator=self.coordinator,
             native_value=self.native_value,
             cached_data=cached_data,
-            config_entry=self.coordinator.config_entry,
         )
 
     def _get_rolling_hour_level_for_cached_data(self, key: str) -> str | None:
@@ -1194,36 +931,3 @@ class TibberPricesSensor(TibberPricesEntity, RestoreSensor):
             chart_data_last_update=self._chart_data_last_update,
             chart_data_error=self._chart_data_error,
         )
-
-    def _get_chart_metadata_value(self) -> str | None:
-        """Return state for chart_metadata sensor."""
-        return get_chart_metadata_state(
-            chart_metadata_response=self._chart_metadata_response,
-            chart_metadata_error=self._chart_metadata_error,
-        )
-
-    async def _refresh_chart_metadata(self) -> None:
-        """Refresh chart metadata by calling get_chartdata service with metadata=only."""
-        response, error = await call_chartdata_service_for_metadata_async(
-            hass=self.hass,
-            coordinator=self.coordinator,
-            config_entry=self.coordinator.config_entry,
-        )
-        self._chart_metadata_response = response
-        time = self.coordinator.time
-        self._chart_metadata_last_update = time.now()
-        self._chart_metadata_error = error
-        # Trigger state update after refresh
-        self.async_write_ha_state()
-
-    def _get_chart_metadata_attributes(self) -> dict[str, object] | None:
-        """
-        Return chart metadata from last service call as attributes.
-
-        Delegates to chart_metadata module for attribute building.
-        """
-        return build_chart_metadata_attributes(
-            chart_metadata_response=self._chart_metadata_response,
-            chart_metadata_last_update=self._chart_metadata_last_update,
-            chart_metadata_error=self._chart_metadata_error,
-        )

custom_components/tibber_prices/sensor/definitions.py

@@ -68,13 +68,13 @@ INTERVAL_PRICE_SENSORS = (
         suggested_display_precision=2,
     ),
     SensorEntityDescription(
-        key="current_interval_price_base",
+        key="current_interval_price_major",
-        translation_key="current_interval_price_base",
+        translation_key="current_interval_price_major",
         name="Current Electricity Price (Energy Dashboard)",
         icon="mdi:cash",  # Dynamic: shows cash-multiple/plus/cash/minus/remove based on price level
         device_class=SensorDeviceClass.MONETARY,
         state_class=SensorStateClass.TOTAL,  # MONETARY requires TOTAL or None for Energy Dashboard
-        suggested_display_precision=4,  # More precision for base currency (e.g., 0.2534 EUR/kWh)
+        suggested_display_precision=4,  # More precision for major currency (e.g., 0.2534 EUR/kWh)
     ),
     SensorEntityDescription(
         key="next_interval_price",
@@ -181,7 +181,7 @@ ROLLING_HOUR_PRICE_SENSORS = (
         icon="mdi:cash",  # Dynamic: shows cash-multiple/plus/cash/minus/remove based on aggregated price level
         device_class=SensorDeviceClass.MONETARY,
         state_class=SensorStateClass.TOTAL,  # MONETARY requires TOTAL or None
-        suggested_display_precision=2,
+        suggested_display_precision=1,
     ),
     SensorEntityDescription(
         key="next_hour_average_price",
@@ -190,7 +190,7 @@ ROLLING_HOUR_PRICE_SENSORS = (
         icon="mdi:cash-fast",  # Dynamic: shows cash-multiple/plus/cash/minus/remove based on aggregated price level
         device_class=SensorDeviceClass.MONETARY,
         state_class=SensorStateClass.TOTAL,  # MONETARY requires TOTAL or None
-        suggested_display_precision=2,
+        suggested_display_precision=1,
     ),
 )
 
@@ -259,7 +259,7 @@ DAILY_STAT_SENSORS = (
         icon="mdi:arrow-collapse-down",
         device_class=SensorDeviceClass.MONETARY,
         state_class=SensorStateClass.TOTAL,  # MONETARY requires TOTAL or None
-        suggested_display_precision=2,
+        suggested_display_precision=1,
     ),
     SensorEntityDescription(
         key="highest_price_today",
@@ -268,7 +268,7 @@ DAILY_STAT_SENSORS = (
         icon="mdi:arrow-collapse-up",
         device_class=SensorDeviceClass.MONETARY,
         state_class=SensorStateClass.TOTAL,  # MONETARY requires TOTAL or None
-        suggested_display_precision=2,
+        suggested_display_precision=1,
     ),
     SensorEntityDescription(
         key="average_price_today",
@@ -277,7 +277,7 @@ DAILY_STAT_SENSORS = (
         icon="mdi:chart-line",
         device_class=SensorDeviceClass.MONETARY,
         state_class=SensorStateClass.TOTAL,  # MONETARY requires TOTAL or None
-        suggested_display_precision=2,
+        suggested_display_precision=1,
     ),
     SensorEntityDescription(
         key="lowest_price_tomorrow",
@@ -286,7 +286,7 @@ DAILY_STAT_SENSORS = (
         icon="mdi:arrow-collapse-down",
         device_class=SensorDeviceClass.MONETARY,
         state_class=SensorStateClass.TOTAL,  # MONETARY requires TOTAL or None
-        suggested_display_precision=2,
+        suggested_display_precision=1,
     ),
     SensorEntityDescription(
         key="highest_price_tomorrow",
@@ -295,7 +295,7 @@ DAILY_STAT_SENSORS = (
         icon="mdi:arrow-collapse-up",
         device_class=SensorDeviceClass.MONETARY,
         state_class=SensorStateClass.TOTAL,  # MONETARY requires TOTAL or None
-        suggested_display_precision=2,
+        suggested_display_precision=1,
     ),
     SensorEntityDescription(
         key="average_price_tomorrow",
@@ -304,7 +304,7 @@ DAILY_STAT_SENSORS = (
         icon="mdi:chart-line",
         device_class=SensorDeviceClass.MONETARY,
         state_class=SensorStateClass.TOTAL,  # MONETARY requires TOTAL or None
-        suggested_display_precision=2,
+        suggested_display_precision=1,
     ),
 )
 
