_resolve_time_with_day_offset() was calling dt_util.now() internally
instead of using the injected now parameter. This caused incorrect date
calculations in tests and any caller that passes a specific reference time.
Also add missing price_rank_* sensor keys to TIME_SENSITIVE_ENTITY_KEYS
in coordinator/constants.py so quarter-hour refresh is registered for all
11 price rank sensors (current/next/previous interval and hour variants).
Rename dt as dt_utils → dt as dt_util (ICN001) across 11 files to follow
the project-wide import alias convention. Apply ruff auto-fixes for import
ordering and collapsing single-item imports throughout the codebase.
Released-Bug: no
Add three new price rank sensors that show where today's/tomorrow's/combined
average price falls relative to all intervals in the evaluated window:
- price_rank_today: today's average price percentile rank (0–100%)
- price_rank_tomorrow: tomorrow's average price percentile rank
- price_rank_today_tomorrow: combined today+tomorrow percentile rank
Extend all volatility sensors with IQR-based band statistics:
- price_typical_spread: interquartile range (IQR) in currency subunit
- price_typical_spread_%: IQR as percentage of daily average
- price_spike_count: number of intervals outside Tukey fences (outliers)
Add calculate_iqr_stats() utility function in utils/price.py that computes
the 25th/75th percentiles, IQR, outer fences (Q1 - 1.5×IQR / Q3 + 1.5×IQR),
and outlier count for any list of price values. Entity keys and attribute
names use plain language (`price_rank`, `price_typical_spread`) as primary
labels; technical terms (percentile rank, IQR) are included parenthetically
in descriptions and documentation.
Impact: Users can now see where current day prices rank compared to their window and how tightly clustered or spike-prone a day's prices are.
- 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.
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.
