Added optional diagnostic binary sensor that exposes get_chartdata
service results as entity attributes for legacy dashboard tools.
Key features:
- Entity: binary_sensor.tibber_home_NAME_chart_data_export
- Configurable via Options Flow Step 7 (YAML parameters)
- Calls get_chartdata service with user configuration
- Exposes response as attributes for chart cards
- Disabled by default (opt-in)
- Auto-refreshes on coordinator updates
- Manual refresh via homeassistant.update_entity
Implementation details:
- Added chart_data_export entity description to definitions.py
- Implemented state/attribute logic in binary_sensor/core.py
- Added YAML configuration schema in schemas.py
- Added validation in options_flow.py (Step 7)
- Service call validation with detailed error messages
- Attribute ordering: metadata first, descriptions next, service data last
- Dynamic icon mapping (database-export/database-alert)
Translations:
- Added chart_data_export_config to all 5 languages
- Added Step 7 descriptions with legacy warning
- Added invalid_yaml_syntax/invalid_yaml_structure error messages
- Added custom_translations for sensor descriptions
Documentation:
- Added Chart Data Export section to sensors.md
- Added comprehensive service guide to services.md
- Migration path from sensor to service
- Configuration instructions via Options Flow
Impact: Provides backward compatibility for dashboard tools that can
only read entity attributes (e.g., older ApexCharts versions). New
integrations should use tibber_prices.get_chartdata service directly.
Complete overhaul of the ApexCharts integration service layer to support
modern chart card workflows with flexible data formatting and filtering.
Replaced services:
- Removed: get_price, get_apexcharts_data (legacy, entity-based)
- Added: get_chartdata (flexible data service)
- Improved: get_apexcharts_yaml (now uses get_chartdata internally)
New get_chartdata service features:
- Multiple output formats (array_of_objects, array_of_arrays)
- Customizable field names for chart compatibility
- Resolution options (15-min intervals, hourly averages)
- Advanced filtering (level_filter, rating_level_filter)
- NULL insertion modes (none, segments, all) for clean gaps
- Minor currency support (cents/øre) with custom rounding
- Optional fields (level, rating_level, average)
- Multi-day support (yesterday/today/tomorrow)
Enhanced get_apexcharts_yaml service:
- Direct entry_id parameter (no entity_id lookup needed)
- Uses get_chartdata with WebSocket API (data_generator)
- Improved ApexCharts configuration:
* Gradient fill (70% opacity → 20%)
* Grid styling with dashed lines
* Zoom & Pan tools (animations disabled for performance)
* Optimized legend (top-left, compact markers)
* Y-axis auto-scaling (min: 0 for visibility, supports negative prices)
* 2 decimal places (improved precision)
* Browser locale formatting (automatic comma/point)
* insert_nulls='segments' for clean gaps between levels
- Multi-language support (translated titles, series names)
- Day selection (yesterday/today/tomorrow with correct span config)
Service translations:
- Added comprehensive field descriptions (all 5 languages: de, en, nb, nl, sv)
- Selector translations for all options (day, resolution, output_format, etc.)
- ApexCharts title translations in custom_translations/
Technical improvements:
- Hourly aggregation uses exact 4-interval windows (:00/:15/:30/:45)
- Level/rating aggregation follows sensor logic (aggregate_level_data, aggregate_rating_data)
- Midnight extension for last interval of filtered data (seamless day transitions)
- Case-insensitive filter matching (normalized to uppercase)
- Ruff complexity fixed (extracted _get_level_translation helper)
Impact: Users can now generate production-ready ApexCharts YAML with a single
service call, or use get_chartdata flexibly with any chart card (ApexCharts,
Plotly, Mini Graph, etc.). Supports complex filtering scenarios (e.g., "show
only LOW rating periods") with clean visual gaps. Full multi-language support.
Replaced absolute volatility thresholds (ct/øre) with relative coefficient
of variation (CV = std_dev / mean * 100%) for scale-independent volatility
measurement that works across all price levels.
Changes to volatility calculation:
- price_utils.py: Rewrote calculate_volatility_level() to accept price list
instead of spread value, using statistics.mean() and statistics.stdev()
- sensor.py: Updated volatility sensors to pass price lists (not spread)
- services.py: Modified _get_price_stats() to calculate CV from prices
- period_statistics.py: Extract prices for CV calculation in period summaries
- const.py: Updated default thresholds to 15%/30%/50% (was 5/15/30 ct)
with comprehensive documentation explaining CV-based approach
Dead code removal:
- period_utils/core.py: Removed filter_periods_by_volatility() function
(86 lines of code that was never actually called)
- period_utils/__init__.py: Removed dead function export
- period_utils/relaxation.py: Simplified callback signature from
Callable[[str|None, str|None], bool] to Callable[[str|None], bool]
- coordinator.py: Updated lambda callbacks to match new signature
- const.py: Replaced RELAXATION_VOLATILITY_ANY with RELAXATION_LEVEL_ANY
Bug fix:
- relaxation.py: Added int() conversion for max_relaxation_attempts
(line 435: attempts = max(1, int(max_relaxation_attempts)))
Fixes TypeError when config value arrives as float
Configuration UI:
- config_flow.py: Changed volatility threshold unit display from "ct" to "%"
Translations (all 5 languages):
- Updated volatility descriptions to explain coefficient of variation
- Changed threshold labels from "spread ≥ value" to "CV ≥ percentage"
- Languages: de, en, nb, nl, sv
Documentation:
- period-calculation.md: Removed volatility filter section (dead feature)
Impact: Breaking change for users with custom volatility thresholds.
