57 commits

Author	SHA1	Message	Date
Marko Djordjevic	847ff67986	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.	2026-02-15 19:18:28 +01:00
Marko Djordjevic	3a83fd38e9	feat(ml): implement FastAPI inference service with model loading, preprocessing, and prediction endpoints	2026-02-15 14:29:07 +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
Marko Djordjevic	16763b967e	feat(ml): implement annotation ingestion with windowed/BIO encoding and TA-Lib patterns	2026-02-15 12:28:58 +01:00
Marko Djordjevic	fd29ab91e0	feat(ml): implement feature engineering pipeline ... - Create pipeline.py with CLI argument parsing for running stages - Implement TA-Lib indicator computation with multi-output support - Add candle feature extraction (body_size, wicks, ratios, etc.) - Create custom feature loader with dynamic module import - Wire all feature engineering stages with NaN handling - Tasks completed: 2.2, 2.3, 3.1, 3.2, 3.3, 3.4, 3.5	2026-02-15 12:22:59 +01:00
Marko Djordjevic	ea339a54a7	feat(ml): add database schema, config parser, and DVC setup ... - Initialize DVC with local storage backend (task 1.6) - Create PostgreSQL schema for training_runs table (task 1.7) - Add SQLAlchemy database connection setup (task 1.8) - Create Pydantic config models for pipeline.yaml (task 2.1) - Add migration runner for database setup - Fix pyproject.toml package discovery config	2026-02-15 12:08:53 +01:00
Marko Djordjevic	1a653c5866	feat: add ML service scaffolding with Python FastAPI, Docker, and MLflow setup	2026-02-15 11:58:31 +01:00