refactor(sensors): Transform sensor platform into package

AGENTS.md

@@ -4,8 +4,8 @@ This is a **Home Assistant custom component** for Tibber electricity price data,
 
 ## Documentation Metadata
 
--   **Last Major Update**: 2025-11-15
--   **Last Architecture Review**: 2025-11-15 (Sensor.py refactoring completed - unified handler methods for interval/rolling hour/daily stats/24h windows. Sensor organization changed from feature-type to calculation-method grouping. Common Tasks section updated with new patterns.)
+-   **Last Major Update**: 2025-11-17
+-   **Last Architecture Review**: 2025-11-17 (Module splitting refactoring completed - sensor.py split into sensor/ package with core.py, definitions.py, helpers.py, attributes.py. Created entity_utils/ package for shared icon/color/attribute logic. All phases complete.)
 -   **Documentation Status**: ✅ Current (verified against codebase)
 
 _Note: When proposing significant updates to this file, update the metadata above with the new date and brief description of changes._
@@ -112,7 +112,7 @@ This ensures the documentation stays accurate and useful as the codebase evolves
 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 (`price_utils.py`, `average_utils.py`) calculate trailing/leading 24h averages, price differences, and rating levels for each 15-minute interval
-4. Entity platforms (`sensor.py`, `binary_sensor.py`) expose enriched data as Home Assistant entities
+4. Entity platforms (`sensor/` package, `binary_sensor.py`) expose enriched data as Home Assistant entities
 5. Custom services (`services.py`) provide API endpoints for integrations like ApexCharts
 
 **Key Patterns:**
@@ -179,7 +179,7 @@ This ensures the documentation stays accurate and useful as the codebase evolves
       "rating_level": "NORMAL"      # Added: LOW/NORMAL/HIGH based on thresholds
     }
     ```
--   **Sensor organization (refactored Nov 2025)**: Sensors in `sensor.py` are grouped by **calculation method** rather than feature type, enabling code reuse through unified handler methods:
+-   **Sensor organization (refactored Nov 2025)**: The `sensor/` package is organized by **calculation method** rather than feature type, enabling code reuse through unified handler methods:
     -   **Interval-based sensors**: Use `_get_interval_value(interval_offset, value_type)` for current/next/previous interval data
     -   **Rolling hour sensors**: Use `_get_rolling_hour_value(hour_offset, value_type)` for 5-interval windows
     -   **Daily statistics**: Use `_get_daily_stat_value(day, stat_func)` for calendar day min/max/avg
@@ -204,9 +204,19 @@ custom_components/tibber_prices/
 ├── price_utils.py        # Price enrichment, level/rating calculations
 ├── average_utils.py      # Trailing/leading average utilities
 ├── services.py           # Custom services (get_price, ApexCharts, etc.)
-├── sensor.py             # Price/stats/diagnostic sensors
+├── sensor/               # Sensor platform (package)
+│   ├── __init__.py       #   Platform setup (async_setup_entry)
+│   ├── core.py           #   TibberPricesSensor class
+│   ├── definitions.py    #   ENTITY_DESCRIPTIONS
+│   ├── helpers.py        #   Pure helper functions
+│   └── attributes.py     #   Attribute builders
 ├── binary_sensor.py      # Peak/best hour binary sensors
 ├── entity.py             # Base TibberPricesEntity class
+├── entity_utils/         # Shared entity helpers (both platforms)
+│   ├── __init__.py       #   Package exports
+│   ├── icons.py          #   Icon mapping logic
+│   ├── colors.py         #   Color mapping logic
+│   └── attributes.py     #   Common attribute builders
 ├── data.py               # @dataclass TibberPricesData
 ├── const.py              # Constants, translation loaders, currency helpers
 ├── config_flow.py        # UI configuration flow
@@ -1768,7 +1778,7 @@ After the sensor.py refactoring (completed Nov 2025), sensors are organized by *
     - **Volatility**: Statistical analysis of price variation
     - **Diagnostic**: System information and metadata
 
-2. **Add entity description** to appropriate sensor group in `sensor.py`:
+2. **Add entity description** to appropriate sensor group in `sensor/definitions.py`:
 
     - `INTERVAL_PRICE_SENSORS`, `INTERVAL_LEVEL_SENSORS`, or `INTERVAL_RATING_SENSORS`
     - `ROLLING_HOUR_PRICE_SENSORS`, `ROLLING_HOUR_LEVEL_SENSORS`, or `ROLLING_HOUR_RATING_SENSORS`
@@ -1778,7 +1788,7 @@ After the sensor.py refactoring (completed Nov 2025), sensors are organized by *
     - `VOLATILITY_SENSORS`
     - `DIAGNOSTIC_SENSORS`
 
-3. **Add handler mapping** in `_get_value_getter()` method:
+3. **Add handler mapping** in `sensor/core.py` → `_get_value_getter()` method:
 
     - For interval-based: Use `_get_interval_value(interval_offset, value_type)`
     - For rolling hour: Use `_get_rolling_hour_value(hour_offset, value_type)`
@@ -1793,7 +1803,7 @@ After the sensor.py refactoring (completed Nov 2025), sensors are organized by *
 **Example - Adding a "2 hours ago" interval sensor:**
 
 ```python
-# 1. Add to INTERVAL_PRICE_SENSORS group in sensor.py
+# 1. Add to INTERVAL_PRICE_SENSORS group in sensor/definitions.py
 SensorEntityDescription(
     key="two_hours_ago_price",
     translation_key="two_hours_ago_price",
@@ -1804,7 +1814,7 @@ SensorEntityDescription(
     suggested_display_precision=2,
 )
 
-# 2. Add handler in _get_value_getter()
+# 2. Add handler in sensor/core.py → _get_value_getter()
 "two_hours_ago_price": lambda: self._get_interval_value(
     interval_offset=-8,  # 2 hours = 8 intervals (15 min each)
     value_type="price",
@@ -1834,7 +1844,7 @@ SensorEntityDescription(
 
 **Unified Handler Methods (Post-Refactoring):**
 
-The refactoring consolidated duplicate logic into unified methods:
+The refactoring consolidated duplicate logic into unified methods in `sensor/core.py`:
 
 -   **`_get_interval_value(interval_offset, value_type, in_euro=False)`**
 

custom_components/tibber_prices/binary_sensor.py

@@ -16,6 +16,7 @@ from homeassistant.util import dt as dt_util
 
 from .coordinator import TIME_SENSITIVE_ENTITY_KEYS
 from .entity import TibberPricesEntity
+from .entity_utils import add_icon_color_attribute, get_binary_sensor_icon
 
 if TYPE_CHECKING:
     from collections.abc import Callable
@@ -28,8 +29,6 @@ if TYPE_CHECKING:
     from .data import TibberPricesConfigEntry
 
 from .const import (
-    BINARY_SENSOR_COLOR_MAPPING,
-    BINARY_SENSOR_ICON_MAPPING,
     CONF_EXTENDED_DESCRIPTIONS,
     DEFAULT_EXTENDED_DESCRIPTIONS,
     async_get_entity_description,
@@ -446,24 +445,15 @@ class TibberPricesBinarySensor(TibberPricesEntity, BinarySensorEntity):
         """Return the icon based on binary sensor state."""
         key = self.entity_description.key
 
-        # Dynamic icons for best/peak price period sensors
-        if key in BINARY_SENSOR_ICON_MAPPING:
-            if self.is_on:
-                # Sensor is ON - use "on" icon
-                icon = BINARY_SENSOR_ICON_MAPPING[key].get("on")
-            else:
-                # Sensor is OFF - check if future periods exist
-                has_future_periods = self._has_future_periods()
-                if has_future_periods:
-                    icon = BINARY_SENSOR_ICON_MAPPING[key].get("off")
-                else:
-                    icon = BINARY_SENSOR_ICON_MAPPING[key].get("off_no_future")
+        # Use shared icon utility
+        icon = get_binary_sensor_icon(
+            key,
+            is_on=self.is_on,
+            has_future_periods_callback=self._has_future_periods,
+        )
 
-            if icon:
-                return icon
-
-        # For all other sensors, use static icon from entity description
-        return self.entity_description.icon
+        # Fall back to static icon from entity description
+        return icon or self.entity_description.icon
 
     def _has_future_periods(self) -> bool:
         """
@@ -514,15 +504,10 @@ class TibberPricesBinarySensor(TibberPricesEntity, BinarySensorEntity):
                     clean_attrs = {k: v for k, v in dynamic_attrs.items() if not k.startswith("_")}
                     attributes.update(clean_attrs)
 
-            # Add icon_color for best/peak price period sensors
-            key = self.entity_description.key
-            if key in BINARY_SENSOR_COLOR_MAPPING:
-                state = "on" if self.is_on else "off"
-                color = BINARY_SENSOR_COLOR_MAPPING[key].get(state)
-                if color:
-                    attributes["icon_color"] = color
+            # Add icon_color for best/peak price period sensors using shared utility
+            add_icon_color_attribute(attributes, self.entity_description.key, is_on=self.is_on)
 
-            # Add descriptions from the custom translations file
+            # Add description from the custom translations file
             if self.entity_description.translation_key and self.hass is not None:
                 # Get user's language preference
                 language = self.hass.config.language if self.hass.config.language else "en"
@@ -596,13 +581,8 @@ class TibberPricesBinarySensor(TibberPricesEntity, BinarySensorEntity):
                     clean_attrs = {k: v for k, v in dynamic_attrs.items() if not k.startswith("_")}
                     attributes.update(clean_attrs)
 
-            # Add icon_color for best/peak price period sensors
-            key = self.entity_description.key
-            if key in BINARY_SENSOR_COLOR_MAPPING:
-                state = "on" if self.is_on else "off"
-                color = BINARY_SENSOR_COLOR_MAPPING[key].get(state)
-                if color:
-                    attributes["icon_color"] = color
+            # Add icon_color for best/peak price period sensors using shared utility
+            add_icon_color_attribute(attributes, self.entity_description.key, is_on=self.is_on)
 
             # Add descriptions from the cache (non-blocking)
             if self.entity_description.translation_key and self.hass is not None:

custom_components/tibber_prices/config_flow.py

@@ -526,7 +526,7 @@ class TibberPricesOptionsFlowHandler(OptionsFlow):
 
         if user_input is not None:
             self._options.update(user_input)
-            return await self.async_step_price_rating()
+            return await self.async_step_current_interval_price_rating()
 
         return self.async_show_form(
             step_id="init",
@@ -546,7 +546,9 @@ class TibberPricesOptionsFlowHandler(OptionsFlow):
             },
         )
 
-    async def async_step_price_rating(self, user_input: dict[str, Any] | None = None) -> ConfigFlowResult:
+    async def async_step_current_interval_price_rating(
+        self, user_input: dict[str, Any] | None = None
+    ) -> ConfigFlowResult:
         """Configure price rating thresholds."""
         if user_input is not None:
             self._options.update(user_input)

custom_components/tibber_prices/const.py

@@ -183,8 +183,8 @@ VOLATILITY_VERY_HIGH = "VERY_HIGH"
 
 # Sensor options (lowercase versions for ENUM device class)
 # NOTE: These constants define the valid enum options, but they are not used directly
-# in sensor.py due to import timing issues. Instead, the options are defined inline
-# in the SensorEntityDescription objects. Keep these in sync with sensor.py!