@@ -395,7 +395,7 @@ WINDOW_24H_SENSORS = (
         device_class=SensorDeviceClass.MONETARY,
         state_class=SensorStateClass.TOTAL,  # MONETARY requires TOTAL or None
         entity_registry_enabled_default=False,
-        suggested_display_precision=2,
+        suggested_display_precision=1,
     ),
     SensorEntityDescription(
         key="leading_price_average",
@@ -405,7 +405,7 @@ WINDOW_24H_SENSORS = (
         device_class=SensorDeviceClass.MONETARY,
         state_class=SensorStateClass.TOTAL,  # MONETARY requires TOTAL or None
         entity_registry_enabled_default=False,  # Advanced use case
-        suggested_display_precision=2,
+        suggested_display_precision=1,
     ),
     SensorEntityDescription(
         key="trailing_price_min",
@@ -415,7 +415,7 @@ WINDOW_24H_SENSORS = (
         device_class=SensorDeviceClass.MONETARY,
         state_class=SensorStateClass.TOTAL,  # MONETARY requires TOTAL or None
         entity_registry_enabled_default=False,
-        suggested_display_precision=2,
+        suggested_display_precision=1,
     ),
     SensorEntityDescription(
         key="trailing_price_max",
@@ -425,7 +425,7 @@ WINDOW_24H_SENSORS = (
         device_class=SensorDeviceClass.MONETARY,
         state_class=SensorStateClass.TOTAL,  # MONETARY requires TOTAL or None
         entity_registry_enabled_default=False,
-        suggested_display_precision=2,
+        suggested_display_precision=1,
     ),
     SensorEntityDescription(
         key="leading_price_min",
@@ -435,7 +435,7 @@ WINDOW_24H_SENSORS = (
         device_class=SensorDeviceClass.MONETARY,
         state_class=SensorStateClass.TOTAL,  # MONETARY requires TOTAL or None
         entity_registry_enabled_default=False,  # Advanced use case
-        suggested_display_precision=2,
+        suggested_display_precision=1,
     ),
     SensorEntityDescription(
         key="leading_price_max",
@@ -445,7 +445,7 @@ WINDOW_24H_SENSORS = (
         device_class=SensorDeviceClass.MONETARY,
         state_class=SensorStateClass.TOTAL,  # MONETARY requires TOTAL or None
         entity_registry_enabled_default=False,  # Advanced use case
-        suggested_display_precision=2,
+        suggested_display_precision=1,
     ),
 )
 
@@ -454,7 +454,7 @@ WINDOW_24H_SENSORS = (
 # ----------------------------------------------------------------------------
 # Calculate averages and trends for upcoming time windows
 
