- 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.
- Introduced `TimeframeAggregator` class for real-time aggregation of minute-level data to higher timeframes, enhancing the `IncStrategyBase` functionality.
- Updated `IncStrategyBase` to include `update_minute_data()` method, allowing strategies to process minute-level OHLCV data seamlessly.
- Enhanced existing strategies (`IncMetaTrendStrategy`, `IncRandomStrategy`) to utilize the new aggregation features, simplifying their implementations and improving performance.
- Added comprehensive documentation in `IMPLEMENTATION_SUMMARY.md` detailing the new architecture and usage examples for the aggregation feature.
- Updated performance metrics and logging to monitor minute data processing effectively.
- Ensured backward compatibility with existing `update()` methods, maintaining functionality for current 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.
- Added new dependencies: `plotly`, `websocket`, `cffi`, `gevent`, `greenlet`, and `narwhals` to `pyproject.toml` and `uv.lock`.
- Updated `.gitignore` to exclude the `frontend/` directory.
- Modified configuration files to set `start_date` to `2025-01-01` in `config_bbrs.json` and `config_default.json`, with `stop_date` set to `null` in both.
- Introduced a new project metadata file `.cursor/project.mdc` for project documentation and management.
- Introduced the CryptoComTrader class to handle WebSocket connections for real-time trading data and operations.
- Implemented methods for fetching price, order book, user balance, and placing orders.
- Added functionality to retrieve candlestick data and available trading instruments.
- Created a main script to initialize the trader, fetch instruments, and display candlestick data in a loop.
- Integrated Plotly for visualizing candlestick data, enhancing user interaction and data representation.
- Expanded the README.md to provide a comprehensive overview of the Cycles framework, including features, quick start instructions, and configuration examples.
- Updated strategies documentation to detail the architecture, available strategies, and their configurations, emphasizing the new multi-timeframe capabilities.
- Added a new timeframe system documentation to explain the strategy-controlled timeframe management and automatic data resampling.
- Improved the strategy manager documentation to clarify its role in orchestrating multiple strategies and combining signals effectively.
- Adjusted configuration examples to reflect recent changes in strategy parameters and usage.
- Removed JSON files from .gitignore to allow tracking of configuration files.
- Added multiple new configuration files for the BBRS strategy, including multi-timeframe and default settings.
- Introduced a combined configuration file to support weighted strategy execution.
- Established a default configuration for 5-minute and 15-minute timeframes, enhancing flexibility for strategy testing.
- Added BBRS strategy implementation, incorporating Bollinger Bands and RSI for trading signals.
- Introduced multi-timeframe analysis support, allowing strategies to handle internal resampling.
- Enhanced StrategyManager to log strategy initialization and unique timeframes in use.
- Updated DefaultStrategy to support flexible timeframe configurations and improved stop-loss execution.
- Improved plotting logic in BacktestCharts for better visualization of strategy outputs and trades.
- Refactored strategy base class to facilitate resampling and data handling across different timeframes.
- Introduced a new strategies module containing the StrategyManager class to orchestrate multiple trading strategies.
- Implemented StrategyBase and StrategySignal as foundational components for strategy development.
- Added DefaultStrategy for meta-trend analysis and BBRSStrategy for Bollinger Bands + RSI trading.
- Enhanced documentation to provide clear usage examples and configuration guidelines for the new system.
- Established a modular architecture to support future strategy additions and improvements.
- Added validation to ensure the specified price column exists in the DataFrame for Bollinger Bands calculations.
- Introduced a new method to ensure the DataFrame has a proper DatetimeIndex, improving time-series operations in strategy processing.
- Updated strategy run method to call the new DatetimeIndex validation method before processing data.
- Improved logging for better traceability of data transformations and potential issues.
- Refactored the Backtest class to encapsulate state and behavior, enhancing clarity and maintainability.
- Updated strategy functions to accept the Backtest instance, streamlining data access and manipulation.
- Introduced a new plotting method in BacktestCharts for visualizing close prices with trend indicators.
- Improved handling of meta_trend data to ensure proper visualization and trend representation.
- Adjusted main execution logic to support the new Backtest structure and enhanced debugging capabilities.
- Introduced a new method to standardize output column names across different strategies, ensuring consistency in data handling and plotting.
- Updated plotting logic in test_bbrsi.py to utilize standardized column names, improving clarity and maintainability.
- Enhanced error handling for missing data in plots and adjusted visual elements for better representation of trading signals.
- Improved the overall structure of strategy implementations to support additional indicators and metadata for better analysis.
- Removed unused configuration for daily data and consolidated minute configuration into a single config dictionary.
- Updated plotting logic to dynamically handle different strategies, ensuring appropriate bands and signals are displayed based on the selected strategy.
- Improved error handling and logging for missing data in plots.
- Enhanced the Bollinger Bands and RSI classes to support adaptive parameters based on market regimes, improving flexibility in strategy execution.
- Added new CryptoTradingStrategy with multi-timeframe analysis and volume confirmation for better trading signal accuracy.
- Updated documentation to reflect changes in strategy implementations and configuration requirements.
