Julian Pawlowski 6f5261785b feat(sensor): add price rank sensors and IQR-based volatility attributes ... 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.		2026-04-12 14:13:47 +00:00
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tibber_prices	feat(sensor): add price rank sensors and IQR-based volatility attributes	2026-04-12 14:13:47 +00:00