+# in sensor/definitions.py due to import timing issues. Instead, the options are defined inline
+# in the SensorEntityDescription objects. Keep these in sync with sensor/definitions.py!
 PRICE_LEVEL_OPTIONS = [
     PRICE_LEVEL_VERY_CHEAP.lower(),
     PRICE_LEVEL_CHEAP.lower(),

custom_components/tibber_prices/entity_utils/__init__.py

@@ -0,0 +1,31 @@
+"""Entity utilities for Tibber Prices integration."""
+
+from __future__ import annotations
+
+from .attributes import build_period_attributes, build_timestamp_attribute
+from .colors import add_icon_color_attribute, get_icon_color
+from .icons import (
+    get_binary_sensor_icon,
+    get_dynamic_icon,
+    get_level_sensor_icon,
+    get_price_level_for_icon,
+    get_price_sensor_icon,
+    get_rating_sensor_icon,
+    get_trend_icon,
+    get_volatility_sensor_icon,
+)
+
+__all__ = [
+    "add_icon_color_attribute",
+    "build_period_attributes",
+    "build_timestamp_attribute",
+    "get_binary_sensor_icon",
+    "get_dynamic_icon",
+    "get_icon_color",
+    "get_level_sensor_icon",
+    "get_price_level_for_icon",
+    "get_price_sensor_icon",
+    "get_rating_sensor_icon",
+    "get_trend_icon",
+    "get_volatility_sensor_icon",
+]

custom_components/tibber_prices/entity_utils/attributes.py

@@ -0,0 +1,42 @@
+"""Common attribute utilities for Tibber Prices entities."""
+
+from __future__ import annotations
+
+
+def build_timestamp_attribute(interval_data: dict | None) -> str | None:
+    """
+    Build timestamp attribute from interval data.
+
+    Extracts startsAt field consistently across all sensors.
+
+    Args:
+        interval_data: Interval data dictionary containing startsAt field
+
+    Returns:
+        ISO format timestamp string or None
+
+    """
+    if not interval_data:
+        return None
+    return interval_data.get("startsAt")
+
+
+def build_period_attributes(period_data: dict) -> dict:
+    """
+    Build common period attributes (start, end, duration, timestamp).
+
+    Used by binary sensors for period-based entities.
+
+    Args:
+        period_data: Period data dictionary
+
+    Returns:
+        Dictionary with common period attributes
+
+    """
+    return {
+        "start": period_data.get("start"),
+        "end": period_data.get("end"),
+        "duration_minutes": period_data.get("duration_minutes"),
+        "timestamp": period_data.get("start"),  # Timestamp = period start
+    }

custom_components/tibber_prices/entity_utils/colors.py

@@ -0,0 +1,79 @@
+"""Color utilities for Tibber Prices entities."""
+
+from __future__ import annotations
+
+from typing import Any
+
+from custom_components.tibber_prices.const import (
+    BINARY_SENSOR_COLOR_MAPPING,
+    PRICE_LEVEL_COLOR_MAPPING,
+    PRICE_RATING_COLOR_MAPPING,
+    VOLATILITY_COLOR_MAPPING,
+)
+
+
+def add_icon_color_attribute(
+    attributes: dict,
+    key: str,
+    state_value: Any = None,
+    *,
+    is_on: bool | None = None,
+) -> None:
+    """
+    Add icon_color attribute if color mapping exists.
+
+    Used by both sensor and binary_sensor platforms.
+
+    Args:
+        attributes: Attribute dictionary to update
+        key: Entity description key
+        state_value: Sensor value (for sensors) or None (for binary sensors)
+        is_on: Binary sensor state (for binary sensors) or None (for sensors)
+
+    """
+    color = get_icon_color(key, state_value, is_on=is_on)
+    if color:
+        attributes["icon_color"] = color
+
+
+def get_icon_color(
+    key: str,
+    state_value: Any = None,
+    *,
+    is_on: bool | None = None,
+) -> str | None:
+    """
+    Get icon color from various mappings.
+
+    Args:
+        key: Entity description key
+        state_value: Sensor value (for sensors)
+        is_on: Binary sensor state (for binary sensors)
+
+    Returns:
+        CSS color variable string or None
+
+    """
+    # Binary sensor colors (based on on/off state)
+    if key in BINARY_SENSOR_COLOR_MAPPING and is_on is not None:
+        state_key = "on" if is_on else "off"
+        return BINARY_SENSOR_COLOR_MAPPING[key].get(state_key)
+
+    # Trend sensor colors (based on trend state)
+    if key.startswith("price_trend_") and isinstance(state_value, str):
+        trend_colors = {
+            "rising": "var(--error-color)",  # Red/Orange for rising prices
+            "falling": "var(--success-color)",  # Green for falling prices
+            "stable": "var(--state-icon-color)",  # Default gray for stable
+        }
+        return trend_colors.get(state_value)
+
+    # Price level/rating/volatility colors (based on uppercase value)
+    if isinstance(state_value, str):
+        return (
+            PRICE_LEVEL_COLOR_MAPPING.get(state_value.upper())
+            or PRICE_RATING_COLOR_MAPPING.get(state_value.upper())
+            or VOLATILITY_COLOR_MAPPING.get(state_value.upper())
+        )
+
+    return None

custom_components/tibber_prices/entity_utils/icons.py

@@ -0,0 +1,206 @@
+"""Icon utilities for Tibber Prices entities."""
+
+from __future__ import annotations
+
+from datetime import timedelta
+from typing import TYPE_CHECKING, Any
+
+from custom_components.tibber_prices.const import (
+    BINARY_SENSOR_ICON_MAPPING,
+    PRICE_LEVEL_CASH_ICON_MAPPING,
+    PRICE_LEVEL_ICON_MAPPING,
+    PRICE_RATING_ICON_MAPPING,
+    VOLATILITY_ICON_MAPPING,
+)
+from custom_components.tibber_prices.price_utils import find_price_data_for_interval
+from homeassistant.util import dt as dt_util
+
+if TYPE_CHECKING:
+    from collections.abc import Callable
+
+# Constants imported from price_utils
+MINUTES_PER_INTERVAL = 15
+
+
+def get_dynamic_icon(
+    key: str,
+    value: Any,
+    *,
+    is_on: bool | None = None,
+    coordinator_data: dict | None = None,
+    has_future_periods_callback: Callable[[], bool] | None = None,
+) -> str | None:
+    """
+    Get dynamic icon based on sensor state.
+
+    Unified function for both sensor and binary_sensor platforms.
+
+    Args:
+        key: Entity description key
+        value: Native value of the sensor
+        is_on: Binary sensor state (None for regular sensors)
+        coordinator_data: Coordinator data for price level lookups
+        has_future_periods_callback: Callback to check if future periods exist (binary sensors)
+
+    Returns:
+        Icon string or None if no dynamic icon applies
+
+    """
+    # Try various icon sources in order
+    return (
+        get_trend_icon(key, value)
+        or get_price_sensor_icon(key, coordinator_data)
+        or get_level_sensor_icon(key, value)
+        or get_rating_sensor_icon(key, value)
+        or get_volatility_sensor_icon(key, value)
+        or get_binary_sensor_icon(key, is_on=is_on, has_future_periods_callback=has_future_periods_callback)
+    )
+
+
+def get_trend_icon(key: str, value: Any) -> str | None:
+    """Get icon for trend sensors."""
+    if not key.startswith("price_trend_") or not isinstance(value, str):
+        return None
+
+    trend_icons = {
+        "rising": "mdi:trending-up",
+        "falling": "mdi:trending-down",
+        "stable": "mdi:trending-neutral",
+    }
+    return trend_icons.get(value)
+
+
+def get_price_sensor_icon(key: str, coordinator_data: dict | None) -> str | None:
+    """
+    Get icon for current price sensors (dynamic based on price level).
+
+    Only current_interval_price and current_hour_average have dynamic icons.
+    Other price sensors (next/previous) use static icons from entity description.
+
+    Args:
+        key: Entity description key
+        coordinator_data: Coordinator data for price level lookups
+
+    Returns:
+        Icon string or None if not a current price sensor
+
+    """
+    if not coordinator_data:
+        return None
+
+    # Only current price sensors get dynamic icons
+    if key == "current_interval_price":
+        level = get_price_level_for_icon(coordinator_data, interval_offset=0)
+        if level:
+            return PRICE_LEVEL_CASH_ICON_MAPPING.get(level.upper())
+    elif key == "current_hour_average":
+        # For hour average, we cannot use this helper (needs sensor rolling hour logic)
+        # Return None and let sensor handle it
+        return None
+
+    # For all other price sensors, let entity description handle the icon
+    return None
+
+
+def get_level_sensor_icon(key: str, value: Any) -> str | None:
+    """Get icon for price level sensors."""
+    if key not in [
+        "current_interval_price_level",
+        "next_interval_price_level",
+        "previous_interval_price_level",
+        "current_hour_price_level",
+        "next_hour_price_level",
+    ] or not isinstance(value, str):
+        return None
+
+    return PRICE_LEVEL_ICON_MAPPING.get(value.upper())
+
+
+def get_rating_sensor_icon(key: str, value: Any) -> str | None:
+    """Get icon for price rating sensors."""
+    if key not in [
+        "current_interval_price_rating",
+        "next_interval_price_rating",
+        "previous_interval_price_rating",
+        "current_hour_price_rating",
+        "next_hour_price_rating",
+    ] or not isinstance(value, str):
+        return None
+
+    return PRICE_RATING_ICON_MAPPING.get(value.upper())
+
+
+def get_volatility_sensor_icon(key: str, value: Any) -> str | None:
+    """Get icon for volatility sensors."""
