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.

DEPLOYMENT.md

@@ -196,7 +196,8 @@ sudo make install
 
 ```bash
 cd services/ml
-pip install -r requirements.txt
+uv sync
+#pip install -r requirements.txt
 ```
 
 #### 3. Setup PostgreSQL
@@ -217,7 +218,7 @@ DVC is used for dataset versioning:
 
 ```bash
 cd services/ml
-dvc init
+dvc init #--subdir
 dvc remote add -d local /path/to/dvc-storage
 ```