Improved validation logic for service parameters in find_cheapest_hours, find_cheapest_schedule, and chartdata services. Added checks for unique task names, ensured that segment durations do not exceed total duration, and clarified error messages for better user understanding.
Impact: Users will receive clearer error messages and improved validation when using the services, leading to a more robust experience.
Updated the filter logic to include period_filter alongside level_filter and rating_level_filter for segment definitions. This change ensures that users can utilize period_filter effectively when defining segments.
Impact: Users can now use period_filter in addition to existing filters for more flexible segment definitions.
Updated the long descriptions and usage tips for the price trend change sensors in multiple languages (de, en, nb, nl, sv) to provide clearer guidance on detection mechanics and expected behavior during V-shaped price days.
Impact: Users will have a better understanding of how the sensors operate and can make more informed decisions regarding automation based on price trends.
Rename the three existing price rank sensors from price_rank_* to
current_interval_price_rank_* to clarify they rank the current
quarter-hour interval's price, not a daily aggregate — consistent with
current_interval_price_level / current_interval_price_rating naming.
Add 8 new rank sensors covering additional subjects and reference windows:
- next_interval_price_rank_{today,today_tomorrow}
- previous_interval_price_rank_{today,today_tomorrow}
- current_hour_price_rank_{today,today_tomorrow} (5-interval rolling avg)
- next_hour_price_rank_{today,today_tomorrow} (5-interval rolling avg)
All new sensors are disabled by default. The volatility calculator gains a
subject parameter (_get_subject_price / _get_subject_price_attr_key /
_get_rolling_hour_avg_price) to select which price to rank. Sensor key
routing in value_getters.py and attributes/__init__.py updated accordingly.
No migration entries needed — the original price_rank_* sensors were never
released to users.
All 5 translation files updated. sensor-reference.md regenerated (129 entities).
Impact: Users can now track price rank for the next interval (look-ahead),
the previous interval (logging), and rolling hourly averages — for both
same-day and two-day reference windows.
Add entity name translations for price_rank_today, price_rank_tomorrow, and
price_rank_today_tomorrow sensors in English, German, Norwegian, Dutch, and
Swedish.
Impact: Sensor display names appear correctly in the Home Assistant UI for all
supported languages.
Add structured reason codes to no-result responses for find_cheapest_block,
find_cheapest_hours, and find_cheapest_schedule. Each handler now classifies
why no result was returned: no_data_in_range, no_intervals_matching_level_filter,
insufficient_intervals_after_filter, or insufficient_contiguous_window.
Add include_comparison_details flag to find_cheapest_schedule. When enabled,
each scheduled task includes a price_comparison field showing the most expensive
alternative window (mean, min, max, start, end) for cost-savings context.
Document stable reason code contracts in en.json service descriptions.
Add corresponding field translations to all locales (de, nb, nl, sv).
Impact: Automations and scripts can now react to why no window was found,
and schedules can display concrete savings vs. worst-case pricing.
Phase 3: When geometric bonus intervals cause CV gate failure, strip them
from period edges (unextended boundaries) and set geometric_extension_attempted=True
on the summary. Previously only geometric_extension_active was tracked.
Moved LOW_PRICE_QUALITY_BYPASS_THRESHOLD constant to types.py for shared access.
Phase 4: Add time_range: tuple[datetime, datetime] | None parameter to
build_periods(), calculate_periods(), and calculate_periods_with_relaxation().
Filters candidate intervals to [start, end) without affecting day-wide reference
prices. Refactored _apply_segment_forcing() to use time_range instead of the
restricted_prices list approach.
Impact: Period statistics now accurately reflect when geometric flex extension
was attempted but reverted due to quality gate failure. Segment forcing uses
a cleaner API that preserves full price context for reference calculations.
Uses valley/peak knee points from day pattern analysis to grant extra
flex to price intervals that fall inside detected geometric zones,
making period detection more permissive within V-shape (best price)
or Λ-shape (peak price) price formations.
New options:
- CONF_BEST_PRICE_GEOMETRIC_FLEX (0-25%, default 0 = disabled)
- CONF_PEAK_PRICE_GEOMETRIC_FLEX (0-25%, default 0 = disabled)
Implementation:
- compute_geometric_flex_bonus() in level_filtering.py checks if
interval falls inside valley/peak zone and returns extra_flex
- period_building.py applies geo bonus per-interval via
criteria._replace(flex=...) and sets geometric_bonus_applied flag
- period_statistics.py reports geometric_extension_active and
geometric_extension_intervals in period summaries
- Day patterns threaded through full pipeline:
data_transformation → coordinator/core → periods →
relaxation → calculate_periods → price_context
- UI sliders in both extension_settings sections
- Translations: en, de, nb, nl, sv
Impact: Users with clearly V-shaped or Λ-shaped daily price curves
can enable geometric flex to improve period detection accuracy within
those characteristic shapes without increasing global flex.