-FUTURE_MEAN_SENSORS = (
+FUTURE_AVG_SENSORS = (
     # Default enabled: 1h-5h
     SensorEntityDescription(
         key="next_avg_1h",
@@ -463,7 +463,7 @@ FUTURE_MEAN_SENSORS = (
         icon="mdi:chart-line",
         device_class=SensorDeviceClass.MONETARY,
         state_class=SensorStateClass.TOTAL,  # MONETARY requires TOTAL or None
-        suggested_display_precision=2,
+        suggested_display_precision=1,
         entity_registry_enabled_default=True,
     ),
     SensorEntityDescription(
@@ -473,7 +473,7 @@ FUTURE_MEAN_SENSORS = (
         icon="mdi:chart-line",
         device_class=SensorDeviceClass.MONETARY,
         state_class=SensorStateClass.TOTAL,  # MONETARY requires TOTAL or None
-        suggested_display_precision=2,
+        suggested_display_precision=1,
         entity_registry_enabled_default=True,
     ),
     SensorEntityDescription(
@@ -483,7 +483,7 @@ FUTURE_MEAN_SENSORS = (
         icon="mdi:chart-line",
         device_class=SensorDeviceClass.MONETARY,
         state_class=SensorStateClass.TOTAL,  # MONETARY requires TOTAL or None
-        suggested_display_precision=2,
+        suggested_display_precision=1,
         entity_registry_enabled_default=True,
     ),
     SensorEntityDescription(
@@ -493,7 +493,7 @@ FUTURE_MEAN_SENSORS = (
         icon="mdi:chart-line",
         device_class=SensorDeviceClass.MONETARY,
         state_class=SensorStateClass.TOTAL,  # MONETARY requires TOTAL or None
-        suggested_display_precision=2,
+        suggested_display_precision=1,
         entity_registry_enabled_default=True,
     ),
     SensorEntityDescription(
@@ -503,7 +503,7 @@ FUTURE_MEAN_SENSORS = (
         icon="mdi:chart-line",
         device_class=SensorDeviceClass.MONETARY,
         state_class=SensorStateClass.TOTAL,  # MONETARY requires TOTAL or None
-        suggested_display_precision=2,
+        suggested_display_precision=1,
         entity_registry_enabled_default=True,
     ),
     # Disabled by default: 6h, 8h, 12h (advanced use cases)
@@ -514,7 +514,7 @@ FUTURE_MEAN_SENSORS = (
         icon="mdi:chart-line",
         device_class=SensorDeviceClass.MONETARY,
         state_class=SensorStateClass.TOTAL,  # MONETARY requires TOTAL or None
-        suggested_display_precision=2,
+        suggested_display_precision=1,
         entity_registry_enabled_default=False,
     ),
     SensorEntityDescription(
@@ -524,7 +524,7 @@ FUTURE_MEAN_SENSORS = (
         icon="mdi:chart-line",
         device_class=SensorDeviceClass.MONETARY,
         state_class=SensorStateClass.TOTAL,  # MONETARY requires TOTAL or None
-        suggested_display_precision=2,
+        suggested_display_precision=1,
         entity_registry_enabled_default=False,
     ),
     SensorEntityDescription(
@@ -534,7 +534,7 @@ FUTURE_MEAN_SENSORS = (
         icon="mdi:chart-line",
         device_class=SensorDeviceClass.MONETARY,
         state_class=SensorStateClass.TOTAL,  # MONETARY requires TOTAL or None
-        suggested_display_precision=2,
+        suggested_display_precision=1,
         entity_registry_enabled_default=False,
     ),
 )
@@ -548,7 +548,7 @@ FUTURE_TREND_SENSORS = (
         icon="mdi:trending-up",  # Dynamic: trending-up/trending-down/trending-neutral based on current trend
         device_class=SensorDeviceClass.ENUM,
         state_class=None,  # Enum values: no statistics
-        options=["strongly_falling", "falling", "stable", "rising", "strongly_rising"],
+        options=["rising", "falling", "stable"],
         entity_registry_enabled_default=True,
     ),
     # Next trend change sensor (when will trend change?)
@@ -570,7 +570,7 @@ FUTURE_TREND_SENSORS = (
         icon="mdi:trending-up",  # Dynamic: shows trending-up/trending-down/trending-neutral based on trend value
         device_class=SensorDeviceClass.ENUM,
         state_class=None,  # Enum values: no statistics
-        options=["strongly_falling", "falling", "stable", "rising", "strongly_rising"],
+        options=["rising", "falling", "stable"],
         entity_registry_enabled_default=True,
     ),
     SensorEntityDescription(
@@ -580,7 +580,7 @@ FUTURE_TREND_SENSORS = (
         icon="mdi:trending-up",  # Dynamic: shows trending-up/trending-down/trending-neutral based on trend value
         device_class=SensorDeviceClass.ENUM,
         state_class=None,  # Enum values: no statistics
-        options=["strongly_falling", "falling", "stable", "rising", "strongly_rising"],
+        options=["rising", "falling", "stable"],
         entity_registry_enabled_default=True,
     ),
     SensorEntityDescription(
@@ -590,7 +590,7 @@ FUTURE_TREND_SENSORS = (
         icon="mdi:trending-up",  # Dynamic: shows trending-up/trending-down/trending-neutral based on trend value
         device_class=SensorDeviceClass.ENUM,
         state_class=None,  # Enum values: no statistics
-        options=["strongly_falling", "falling", "stable", "rising", "strongly_rising"],
+        options=["rising", "falling", "stable"],
         entity_registry_enabled_default=True,
     ),
     SensorEntityDescription(
@@ -600,7 +600,7 @@ FUTURE_TREND_SENSORS = (
         icon="mdi:trending-up",  # Dynamic: shows trending-up/trending-down/trending-neutral based on trend value
         device_class=SensorDeviceClass.ENUM,
         state_class=None,  # Enum values: no statistics
-        options=["strongly_falling", "falling", "stable", "rising", "strongly_rising"],
+        options=["rising", "falling", "stable"],
         entity_registry_enabled_default=True,
     ),
     SensorEntityDescription(
@@ -610,7 +610,7 @@ FUTURE_TREND_SENSORS = (
         icon="mdi:trending-up",  # Dynamic: shows trending-up/trending-down/trending-neutral based on trend value
         device_class=SensorDeviceClass.ENUM,
         state_class=None,  # Enum values: no statistics
-        options=["strongly_falling", "falling", "stable", "rising", "strongly_rising"],
+        options=["rising", "falling", "stable"],
         entity_registry_enabled_default=True,
     ),
     # Disabled by default: 6h, 8h, 12h
@@ -621,7 +621,7 @@ FUTURE_TREND_SENSORS = (
         icon="mdi:trending-up",  # Dynamic: shows trending-up/trending-down/trending-neutral based on trend value
         device_class=SensorDeviceClass.ENUM,
         state_class=None,  # Enum values: no statistics
-        options=["strongly_falling", "falling", "stable", "rising", "strongly_rising"],
+        options=["rising", "falling", "stable"],
         entity_registry_enabled_default=False,
     ),
     SensorEntityDescription(
@@ -631,7 +631,7 @@ FUTURE_TREND_SENSORS = (
         icon="mdi:trending-up",  # Dynamic: shows trending-up/trending-down/trending-neutral based on trend value
         device_class=SensorDeviceClass.ENUM,
         state_class=None,  # Enum values: no statistics
-        options=["strongly_falling", "falling", "stable", "rising", "strongly_rising"],
+        options=["rising", "falling", "stable"],
         entity_registry_enabled_default=False,
     ),
     SensorEntityDescription(
@@ -641,7 +641,7 @@ FUTURE_TREND_SENSORS = (
         icon="mdi:trending-up",  # Dynamic: shows trending-up/trending-down/trending-neutral based on trend value
         device_class=SensorDeviceClass.ENUM,
         state_class=None,  # Enum values: no statistics
-        options=["strongly_falling", "falling", "stable", "rising", "strongly_rising"],
+        options=["rising", "falling", "stable"],
         entity_registry_enabled_default=False,
     ),
 )
@@ -731,9 +731,9 @@ BEST_PRICE_TIMING_SENSORS = (
         name="Best Price Period Duration",
         icon="mdi:timer",
         device_class=SensorDeviceClass.DURATION,
-        native_unit_of_measurement=UnitOfTime.HOURS,
+        native_unit_of_measurement=UnitOfTime.MINUTES,
-        state_class=None,  # Duration not needed in long-term statistics
+        state_class=None,  # Changes with each period: no statistics
-        suggested_display_precision=2,
+        suggested_display_precision=0,
         entity_registry_enabled_default=False,
     ),
     SensorEntityDescription(
@@ -741,10 +741,9 @@ BEST_PRICE_TIMING_SENSORS = (
         translation_key="best_price_remaining_minutes",
         name="Best Price Remaining Time",
         icon="mdi:timer-sand",
-        device_class=SensorDeviceClass.DURATION,
+        native_unit_of_measurement=UnitOfTime.MINUTES,
-        native_unit_of_measurement=UnitOfTime.HOURS,
+        state_class=None,  # Countdown timer: no statistics
-        state_class=None,  # Countdown timers excluded from statistics
+        suggested_display_precision=0,
-        suggested_display_precision=2,
     ),
     SensorEntityDescription(
         key="best_price_progress",
@@ -768,10 +767,9 @@ BEST_PRICE_TIMING_SENSORS = (
         translation_key="best_price_next_in_minutes",
         name="Best Price Starts In",
         icon="mdi:timer-outline",
-        device_class=SensorDeviceClass.DURATION,
+        native_unit_of_measurement=UnitOfTime.MINUTES,
-        native_unit_of_measurement=UnitOfTime.HOURS,
+        state_class=None,  # Countdown timer: no statistics
-        state_class=None,  # Next-start timers excluded from statistics
+        suggested_display_precision=0,
-        suggested_display_precision=2,
     ),
 )
 
