Module interbar_cache 

Inter-bar feature result caching for streaming optimization

GitHub Issue: https://github.com/terrylica/opendeviationbar-py/issues/96 GitHub Issue: https://github.com/terrylica/opendeviationbar-py/issues/59 Task #144 Phase 4: Result caching for deterministic sequences

Caches computed inter-bar features (OFI, VWAP, Kyle Lambda, Hurst, etc.) keyed by trade count and window hash to avoid redundant computation in streaming scenarios where similar lookback windows repeat.

Benefits

• Latency reduction: 20-40% for repeated window patterns (Task #144 target)
• Memory efficient: LRU eviction, bounded capacity
• Transparent: Optional, backward compatible

Architecture

Cache key: (trade_count, window_hash) → InterBarFeatures

Window hash captures:

• Price movement pattern (OHLC bounds)
• Volume distribution
• Temporal characteristics (duration, trade frequency)

Structs

InterBarCacheKey

Cache key for inter-bar feature results

InterBarFeatureCache

LRU cache for inter-bar feature computation results

Constants

INTERBAR_FEATURE_CACHE_CAPACITY

Maximum capacity for inter-bar feature cache Trade-off: Larger → higher hit ratio; smaller → less memory