After period detection, optionally walk left/right from each period boundary
to absorb adjacent VERY_CHEAP (best price) or VERY_EXPENSIVE (peak price)
intervals (step 7.5 in the pipeline).
New constants: CONF_BEST_PRICE_EXTEND_TO_VERY_CHEAP, CONF_BEST_PRICE_MAX_EXTENSION_INTERVALS,
CONF_PEAK_PRICE_EXTEND_TO_VERY_EXPENSIVE, CONF_PEAK_PRICE_MAX_EXTENSION_INTERVALS.
Defaults: off / 4 intervals (1 hour per side). Hard maximum: 12 intervals (3 hours).
Config stored under "extension_settings" section, reflected in period hash
for correct cache invalidation.
New module: coordinator/period_handlers/shape_extension.py handles the
boundary walk, stat recalculation, and extension_intervals_added bookkeeping.
Impact: Users can opt-in to wider best/peak price windows that include
extreme-level adjacent intervals, reducing missed very cheap/expensive slots
at period edges.
Introduces a new day_pattern.py module that analyses the 15-min price curve
for each calendar day (yesterday/today/tomorrow) and classifies its shape.
New sensors:
day_pattern_yesterday / day_pattern_today / day_pattern_tomorrow
EntityCategory.DIAGNOSTIC, SensorDeviceClass.ENUM
Patterns: valley, peak, double_valley, double_peak, flat, rising, falling, mixed
The detector uses centred-rolling smoothing, prominence-filtered extrema,
Kneedle-based knee detection, and monotone segment building.
Coordinator populates transformed_data["dayPatterns"] after priceInfo enrichment.
Impact: Users can trigger automations based on the shape of the day's price
curve, e.g. pre-heat when tomorrow is a valley day.
New services for finding optimal electricity price windows:
- find_cheapest_block: Cheapest contiguous time block (e.g., dishwasher)
- find_cheapest_hours: Cheapest N hours, non-contiguous (e.g., EV charging)
- find_cheapest_schedule: Multi-task scheduling with no-overlap (e.g., shared circuit)
- find_most_expensive_block: Most expensive contiguous block (peak avoidance)
- find_most_expensive_hours: Most expensive N hours (consumption shifting)
Key features:
- Flexible search range (today, tomorrow, today+tomorrow, rolling window)
- Power profile support for variable consumption patterns
- Price level filtering (e.g., only CHEAP/VERY_CHEAP intervals)
- Comparison details showing savings vs. alternatives
- Sliding window algorithm (O(n)) for block search, greedy scheduling
for multi-task optimization
Also includes:
- Shared validation utilities (search range, price level, power profile)
- entry_id now optional on all services (auto-selects single home)
- Input validation for existing services (time range, filter conflicts)
- Service icons for all new and existing services
- Translations for all 5 languages (en, de, nb, nl, sv)
- Removed 10 unused config.error translation keys (replaced by exceptions)
- Tests for price window algorithms and search range resolution
Impact: Users can find optimal time windows for appliances, EV charging,
and multi-device scheduling via HA service calls. Existing services
improved with optional entry_id and better input validation.
Adds migrations.py with automatic entity registry migration for renamed
sensor keys. Separated from coordinator/repairs.py (runtime issues) and
__init__.py _migrate_config_options() (config format changes).
- ENTITY_KEY_RENAMES dict maps old→new entity keys (extensible)
- _auto_migrate_entity_keys() updates unique_id, preserves entity_id
- Handles partial migration (new entity already exists → remove old)
- Creates persistent HA repair issue after migration via ir.async_create_issue()
- Called in async_setup_entry() after _migrate_config_options()
Migrates: trend_change_in_minutes → next_price_trend_change_in
Repair issue informs users about:
- Auto-migrated entity renames (entity_id preserved, no action needed)
- Duration sensor value unit change (hours → minutes): update automation
thresholds from `state < 0.25` to `state < 15` for 15-minute checks
All 5 language files (en, de, nb, nl, sv) updated with translations.
BREAKING CHANGE: Duration sensors (remaining time, starts in, period
duration, trend change countdown) now report state values in minutes
instead of hours. Display unit in dashboards remains hours by default.
Update numeric comparisons in automations accordingly.
Impact: Users upgrading from previous releases see an informational
repair notice guiding them through any required automation updates.