@@ -790,9 +788,9 @@ PEAK_PRICE_TIMING_SENSORS = (
         name="Peak Price Period Duration",
         icon="mdi:timer",
         device_class=SensorDeviceClass.DURATION,
-        native_unit_of_measurement=UnitOfTime.HOURS,
+        native_unit_of_measurement=UnitOfTime.MINUTES,
-        state_class=None,  # Duration not needed in long-term statistics
+        state_class=None,  # Changes with each period: no statistics
-        suggested_display_precision=2,
+        suggested_display_precision=0,
         entity_registry_enabled_default=False,
     ),
     SensorEntityDescription(
@@ -800,10 +798,9 @@ PEAK_PRICE_TIMING_SENSORS = (
         translation_key="peak_price_remaining_minutes",
         name="Peak Price Remaining Time",
         icon="mdi:timer-sand",
-        device_class=SensorDeviceClass.DURATION,
+        native_unit_of_measurement=UnitOfTime.MINUTES,
-        native_unit_of_measurement=UnitOfTime.HOURS,
+        state_class=None,  # Countdown timer: no statistics
-        state_class=None,  # Countdown timers excluded from statistics
+        suggested_display_precision=0,
-        suggested_display_precision=2,
     ),
     SensorEntityDescription(
         key="peak_price_progress",
@@ -827,10 +824,9 @@ PEAK_PRICE_TIMING_SENSORS = (
         translation_key="peak_price_next_in_minutes",
         name="Peak Price Starts In",
         icon="mdi:timer-outline",
-        device_class=SensorDeviceClass.DURATION,
+        native_unit_of_measurement=UnitOfTime.MINUTES,
-        native_unit_of_measurement=UnitOfTime.HOURS,
+        state_class=None,  # Countdown timer: no statistics
-        state_class=None,  # Next-start timers excluded from statistics
+        suggested_display_precision=0,
-        suggested_display_precision=2,
     ),
 )
 
@@ -847,7 +843,6 @@ DIAGNOSTIC_SENSORS = (
         options=["cached", "fresh", "refreshing", "searching_tomorrow", "turnover_pending", "error"],
         state_class=None,  # Status value: no statistics
         entity_category=EntityCategory.DIAGNOSTIC,
-        entity_registry_enabled_default=True,  # Critical for debugging
     ),
     # Home metadata from user data
     SensorEntityDescription(
@@ -1008,16 +1003,6 @@ DIAGNOSTIC_SENSORS = (
         entity_category=EntityCategory.DIAGNOSTIC,
         entity_registry_enabled_default=False,  # Opt-in
     ),
-    SensorEntityDescription(
-        key="chart_metadata",
-        translation_key="chart_metadata",
-        name="Chart Metadata",
-        icon="mdi:chart-box-outline",
-        device_class=SensorDeviceClass.ENUM,
-        options=["pending", "ready", "error"],
-        entity_category=EntityCategory.DIAGNOSTIC,
-        entity_registry_enabled_default=True,  # Critical for chart features
-    ),
 )
 
