Refactored trend calculator with direction-group-based trend change detection
(rising/strongly_rising treated as same group, falling/strongly_falling as same
group). Added minimum absolute price change thresholds (noise floor) to prevent
spurious trends at low price levels. Both percentage AND absolute conditions
must now be met.
Updated strongly threshold defaults from ±6% to ±9% (3x base for perceptual
scaling). Added missing strongly thresholds and new config keys to
get_default_options(). calculate_price_trend() now returns volatility_factor
as 4th tuple element for threshold transparency.
Added CONF_PRICE_TREND_CHANGE_CONFIRMATION (default: 3 intervals = 45min)
and CONF_PRICE_TREND_MIN_PRICE_CHANGE / _STRONGLY with validation limits.
Updated tests for new 4-tuple return value.
Impact: More stable trend detection — fewer false trend changes during low-price
periods. Direction-group logic prevents noise from "rising ↔ strongly_rising"
oscillations. Users can fine-tune noise floor for their market.
Add calculate_coefficient_of_variation() as central utility function:
- CV = (std_dev / mean) * 100 as standardized volatility measure
- calculate_volatility_with_cv() returns both level and numeric CV
- Volatility sensors now expose CV in attributes for transparency
Used as foundation for quality gates, adaptive smoothing, and period statistics.
Impact: Volatility sensors show numeric CV percentage alongside categorical level,
enabling users to see exact price variation.
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.
The gap tolerance algorithm now looks through small intermediate blocks
to find the first LARGE block (> gap_tolerance) in each direction.
This ensures small isolated rating intervals are merged into the
correct dominant block, not just the nearest neighbor.
Example: NORMAL(large) HIGH(1) NORMAL(1) HIGH(large)
Before: HIGH at 05:45 merged into NORMAL (wrong - nearest neighbor)
After: NORMAL at 06:00 merged into HIGH (correct - dominant block)
Also collects all merge decisions BEFORE applying them, preventing
order-dependent outcomes when multiple small blocks are adjacent.
Impact: Rating transitions now appear at visually logical positions
where prices actually change direction, not at arbitrary boundaries.
Implemented configurable display format (mean/median/both) while always
calculating and exposing both price_mean and price_median attributes.
Core changes:
- utils/average.py: Refactored calculate_mean_median() to always return both
values, added comprehensive None handling (117 lines changed)
- sensor/attributes/helpers.py: Always include both attributes regardless of
user display preference (41 lines)
- sensor/core.py: Dynamic _unrecorded_attributes based on display setting
(55 lines), extracted helper methods to reduce complexity
- Updated all calculators (rolling_hour, trend, volatility, window_24h) to
use new always-both approach
Impact: Users can switch display format in UI without losing historical data.
Automation authors always have access to both statistical measures.
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>
Updated attribute ordering documentation to use correct names:
- "periods" → "pricePeriods" (matches code since refactoring)
- "intervals" → "priceInfo" (flat list structure)
Impact: Documentation now matches actual code structure.
- Introduced `get_intervals_for_day_offsets` helper to streamline access to price intervals for yesterday, today, and tomorrow.
- Updated various components to replace direct access to `priceInfo` with the new helper, ensuring a flat structure for price intervals.
- Adjusted calculations and data processing methods to accommodate the new data structure.
- Enhanced documentation to reflect changes in caching strategy and data structure.
Fixed multiple calculation issues with negative prices (Norway/Germany
renewable surplus scenarios):
Bug #6: Rating threshold validation with dead code
- Added threshold validation (low >= high) with warning
- Returns NORMAL as fallback for misconfigured thresholds
Bug #7: Min/Max functions returning 0.0 instead of None
- Changed default from 0.0 to None when window is empty
- Prevents misinterpretation (0.0 looks like price with negatives)
Bug #9: Period price diff percentage wrong sign with negative reference
- Use abs(ref_price) in percentage calculation
- Correct percentage direction for negative prices
Bug #10: Trend diff percentage wrong sign with negative current price
- Use abs(current_interval_price) in percentage calculation
- Correct trend direction when prices cross zero
Bug #11: later_half_diff calculation failed for negative prices
- Changed condition from `if current_interval_price > 0` to `!= 0`
- Use abs(current_interval_price) for percentage
Changes:
- utils/price.py: Add threshold validation, use abs() in percentages
- utils/average.py: Return None instead of 0.0 for empty windows
- period_statistics.py: Use abs() for reference prices
- trend.py: Use abs() for current prices, fix zero-check condition
- tests: 95+ new tests covering negative/zero/mixed price scenarios
Impact: All calculations work correctly with negative electricity prices.
Percentages show correct direction regardless of sign.
Introduce TimeService as single source of truth for all datetime operations,
replacing direct dt_util calls throughout the codebase. This establishes
consistent time context across update cycles and enables future time-travel
testing capability.
Core changes:
- NEW: coordinator/time_service.py with timezone-aware datetime API
- Coordinator now creates TimeService per update cycle, passes to calculators
- Timer callbacks (#2, #3) inject TimeService into entity update flow
- All sensor calculators receive TimeService via coordinator reference
- Attribute builders accept time parameter for timestamp calculations
Key patterns replaced:
- dt_util.now() → time.now() (single reference time per cycle)
- dt_util.parse_datetime() + as_local() → time.get_interval_time()
- Manual interval arithmetic → time.get_interval_offset_time()
- Manual day boundaries → time.get_day_boundaries()
- round_to_nearest_quarter_hour() → time.round_to_nearest_quarter()
Import cleanup:
- Removed dt_util imports from ~30 files (calculators, attributes, utils)
- Restricted dt_util to 3 modules: time_service.py (operations), api/client.py
(rate limiting), entity_utils/icons.py (cosmetic updates)
- datetime/timedelta only for TYPE_CHECKING (type hints) or duration arithmetic
Interval resolution abstraction:
- Removed hardcoded MINUTES_PER_INTERVAL constant from 10+ files
- New methods: time.minutes_to_intervals(), time.get_interval_duration()
- Supports future 60-minute resolution (legacy data) via TimeService config
Timezone correctness:
- API timestamps (startsAt) already localized by data transformation
- TimeService operations preserve HA user timezone throughout
- DST transitions handled via get_expected_intervals_for_day() (future use)
Timestamp ordering preserved:
- Attribute builders generate default timestamp (rounded quarter)
- Sensors override when needed (next interval, daily midnight, etc.)
- Platform ensures timestamp stays FIRST in attribute dict
Timer integration:
- Timer #2 (quarter-hour): Creates TimeService, calls _handle_time_sensitive_update(time)
- Timer #3 (30-second): Creates TimeService, calls _handle_minute_update(time)
- Consistent time reference for all entities in same update batch
Time-travel readiness:
- TimeService.with_reference_time() enables time injection (not yet used)
- All calculations use time.now() → easy to simulate past/future states
- Foundation for debugging period calculations with historical data
Impact: Eliminates timestamp drift within update cycles (previously 60+ independent
dt_util.now() calls could differ by milliseconds). Establishes architecture for
time-based testing and debugging features.