Entity renames are handled transparently with no loss of history.
Renamed trend_change_in_minutes → next_price_trend_change_in to align
with its sibling sensor next_price_trend_change (timestamp variant).
Follows the established best/peak price naming pattern where related
sensors share a common prefix (e.g. best_price_next_start_time /
best_price_next_in_minutes).
Updated entity key, translation key, friendly names (all 5 languages),
custom translations, coordinator constants, attribute routing, and
cache-clear mapping.
BREAKING CHANGE: Entity ID changes from
sensor.<home>_trend_change_in_minutes to
sensor.<home>_next_price_trend_change_in. Automations and dashboards
referencing the old entity ID must be updated.
Impact: Users with automations or dashboard cards referencing the old
sensor name need to update references. The sensor retains identical
functionality and attributes.
Add four optional parameters to the get_chartdata service:
- include_energy: Include raw energy/spot price (default: false)
- include_tax: Include tax component (default: false)
- energy_field: Custom field name (default: energy_price)
- tax_field: Custom field name (default: tax)
Custom field names allow direct compatibility with ApexCharts
and other charting tools without post-processing.
All code paths (all/segments/none insert_nulls modes) and the
last-interval handler include energy/tax when enabled.
Added translations for all 5 languages (en, de, nl, nb, sv).
Impact: Users can include price composition data in chart exports,
enabling visual breakdowns of energy cost vs. taxes in dashboards.
Renamed 8 price_trend_Xh entries to price_outlook_Xh and added 15 new
price_trajectory_Xh entries (2h–12h) in all 5 languages (de, en, nb, nl, sv).
translations/ (HA-native: name + 5 states per sensor):
- EN: "Price Outlook (Xh)" / "Price Trajectory (Xh)"
- DE: "Preisausblick (Xh)" / "Preisverlauf (Xh)"
- NB: "Prisutblikk (Xt)" / "Prisforløp (Xt)"
- NL: "Prijsvooruitzicht (Xu)" / "Prijstrajectorie (Xu)"
- SV: "Prisöversikt (Xh)" / "Prisutveckling (Xh)"
custom_translations/ (description + long_description + usage_tips):
- Outlook descriptions updated to explain window-average comparison
semantics (not price direction)
- Trajectory descriptions explain first-half vs second-half logic and
the "outlook: falling + trajectory: rising = you're AT the minimum" pattern
- Trajectory long_description and usage_tips in English for all languages;
description field in native language
Impact: Entity display names update to reflect the corrected semantic meaning.
When runtime config override entities (number/switch) are enabled,
the Options Flow now displays warning indicators at the top of each
affected section. Users see which fields are being managed by config
entities and can still edit the base values if needed.
Changes:
- Add ConstantSelector warnings in Best Price/Peak Price sections
- Implement multi-language support for override warnings (de, en, nb, nl, sv)
- Add _get_override_translations() to load translated field labels
- Add _get_active_overrides() to detect enabled override entities
- Extend get_best_price_schema/get_peak_price_schema with translations param
- Add 14 number/switch config entities for runtime period tuning
- Document runtime configuration entities in user docs
Warning format adapts to overridden fields:
- Single: "⚠️ Flexibility controlled by config entity"
- Multiple: "⚠️ Flexibility and Minimum Distance controlled by config entity"
Impact: Users can now dynamically adjust period calculation parameters
via Home Assistant automations, scripts, or dashboards without entering
the Options Flow. Clear UI indicators show which settings are currently
overridden.
Add resolution parameter to get_chartdata and get_apexcharts_yaml services,
allowing users to choose between original 15-minute intervals or aggregated
hourly values for chart visualization.
Implementation uses rolling 5-interval window aggregation (-2, -1, 0, +1, +2
around :00 of each hour = 60 minutes total), matching the sensor rolling
hour methodology. Respects user's CONF_AVERAGE_SENSOR_DISPLAY setting for
mean vs median calculation.
Changes:
- formatters.py: Add aggregate_to_hourly() function preserving original
field names (startsAt, total, level, rating_level) for unified processing
- get_chartdata.py: Pre-aggregate data before processing when resolution is
'hourly', enabling same code path for filters/insert_nulls/connect_segments
- get_apexcharts_yaml.py: Add resolution parameter, pass to all 4 get_chartdata
service calls in generated JavaScript
- services.yaml: Add resolution field with interval/hourly selector
- icons.json: Add section icons for get_apexcharts_yaml fields
- translations: Add highlight_peak_price and resolution field translations
for all 5 languages (en, de, sv, nb, nl)
Impact: Users can now generate cleaner charts with 24 hourly data points
instead of 96 quarter-hourly intervals. The unified processing approach
ensures all chart features (filters, null insertion, segment connection)
work identically for both resolutions.
