847ff67986
Add complete workflow for using TA-Lib to bootstrap training data: - generate_talib_annotations.py: Python script to run TA-Lib CDL* functions and output span annotations in UI-compatible format - import_talib_annotations.ts: TypeScript script to import generated annotations into the UI database with auto-label-type creation - npm script 'import-annotations' for easy execution - TALIB_WORKFLOW.md: Comprehensive guide covering the full cycle: * Generate patterns with TA-Lib * Import into UI * Review and edit in browser * Export and train model * Compare predictions with TA-Lib detections * Iterate for improvement This enables the intended workflow: use TA-Lib for initial annotations, manually refine them, then train a model that learns from corrections.
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537 B
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22 lines
No EOL
537 B
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pyproject.toml
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app/__init__.py
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app/annotation_ingestion.py
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app/config.py
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app/db.py
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app/main.py
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app/preprocessing.py
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candle_ml.egg-info/PKG-INFO
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candle_ml.egg-info/SOURCES.txt
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candle_ml.egg-info/dependency_links.txt
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candle_ml.egg-info/requires.txt
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candle_ml.egg-info/top_level.txt
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features/candle_features.py
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features/custom_loader.py
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features/engineer.py
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features/talib_features.py
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training/__init__.py
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training/evaluation.py
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training/train.py
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training/models/__init__.py
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training/models/random_forest.py
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training/models/xgboost_model.py |