- Change pair and timeframe fields from required to optional
- Frontend only sends candles array, not pair/timeframe metadata
- These fields are only used for logging, not prediction logic
- Update logging to handle None values with 'unknown' fallback
- Fixes 422 validation error on /predict endpoint
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.
