- 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.
- Added error handling in DataLoader to attempt reading CSV files with a fallback to the Python engine if the default engine fails.
- Converted numpy float32 columns to Python float for compatibility in DataLoader.
- Updated MinuteDataBuffer to accept both Python and numpy numeric types, ensuring consistent data validation and conversion.
- Introduced a comprehensive framework for incremental trading strategies, including modules for strategy execution, backtesting, and data processing.
- Added key components such as `IncTrader`, `IncBacktester`, and various trading strategies (e.g., `MetaTrendStrategy`, `BBRSStrategy`, `RandomStrategy`) to facilitate real-time trading and backtesting.
- Implemented a robust backtesting framework with configuration management, parallel execution, and result analysis capabilities.
- Developed an incremental indicators framework to support real-time data processing with constant memory usage.
- Enhanced documentation to provide clear usage examples and architecture overview, ensuring maintainability and ease of understanding for future development.
- Ensured compatibility with existing strategies and maintained a focus on performance and scalability throughout the implementation.