Added dynamic warnings when users configure settings for sensors that
are currently disabled. This improves UX by informing users that their
configuration changes won't have any visible effect until they enable
the relevant sensors.
Changes:
- Created entity_check.py helper module with sensor-to-step mappings
- Added check_relevant_entities_enabled() to detect disabled sensors
- Integrated warnings into 6 options flow steps (price_rating,
price_level, best_price, peak_price, price_trend, volatility)
- Made Chart Data Export info page content-aware: shows configuration
guide when sensor is enabled, shows enablement instructions when disabled
- Updated all 5 translation files (de, en, nb, nl, sv) with dynamic
placeholders {entity_warning} and {sensor_status_info}
Impact: Users now receive clear feedback when configuring settings for
disabled sensors, reducing confusion about why changes aren't visible.
Chart Data Export page now provides context-appropriate guidance.
Expose the `price_coefficient_variation_%` value across period statistics, binary sensor attributes, and the volatility calculator, and refresh the volatility descriptions/translations to mention the coefficient-of-variation metric.
Add debug service to clear tomorrow data from interval pool, enabling
testing of tomorrow data refresh cycle without waiting for next day.
Service available only in DevContainer (TIBBER_PRICES_DEV=1 env var).
Removes intervals from both Pool index and coordinator.data["priceInfo"]
so sensors properly show "unknown" state.
Changes:
- Add debug_clear_tomorrow.py service handler
- Register conditionally based on TIBBER_PRICES_DEV env var
- Add service schema and translations
- Set TIBBER_PRICES_DEV=1 in devcontainer.json
Usage: Developer Tools → Services → tibber_prices.debug_clear_tomorrow
Impact: Enables rapid testing of tomorrow data refresh cycle during
development without waiting or restarting HA.
Implement gap tolerance smoothing for Tibber's price level classification
(VERY_CHEAP/CHEAP/NORMAL/EXPENSIVE/VERY_EXPENSIVE), separate from the existing
rating_level gap tolerance (LOW/NORMAL/HIGH).
New feature:
- Add CONF_PRICE_LEVEL_GAP_TOLERANCE config option with separate UI step
- Implement _apply_level_gap_tolerance() using same bidirectional gravitational
pull algorithm as rating gap tolerance
- Add _build_level_blocks() and _merge_small_level_blocks() helper functions
Config flow changes:
- Add new "price_level" options step with dedicated schema
- Add menu entry "🏷️ Preisniveau" / "🏷️ Price Level"
- Include translations for all 5 languages (de, en, nb, nl, sv)
Bug fixes:
- Use copy.deepcopy() for price intervals before enrichment to prevent
in-place modification of cached raw API data, which caused gap tolerance
changes to not take effect when reverting settings
- Clear transformation cache in invalidate_config_cache() to ensure
re-enrichment with new settings
Logging improvements:
- Reduce options update handler from 4 INFO messages to 1 DEBUG message
- Move level_filtering and period_overlap debug logs to .details logger
for granular control via configuration.yaml
Technical details:
- level_gap_tolerance is tracked separately in transformation config hash
- Algorithm: Identifies small blocks (≤ tolerance) and merges them into
the larger neighboring block using gravitational pull calculation
- Default: 1 (smooth single isolated intervals), Range: 0-4
Impact: Users can now stabilize Tibber's price level classification
independently from the internal rating_level calculation. Prevents
automation flickering caused by brief price level changes in Tibber's API.
Added UI controls for price rating stabilization parameters that were
previously hardcoded. Users can now fine-tune rating stability to match
their automation needs.
Changes:
- Added CONF_PRICE_RATING_HYSTERESIS constant (0-5%, step 0.5%, default 2%)
- Added CONF_PRICE_RATING_GAP_TOLERANCE constant (0-4 intervals, default 1)
- Extended get_price_rating_schema() with two new sliders
- Updated data_transformation.py to pass both parameters to enrichment function
- Improved descriptions in all 5 languages (de, en, nb, nl, sv) to focus on
automation stability instead of chart appearance
- Both settings included in factory reset via get_default_options()
Hysteresis explanation: Prevents rapid state changes when prices hover near
thresholds (e.g., LOW requires price > threshold+hysteresis to leave).
Gap tolerance explanation: Merges small isolated rating blocks into dominant
neighboring blocks using "look through" algorithm (fixed in previous commit).