+    if not key.endswith("_volatility") or not isinstance(value, str):
+        return None
+
+    return VOLATILITY_ICON_MAPPING.get(value.upper())
+
+
+def get_binary_sensor_icon(
+    key: str,
+    *,
+    is_on: bool | None,
+    has_future_periods_callback: Callable[[], bool] | None = None,
+) -> str | None:
+    """
+    Get icon for binary sensors with dynamic state-based icons.
+
+    Args:
+        key: Entity description key
+        is_on: Binary sensor state
+        has_future_periods_callback: Callback to check if future periods exist
+
+    Returns:
+        Icon string or None if not a binary sensor with dynamic icons
+
+    """
+    if key not in BINARY_SENSOR_ICON_MAPPING or is_on is None:
+        return None
+
+    if is_on:
+        # Sensor is ON - use "on" icon
+        return BINARY_SENSOR_ICON_MAPPING[key].get("on")
+
+    # Sensor is OFF - check if future periods exist
+    has_future_periods = has_future_periods_callback() if has_future_periods_callback else False
+
+    if has_future_periods:
+        return BINARY_SENSOR_ICON_MAPPING[key].get("off")
+
+    return BINARY_SENSOR_ICON_MAPPING[key].get("off_no_future")
+
+
+def get_price_level_for_icon(
+    coordinator_data: dict,
+    *,
+    interval_offset: int | None = None,
+) -> str | None:
+    """
+    Get the price level for icon determination.
+
+    Supports interval-based lookups (current/next/previous interval).
+
+    Args:
+        coordinator_data: Coordinator data
+        interval_offset: Interval offset (0=current, 1=next, -1=previous)
+
+    Returns:
+        Price level string or None if not found
+
+    """
+    if not coordinator_data or interval_offset is None:
+        return None
+
+    price_info = coordinator_data.get("priceInfo", {})
+    now = dt_util.now()
+
+    # Interval-based lookup
+    target_time = now + timedelta(minutes=MINUTES_PER_INTERVAL * interval_offset)
+    interval_data = find_price_data_for_interval(price_info, target_time)
+
+    if not interval_data or "level" not in interval_data:
+        return None
+
+    return interval_data["level"]

custom_components/tibber_prices/sensor/__init__.py

@@ -0,0 +1,30 @@
+"""Sensor platform for Tibber Prices integration."""
+
+from __future__ import annotations
+
+from typing import TYPE_CHECKING
+
+from .core import TibberPricesSensor
+from .definitions import 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 sensor based on a config entry."""
+    coordinator = entry.runtime_data.coordinator
+
+    async_add_entities(
+        TibberPricesSensor(
+            coordinator=coordinator,
+            entity_description=entity_description,
+        )
+        for entity_description in ENTITY_DESCRIPTIONS
+    )

custom_components/tibber_prices/sensor/attributes.py

@@ -0,0 +1,767 @@
+"""
+Attribute builders for Tibber Prices sensors.
+
+This module contains all the attribute building logic extracted from TibberPricesSensor.
+Each function takes explicit parameters instead of accessing instance variables.
+"""
+
+from __future__ import annotations
+
+from datetime import datetime, timedelta
+from typing import TYPE_CHECKING, Any
+
+from custom_components.tibber_prices.const import (
+    PRICE_LEVEL_MAPPING,
+    PRICE_RATING_MAPPING,
+)
+from custom_components.tibber_prices.entity_utils import add_icon_color_attribute
+from custom_components.tibber_prices.price_utils import (
+    MINUTES_PER_INTERVAL,
+    calculate_volatility_level,
+    find_price_data_for_interval,
+)
+from homeassistant.const import PERCENTAGE
+from homeassistant.util import dt as dt_util
+
+if TYPE_CHECKING:
+    from custom_components.tibber_prices.coordinator import (
+        TibberPricesDataUpdateCoordinator,
+    )
+
+# Constants
+MAX_FORECAST_INTERVALS = 8  # Show up to 8 future intervals (2 hours with 15-min intervals)
+
+
+def build_sensor_attributes(
+    key: str,
+    coordinator: TibberPricesDataUpdateCoordinator,
+    native_value: Any,
+    cached_data: dict,
+) -> dict | None:
+    """
+    Build attributes for a sensor based on its key.
+
+    Args:
+        key: The sensor entity key
+        coordinator: The data update coordinator
+        native_value: The current native value of the sensor
+        cached_data: Dictionary containing cached sensor data
+                    (_last_extreme_interval, _trend_attributes, _volatility_attributes, etc.)
+
+    Returns:
+        Dictionary of attributes or None if no attributes should be added
+
+    """
+    if not coordinator.data:
+        return None
+
+    try:
+        attributes: dict[str, Any] = {}
+
+        # For trend sensors, use the cached _trend_attributes
+        if key.startswith("price_trend_") and cached_data.get("trend_attributes"):
+            attributes.update(cached_data["trend_attributes"])
+
+        # Group sensors by type and delegate to specific handlers
+        if key in [
+            "current_interval_price",
+            "current_interval_price_level",
+            "next_interval_price",
+            "previous_interval_price",
+            "current_hour_average",
+            "next_hour_average",
+            "next_interval_price_level",
+            "previous_interval_price_level",
+            "current_hour_price_level",
+            "next_hour_price_level",
+            "next_interval_price_rating",
+            "previous_interval_price_rating",
+            "current_hour_price_rating",
+            "next_hour_price_rating",
+        ]:
+            add_current_interval_price_attributes(
+                attributes=attributes,
+                key=key,
+                coordinator=coordinator,
+                native_value=native_value,
+                cached_data=cached_data,
+            )
+        elif key in [
+            "trailing_price_average",
+            "leading_price_average",
+            "trailing_price_min",
+            "trailing_price_max",
+            "leading_price_min",
+            "leading_price_max",
+        ]:
+            add_average_price_attributes(attributes=attributes, key=key, coordinator=coordinator)
+        elif key.startswith("next_avg_"):
+            add_next_avg_attributes(attributes=attributes, key=key, coordinator=coordinator)
+        elif any(pattern in key for pattern in ["_price_today", "_price_tomorrow", "rating", "data_timestamp"]):
+            add_statistics_attributes(
+                attributes=attributes,
+                key=key,
+                coordinator=coordinator,
+                cached_data=cached_data,
+            )
+        elif key == "price_forecast":
+            add_price_forecast_attributes(attributes=attributes, coordinator=coordinator)
+        elif key.endswith("_volatility"):
+            add_volatility_attributes(attributes=attributes, cached_data=cached_data)
+
+        # For price_level, add the original level as attribute
+        if key == "current_interval_price_level" and cached_data.get("last_price_level") is not None:
+            attributes["level_id"] = cached_data["last_price_level"]
+
+    except (KeyError, ValueError, TypeError) as ex:
+        coordinator.logger.exception(
+            "Error getting sensor attributes",
+            extra={
+                "error": str(ex),
+                "entity": key,
+            },
+        )
+        return None
+    else:
+        return attributes if attributes else None
+
+
+def add_current_interval_price_attributes(
+    attributes: dict,
+    key: str,
+    coordinator: TibberPricesDataUpdateCoordinator,
+    native_value: Any,
+    cached_data: dict,
+) -> None:
+    """
+    Add attributes for current interval price sensors.
+
+    Args:
+        attributes: Dictionary to add attributes to
+        key: The sensor entity key
+        coordinator: The data update coordinator
+        native_value: The current native value of the sensor
+        cached_data: Dictionary containing cached sensor data
+
+    """
+    price_info = coordinator.data.get("priceInfo", {}) if coordinator.data else {}
+    now = dt_util.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",
+        "next_hour_price_level",
+        "next_hour_price_rating",
+    ]
+    current_hour_sensors = [
+        "current_hour_average",
+        "current_hour_price_level",
+        "current_hour_price_rating",
+    ]
+
+    # Set timestamp and interval data based on sensor type
+    interval_data = None
+    if key in next_interval_sensors:
+        target_time = now + timedelta(minutes=MINUTES_PER_INTERVAL)
+        interval_data = find_price_data_for_interval(price_info, target_time)
+        attributes["timestamp"] = interval_data["startsAt"] if interval_data else None
+    elif key in previous_interval_sensors:
+        target_time = now - timedelta(minutes=MINUTES_PER_INTERVAL)
+        interval_data = find_price_data_for_interval(price_info, target_time)
+        attributes["timestamp"] = interval_data["startsAt"] if interval_data else None
+    elif key in next_hour_sensors:
+        target_time = now + timedelta(hours=1)
+        interval_data = find_price_data_for_interval(price_info, target_time)
+        attributes["timestamp"] = interval_data["startsAt"] if interval_data else None
+    elif key in current_hour_sensors:
+        current_interval_data = get_current_interval_data(coordinator)
+        attributes["timestamp"] = current_interval_data["startsAt"] if current_interval_data else None
+    else:
+        current_interval_data = get_current_interval_data(coordinator)
+        attributes["timestamp"] = current_interval_data["startsAt"] if current_interval_data else None
+
+    # Add icon_color for price sensors (based on their price level)
+    if key in ["current_interval_price", "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"]
+            add_icon_color_attribute(attributes, key="price_level", state_value=level)
+    elif key in ["current_hour_average", "next_hour_average"]:
+        # For hour-based price sensors, get level from cached_data
+        level = cached_data.get("rolling_hour_level")
+        if level:
+            add_icon_color_attribute(attributes, key="price_level", state_value=level)
+
+    # Add price level attributes for all level sensors
+    add_level_attributes_for_sensor(
+        attributes=attributes,
+        key=key,
+        interval_data=interval_data,
+        coordinator=coordinator,
+        native_value=native_value,
+    )
+
+    # Add price rating attributes for all rating sensors
+    add_rating_attributes_for_sensor(
+        attributes=attributes,
+        key=key,
+        interval_data=interval_data,
+        coordinator=coordinator,
+        native_value=native_value,
+    )
+
+
+def add_level_attributes_for_sensor(
+    attributes: dict,
+    key: str,
+    interval_data: dict | None,
+    coordinator: TibberPricesDataUpdateCoordinator,
+    native_value: Any,
+) -> None:
+    """
+    Add price level attributes based on sensor type.
