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https://github.com/jpawlowski/hass.tibber_prices.git
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6f5261785b
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. |
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| .. | ||
| __init__.py | ||
| daily_stat.py | ||
| future.py | ||
| helpers.py | ||
| interval.py | ||
| lifecycle.py | ||
| metadata.py | ||
| timing.py | ||
| trend.py | ||
| volatility.py | ||
| window_24h.py | ||