- 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.
- Introduced a new boundary utilization score to measure price proximity to triangle boundaries, improving the accuracy of strength assessments.
- Updated scoring weights to incorporate boundary utilization, adjusting the contributions of convergence, volume, and fitting scores.
- Added detailed chart mode in the stock viewer, allowing users to toggle between standard and detailed views with additional metrics displayed.
- Enhanced documentation to reflect new features, including usage instructions for the boundary utilization score and detailed chart mode.
- Improved error handling in the stock viewer for better user experience.
- Introduced an interactive HTML stock viewer for visualizing strength scores and filtering stocks based on user-defined thresholds.
- Added `--all-stocks` parameter to generate charts for all 108 stocks, including those not meeting convergence criteria.
- Implemented a new scoring system for breakout strength, incorporating fitting adherence to improve accuracy.
- Updated multiple documentation files, including usage instructions and feature overviews, to reflect recent enhancements.
- Improved error handling and file naming conventions to ensure compatibility across platforms.
- Added `--show-details` parameter to `pipeline_converging_triangle.py` for generating detailed charts that display all pivot points and fitting lines.
- Implemented an iterative outlier removal algorithm in `fit_pivot_line` to improve the accuracy of pivot point fitting by eliminating weak points.
- Updated `USAGE.md` to include new command examples for the detailed mode.
- Revised multiple documentation files to reflect recent changes and improvements in the pivot detection and visualization processes.
- 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.
- Revised README.md to enhance clarity on core functionalities and usage instructions.
- Updated USAGE.md to reflect the new pipeline script and its parameters.
- Modified .gitignore to include additional output files for better management.
- Removed outdated output files (all_results.csv, report.md, strong_breakout_up.csv, strong_breakout_down.csv) to streamline data handling.
- Improved structure and descriptions in documentation for better user guidance.
- 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.