+
+    Args:
+        attributes: Dictionary to add attributes to
+        key: The sensor entity key
+        interval_data: Interval data for next/previous sensors
+        coordinator: The data update coordinator
+        native_value: The current native value of the sensor
+
+    """
+    # For interval-based level sensors (next/previous), use interval data
+    if key in ["next_interval_price_level", "previous_interval_price_level"]:
+        if interval_data and "level" in interval_data:
+            add_price_level_attributes(attributes, interval_data["level"])
+    # For hour-aggregated level sensors, use native_value
+    elif key in ["current_hour_price_level", "next_hour_price_level"]:
+        level_value = native_value
+        if level_value and isinstance(level_value, str):
+            add_price_level_attributes(attributes, level_value.upper())
+    # For current price level sensor
+    elif key == "current_interval_price_level":
+        current_interval_data = get_current_interval_data(coordinator)
+        if current_interval_data and "level" in current_interval_data:
+            add_price_level_attributes(attributes, current_interval_data["level"])
+
+
+def add_price_level_attributes(attributes: dict, level: str) -> None:
+    """
+    Add price level specific attributes.
+
+    Args:
+        attributes: Dictionary to add attributes to
+        level: The price level value (e.g., VERY_CHEAP, NORMAL, etc.)
+
+    """
+    if level in PRICE_LEVEL_MAPPING:
+        attributes["level_value"] = PRICE_LEVEL_MAPPING[level]
+    attributes["level_id"] = level
+
+    # Add icon_color for dynamic styling
+    add_icon_color_attribute(attributes, key="price_level", state_value=level)
+
+
+def add_rating_attributes_for_sensor(
+    attributes: dict,
+    key: str,
+    interval_data: dict | None,
+    coordinator: TibberPricesDataUpdateCoordinator,
+    native_value: Any,
+) -> None:
+    """
+    Add price rating attributes based on sensor type.
+
+    Args:
+        attributes: Dictionary to add attributes to
+        key: The sensor entity key
+        interval_data: Interval data for next/previous sensors
+        coordinator: The data update coordinator
+        native_value: The current native value of the sensor
+
+    """
+    # For interval-based rating sensors (next/previous), use interval data
+    if key in ["next_interval_price_rating", "previous_interval_price_rating"]:
+        if interval_data and "rating_level" in interval_data:
+            add_price_rating_attributes(attributes, interval_data["rating_level"])
+    # For hour-aggregated rating sensors, use native_value
+    elif key in ["current_hour_price_rating", "next_hour_price_rating"]:
+        rating_value = native_value
+        if rating_value and isinstance(rating_value, str):
+            add_price_rating_attributes(attributes, rating_value.upper())
+    # For current price rating sensor
+    elif key == "current_interval_price_rating":
+        current_interval_data = get_current_interval_data(coordinator)
+        if current_interval_data and "rating_level" in current_interval_data:
+            add_price_rating_attributes(attributes, current_interval_data["rating_level"])
+
+
+def add_price_rating_attributes(attributes: dict, rating: str) -> None:
+    """
+    Add price rating specific attributes.
+
+    Args:
+        attributes: Dictionary to add attributes to
+        rating: The price rating value (e.g., LOW, NORMAL, HIGH)
+
+    """
+    if rating in PRICE_RATING_MAPPING:
+        attributes["rating_value"] = PRICE_RATING_MAPPING[rating]
+    attributes["rating_id"] = rating
+
+    # Add icon_color for dynamic styling
+    add_icon_color_attribute(attributes, key="price_rating", state_value=rating)
+
+
+def add_statistics_attributes(
+    attributes: dict,
+    key: str,
+    coordinator: TibberPricesDataUpdateCoordinator,
+    cached_data: dict,
+) -> None:
+    """
+    Add attributes for statistics and rating sensors.
+
+    Args:
+        attributes: Dictionary to add attributes to
+        key: The sensor entity key
+        coordinator: The data update coordinator
+        cached_data: Dictionary containing cached sensor data
+
+    """
+    price_info = coordinator.data.get("priceInfo", {})
+    now = dt_util.now()
+
+    if key == "data_timestamp":
+        # For data_timestamp sensor, use the latest timestamp from cached_data
+        latest_timestamp = cached_data.get("data_timestamp")
+        if latest_timestamp:
+            attributes["timestamp"] = latest_timestamp.isoformat()
+    elif key == "current_interval_price_rating":
+        interval_data = find_price_data_for_interval(price_info, now)
+        attributes["timestamp"] = interval_data["startsAt"] if interval_data else None
+        if cached_data.get("last_rating_difference") is not None:
+            attributes["diff_" + PERCENTAGE] = cached_data["last_rating_difference"]
+        if cached_data.get("last_rating_level") is not None:
+            attributes["level_id"] = cached_data["last_rating_level"]
+            attributes["level_value"] = PRICE_RATING_MAPPING.get(
+                cached_data["last_rating_level"], cached_data["last_rating_level"]
+            )
+    elif key in [
+        "lowest_price_today",
+        "highest_price_today",
+        "lowest_price_tomorrow",
+        "highest_price_tomorrow",
+    ]:
+        # Use the timestamp from the interval that has the extreme price
+        if cached_data.get("last_extreme_interval"):
+            attributes["timestamp"] = cached_data["last_extreme_interval"].get("startsAt")
+        else:
+            # Fallback: use the first timestamp of the appropriate day
+            day_key = "tomorrow" if "tomorrow" in key else "today"
+            day_data = price_info.get(day_key, [])
+            if day_data:
+                attributes["timestamp"] = day_data[0].get("startsAt")
+    else:
+        # Fallback: use the first timestamp of the appropriate day
+        day_key = "tomorrow" if "tomorrow" in key else "today"
+        day_data = price_info.get(day_key, [])
+        if day_data:
+            attributes["timestamp"] = day_data[0].get("startsAt")
+
+
+def add_average_price_attributes(
+    attributes: dict,
+    key: str,
+    coordinator: TibberPricesDataUpdateCoordinator,
+) -> None:
+    """
+    Add attributes for trailing and leading average price sensors.
+
+    Args:
+        attributes: Dictionary to add attributes to
+        key: The sensor entity key
+        coordinator: The data update coordinator
+
+    """
+    now = dt_util.now()
+
+    # Determine if this is trailing or leading
+    is_trailing = "trailing" in key
+
+    # Get all price intervals
+    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
+
+    # Calculate the time window
+    if is_trailing:
+        window_start = now - timedelta(hours=24)
+        window_end = now
+    else:
+        window_start = now
+        window_end = now + timedelta(hours=24)
+
+    # Find all intervals in the window and get first/last timestamps
+    intervals_in_window = []
+    for price_data in all_prices:
+        starts_at = dt_util.parse_datetime(price_data["startsAt"])
+        if starts_at is None:
+            continue
+        starts_at = dt_util.as_local(starts_at)
+        if window_start <= starts_at < window_end:
+            intervals_in_window.append(price_data)
+
+    # Add timestamp attribute (first interval in the window)
+    if intervals_in_window:
+        attributes["timestamp"] = intervals_in_window[0].get("startsAt")
+        attributes["interval_count"] = len(intervals_in_window)
+
+
+def add_next_avg_attributes(
+    attributes: dict,
+    key: str,
+    coordinator: TibberPricesDataUpdateCoordinator,
+) -> None:
+    """
+    Add attributes for next N hours average price sensors.
+
+    Args:
+        attributes: Dictionary to add attributes to
+        key: The sensor entity key
+        coordinator: The data update coordinator
+
+    """
+    now = dt_util.now()
+
+    # Extract hours from sensor key (e.g., "next_avg_3h" -> 3)
+    try:
+        hours = int(key.replace("next_avg_", "").replace("h", ""))
+    except (ValueError, AttributeError):
+        return
+
+    # Get next interval start time (this is where the calculation begins)
+    next_interval_start = now + timedelta(minutes=MINUTES_PER_INTERVAL)
+
+    # Calculate the end of the time window
+    window_end = next_interval_start + timedelta(hours=hours)
+
+    # Get all price intervals
+    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:
+        starts_at = dt_util.parse_datetime(price_data["startsAt"])
+        if starts_at is None:
+            continue
+        starts_at = dt_util.as_local(starts_at)
+        if next_interval_start <= starts_at < window_end:
+            intervals_in_window.append(price_data)
+
+    # Add timestamp attribute (start of next interval - where calculation begins)
+    if intervals_in_window:
+        attributes["timestamp"] = intervals_in_window[0].get("startsAt")
+        attributes["interval_count"] = len(intervals_in_window)
+        attributes["hours"] = hours
+
+
+def add_price_forecast_attributes(
+    attributes: dict,
+    coordinator: TibberPricesDataUpdateCoordinator,
+) -> None:
+    """
+    Add forecast attributes for the price forecast sensor.
+
+    Args:
+        attributes: Dictionary to add attributes to
+        coordinator: The data update coordinator
+
+    """
+    future_prices = get_future_prices(coordinator, max_intervals=MAX_FORECAST_INTERVALS)
+    if not future_prices:
+        attributes["intervals"] = []
+        attributes["intervals_by_hour"] = []
+        attributes["data_available"] = False
+        return
+
+    # Add timestamp attribute (first future interval)
+    if future_prices:
+        attributes["timestamp"] = future_prices[0]["interval_start"]
+
+    attributes["intervals"] = future_prices
+    attributes["data_available"] = True
+
+    # Group by hour for easier consumption in dashboards
+    hours: dict[str, Any] = {}
+    for interval in future_prices:
+        starts_at = datetime.fromisoformat(interval["interval_start"])
+        hour_key = starts_at.strftime("%Y-%m-%d %H")
+
+        if hour_key not in hours:
+            hours[hour_key] = {
+                "hour": starts_at.hour,
+                "day": interval["day"],
+                "date": starts_at.date().isoformat(),
+                "intervals": [],
+                "min_price": None,
+                "max_price": None,
+                "avg_price": 0,
+                "avg_rating": None,  # Initialize rating tracking
+                "ratings_available": False,  # Track if any ratings are available
+            }
+
+        # Create interval data with both price and rating info
+        interval_data = {
+            "minute": starts_at.minute,
+            "price": interval["price"],
+            "price_minor": interval["price_minor"],
+            "level": interval["level"],  # Price level from priceInfo
+            "time": starts_at.strftime("%H:%M"),
+        }
+
+        # Add rating data if available
+        if interval["rating"] is not None:
+            interval_data["rating"] = interval["rating"]
+            interval_data["rating_level"] = interval["rating_level"]
+            hours[hour_key]["ratings_available"] = True
+
+        hours[hour_key]["intervals"].append(interval_data)
+
+        # Track min/max/avg for the hour
+        price = interval["price"]
+        if hours[hour_key]["min_price"] is None or price < hours[hour_key]["min_price"]:
+            hours[hour_key]["min_price"] = price
+        if hours[hour_key]["max_price"] is None or price > hours[hour_key]["max_price"]:
+            hours[hour_key]["max_price"] = price
+
+    # Calculate averages
+    for hour_data in hours.values():
+        prices = [interval["price"] for interval in hour_data["intervals"]]
+        if prices:
+            hour_data["avg_price"] = sum(prices) / len(prices)
+            hour_data["min_price"] = hour_data["min_price"]
+            hour_data["max_price"] = hour_data["max_price"]
+
+            # Calculate average rating if ratings are available
+            if hour_data["ratings_available"]:
+                ratings = [interval.get("rating") for interval in hour_data["intervals"] if "rating" in interval]
+                if ratings:
+                    hour_data["avg_rating"] = sum(ratings) / len(ratings)
+
+    # Convert to list sorted by hour
+    attributes["intervals_by_hour"] = [hour_data for _, hour_data in sorted(hours.items())]
+
+
+def add_volatility_attributes(
+    attributes: dict,
+    cached_data: dict,
+) -> None:
+    """
+    Add attributes for volatility sensors.