 # ----------------------------------------------------------------------------
@@ -1035,7 +1020,7 @@ ENTITY_DESCRIPTIONS = (
     *DAILY_LEVEL_SENSORS,
     *DAILY_RATING_SENSORS,
     *WINDOW_24H_SENSORS,
-    *FUTURE_MEAN_SENSORS,
+    *FUTURE_AVG_SENSORS,
     *FUTURE_TREND_SENSORS,
     *VOLATILITY_SENSORS,
     *BEST_PRICE_TIMING_SENSORS,

custom_components/tibber_prices/sensor/helpers.py

@@ -23,12 +23,8 @@ from typing import TYPE_CHECKING
 
 if TYPE_CHECKING:
     from custom_components.tibber_prices.coordinator.time_service import TibberPricesTimeService
-    from homeassistant.config_entries import ConfigEntry
 
-from custom_components.tibber_prices.const import get_display_unit_factor
-from custom_components.tibber_prices.coordinator.helpers import get_intervals_for_day_offsets
 from custom_components.tibber_prices.entity_utils.helpers import get_price_value
-from custom_components.tibber_prices.utils.average import calculate_mean, calculate_median
 from custom_components.tibber_prices.utils.price import (
     aggregate_price_levels,
     aggregate_price_rating,
@@ -38,31 +34,22 @@ if TYPE_CHECKING:
     from collections.abc import Callable
 
 
-def aggregate_average_data(
+def aggregate_price_data(window_data: list[dict]) -> float | None:
-    window_data: list[dict],
-    config_entry: ConfigEntry,
-) -> tuple[float | None, float | None]:
     """
-    Calculate average and median price from window data.
+    Calculate average price from window data.
 
     Args:
-        window_data: List of price interval dictionaries with 'total' key.
+        window_data: List of price interval dictionaries with 'total' key
-        config_entry: Config entry to get display unit configuration.
 
     Returns:
-        Tuple of (average price, median price) in display currency units,
+        Average price in minor currency units (cents/øre), or None if no prices
-        or (None, None) if no prices.
 
     """
     prices = [float(i["total"]) for i in window_data if "total" in i]
     if not prices:
-        return None, None
+        return None
-    # Calculate both mean and median
+    # Return in minor currency units (cents/øre)
-    mean = calculate_mean(prices)
+    return round((sum(prices) / len(prices)) * 100, 2)
-    median = calculate_median(prices)
-    # Convert to display currency unit based on configuration
-    factor = get_display_unit_factor(config_entry)
-    return round(mean * factor, 2), round(median * factor, 2) if median is not None else None
 
 
 def aggregate_level_data(window_data: list[dict]) -> str | None:
@@ -113,29 +100,25 @@ def aggregate_window_data(
     value_type: str,
     threshold_low: float,
     threshold_high: float,
-    config_entry: ConfigEntry,
 ) -> str | float | None:
     """
     Aggregate data from multiple intervals based on value type.
 
     Unified helper that routes to appropriate aggregation function.
 
-    NOTE: This function is legacy code - rolling_hour calculator has its own implementation.
-
     Args:
-        window_data: List of price interval dictionaries.
+        window_data: List of price interval dictionaries
-        value_type: Type of value to aggregate ('price', 'level', or 'rating').
+        value_type: Type of value to aggregate ('price', 'level', or 'rating')
-        threshold_low: Low threshold for rating calculation.
+        threshold_low: Low threshold for rating calculation
-        threshold_high: High threshold for rating calculation.
+        threshold_high: High threshold for rating calculation
-        config_entry: Config entry to get display unit configuration.
 
     Returns:
-        Aggregated value (price as float, level/rating as str), or None if no data.
+        Aggregated value (price as float, level/rating as str), or None if no data
 
     """
     # Map value types to aggregation functions
     aggregators: dict[str, Callable] = {
-        "price": lambda data: aggregate_average_data(data, config_entry)[0],  # Use only average from tuple
+        "price": lambda data: aggregate_price_data(data),
         "level": lambda data: aggregate_level_data(data),
         "rating": lambda data: aggregate_rating_data(data, threshold_low, threshold_high),
     }
@@ -147,7 +130,7 @@ def aggregate_window_data(
 
 
 def get_hourly_price_value(
-    coordinator_data: dict,
+    price_info: dict,
     *,
     hour_offset: int,
     in_euro: bool,
@@ -160,9 +143,9 @@ def get_hourly_price_value(
     Kept for potential backward compatibility.
 
     Args:
-        coordinator_data: Coordinator data dict
+        price_info: Price information dict with 'today' and 'tomorrow' keys
         hour_offset: Hour offset from current time (positive=future, negative=past)
-        in_euro: If True, return price in base currency (EUR), else minor (cents/øre)
+        in_euro: If True, return price in major currency (EUR), else minor (cents/øre)
         time: TibberPricesTimeService instance (required)
 
     Returns:
@@ -178,18 +161,30 @@ def get_hourly_price_value(
     target_hour = target_datetime.hour
     target_date = target_datetime.date()
 
-    # Get all intervals (yesterday, today, tomorrow) via helper
+    # Determine which day's data we need
-    all_intervals = get_intervals_for_day_offsets(coordinator_data, [-1, 0, 1])
+    day_key = "tomorrow" if target_date > now.date() else "today"
 
-    # Search through all intervals to find the matching hour
+    for price_data in price_info.get(day_key, []):
-    for price_data in all_intervals:
         # Parse the timestamp and convert to local time
         starts_at = time.get_interval_time(price_data)
         if starts_at is None:
             continue
 
+        # Make sure it's in the local timezone for proper comparison
+
         # Compare using both hour and date for accuracy
         if starts_at.hour == target_hour and starts_at.date() == target_date:
             return get_price_value(float(price_data["total"]), in_euro=in_euro)
 
