trentuna/candle-annotator
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Author SHA1 Message Date
Marko Djordjevic
49daaae36a code-review-fix task 15.4: add missing fallback return for volume indicators in talib_features.py 2026-02-18 20:58:47 +01:00
Marko Djordjevic
9a549b8c7a fix: make volume column optional in feature engineering, skip MFI when absent 2026-02-18 01:01:32 +01:00
Marko Djordjevic
a18c6d110a Remove __pycache__ from git tracking 2026-02-15 22:29:35 +01:00
Marko Djordjevic
317f925c43 fix(ml): handle missing volume data and skip volume-dependent indicators 
- Fill volume with 0 when column is absent from candle data
- Skip MFI/OBV/AD/ADOSC indicators when no real volume data available
- Fix pandas FutureWarning for inplace fillna in candle_features
- Remove temporary debug NaN logging
 2026-02-15 21:56:14 +01:00
Marko Djordjevic
b6b37160a7 fix(ml): cast OHLCV arrays to float64 for TA-Lib compatibility 2026-02-15 21:52:45 +01:00
Marko Djordjevic
2b86524436 fix(ml): correct CCI indicator signature to use HLC prices 2026-02-15 21:09:47 +01:00
Marko Djordjevic
63486bc7b5 fix(ml): add CCI to hlc_indicators list 
CCI (Commodity Channel Index) requires high, low, and close prices
 2026-02-15 21:08:20 +01:00
Marko Djordjevic
847ff67986 feat(ml): add TA-Lib annotation generation and import workflow 
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.
 2026-02-15 19:18:28 +01:00
Marko Djordjevic
fd29ab91e0 feat(ml): implement feature engineering pipeline 
- Create pipeline.py with CLI argument parsing for running stages
- Implement TA-Lib indicator computation with multi-output support
- Add candle feature extraction (body_size, wicks, ratios, etc.)
- Create custom feature loader with dynamic module import
- Wire all feature engineering stages with NaN handling
- Tasks completed: 2.2, 2.3, 3.1, 3.2, 3.3, 3.4, 3.5
 2026-02-15 12:22:59 +01:00