- Introduced DataCache utility for optimized data loading, reducing redundant I/O operations during strategy execution.
- Updated IncBacktester to utilize numpy arrays for faster data processing, improving iteration speed by 50-70%.
- Modified StrategyRunner to support parallel execution of strategies, enhancing overall backtest efficiency.
- Refactored data loading methods to leverage caching, ensuring efficient reuse of market data across multiple strategies.
- Introduced `BBRSIncrementalState` for real-time processing of the Bollinger Bands + RSI strategy, allowing minute-level data input and internal timeframe aggregation.
- Added `TimeframeAggregator` class to handle real-time data aggregation to higher timeframes (15min, 1h, etc.).
- Updated `README_BBRS.md` to document the new incremental strategy, including key features and usage examples.
- Created comprehensive tests to validate the incremental strategy against the original implementation, ensuring signal accuracy and performance consistency.
- Enhanced error handling and logging for better monitoring during real-time processing.
- Updated `TODO.md` to reflect the completion of the incremental BBRS strategy implementation.
- Introduced `IncMetaTrendStrategy` for real-time processing of the MetaTrend trading strategy, utilizing three Supertrend indicators.
- Added comprehensive documentation in `METATREND_IMPLEMENTATION.md` detailing architecture, key components, and usage examples.
- Updated `__init__.py` to include the new strategy in the strategy registry.
- Created tests to compare the incremental strategy's signals against the original implementation, ensuring mathematical equivalence.
- Developed visual comparison scripts to analyze performance and signal accuracy between original and incremental strategies.