+    # If we didn't find the price in the expected day's data, check the other day
+    # This is a fallback for potential edge cases
+    other_day_key = "today" if day_key == "tomorrow" else "tomorrow"
+    for price_data in price_info.get(other_day_key, []):
+        starts_at = time.get_interval_time(price_data)
+        if starts_at is None:
+            continue
+
+        if starts_at.hour == target_hour and starts_at.date() == target_date:
+            return get_price_value(float(price_data["total"]), in_euro=in_euro)
+
     return None

custom_components/tibber_prices/sensor/types.py

@@ -1,261 +0,0 @@
-"""
-Type definitions for Tibber Prices sensor attributes.
-
-These TypedDict definitions serve as **documentation** of the attribute structure
-for each sensor type. They enable IDE autocomplete and type checking when working
-with attribute dictionaries.
-
-NOTE: In function signatures, we still use dict[str, Any] for flexibility,
-but these TypedDict definitions document what keys and types are expected.
-
-IMPORTANT: The Literal types defined here should be kept in sync with the
-string constants in const.py, which are the single source of truth for runtime values.
-"""
-
-from __future__ import annotations
-
-from typing import Literal, TypedDict
-
-# ============================================================================
-# Literal Type Definitions
-# ============================================================================
-# SYNC: Keep these in sync with constants in const.py
-#
-# const.py defines the runtime constants (single source of truth):
-#   - PRICE_LEVEL_VERY_CHEAP, PRICE_LEVEL_CHEAP, etc.
-#   - PRICE_RATING_LOW, PRICE_RATING_NORMAL, etc.
-#   - VOLATILITY_LOW, VOLATILITY_MODERATE, etc.
-#
-# These Literal types should mirror those constants for type safety.
-
-# Price level literals (from Tibber API)
-PriceLevel = Literal[
-    "VERY_CHEAP",
-    "CHEAP",
-    "NORMAL",
-    "EXPENSIVE",
-    "VERY_EXPENSIVE",
-]
-
-# Price rating literals (calculated values)
-PriceRating = Literal[
-    "LOW",
-    "NORMAL",
-    "HIGH",
-]
-
-# Volatility level literals (based on coefficient of variation)
-VolatilityLevel = Literal[
-    "LOW",
-    "MODERATE",
-    "HIGH",
-    "VERY_HIGH",
-]
-
-# Data completeness literals
-DataCompleteness = Literal[
-    "complete",
-    "partial_yesterday",
-    "partial_today",
-    "partial_tomorrow",
-    "missing_yesterday",
-    "missing_today",
-    "missing_tomorrow",
-]
-
-
-# ============================================================================
-# TypedDict Definitions
-# ============================================================================
-
-
-class BaseAttributes(TypedDict, total=False):
-    """
-    Base attributes common to all sensors.
-
-    All sensor attributes include at minimum:
-    - timestamp: ISO 8601 string indicating when the state/attributes are valid
-    - error: Optional error message if something went wrong
-    """
-
-    timestamp: str
-    error: str
-
-
-class IntervalPriceAttributes(BaseAttributes, total=False):
-    """
-    Attributes for interval price sensors (current/next/previous).
-
-    These sensors show price information for a specific 15-minute interval.
-    """
-
-    level_value: int  # Numeric value for price level (1-5)
-    level_id: PriceLevel  # String identifier for price level
-    icon_color: str  # Optional icon color based on level
-
-
-class IntervalLevelAttributes(BaseAttributes, total=False):
-    """
-    Attributes for interval level sensors.
-
-    These sensors show the price level classification for an interval.
-    """
-
-    icon_color: str  # Icon color based on level
-
-
-class IntervalRatingAttributes(BaseAttributes, total=False):
-    """
-    Attributes for interval rating sensors.
-
-    These sensors show the price rating (LOW/NORMAL/HIGH) for an interval.
-    """
-
-    rating_value: int  # Numeric value for price rating (1-3)
-    rating_id: PriceRating  # String identifier for price rating
-    icon_color: str  # Optional icon color based on rating
-
-
-class RollingHourAttributes(BaseAttributes, total=False):
-    """
-    Attributes for rolling hour sensors.
-
-    These sensors aggregate data across 5 intervals (2 before + current + 2 after).
-    """
-
-    icon_color: str  # Optional icon color based on aggregated level
-
-
-class DailyStatPriceAttributes(BaseAttributes, total=False):
-    """
-    Attributes for daily statistics price sensors (min/max/avg).
-
-    These sensors show price statistics for a full calendar day.
-    """
-
-    # No additional attributes for daily price stats beyond base
-
-
-class DailyStatRatingAttributes(BaseAttributes, total=False):
-    """
-    Attributes for daily statistics rating sensors.
-
-    These sensors show rating statistics for a full calendar day.
-    """
-
-    diff_percent: str  # Key is actually "diff_%" - percentage difference
-    level_id: PriceRating  # Rating level identifier
-    level_value: int  # Numeric rating value (1-3)
-
-
-class Window24hAttributes(BaseAttributes, total=False):
-    """
-    Attributes for 24-hour window sensors (trailing/leading).
-
-    These sensors analyze price data across a 24-hour window from current time.
-    """
-
-    interval_count: int  # Number of intervals in the window
-
-
-class VolatilityAttributes(BaseAttributes, total=False):
-    """
-    Attributes for volatility sensors.
-
-    These sensors analyze price variation and spread across time periods.
-    """
-
-    today_spread: float  # Price range for today (max - min)
-    today_volatility: str  # Volatility level for today
-    interval_count_today: int  # Number of intervals analyzed today
-    tomorrow_spread: float  # Price range for tomorrow (max - min)
-    tomorrow_volatility: str  # Volatility level for tomorrow
-    interval_count_tomorrow: int  # Number of intervals analyzed tomorrow
-
-
-class TrendAttributes(BaseAttributes, total=False):
-    """
-    Attributes for trend sensors.
-
-    These sensors analyze price trends and forecast future movements.
-    Trend attributes are complex and may vary based on trend type.
-    """
-
-    # Trend attributes are dynamic and vary by sensor type
-    # Keep flexible with total=False
-
-
-class TimingAttributes(BaseAttributes, total=False):
-    """
-    Attributes for period timing sensors (best_price/peak_price timing).
-
-    These sensors track timing information for best/peak price periods.
-    """
-
-    icon_color: str  # Icon color based on timing status
-
-
-class FutureAttributes(BaseAttributes, total=False):
-    """
-    Attributes for future forecast sensors.
-
-    These sensors provide N-hour forecasts starting from next interval.
-    """
-
-    interval_count: int  # Number of intervals in forecast
-    hours: int  # Number of hours in forecast window
-
-
-class LifecycleAttributes(BaseAttributes, total=False):
-    """
-    Attributes for lifecycle/diagnostic sensors.
-
-    These sensors provide system information and cache status.
-    """
-
-    cache_age: str  # Human-readable cache age
-    cache_age_minutes: int  # Cache age in minutes
-    cache_validity: str  # Cache validity status
-    last_api_fetch: str  # ISO 8601 timestamp of last API fetch
-    last_cache_update: str  # ISO 8601 timestamp of last cache update
-    data_completeness: DataCompleteness  # Data completeness status
-    yesterday_available: bool  # Whether yesterday data exists
-    today_available: bool  # Whether today data exists
-    tomorrow_available: bool  # Whether tomorrow data exists
-    tomorrow_expected_after: str  # Time when tomorrow data expected
-    next_api_poll: str  # ISO 8601 timestamp of next API poll
-    next_midnight_turnover: str  # ISO 8601 timestamp of next midnight turnover
-    updates_today: int  # Number of API updates today
-    last_turnover: str  # ISO 8601 timestamp of last midnight turnover
-    last_error: str  # Last error message if any
-
-
-class MetadataAttributes(BaseAttributes, total=False):
-    """
-    Attributes for metadata sensors (home info, metering point).
-
-    These sensors provide Tibber account and home metadata.
-    Metadata attributes vary by sensor type.
-    """
-
-    # Metadata attributes are dynamic and vary by sensor type
-    # Keep flexible with total=False
-
-
-# Union type for all sensor attributes (for documentation purposes)
-# In actual code, use dict[str, Any] for flexibility
-SensorAttributes = (
-    IntervalPriceAttributes
-    | IntervalLevelAttributes
-    | IntervalRatingAttributes
-    | RollingHourAttributes
-    | DailyStatPriceAttributes
-    | DailyStatRatingAttributes
-    | Window24hAttributes
-    | VolatilityAttributes
-    | TrendAttributes
-    | TimingAttributes
-    | FutureAttributes
-    | LifecycleAttributes
-    | MetadataAttributes
-)