Impact: Users can now adjust rating stability for their specific use cases.
Lower hysteresis (0-1%) for responsive automations, higher (3-5%) for stable
long-running processes. Gap tolerance prevents brief rating spikes from
triggering unnecessary automation actions.
Synchronized all translation files (de, en, nb, nl, sv) with:
1. Custom translations: Added 'configurable display format' messaging to
sensor descriptions
2. Standard translations: Added detailed bullet-point descriptions for
average_sensor_display config option
Changes affect both /custom_translations/ and /translations/ directories,
ensuring UI shows complete information about the new display configuration
option across all supported languages.
Fixes configuration wizard not saving settings (#59):
Root cause was twofold:
1. Linear multi-step flow pattern didn't properly persist changes between steps
2. Best/peak price settings used nested sections format - values were saved
in sections (period_settings, flexibility_settings, etc.) but read from
flat structure, causing configured values to be ignored on subsequent runs
Solution:
- Replaced linear step-through flow with menu-based navigation system
- Each configuration area now has dedicated "Save & Back" buttons
- Removed nested sections from all steps except best/peak price (where they
provide better UX for grouping related settings)
- Fixed best/peak price steps to correctly extract values from sections:
period_settings, flexibility_settings, relaxation_and_target_periods
- Added reset-to-defaults functionality with confirmation dialog
UI/UX improvements:
- Menu structure: General Settings, Currency Display, Price Rating Thresholds,
Volatility, Best Price Period, Peak Price Period, Price Trend,
Chart Data Export, Reset to Defaults, Back
- Removed confusing step progress indicators ("{step_num} / {total_steps}")
- Changed all submit buttons from "Continue →" to "↩ Save & Back"
- Clear grouping of settings by functional area
Translation updates (nl.json + sv.json):
- Refined volatility threshold descriptions with CV formula explanations
- Clarified price trend thresholds (compares current vs. future N-hour average,
not "per hour increase")
- Standardized terminology (e.g., "entry" → "item", compound word consistency)
- Consistently formatted all sensor names and descriptions
- Added new data lifecycle status sensor names
Technical changes:
- Options flow refactored from linear to menu pattern with menu_options dict
- New reset_to_defaults step with confirmation and abort handlers
- Section extraction logic in best_price/peak_price steps now correctly reads
from nested structure (period_settings.*, flexibility_settings.*, etc.)
- Removed sections from general_settings, display_settings, volatility, etc.
(simpler flat structure via menu navigation)
Impact: Configuration wizard now reliably saves all settings. Users can
navigate between setting areas without restarting the flow. Reset function
enables quick recovery when experimenting with thresholds. Previously
configured best/peak price settings are now correctly applied.
Add user-configurable option to choose between median and arithmetic mean
as the displayed value for all 14 average price sensors, with the alternate
value exposed as attribute.
BREAKING CHANGE: Average sensor default changed from arithmetic mean to
median. Users who rely on arithmetic mean behavior may use the price_mean attribue now, or must manually reconfigure
via Settings → Devices & Services → Tibber Prices → Configure → General
Settings → "Average Sensor Display" → Select "Arithmetic Mean" to get this as sensor state.
Affected sensors (14 total):
- Daily averages: average_price_today, average_price_tomorrow
- 24h windows: trailing_price_average, leading_price_average
- Rolling hour: current_hour_average_price, next_hour_average_price
- Future forecasts: next_avg_3h, next_avg_6h, next_avg_9h, next_avg_12h
Implementation:
- All average calculators now return (mean, median) tuples
- User preference controls which value appears in sensor state
- Alternate value automatically added to attributes
- Period statistics (best_price/peak_price) extended with both values
Technical changes:
- New config option: CONF_AVERAGE_SENSOR_DISPLAY (default: "median")
- Calculator functions return tuples: (avg, median)
- Attribute builders: add_alternate_average_attribute() helper function
- Period statistics: price_avg → price_mean + price_median
- Translations: Updated all 5 languages (de, en, nb, nl, sv)
- Documentation: AGENTS.md, period-calculation.md, recorder-optimization.md
Migration path:
Users can switch back to arithmetic mean via:
Settings → Integrations → Tibber Prices → Configure
→ General Settings → "Average Sensor Display" → "Arithmetic Mean"
Impact: Median is more resistant to price spikes, providing more stable
automation triggers. Statistical analysis from coordinator still uses
arithmetic mean (e.g., trailing_avg_24h for rating calculations).
