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.

services/ml/candle_ml.egg-info/SOURCES.txt

@@ -1,6 +1,22 @@
 pyproject.toml
+app/__init__.py
+app/annotation_ingestion.py
+app/config.py
+app/db.py
+app/main.py
+app/preprocessing.py
 candle_ml.egg-info/PKG-INFO
 candle_ml.egg-info/SOURCES.txt
 candle_ml.egg-info/dependency_links.txt
 candle_ml.egg-info/requires.txt
-candle_ml.egg-info/top_level.txt
+candle_ml.egg-info/top_level.txt
+features/candle_features.py
+features/custom_loader.py
+features/engineer.py
+features/talib_features.py
+training/__init__.py
+training/evaluation.py
+training/train.py
+training/models/__init__.py
+training/models/random_forest.py
+training/models/xgboost_model.py