- Fix CCI indicator to use HLC prices instead of close only
- Parse datetime column when loading enriched CSV
- Strip timezone from annotation timestamps
- Fix TA-Lib pattern names (CDL3WHITESOLDIERS, CDL3BLACKCROWS)
- Exclude programmatic label columns from training features
- Fix classification report to handle missing classes
- Update MLflow tracking to use localhost:5000
- Grant PostgreSQL permissions to ml_user
Pipeline now runs successfully end-to-end:
- Feature engineering: 2543 rows, 31 columns
- Annotation ingestion: 286 samples
- Training: 89.47% test accuracy with Random Forest
- Create RandomForestModel and XGBoostModel wrappers with class weight support
- Implement temporal and random train/val/test splitting
- Add MLflow experiment tracking with full parameter and metric logging
- Create evaluation module for confusion matrix, feature importance, and classification reports
- Implement model training with sklearn/xgboost flavor logging and optional registry registration
- Store training run metadata in PostgreSQL
- Wire training stage into pipeline.py orchestrator
- Support both RandomForest and XGBoost models with configurable hyperparameters