Co-developed-with: GitHub Copilot <copilot@github.com>
Add repair notification system with three auto-clearing repair types:
- Tomorrow data missing (after 18:00)
- API rate limit exceeded (3+ consecutive errors)
- Home not found in Tibber account
Includes:
- coordinator/repairs.py: Complete TibberPricesRepairManager implementation
- Enhanced API error handling with explicit 5xx handling
- Translations for 5 languages (EN, DE, NB, NL, SV)
- Developer documentation in docs/developer/docs/repairs-system.md
Impact: Users receive actionable notifications for important issues instead
of only seeing stale data in logs.
Unified enum representation across all translation files and improved
consistency of localization patterns.
Key changes:
- Replaced uppercase enum constants (VERY_CHEAP, LOW, RISING) with
localized lowercase values (sehr günstig, niedrig, steigend) across
all languages in both translations/ and custom_translations/
- Removed **bold** markdown from sensor attributes (custom_translations/)
as it doesn't render in extra_state_attributes UI
- Preserved **bold** in Config Flow descriptions (translations/) where
markdown is properly rendered
- Corrected German formality: "Sie" → "du" throughout all descriptions
- Completed missing Config Flow translations in Dutch, Swedish, and
Norwegian (~45 fields: period_settings, flexibility_settings,
relaxation_and_target_periods sections)
- Fixed chart_data_export and chart_metadata sensor classification
(moved from binary_sensor to sensor as they are ENUM type)
- Corrected sensor placement in custom_translations/ (all 5 languages)
Files changed: 10 (5 translations/ + 5 custom_translations/)
Lines: +203, -222
Impact: All 5 languages now use consistent, properly formatted
localized enum values. Config Flow UI displays correctly formatted
examples with bold highlighting. Sensor attributes show clean text
without raw markdown syntax. German uses informal "du" tone throughout.
Implemented new chart_metadata diagnostic sensor that provides essential
chart configuration values (yaxis_min, yaxis_max, gradient_stop) as
attributes, enabling dynamic chart configuration without requiring
async service calls in templates.
Sensor implementation:
- New chart_metadata.py module with metadata-only service calls
- Automatically calls get_chartdata with metadata="only" parameter
- Refreshes on coordinator updates (new price data or user data)
- State values: "pending", "ready", "error"
- Enabled by default (critical for chart features)
ApexCharts YAML generator integration:
- Checks for chart_metadata sensor availability before generation
- Uses template variables to read sensor attributes dynamically
- Fallback to fixed values (gradient_stop=50%) if sensor unavailable
- Creates separate notifications for two independent issues:
1. Chart metadata sensor disabled (reduced functionality warning)
2. Required custom cards missing (YAML won't work warning)
- Both notifications explain YAML generation context and provide
complete fix instructions with regeneration requirement
Configuration:
- Supports configuration.yaml: tibber_prices.chart_metadata_config
- Optional parameters: day, minor_currency, resolution
- Defaults to minor_currency=True for ApexCharts compatibility
Translation additions:
- Entity name and state translations (all 5 languages)
- Notification messages for sensor unavailable and missing cards
- best_price_period_name for tooltip formatter
Binary sensor improvements:
- tomorrow_data_available now enabled by default (critical for automations)
- data_lifecycle_status now enabled by default (critical for debugging)
Impact: Users get dynamic chart configuration with optimized Y-axis scaling
and gradient positioning without manual calculations. ApexCharts YAML
generation now provides clear, actionable notifications when issues occur,
ensuring users understand why functionality is limited and how to fix it.
Implemented comprehensive metadata calculation for chart data export service
with automatic Y-axis scaling and gradient positioning based on actual price
statistics.
Changes:
- Added 'metadata' parameter to get_chartdata service (include/only/none)
- Implemented _calculate_metadata() with per-day price statistics
* min/max/avg/median prices
* avg_position and median_position (0-1 scale for gradient stops)
* yaxis_suggested bounds (floor(min)-1, ceil(max)+1)
* time_range with day boundaries
* currency info with symbol and unit
- Integrated metadata into rolling_window modes via config-template-card
* Pre-calculated yaxis bounds (no async issues in templates)
* Dynamic gradient stops based on avg_position
* Server-side calculation ensures consistency
Visual refinements:
- Best price overlay opacity reduced to 0.05 (ultra-subtle green hint)
- Stroke width increased to 1.5 for better visibility
- Gradient opacity adjusted to 0.45 with "light" shade
- Marker configuration: size 0, hover size 2, strokeWidth 1
- Header display: Only show LOW/HIGH rating_levels (min/max prices)
* Conditional logic excludes NORMAL and level types
* Entity state shows meaningful extrema values
- NOW marker label removed for rolling_window_autozoom mode
* Static position at 120min lookback makes label misleading
Code cleanup:
- Removed redundant all_series_config (server-side data formatting)
- Currency names capitalized (Cents, Øre, Öre, Pence)
Translation updates:
- Added metadata selector translations (de, en, nb, nl, sv)
- Added metadata field description in services
- Synchronized all language files
Impact: Users get dynamic Y-axis scaling based on actual price data,
eliminating manual configuration. Rolling window charts automatically
adjust axis bounds and gradient positioning. Header shows only
meaningful extreme values (daily min/max). All data transformation
happens server-side for optimal performance and consistency.
