- Added support for daily best stocks reporting, including a new CSV output for daily best triangles based on strength.
- Introduced a logging mechanism to capture detailed execution logs, improving traceability and debugging.
- Implemented a v2 optimization for batch detection, significantly reducing detection time from 92 seconds to under 2 seconds.
- Updated the .gitignore file to include new log files and outputs for better management.
- Enhanced the pipeline script to allow for flexible configuration of detection parameters and improved user experience.
Files modified:
- scripts/run_converging_triangle.py: Added logging and v2 optimization.
- scripts/pipeline_converging_triangle.py: Updated for new features and logging.
- scripts/plot_converging_triangles.py: Adjusted for new plotting options.
- New files: discuss/20260127-拟合线.md, discuss/20260128-拟合线.md, and several images for visual documentation.
- Added support for a detailed chart mode in plot_converging_triangles.py, allowing users to visualize all pivot points and fitting lines.
- Improved pivot fitting logic to utilize multiple representative points, enhancing detection accuracy and reducing false positives.
- Introduced a new real-time detection mode with flexible zone parameters for better responsiveness in stock analysis.
- Updated README.md and USAGE.md to reflect new features and usage instructions.
- Added multiple documentation files detailing recent improvements, including pivot point fitting and visualization enhancements.
- Cleaned up and archived outdated scripts to streamline the project structure.
- Added pipeline_converging_triangle.py for streamlined execution of detection, reporting, and chart generation.
- Introduced triangle_config.py for centralized parameter management across scripts.
- Updated plot_converging_triangles.py to utilize parameters from the new config file.
- Revised report_converging_triangles.py to reflect dynamic detection window based on configuration.
- Enhanced existing scripts for improved error handling and output consistency.
- Added new documentation files for usage instructions and parameter configurations.
- Updated all_results.csv with additional stock data and breakout strength metrics.
- Revised report.md to improve clarity and detail on stock selection criteria and results.
- Expanded strong_breakout_down.csv and strong_breakout_up.csv with new entries reflecting recent analysis.
- Introduced new chart images for selected stocks to visualize breakout patterns.
- Added plot_converging_triangles.py script for generating visualizations of stocks meeting convergence criteria.
- Enhanced report_converging_triangles.py to allow for date-specific reporting and improved output formatting.
- Optimized run_converging_triangle.py for performance and added execution time logging.
- Created README.md and USAGE.md for project overview and usage instructions.
- Added core algorithm in src/converging_triangle.py for batch processing of stock data.
- Introduced data files (open.pkl, high.pkl, low.pkl, close.pkl, volume.pkl) for OHLCV data.
- Developed output documentation for results and breakout strength calculations.
- Implemented scripts for running the detection and generating reports.
- Added SVG visualizations and markdown documentation for algorithm details and usage examples.