custom_components/tibber_prices/sensor/value_getters.py

@@ -2,17 +2,15 @@
 
 from __future__ import annotations
 
-from typing import TYPE_CHECKING, cast
+from typing import TYPE_CHECKING
 
 from custom_components.tibber_prices.utils.average import (
+    calculate_current_leading_avg,
     calculate_current_leading_max,
-    calculate_current_leading_mean,
     calculate_current_leading_min,
+    calculate_current_trailing_avg,
     calculate_current_trailing_max,
-    calculate_current_trailing_mean,
     calculate_current_trailing_min,
-    calculate_mean,
-    calculate_median,
 )
 
 if TYPE_CHECKING:
@@ -43,7 +41,6 @@ def get_value_getter_mapping(  # noqa: PLR0913 - needs all calculators as parame
     get_next_avg_n_hours_value: Callable[[int], float | None],
     get_data_timestamp: Callable[[], datetime | None],
     get_chart_data_export_value: Callable[[], str | None],
-    get_chart_metadata_value: Callable[[], str | None],
 ) -> dict[str, Callable]:
     """
     Build mapping from entity key to value getter callable.
@@ -64,20 +61,11 @@ def get_value_getter_mapping(  # noqa: PLR0913 - needs all calculators as parame
         get_next_avg_n_hours_value: Method for next N-hour average forecasts
         get_data_timestamp: Method for data timestamp sensor
         get_chart_data_export_value: Method for chart data export sensor
-        get_chart_metadata_value: Method for chart metadata sensor
 
     Returns:
         Dictionary mapping entity keys to their value getter callables.
 