+
+    Args:
+        attributes: Dictionary to add attributes to
+        cached_data: Dictionary containing cached sensor data
+
+    """
+    if cached_data.get("volatility_attributes"):
+        attributes.update(cached_data["volatility_attributes"])
+
+
+def get_prices_for_volatility(
+    volatility_type: str,
+    price_info: dict,
+) -> list[float]:
+    """
+    Get price list for volatility calculation based on type.
+
+    Args:
+        volatility_type: One of "today", "tomorrow", "next_24h", "today_tomorrow"
+        price_info: Price information dictionary from coordinator data
+
+    Returns:
+        List of prices to analyze
+
+    """
+    if volatility_type == "today":
+        return [float(p["total"]) for p in price_info.get("today", []) if "total" in p]
+
+    if volatility_type == "tomorrow":
+        return [float(p["total"]) for p in price_info.get("tomorrow", []) if "total" in p]
+
+    if volatility_type == "next_24h":
+        # Rolling 24h from now
+        now = dt_util.now()
+        end_time = now + timedelta(hours=24)
+        prices = []
+
+        for day_key in ["today", "tomorrow"]:
+            for price_data in price_info.get(day_key, []):
+                starts_at = dt_util.parse_datetime(price_data.get("startsAt"))
+                if starts_at is None:
+                    continue
+                starts_at = dt_util.as_local(starts_at)
+
+                if now <= starts_at < end_time and "total" in price_data:
+                    prices.append(float(price_data["total"]))
+        return prices
+
+    if volatility_type == "today_tomorrow":
+        # Combined today + tomorrow
+        prices = []
+        for day_key in ["today", "tomorrow"]:
+            for price_data in price_info.get(day_key, []):
+                if "total" in price_data:
+                    prices.append(float(price_data["total"]))
+        return prices
+
+    return []
+
+
+def add_volatility_type_attributes(
+    volatility_attributes: dict,
+    volatility_type: str,
+    price_info: dict,
+    thresholds: dict,
+) -> None:
+    """
+    Add type-specific attributes for volatility sensors.
+
+    Args:
+        volatility_attributes: Dictionary to add type-specific attributes to
+        volatility_type: Type of volatility calculation
+        price_info: Price information dictionary from coordinator data
+        thresholds: Volatility thresholds configuration
+
+    """
+    if volatility_type == "today_tomorrow":
+        # Add breakdown for today vs tomorrow
+        today_prices = [float(p["total"]) for p in price_info.get("today", []) if "total" in p]
+        tomorrow_prices = [float(p["total"]) for p in price_info.get("tomorrow", []) if "total" in p]
+
+        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":
+        # Add time window info
+        now = dt_util.now()
+        volatility_attributes["timestamp"] = now.isoformat()
+
+
+def get_future_prices(
+    coordinator: TibberPricesDataUpdateCoordinator,
+    max_intervals: int | None = None,
+) -> list[dict] | None:
+    """
+    Get future price data for multiple upcoming intervals.
+
+    Args:
+        coordinator: The data update coordinator
+        max_intervals: Maximum number of future intervals to return
+
+    Returns:
+        List of upcoming price intervals with timestamps and prices
+
+    """
+    if not coordinator.data:
+        return None
+
+    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 None
+
+    now = dt_util.now()
+
+    # Initialize the result list
+    future_prices = []
+
+    # Track the maximum intervals to return
+    intervals_to_return = MAX_FORECAST_INTERVALS if max_intervals is None else max_intervals
+
+    for day_key in ["today", "tomorrow"]:
+        for price_data in price_info.get(day_key, []):
+            starts_at = dt_util.parse_datetime(price_data["startsAt"])
+            if starts_at is None:
+                continue
+
+            starts_at = dt_util.as_local(starts_at)
+            interval_end = starts_at + timedelta(minutes=MINUTES_PER_INTERVAL)
+
+            if starts_at > now:
+                future_prices.append(
+                    {
+                        "interval_start": starts_at.isoformat(),
+                        "interval_end": interval_end.isoformat(),
+                        "price": float(price_data["total"]),
+                        "price_minor": round(float(price_data["total"]) * 100, 2),
+                        "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"])
+
+    # Limit to the requested number of intervals
+    return future_prices[:intervals_to_return] if future_prices else None
+
+
+def get_current_interval_data(
+    coordinator: TibberPricesDataUpdateCoordinator,
+) -> dict | None:
+    """
+    Get the current interval data from coordinator.
+
+    Args:
+        coordinator: The data update coordinator
+
+    Returns:
+        Current interval data dictionary or None
+
+    """
+    if not coordinator.data:
+        return None
+
+    price_info = coordinator.data.get("priceInfo", {})
+    now = dt_util.now()
+    return find_price_data_for_interval(price_info, now)

custom_components/tibber_prices/sensor/definitions.py

@@ -0,0 +1,564 @@
+"""
+Sensor entity definitions for Tibber Prices.
+
+This module contains all SensorEntityDescription definitions organized by
+calculation method. Sensor definitions are declarative and independent of
+the implementation logic.
+
+Organization by calculation pattern:
+    1. Interval-based: Time offset from current interval
+    2. Rolling hour: 5-interval aggregation windows
+    3. Daily statistics: Calendar day min/max/avg
+    4. 24h windows: Trailing/leading statistics
+    5. Future forecast: N-hour windows from next interval
+    6. Volatility: Price variation analysis
+    7. Diagnostic: System metadata
+"""
+
+from __future__ import annotations
+
+from homeassistant.components.sensor import (
+    SensorDeviceClass,
+    SensorEntityDescription,
+)
+from homeassistant.const import EntityCategory
+
+# ============================================================================
+# SENSOR DEFINITIONS - Grouped by calculation method
+# ============================================================================
+#
+# Sensors are organized by HOW they calculate values, not WHAT they display.
+# This groups sensors that share common logic and enables code reuse through
+# unified handler methods.
+#
+# Calculation patterns:
+#   1. Interval-based: Use time offset from current interval
+#   2. Rolling hour: Aggregate 5-interval window (2 before + center + 2 after)
+#   3. Daily statistics: Min/max/avg within calendar day boundaries
+#   4. 24h windows: Trailing/leading from current interval
+#   5. Future forecast: N-hour windows starting from next interval
+#   6. Volatility: Statistical analysis of price variation
+#   7. Diagnostic: System information and metadata
+# ============================================================================
+
+# ----------------------------------------------------------------------------
+# 1. INTERVAL-BASED SENSORS (offset: -1, 0, +1 from current interval)
+# ----------------------------------------------------------------------------
+# All use find_price_data_for_interval() with time offset
+# Shared handler: _get_interval_value(interval_offset, value_type)
+
+INTERVAL_PRICE_SENSORS = (
+    SensorEntityDescription(
+        key="current_interval_price",
+        translation_key="current_interval_price",
+        name="Current Electricity Price",
+        icon="mdi:cash",  # Dynamic: will show cash-multiple/plus/cash/minus/remove based on level
+        device_class=SensorDeviceClass.MONETARY,
+        suggested_display_precision=2,
+    ),
+    SensorEntityDescription(
+        key="next_interval_price",
+        translation_key="next_interval_price",
+        name="Next Price",
+        icon="mdi:cash-fast",  # Static: motion lines indicate "coming soon"
+        device_class=SensorDeviceClass.MONETARY,
+        suggested_display_precision=2,
+    ),
+    SensorEntityDescription(
+        key="previous_interval_price",
+        translation_key="previous_interval_price",
+        name="Previous Electricity Price",
+        icon="mdi:cash-refund",  # Static: arrow back indicates "past"
+        device_class=SensorDeviceClass.MONETARY,
+        entity_registry_enabled_default=False,
+        suggested_display_precision=2,
+    ),
+)
+
+# NOTE: Enum options are defined inline (not imported from const.py) to avoid
+# import timing issues with Home Assistant's entity platform initialization.
+# Keep in sync with PRICE_LEVEL_OPTIONS in const.py!
+INTERVAL_LEVEL_SENSORS = (
+    SensorEntityDescription(
+        key="current_interval_price_level",
+        translation_key="current_interval_price_level",
+        name="Current Price Level",
+        icon="mdi:gauge",
+        device_class=SensorDeviceClass.ENUM,
+        options=["very_cheap", "cheap", "normal", "expensive", "very_expensive"],
+    ),
+    SensorEntityDescription(
+        key="next_interval_price_level",
+        translation_key="next_interval_price_level",
+        name="Next Price Level",
+        icon="mdi:gauge-empty",
+        device_class=SensorDeviceClass.ENUM,
+        options=["very_cheap", "cheap", "normal", "expensive", "very_expensive"],
+    ),
+    SensorEntityDescription(
+        key="previous_interval_price_level",
+        translation_key="previous_interval_price_level",
+        name="Previous Price Level",
+        icon="mdi:gauge-empty",
+        entity_registry_enabled_default=False,
+        device_class=SensorDeviceClass.ENUM,
+        options=["very_cheap", "cheap", "normal", "expensive", "very_expensive"],
+    ),
+)
+
+# NOTE: Enum options are defined inline (not imported from const.py) to avoid
+# import timing issues with Home Assistant's entity platform initialization.