Added two new rolling window options for get_apexcharts_yaml service to provide
flexible dynamic chart visualization:
- rolling_window: Fixed 48h window that automatically shifts between
yesterday+today and today+tomorrow based on data availability
- rolling_window_autozoom: Same as rolling_window but with progressive zoom-in
(2h lookback + remaining time until midnight, updates every 15min)
Implementation changes:
- Updated service schema validation to accept new day options
- Added entity mapping patterns for both rolling modes
- Implemented minute-based graph_span calculation with quarter-hour alignment
- Added config-template-card integration for dynamic span updates
- Used current_interval_price sensor as 15-minute update trigger
- Unified data loading: both rolling modes omit day parameter for dynamic selection
- Applied ternary operator pattern for cleaner day_param logic
- Made grid lines more subtle (borderColor #f5f5f5, strokeDashArray 0)
Translation updates:
- Added selector options in all 5 languages (de, en, nb, nl, sv)
- Updated field descriptions to include default behavior and new options
- Documented that rolling window is default when day parameter omitted
Documentation updates:
- Updated user docs (actions.md, automation-examples.md) with new options
- Added detailed explanation of day parameter options
- Included examples for both rolling_window and rolling_window_autozoom modes
Impact: Users can now create auto-adapting ApexCharts that show 48h rolling
windows with optional progressive zoom throughout the day. Requires
config-template-card for dynamic behavior.
Restructured 5 options flow steps (current_interval_price_rating, best_price,
peak_price, price_trend, volatility) to use Home Assistant's sections feature
for better UI organization and logical grouping.
Changes:
- current_interval_price_rating: Single section "price_rating_thresholds"
- best_price: Three sections (period_settings, flexibility_settings,
relaxation_and_target_periods)
- peak_price: Three sections (period_settings, flexibility_settings,
relaxation_and_target_periods)
- price_trend: Single section "price_trend_thresholds"
- volatility: Single section "volatility_thresholds"
Each section includes name, description, data fields, and data_description
fields following HA translation schema requirements.
Updated all 5 language files (de, en, nb, nl, sv) with new section structure
while preserving existing field descriptions and translations.
Impact: Options flow now displays configuration fields in collapsible,
logically grouped sections with clear section headers, improving UX for
complex multi-parameter configuration steps. No functional changes to
configuration logic or validation.
Add dynamic rolling window mode to get_chartdata and get_apexcharts_yaml
services that automatically adapts to data availability.
When 'day' parameter is omitted, services return 48-hour window:
- With tomorrow data (after ~13:00): today + tomorrow
- Without tomorrow data: yesterday + today
Changes:
- Implement rolling window logic in get_chartdata using has_tomorrow_data()
- Generate config-template-card wrapper in get_apexcharts_yaml for dynamic
ApexCharts span.offset based on tomorrow_data_available binary sensor
- Update service descriptions in services.yaml
- Add rolling window descriptions to all translations (de, en, nb, nl, sv)
- Document rolling window mode in docs/user/services.md
- Add ApexCharts examples with prerequisites in docs/user/automation-examples.md
BREAKING CHANGE: get_apexcharts_yaml rolling window mode requires
config-template-card in addition to apexcharts-card for dynamic offset
calculation.
Impact: Users can create auto-adapting 48h price charts without manual day
selection. Fixed day views (day: today/yesterday/tomorrow) still work with
apexcharts-card only.
Added complete localization support for time offset descriptions:
- Convert hardcoded English strings "(X days ago)" to translatable keys
- Add time_units translations (day/days, hour/hours, minute/minutes, ago, now)
- Support singular/plural forms in all 5 languages (de, en, nb, nl, sv)
- German: Proper Dativ case "Tagen" with preposition "vor"
- Compact format for mixed offsets: "7 Tagen - 02:30"
Config flow improvements:
- Replace hardcoded "Enter new API token" with translated "Add new Tibber account API token"
- Use get_translation() for account_choice dropdown labels
- Fix SelectOptionDict usage (no mixing with translation_key parameter)
- Convert days slider from float to int (prevents "2.0 Tage" display)
- DurationSelector: default {"hours": 0, "minutes": 0} to fix validation errors
Translation keys added:
- selector.account_choice.options.new_token
- time_units (day, days, hour, hours, minute, minutes, ago, now)
- config.step.time_offset_description guidance text
Impact: Config flow works fully translated in all 5 languages with proper grammar.
