- 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.
- Introduced a new Strategy class to encapsulate trading strategies, including the Market Regime Strategy that adapts to different market conditions.
- Refactored BollingerBands and RSI classes to accept configuration parameters for improved flexibility and maintainability.
- Updated test_bbrsi.py to utilize the new strategy implementation and adjusted date ranges for testing.
- Enhanced documentation to include details about the new Strategy class and its components.
- Updated the method for determining the start index of the current trade to directly use the DataFrame index, enhancing clarity and performance.
- Removed the deprecated get_current_min1_end_idx method to streamline the codebase.
- Replaced TrendDetectorSimple with a new Backtest class for improved backtesting functionality.
- Integrated argparse for configuration file input, allowing dynamic parameter setting.
- Added MarketFees and Supertrends classes to handle fee calculations and trend detection, respectively.
- Removed deprecated main_debug.py and trend_detector_simple.py files to streamline the codebase.
- Enhanced process_timeframe_data to utilize the new Backtest class for executing trades and calculating results.
- Updated Storage class to support writing backtest results with metadata.
- Included total_fees_usd in the results dictionary of the Storage class to enhance fee tracking in the output.
- removed plots from TrendDetectorSimple
- Added total fees calculation to process_timeframe_data and aggregate_results functions in main.py.
- Updated trade logging in TrendDetectorSimple to include transaction fees in USD.
- Introduced calculate_okx_fee function for consistent fee calculations based on maker/taker status.
- Adjusted backtesting logic to account for fees when buying and selling, ensuring accurate profit calculations.
- Expanded stop loss percentages and timeframes for broader analysis in main.py.
- Introduced Taxes class in taxes.py to calculate and apply taxes on profits in backtest results.
- Updated main.py to include tax calculations in the results processing flow.
- Refactored trade logging in TrendDetectorSimple to account for transaction fees and ensure accurate profit calculations.
- Added a utility script (apply_taxes_to_file.py) for applying taxes to existing CSV files.
- Adjusted date range and timeframe settings in main.py for broader analysis.
- Added GSheetBatchPusher class to handle background updates to Google Sheets.
- Refactored write_results_per_combination function to write results directly to Google Sheets instead of CSV files.
- Updated process_timeframe function to handle single stop loss percentages.
- Introduced a global queue for batching results and trades for efficient updates.
- Enhanced error handling for Google Sheets API quota limits.
- Adjusted main execution flow to start the batch pusher and ensure all results are pushed after processing.
- Introduced BacktestCharts class in charts.py to plot profit ratio vs stop loss and average trade vs stop loss for different timeframes.
- Updated main.py to integrate new charting functionality and streamline data processing without monthly splits.
- Enhanced backtesting logic in TrendDetectorSimple to include transaction costs and improved stop loss handling using 1-minute data for accuracy.
- Added functionality to write results to individual CSV files for better organization and analysis.