Old absolute values (e.g., 5 ct) will be interpreted as percentages (5%).
However, new defaults (15%/30%/50%) are more conservative and work
universally across all currencies and price levels. No data migration
needed - existing configs continue to work with new interpretation.
Moved filter logic and all period attribute calculations from binary_sensor.py
to coordinator.py and period_utils.py, following Home Assistant best practices
for data flow architecture.
ARCHITECTURE CHANGES:
Binary Sensor Simplification (~225 lines removed):
- Removed _build_periods_summary, _add_price_diff_for_period (calculation logic)
- Removed _get_period_intervals_from_price_info (107 lines, interval reconstruction)
- Removed _should_show_periods, _check_volatility_filter, _check_level_filter
- Removed _build_empty_periods_result (filtering result builder)
- Removed _get_price_hours_attributes (24 lines, dead code)
- Removed datetime import (unused after cleanup)
- New: _build_final_attributes_simple (~20 lines, timestamp-only)
- Result: Pure display-only logic, reads pre-calculated data from coordinator
Coordinator Enhancement (+160 lines):
- Added _should_show_periods(): UND-Verknüpfung of volatility and level filters
- Added _check_volatility_filter(): Checks min_volatility threshold
- Added _check_level_filter(): Checks min/max level bounds
- Enhanced _calculate_periods_for_price_info(): Applies filters before period calculation
- Returns empty periods when filters don't match (instead of calculating unnecessarily)
- Passes volatility thresholds (moderate/high/very_high) to PeriodConfig
Period Utils Refactoring (+110 lines):
- Extended PeriodConfig with threshold_volatility_moderate/high/very_high
- Added PeriodData NamedTuple: Groups timing data (start, end, length, position)
- Added PeriodStatistics NamedTuple: Groups calculated stats (prices, volatility, ratings)
- Added ThresholdConfig NamedTuple: Groups all thresholds + reverse_sort flag
- New _calculate_period_price_statistics(): Extracts price_avg/min/max/spread calculation
- New _build_period_summary_dict(): Builds final dict with correct attribute ordering
- Enhanced _extract_period_summaries(): Now calculates ALL attributes (no longer lightweight):
* price_avg, price_min, price_max, price_spread (in minor units: ct/øre)
* volatility (low/moderate/high/very_high based on absolute thresholds)
* rating_difference_% (average of interval differences)
* period_price_diff_from_daily_min/max (period avg vs daily reference)
* aggregated level and rating_level
* period_interval_count (renamed from interval_count for clarity)
- Removed interval_starts array (redundant - start/end/count sufficient)
- Function signature refactored from 9→4 parameters using NamedTuples
Code Organization (HA Best Practice):
- Moved calculate_volatility_level() from const.py to price_utils.py
- Rule: const.py should contain only constants, no functions
- Removed duplicate VOLATILITY_THRESHOLD_* constants from const.py
- Updated imports in sensor.py, services.py, period_utils.py
DATA FLOW:
Before:
API → Coordinator (basic enrichment) → Binary Sensor (calculate everything on each access)
After:
API → Coordinator (enrichment + filtering + period calculation with ALL attributes) →
Cached Data → Binary Sensor (display + timestamp only)
ATTRIBUTE STRUCTURE:
Period summaries now contain (following copilot-instructions.md ordering):
1. Time: start, end, duration_minutes
2. Decision: level, rating_level, rating_difference_%
3. Prices: price_avg, price_min, price_max, price_spread, volatility
4. Differences: period_price_diff_from_daily_min/max (conditional)
5. Details: period_interval_count, period_position
6. Meta: periods_total, periods_remaining
BREAKING CHANGES: None
- Period data structure enhanced but backwards compatible
- Binary sensor API unchanged (state + attributes)
Impact: Binary sensors now display pre-calculated data from coordinator instead
of calculating on every access. Reduces complexity, improves performance, and
centralizes business logic following Home Assistant coordinator pattern. All
period filtering (volatility + level) now happens in coordinator before caching.
Added comprehensive volatility analysis system:
- 4 new volatility sensors (today, tomorrow, next_24h, today+tomorrow)
- Volatility classification (LOW/MODERATE/HIGH/VERY HIGH) based on price spread
- Configurable thresholds in options flow (step 6 of 6)
- Best/Peak price period filters using volatility and price level
- Price spread calculation in get_price service
Volatility sensors help users decide if price-based optimization is worthwhile.
For example, battery optimization only makes sense when volatility ≥ MODERATE.
Period filters allow AND-logic combinations:
- best_price_min_volatility: Only show cheap periods on volatile days
- best_price_max_level: Only show periods when prices reach desired level
- peak_price_min_volatility: Only show peaks on volatile days
- peak_price_min_level: Only show peaks when expensive levels occur
All 5 language files updated (de, en, nb, nl, sv) with:
- Volatility sensor translations (name, states, descriptions)
- Config flow step 6 "Volatility" with threshold settings
- Step progress indicators added to all config steps
- Period filter translations with usage tips
Impact: Users can now assess daily price volatility and configure period
sensors to only activate when conditions justify battery cycling or load
shifting. Reduces unnecessary battery wear on low-volatility days.