+# Keep in sync with PRICE_RATING_OPTIONS in const.py!
+INTERVAL_RATING_SENSORS = (
+    SensorEntityDescription(
+        key="current_interval_price_rating",
+        translation_key="current_interval_price_rating",
+        name="Current Price Rating",
+        icon="mdi:star-outline",
+        device_class=SensorDeviceClass.ENUM,
+        options=["low", "normal", "high"],
+    ),
+    SensorEntityDescription(
+        key="next_interval_price_rating",
+        translation_key="next_interval_price_rating",
+        name="Next Price Rating",
+        icon="mdi:star-half-full",
+        device_class=SensorDeviceClass.ENUM,
+        options=["low", "normal", "high"],
+    ),
+    SensorEntityDescription(
+        key="previous_interval_price_rating",
+        translation_key="previous_interval_price_rating",
+        name="Previous Price Rating",
+        icon="mdi:star-half-full",
+        entity_registry_enabled_default=False,
+        device_class=SensorDeviceClass.ENUM,
+        options=["low", "normal", "high"],
+    ),
+)
+
+# ----------------------------------------------------------------------------
+# 2. ROLLING HOUR SENSORS (5-interval window: 2 before + center + 2 after)
+# ----------------------------------------------------------------------------
+# All aggregate data from rolling 5-interval window around a specific hour
+# Shared handler: _get_rolling_hour_value(hour_offset, value_type)
+
+ROLLING_HOUR_PRICE_SENSORS = (
+    SensorEntityDescription(
+        key="current_hour_average",
+        translation_key="current_hour_average",
+        name="Current Hour Average Price",
+        icon="mdi:cash",  # Dynamic: will show cash-multiple/plus/cash/minus/remove based on level
+        device_class=SensorDeviceClass.MONETARY,
+        suggested_display_precision=1,
+    ),
+    SensorEntityDescription(
+        key="next_hour_average",
+        translation_key="next_hour_average",
+        name="Next Hour Average Price",
+        icon="mdi:clock-fast",  # Static: clock indicates "next time period"
+        device_class=SensorDeviceClass.MONETARY,
+        suggested_display_precision=1,
+    ),
+)
+
+# NOTE: Enum options are defined inline (not imported from const.py) to avoid
+# import timing issues with Home Assistant's entity platform initialization.
+# Keep in sync with PRICE_LEVEL_OPTIONS in const.py!
+ROLLING_HOUR_LEVEL_SENSORS = (
+    SensorEntityDescription(
+        key="current_hour_price_level",
+        translation_key="current_hour_price_level",
+        name="Current Hour Price Level",
+        icon="mdi:gauge",
+        device_class=SensorDeviceClass.ENUM,
+        options=["very_cheap", "cheap", "normal", "expensive", "very_expensive"],
+    ),
+    SensorEntityDescription(
+        key="next_hour_price_level",
+        translation_key="next_hour_price_level",
+        name="Next Hour Price Level",
+        icon="mdi:gauge-empty",
+        device_class=SensorDeviceClass.ENUM,
+        options=["very_cheap", "cheap", "normal", "expensive", "very_expensive"],
+    ),
+)
+
+# NOTE: Enum options are defined inline (not imported from const.py) to avoid
+# import timing issues with Home Assistant's entity platform initialization.
+# Keep in sync with PRICE_RATING_OPTIONS in const.py!
+ROLLING_HOUR_RATING_SENSORS = (
+    SensorEntityDescription(
+        key="current_hour_price_rating",
+        translation_key="current_hour_price_rating",
+        name="Current Hour Price Rating",
+        icon="mdi:star-outline",
+        device_class=SensorDeviceClass.ENUM,
+        options=["low", "normal", "high"],
+    ),
+    SensorEntityDescription(
+        key="next_hour_price_rating",
+        translation_key="next_hour_price_rating",
+        name="Next Hour Price Rating",
+        icon="mdi:star-half-full",
+        device_class=SensorDeviceClass.ENUM,
+        options=["low", "normal", "high"],
+    ),
+)
+
+# ----------------------------------------------------------------------------
+# 3. DAILY STATISTICS SENSORS (min/max/avg for calendar day boundaries)
+# ----------------------------------------------------------------------------
+# Calculate statistics for specific calendar days (today/tomorrow)
+
+DAILY_STAT_SENSORS = (
+    SensorEntityDescription(
+        key="lowest_price_today",
+        translation_key="lowest_price_today",
+        name="Today's Lowest Price",
+        icon="mdi:arrow-collapse-down",
+        device_class=SensorDeviceClass.MONETARY,
+        suggested_display_precision=1,
+    ),
+    SensorEntityDescription(
+        key="highest_price_today",
+        translation_key="highest_price_today",
+        name="Today's Highest Price",
+        icon="mdi:arrow-collapse-up",
+        device_class=SensorDeviceClass.MONETARY,
+        suggested_display_precision=1,
+    ),
+    SensorEntityDescription(
+        key="average_price_today",
+        translation_key="average_price_today",
+        name="Today's Average Price",
+        icon="mdi:chart-line",
+        device_class=SensorDeviceClass.MONETARY,
+        suggested_display_precision=1,
+    ),
+    SensorEntityDescription(
+        key="lowest_price_tomorrow",
+        translation_key="lowest_price_tomorrow",
+        name="Tomorrow's Lowest Price",
+        icon="mdi:arrow-collapse-down",
+        device_class=SensorDeviceClass.MONETARY,
+        suggested_display_precision=1,
+    ),
+    SensorEntityDescription(
+        key="highest_price_tomorrow",
+        translation_key="highest_price_tomorrow",
+        name="Tomorrow's Highest Price",
+        icon="mdi:arrow-collapse-up",
+        device_class=SensorDeviceClass.MONETARY,
+        suggested_display_precision=1,
+    ),
+    SensorEntityDescription(
+        key="average_price_tomorrow",
+        translation_key="average_price_tomorrow",
+        name="Tomorrow's Average Price",
+        icon="mdi:chart-line",
+        device_class=SensorDeviceClass.MONETARY,
+        suggested_display_precision=1,
+    ),
+)
+
+# ----------------------------------------------------------------------------
+# 4. 24H WINDOW SENSORS (trailing/leading from current interval)
+# ----------------------------------------------------------------------------
+# Calculate statistics over sliding 24-hour windows
+
+WINDOW_24H_SENSORS = (
+    SensorEntityDescription(
+        key="trailing_price_average",
+        translation_key="trailing_price_average",
+        name="Trailing 24h Average Price",
+        icon="mdi:chart-line",
+        device_class=SensorDeviceClass.MONETARY,
+        entity_registry_enabled_default=False,
+        suggested_display_precision=1,
+    ),
+    SensorEntityDescription(
+        key="leading_price_average",
+        translation_key="leading_price_average",
+        name="Leading 24h Average Price",
+        icon="mdi:chart-line-variant",
+        device_class=SensorDeviceClass.MONETARY,
+        suggested_display_precision=1,
+    ),
+    SensorEntityDescription(
+        key="trailing_price_min",
+        translation_key="trailing_price_min",
+        name="Trailing 24h Minimum Price",
+        icon="mdi:arrow-collapse-down",
+        device_class=SensorDeviceClass.MONETARY,
+        entity_registry_enabled_default=False,
+        suggested_display_precision=1,
+    ),
+    SensorEntityDescription(
+        key="trailing_price_max",
+        translation_key="trailing_price_max",
+        name="Trailing 24h Maximum Price",
+        icon="mdi:arrow-collapse-up",
+        device_class=SensorDeviceClass.MONETARY,
+        entity_registry_enabled_default=False,
+        suggested_display_precision=1,
+    ),
+    SensorEntityDescription(
+        key="leading_price_min",
+        translation_key="leading_price_min",
+        name="Leading 24h Minimum Price",
+        icon="mdi:arrow-collapse-down",
+        device_class=SensorDeviceClass.MONETARY,
+        suggested_display_precision=1,
+    ),
+    SensorEntityDescription(
+        key="leading_price_max",
+        translation_key="leading_price_max",
+        name="Leading 24h Maximum Price",
+        icon="mdi:arrow-collapse-up",
+        device_class=SensorDeviceClass.MONETARY,
+        suggested_display_precision=1,
+    ),
+)
+
+# ----------------------------------------------------------------------------
+# 5. FUTURE FORECAST SENSORS (N-hour windows starting from next interval)
+# ----------------------------------------------------------------------------
+# Calculate averages and trends for upcoming time windows
+
+FUTURE_AVG_SENSORS = (
+    # Default enabled: 1h-5h
+    SensorEntityDescription(
+        key="next_avg_1h",
+        translation_key="next_avg_1h",
+        name="Next 1h Average Price",
+        icon="mdi:chart-line",
+        device_class=SensorDeviceClass.MONETARY,
+        suggested_display_precision=1,
+        entity_registry_enabled_default=True,
+    ),
+    SensorEntityDescription(
+        key="next_avg_2h",
+        translation_key="next_avg_2h",
+        name="Next 2h Average Price",
+        icon="mdi:chart-line",
+        device_class=SensorDeviceClass.MONETARY,
+        suggested_display_precision=1,
+        entity_registry_enabled_default=True,
+    ),
+    SensorEntityDescription(
+        key="next_avg_3h",
+        translation_key="next_avg_3h",
+        name="Next 3h Average Price",
+        icon="mdi:chart-line",
+        device_class=SensorDeviceClass.MONETARY,
+        suggested_display_precision=1,
+        entity_registry_enabled_default=True,
+    ),
+    SensorEntityDescription(
+        key="next_avg_4h",
+        translation_key="next_avg_4h",
+        name="Next 4h Average Price",
+        icon="mdi:chart-line",
+        device_class=SensorDeviceClass.MONETARY,
+        suggested_display_precision=1,
+        entity_registry_enabled_default=True,
+    ),
+    SensorEntityDescription(
+        key="next_avg_5h",
+        translation_key="next_avg_5h",
+        name="Next 5h Average Price",
+        icon="mdi:chart-line",
+        device_class=SensorDeviceClass.MONETARY,
+        suggested_display_precision=1,
+        entity_registry_enabled_default=True,
+    ),
+    # Disabled by default: 6h, 8h, 12h (advanced use cases)
+    SensorEntityDescription(
+        key="next_avg_6h",
+        translation_key="next_avg_6h",