Best Price min_distance now uses negative values (-50 to 0) to match
semantic meaning "below average". Peak Price continues using positive
values (0 to 50) for "above average".
Uniform formula: avg * (1 + distance/100) works for both period types.
Sign indicates direction: negative = toward MIN (cheap), positive = toward MAX (expensive).
Changes:
- const.py: DEFAULT_BEST_PRICE_MIN_DISTANCE_FROM_AVG = -5 (was 5)
- schemas.py: Best Price range -50 to 0, Peak Price range 0 to 50
- validators.py: Separate validate_best_price_distance_percentage()
- level_filtering.py: Simplified to uniform formula (removed conditionals)
- translations: Separate error messages for Best/Peak distance validation
- tests: 37 comprehensive validator tests (100% coverage)
Impact: Configuration UI now visually represents direction relative to average.
Users see intuitive negative values for "below average" pricing.
- Added new validation functions for various parameters including flexibility percentage, distance percentage, minimum periods, gap count, relaxation attempts, price rating thresholds, volatility threshold, and price trend thresholds.
- Updated constants in `const.py` to define maximum and minimum limits for the new validation criteria.
- Improved error messages in translations for invalid parameters to provide clearer guidance to users.
- Adjusted existing validation functions to ensure they align with the new constants and validation logic.
Add comprehensive data_lifecycle_status sensor showing real-time cache
vs fresh API data status with 6 states and 13+ detailed attributes.
Key features:
- 6 lifecycle states: cached, fresh, refreshing, searching_tomorrow,
turnover_pending, error
- Push-update system for instant state changes (refreshing→fresh→error)
- Quarter-hour polling for turnover_pending detection at 23:45
- Accurate next_api_poll prediction using Timer #1 offset tracking
- Tomorrow prediction with actual timer schedule (not fixed 13:00)
- 13+ formatted attributes: cache_age, data_completeness, api_calls_today,
next_api_poll, etc.
Implementation:
- sensor/calculators/lifecycle.py: New calculator with state logic
- sensor/attributes/lifecycle.py: Attribute builders with formatting
- coordinator/core.py: Lifecycle tracking + callback system (+16 lines)
- sensor/core.py: Push callback registration (+3 lines)
- coordinator/constants.py: Added to TIME_SENSITIVE_ENTITY_KEYS
- Translations: All 5 languages (de, en, nb, nl, sv)
Timing optimization:
- Extended turnover warning: 5min → 15min (catches 23:45 quarter boundary)
- No minute-timer needed: quarter-hour updates + push = optimal
- Push-updates: <1sec latency for refreshing/fresh/error states
- Timer offset tracking: Accurate tomorrow predictions
Removed obsolete sensors:
- data_timestamp (replaced by lifecycle attributes)
- price_forecast (never implemented, removed from definitions)
Impact: Users can monitor data freshness, API call patterns, cache age,
and understand integration behavior. Perfect for troubleshooting and
visibility into when data updates occur.
Moved Chart Data Export sensor configuration from config flow textarea
to configuration.yaml for better maintainability and consistency with
Home Assistant standards.
Changes:
- __init__.py: Added async_setup() with CONFIG_SCHEMA for tibber_prices.chart_export
- const.py: Added DATA_CHART_CONFIG constant for hass.data storage
- options_flow.py: Simplified chart_data_export step to info-only page
- schemas.py: get_chart_data_export_schema() returns empty schema (no input fields)
- sensor/chart_data.py: Reads config from hass.data instead of config_entry.options
- All 5 translation files: Updated chart_data_export description with:
- Clear heading: "📊 Chart Data Export Sensor"
- Intro line explaining sensor purpose
- Legacy warning (⚠️) recommending service use
- Two valid use cases (✅): attribute-only tools, auto-updating data
- One discouraged use case (❌): automations should use service directly
- 3-step activation instructions
- YAML configuration example with all parameters
- Correct default behavior: today+tomorrow, 15-minute intervals, prices only
Impact: Users configure chart export in configuration.yaml instead of UI.
Sensor remains disabled by default (diagnostic sensor). Config flow shows
prominent info page guiding users toward service usage while keeping
sensor available for legacy dashboard tools that only read attributes.