     """
-
-    def _minutes_to_hours(value: float | None) -> float | None:
-        """Convert minutes to hours for duration-oriented sensors."""
-        if value is None:
-            return None
-
-        return value / 60
-
     return {
         # ================================================================
         # INTERVAL-BASED SENSORS - via IntervalCalculator
@@ -94,7 +82,7 @@ def get_value_getter_mapping(  # noqa: PLR0913 - needs all calculators as parame
         "current_interval_price": lambda: interval_calculator.get_interval_value(
             interval_offset=0, value_type="price", in_euro=False
         ),
-        "current_interval_price_base": lambda: interval_calculator.get_interval_value(
+        "current_interval_price_major": lambda: interval_calculator.get_interval_value(
             interval_offset=0, value_type="price", in_euro=True
         ),
         "next_interval_price": lambda: interval_calculator.get_interval_value(
@@ -140,14 +128,14 @@ def get_value_getter_mapping(  # noqa: PLR0913 - needs all calculators as parame
         "highest_price_today": lambda: daily_stat_calculator.get_daily_stat_value(day="today", stat_func=max),
         "average_price_today": lambda: daily_stat_calculator.get_daily_stat_value(
             day="today",
-            stat_func=lambda prices: (calculate_mean(prices), calculate_median(prices)),
+            stat_func=lambda prices: sum(prices) / len(prices),
         ),
         # Tomorrow statistics sensors
         "lowest_price_tomorrow": lambda: daily_stat_calculator.get_daily_stat_value(day="tomorrow", stat_func=min),
         "highest_price_tomorrow": lambda: daily_stat_calculator.get_daily_stat_value(day="tomorrow", stat_func=max),
         "average_price_tomorrow": lambda: daily_stat_calculator.get_daily_stat_value(
             day="tomorrow",
-            stat_func=lambda prices: (calculate_mean(prices), calculate_median(prices)),
+            stat_func=lambda prices: sum(prices) / len(prices),
         ),
         # Daily aggregated level sensors
         "yesterday_price_level": lambda: daily_stat_calculator.get_daily_aggregated_value(
@@ -172,10 +160,10 @@ def get_value_getter_mapping(  # noqa: PLR0913 - needs all calculators as parame
         # ================================================================
         # Trailing and leading average sensors
         "trailing_price_average": lambda: window_24h_calculator.get_24h_window_value(
-            stat_func=calculate_current_trailing_mean,
+            stat_func=calculate_current_trailing_avg,
         ),
         "leading_price_average": lambda: window_24h_calculator.get_24h_window_value(
-            stat_func=calculate_current_leading_mean,
+            stat_func=calculate_current_leading_avg,
         ),
         # Trailing and leading min/max sensors
         "trailing_price_min": lambda: window_24h_calculator.get_24h_window_value(
@@ -251,17 +239,11 @@ def get_value_getter_mapping(  # noqa: PLR0913 - needs all calculators as parame
         "best_price_end_time": lambda: timing_calculator.get_period_timing_value(
             period_type="best_price", value_type="end_time"
         ),
-        "best_price_period_duration": lambda: _minutes_to_hours(
+        "best_price_period_duration": lambda: timing_calculator.get_period_timing_value(
-            cast(
+            period_type="best_price", value_type="period_duration"
-                "float | None",
-                timing_calculator.get_period_timing_value(period_type="best_price", value_type="period_duration"),
-            )
         ),
-        "best_price_remaining_minutes": lambda: _minutes_to_hours(
+        "best_price_remaining_minutes": lambda: timing_calculator.get_period_timing_value(
-            cast(
+            period_type="best_price", value_type="remaining_minutes"
-                "float | None",
-                timing_calculator.get_period_timing_value(period_type="best_price", value_type="remaining_minutes"),
-            )
         ),
         "best_price_progress": lambda: timing_calculator.get_period_timing_value(
             period_type="best_price", value_type="progress"
@@ -269,27 +251,18 @@ def get_value_getter_mapping(  # noqa: PLR0913 - needs all calculators as parame
         "best_price_next_start_time": lambda: timing_calculator.get_period_timing_value(
             period_type="best_price", value_type="next_start_time"
         ),
-        "best_price_next_in_minutes": lambda: _minutes_to_hours(
+        "best_price_next_in_minutes": lambda: timing_calculator.get_period_timing_value(
-            cast(
+            period_type="best_price", value_type="next_in_minutes"
-                "float | None",
-                timing_calculator.get_period_timing_value(period_type="best_price", value_type="next_in_minutes"),
-            )
         ),
         # Peak Price timing sensors
         "peak_price_end_time": lambda: timing_calculator.get_period_timing_value(
             period_type="peak_price", value_type="end_time"
         ),
-        "peak_price_period_duration": lambda: _minutes_to_hours(
+        "peak_price_period_duration": lambda: timing_calculator.get_period_timing_value(
-            cast(
+            period_type="peak_price", value_type="period_duration"
-                "float | None",
-                timing_calculator.get_period_timing_value(period_type="peak_price", value_type="period_duration"),
-            )
         ),
-        "peak_price_remaining_minutes": lambda: _minutes_to_hours(
+        "peak_price_remaining_minutes": lambda: timing_calculator.get_period_timing_value(
-            cast(
+            period_type="peak_price", value_type="remaining_minutes"
-                "float | None",
-                timing_calculator.get_period_timing_value(period_type="peak_price", value_type="remaining_minutes"),
-            )
         ),
         "peak_price_progress": lambda: timing_calculator.get_period_timing_value(
             period_type="peak_price", value_type="progress"
@@ -297,14 +270,9 @@ def get_value_getter_mapping(  # noqa: PLR0913 - needs all calculators as parame
         "peak_price_next_start_time": lambda: timing_calculator.get_period_timing_value(
             period_type="peak_price", value_type="next_start_time"
         ),
-        "peak_price_next_in_minutes": lambda: _minutes_to_hours(
+        "peak_price_next_in_minutes": lambda: timing_calculator.get_period_timing_value(
-            cast(
+            period_type="peak_price", value_type="next_in_minutes"
-                "float | None",
-                timing_calculator.get_period_timing_value(period_type="peak_price", value_type="next_in_minutes"),
-            )
         ),
         # Chart data export sensor
         "chart_data_export": get_chart_data_export_value,
-        # Chart metadata sensor
-        "chart_metadata": get_chart_metadata_value,
     }