+        name="Next 6h Average Price",
+        icon="mdi:chart-line",
+        device_class=SensorDeviceClass.MONETARY,
+        suggested_display_precision=1,
+        entity_registry_enabled_default=False,
+    ),
+    SensorEntityDescription(
+        key="next_avg_8h",
+        translation_key="next_avg_8h",
+        name="Next 8h Average Price",
+        icon="mdi:chart-line",
+        device_class=SensorDeviceClass.MONETARY,
+        suggested_display_precision=1,
+        entity_registry_enabled_default=False,
+    ),
+    SensorEntityDescription(
+        key="next_avg_12h",
+        translation_key="next_avg_12h",
+        name="Next 12h Average Price",
+        icon="mdi:chart-line",
+        device_class=SensorDeviceClass.MONETARY,
+        suggested_display_precision=1,
+        entity_registry_enabled_default=False,
+    ),
+)
+
+FUTURE_TREND_SENSORS = (
+    # Default enabled: 1h-5h
+    SensorEntityDescription(
+        key="price_trend_1h",
+        translation_key="price_trend_1h",
+        name="Price Trend (1h)",
+        icon="mdi:trending-up",
+        device_class=SensorDeviceClass.ENUM,
+        options=["rising", "falling", "stable"],
+        entity_registry_enabled_default=True,
+    ),
+    SensorEntityDescription(
+        key="price_trend_2h",
+        translation_key="price_trend_2h",
+        name="Price Trend (2h)",
+        icon="mdi:trending-up",
+        device_class=SensorDeviceClass.ENUM,
+        options=["rising", "falling", "stable"],
+        entity_registry_enabled_default=True,
+    ),
+    SensorEntityDescription(
+        key="price_trend_3h",
+        translation_key="price_trend_3h",
+        name="Price Trend (3h)",
+        icon="mdi:trending-up",
+        device_class=SensorDeviceClass.ENUM,
+        options=["rising", "falling", "stable"],
+        entity_registry_enabled_default=True,
+    ),
+    SensorEntityDescription(
+        key="price_trend_4h",
+        translation_key="price_trend_4h",
+        name="Price Trend (4h)",
+        icon="mdi:trending-up",
+        device_class=SensorDeviceClass.ENUM,
+        options=["rising", "falling", "stable"],
+        entity_registry_enabled_default=True,
+    ),
+    SensorEntityDescription(
+        key="price_trend_5h",
+        translation_key="price_trend_5h",
+        name="Price Trend (5h)",
+        icon="mdi:trending-up",
+        device_class=SensorDeviceClass.ENUM,
+        options=["rising", "falling", "stable"],
+        entity_registry_enabled_default=True,
+    ),
+    # Disabled by default: 6h, 8h, 12h
+    SensorEntityDescription(
+        key="price_trend_6h",
+        translation_key="price_trend_6h",
+        name="Price Trend (6h)",
+        icon="mdi:trending-up",
+        device_class=SensorDeviceClass.ENUM,
+        options=["rising", "falling", "stable"],
+        entity_registry_enabled_default=False,
+    ),
+    SensorEntityDescription(
+        key="price_trend_8h",
+        translation_key="price_trend_8h",
+        name="Price Trend (8h)",
+        icon="mdi:trending-up",
+        device_class=SensorDeviceClass.ENUM,
+        options=["rising", "falling", "stable"],
+        entity_registry_enabled_default=False,
+    ),
+    SensorEntityDescription(
+        key="price_trend_12h",
+        translation_key="price_trend_12h",
+        name="Price Trend (12h)",
+        icon="mdi:trending-up",
+        device_class=SensorDeviceClass.ENUM,
+        options=["rising", "falling", "stable"],
+        entity_registry_enabled_default=False,
+    ),
+)
+
+# ----------------------------------------------------------------------------
+# 6. VOLATILITY SENSORS (coefficient of variation analysis)
+# ----------------------------------------------------------------------------
+# NOTE: Enum options are defined inline (not imported from const.py) to avoid
+# import timing issues with Home Assistant's entity platform initialization.
+# Keep in sync with VOLATILITY_OPTIONS in const.py!
+
+VOLATILITY_SENSORS = (
+    SensorEntityDescription(
+        key="today_volatility",
+        translation_key="today_volatility",
+        name="Today's Price Volatility",
+        icon="mdi:chart-bell-curve-cumulative",
+        device_class=SensorDeviceClass.ENUM,
+        options=["low", "moderate", "high", "very_high"],
+    ),
+    SensorEntityDescription(
+        key="tomorrow_volatility",
+        translation_key="tomorrow_volatility",
+        name="Tomorrow's Price Volatility",
+        icon="mdi:chart-bell-curve-cumulative",
+        device_class=SensorDeviceClass.ENUM,
+        options=["low", "moderate", "high", "very_high"],
+    ),
+    SensorEntityDescription(
+        key="next_24h_volatility",
+        translation_key="next_24h_volatility",
+        name="Next 24h Price Volatility",
+        icon="mdi:chart-bell-curve-cumulative",
+        device_class=SensorDeviceClass.ENUM,
+        options=["low", "moderate", "high", "very_high"],
+    ),
+    SensorEntityDescription(
+        key="today_tomorrow_volatility",
+        translation_key="today_tomorrow_volatility",
+        name="Today + Tomorrow Price Volatility",
+        icon="mdi:chart-bell-curve-cumulative",
+        device_class=SensorDeviceClass.ENUM,
+        options=["low", "moderate", "high", "very_high"],
+    ),
+)
+
+# ----------------------------------------------------------------------------
+# 7. DIAGNOSTIC SENSORS (data availability and metadata)
+# ----------------------------------------------------------------------------
+
+DIAGNOSTIC_SENSORS = (
+    SensorEntityDescription(
+        key="data_timestamp",
+        translation_key="data_timestamp",
+        name="Data Expiration",
+        icon="mdi:clock-check",
+        device_class=SensorDeviceClass.TIMESTAMP,
+        entity_category=EntityCategory.DIAGNOSTIC,
+    ),
+    SensorEntityDescription(
+        key="price_forecast",
+        translation_key="price_forecast",
+        name="Price Forecast",
+        icon="mdi:chart-line",
+        entity_category=EntityCategory.DIAGNOSTIC,
+    ),
+)
+
+# ----------------------------------------------------------------------------
+# COMBINED SENSOR DEFINITIONS
+# ----------------------------------------------------------------------------
+
+ENTITY_DESCRIPTIONS = (
+    *INTERVAL_PRICE_SENSORS,
+    *INTERVAL_LEVEL_SENSORS,
+    *INTERVAL_RATING_SENSORS,
+    *ROLLING_HOUR_PRICE_SENSORS,
+    *ROLLING_HOUR_LEVEL_SENSORS,
+    *ROLLING_HOUR_RATING_SENSORS,
+    *DAILY_STAT_SENSORS,
+    *WINDOW_24H_SENSORS,
+    *FUTURE_AVG_SENSORS,
+    *FUTURE_TREND_SENSORS,
+    *VOLATILITY_SENSORS,
+    *DIAGNOSTIC_SENSORS,
+)

custom_components/tibber_prices/sensor/helpers.py

@@ -0,0 +1,178 @@
+"""Helper functions for sensor platform."""
+
+from __future__ import annotations
+
+from datetime import timedelta
+from typing import TYPE_CHECKING
+
+from custom_components.tibber_prices.const import get_price_level_translation
+from custom_components.tibber_prices.price_utils import (
+    aggregate_price_levels,
+    aggregate_price_rating,
+)
+from homeassistant.util import dt as dt_util
+
+if TYPE_CHECKING:
+    from datetime import datetime
+
+    from homeassistant.core import HomeAssistant
+
+
+def aggregate_price_data(window_data: list[dict]) -> float | None:
+    """
+    Calculate average price from window data.
+
+    Args:
+        window_data: List of price interval dictionaries with 'total' key
+
+    Returns:
+        Average price in minor currency units (cents/øre), or None if no prices
+
+    """
+    prices = [float(i["total"]) for i in window_data if "total" in i]
+    if not prices:
+        return None
+    # Return in minor currency units (cents/øre)
+    return round((sum(prices) / len(prices)) * 100, 2)
+
+
+def aggregate_level_data(window_data: list[dict]) -> str | None:
+    """
+    Aggregate price levels from window data.
+
+    Args:
+        window_data: List of price interval dictionaries with 'level' key
+
+    Returns:
+        Aggregated price level (lowercase), or None if no levels
+
+    """
+    levels = [i["level"] for i in window_data if "level" in i]
+    if not levels:
+        return None
+    aggregated = aggregate_price_levels(levels)
+    return aggregated.lower() if aggregated else None
+
+
+def aggregate_rating_data(
+    window_data: list[dict],
+    threshold_low: float,
+    threshold_high: float,
+) -> str | None:
+    """
+    Aggregate price ratings from window data.
+
+    Args:
+        window_data: List of price interval dictionaries with 'difference' and 'rating_level'
+        threshold_low: Low threshold for rating calculation
+        threshold_high: High threshold for rating calculation
+
+    Returns:
+        Aggregated price rating (lowercase), or None if no ratings
+
+    """
+    differences = [i["difference"] for i in window_data if "difference" in i and "rating_level" in i]
+    if not differences:
+        return None
+
+    aggregated, _ = aggregate_price_rating(differences, threshold_low, threshold_high)
+    return aggregated.lower() if aggregated else None
+
+
+def find_rolling_hour_center_index(
+    all_prices: list[dict],
+    current_time: datetime,
+    hour_offset: int,
+) -> int | None:
+    """
+    Find the center index for the rolling hour window.
+
+    Args:
+        all_prices: List of all price interval dictionaries with 'startsAt' key
+        current_time: Current datetime to find the current interval
+        hour_offset: Number of hours to offset from current interval (can be negative)
+
+    Returns:
+        Index of the center interval for the rolling hour window, or None if not found
+
+    """
+    current_idx = None
+
+    for idx, price_data in enumerate(all_prices):
+        starts_at = dt_util.parse_datetime(price_data["startsAt"])
+        if starts_at is None:
+            continue
+        starts_at = dt_util.as_local(starts_at)
+        interval_end = starts_at + timedelta(minutes=15)
+
+        if starts_at <= current_time < interval_end:
+            current_idx = idx
+            break
+
+    if current_idx is None:
+        return None
+
+    return current_idx + (hour_offset * 4)
+
+
+def translate_level(hass: HomeAssistant, level: str) -> str:
+    """
+    Translate price level to the user's language.
