2 commits

Author	SHA1	Message	Date
Marko Djordjevic	aa81d4f3d0	fix(ml): complete ML pipeline fixes and setup ... - 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	2026-02-15 21:29:54 +01:00
Marko Djordjevic	f4c0f9a836	feat(ml): implement training stage with MLflow tracking and model wrappers ... - 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	2026-02-15 14:22:19 +01:00