+
+    Args:
+        hass: HomeAssistant instance for language configuration
+        level: Price level to translate (e.g., VERY_CHEAP, NORMAL, etc.)
+
+    Returns:
+        Translated level string, or original level if translation not found
+
+    """
+    if not hass:
+        return level
+
+    language = hass.config.language or "en"
+    translated = get_price_level_translation(level, language)
+    if translated:
+        return translated
+
+    if language != "en":
+        fallback = get_price_level_translation(level, "en")
+        if fallback:
+            return fallback
+
+    return level
+
+
+def translate_rating_level(rating: str) -> str:
+    """
+    Translate price rating level to the user's language.
+
+    Args:
+        rating: Price rating to translate (e.g., LOW, NORMAL, HIGH)
+
+    Returns:
+        Translated rating string, or original rating if translation not found
+
+    Note:
+        Currently returns the rating as-is. Translation mapping for ratings
+        can be added here when needed, similar to translate_level().
+
+    """
+    # For now, ratings are returned as-is
+    # Add translation mapping here when needed
+    return rating
+
+
+def get_price_value(price: float, *, in_euro: bool) -> float:
+    """
+    Convert price based on unit.
+
+    Args:
+        price: Price value to convert
+        in_euro: If True, return price in euros; if False, return in cents/øre
+
+    Returns:
+        Price in requested unit (euros or minor currency units)
+
+    """
+    return price if in_euro else round((price * 100), 2)

docs/development/README.md

@@ -4,21 +4,21 @@ This section contains documentation for contributors and maintainers of the Tibb
 
 ## 📚 Developer Guides
 
-- **[Setup](setup.md)** - DevContainer, environment setup, and dependencies
-- **[Architecture](architecture.md)** - Code structure, patterns, and conventions
-- **[Testing](testing.md)** - How to run tests and write new test cases
-- **[Release Management](release-management.md)** - Release workflow and versioning process
-- **[Coding Guidelines](coding-guidelines.md)** - Style guide, linting, and best practices
+-   **[Setup](setup.md)** - DevContainer, environment setup, and dependencies
+-   **[Architecture](architecture.md)** - Code structure, patterns, and conventions
+-   **[Testing](testing.md)** - How to run tests and write new test cases
+-   **[Release Management](release-management.md)** - Release workflow and versioning process
+-   **[Coding Guidelines](coding-guidelines.md)** - Style guide, linting, and best practices
 
 ## 🤖 AI Documentation
 
 The main AI/Copilot documentation is in [`AGENTS.md`](../../AGENTS.md). This file serves as long-term memory for AI assistants and contains:
 
-- Detailed architectural patterns
-- Code quality rules and conventions
-- Development workflow guidance
-- Common pitfalls and anti-patterns
-- Project-specific patterns and utilities
+-   Detailed architectural patterns
+-   Code quality rules and conventions
+-   Development workflow guidance
+-   Common pitfalls and anti-patterns
+-   Project-specific patterns and utilities
 
 **Important:** When proposing changes to patterns or conventions, always update [`AGENTS.md`](../../AGENTS.md) to keep AI guidance consistent.
 
@@ -26,29 +26,32 @@ The main AI/Copilot documentation is in [`AGENTS.md`](../../AGENTS.md). This fil
 
 This integration is developed with extensive AI assistance (GitHub Copilot, Claude, and other AI tools). The AI handles:
 
-- **Pattern Recognition**: Understanding and applying Home Assistant best practices
-- **Code Generation**: Implementing features with proper type hints, error handling, and documentation
-- **Refactoring**: Maintaining consistency across the codebase during structural changes
-- **Translation Management**: Keeping 5 language files synchronized
-- **Documentation**: Generating and maintaining comprehensive documentation
+-   **Pattern Recognition**: Understanding and applying Home Assistant best practices
+-   **Code Generation**: Implementing features with proper type hints, error handling, and documentation
+-   **Refactoring**: Maintaining consistency across the codebase during structural changes
+-   **Translation Management**: Keeping 5 language files synchronized
+-   **Documentation**: Generating and maintaining comprehensive documentation
 
 **Quality Assurance:**
-- Automated linting with Ruff (120-char line length, max complexity 25)
-- Home Assistant's type checking and validation
-- Real-world testing in development environment
-- Code review by maintainer before merging
+
+-   Automated linting with Ruff (120-char line length, max complexity 25)
+-   Home Assistant's type checking and validation
+-   Real-world testing in development environment
+-   Code review by maintainer before merging
 
 **Benefits:**
-- Rapid feature development while maintaining quality
-- Consistent code patterns across all modules
-- Comprehensive documentation maintained alongside code
-- Quick bug fixes with proper understanding of context
+
+-   Rapid feature development while maintaining quality
+-   Consistent code patterns across all modules
+-   Comprehensive documentation maintained alongside code
+-   Quick bug fixes with proper understanding of context
 
 **Limitations:**
-- AI may occasionally miss edge cases or subtle bugs
-- Some complex Home Assistant patterns may need human review
-- Translation quality depends on AI's understanding of target language
-- User feedback is crucial for discovering real-world issues
+
+-   AI may occasionally miss edge cases or subtle bugs
+-   Some complex Home Assistant patterns may need human review
+-   Translation quality depends on AI's understanding of target language
+-   User feedback is crucial for discovering real-world issues
 
 If you're working with AI tools on this project, the [`AGENTS.md`](../../AGENTS.md) file provides the context and patterns that ensure consistency.
 
@@ -68,13 +71,13 @@ If you're working with AI tools on this project, the [`AGENTS.md`](../../AGENTS.
 
 The project includes several helper scripts in `./scripts/`:
 
-- `bootstrap` - Initial setup of dependencies
-- `develop` - Start Home Assistant in debug mode
-- `lint` - Auto-fix code issues with ruff
-- `lint-check` - Check code without modifications (CI mode)
-- `setup` - Install development tools (git-cliff, @github/copilot)
-- `prepare-release` - Prepare a new release (bump version, create tag)
-- `generate-release-notes` - Generate release notes from commits
+-   `bootstrap` - Initial setup of dependencies
+-   `develop` - Start Home Assistant in debug mode
+-   `lint` - Auto-fix code issues with ruff
+-   `lint-check` - Check code without modifications (CI mode)
+-   `setup` - Install development tools (git-cliff, @github/copilot)
+-   `prepare-release` - Prepare a new release (bump version, create tag)
+-   `generate-release-notes` - Generate release notes from commits
 
 ## 📦 Project Structure
 
@@ -85,8 +88,17 @@ custom_components/tibber_prices/
 ├── api.py               # Tibber GraphQL API client
 ├── price_utils.py       # Price enrichment functions
 ├── average_utils.py     # Average calculation utilities
-├── sensor.py            # Sensor platform
+├── sensor/              # Sensor platform (package)
+│   ├── __init__.py      #   Platform setup
+│   ├── core.py          #   TibberPricesSensor class
+│   ├── definitions.py   #   Entity descriptions
+│   ├── helpers.py       #   Pure helper functions
+│   └── attributes.py    #   Attribute builders
 ├── binary_sensor.py     # Binary sensor platform
+├── entity_utils/        # Shared entity helpers
+│   ├── icons.py         #   Icon mapping logic
+│   ├── colors.py        #   Color mapping logic
+│   └── attributes.py    #   Common attribute builders
 ├── services.py          # Custom services
 ├── config_flow.py       # UI configuration flow
 ├── const.py             # Constants and helpers
@@ -97,21 +109,24 @@ custom_components/tibber_prices/
 ## 🔍 Key Concepts
 
 **DataUpdateCoordinator Pattern:**
-- Centralized data fetching and caching
-- Automatic entity updates on data changes
-- Persistent storage via `Store`
-- Quarter-hour boundary refresh scheduling
+
+-   Centralized data fetching and caching
+-   Automatic entity updates on data changes
+-   Persistent storage via `Store`
+-   Quarter-hour boundary refresh scheduling
 
 **Price Data Enrichment:**
-- Raw API data is enriched with statistical analysis
-- Trailing/leading 24h averages calculated per interval
-- Price differences and ratings added
-- All via pure functions in `price_utils.py`
+
+-   Raw API data is enriched with statistical analysis
+-   Trailing/leading 24h averages calculated per interval
+-   Price differences and ratings added
+-   All via pure functions in `price_utils.py`
 
 **Translation System:**
-- Dual system: `/translations/` (HA schema) + `/custom_translations/` (extended)
-- Both must stay in sync across all languages (de, en, nb, nl, sv)
-- Async loading at integration setup
+
+-   Dual system: `/translations/` (HA schema) + `/custom_translations/` (extended)
+-   Both must stay in sync across all languages (de, en, nb, nl, sv)
+-   Async loading at integration setup
 
 ## 🧪 Testing
 
@@ -128,11 +143,11 @@ pytest --cov=custom_components.tibber_prices tests/
 
 ## 📝 Documentation Standards
 
-- **User-facing docs** go in `docs/user/`
-- **Developer docs** go in `docs/development/`
-- **AI guidance** goes in `AGENTS.md`
-- Use clear examples and code snippets
-- Keep docs up-to-date with code changes
+-   **User-facing docs** go in `docs/user/`
+-   **Developer docs** go in `docs/development/`
+-   **AI guidance** goes in `AGENTS.md`
+-   Use clear examples and code snippets
+-   Keep docs up-to-date with code changes
 
 ## 🤝 Contributing
 

scripts/generate-release-notes

@@ -201,7 +201,7 @@ generate_with_copilot() {
    - Bug fixes that users experience (translations, api, coordinator, etc.)
 
 8. **Understanding the file paths (use this to assess importance):**
-   - custom_components/tibber_prices/sensor.py = User-facing sensors (HIGH priority for title)
+   - custom_components/tibber_prices/sensor/ = User-facing sensors (HIGH priority for title)
    - custom_components/tibber_prices/binary_sensor.py = User-facing binary sensors (HIGH priority)
    - custom_components/tibber_prices/config_flow.py = User-facing configuration (HIGH priority)
    - custom_components/tibber_prices/services.py = User-facing services (HIGH priority)