7 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	ceb4103ec4	fix(ml): parse datetime column and fix TA-Lib pattern names ... - Add parse_dates parameter when loading enriched CSV - Strip timezone from annotation timestamps to match data - Fix pattern names: CDLTHREEWHITESOLDIERS -> CDL3WHITESOLDIERS - Fix pattern names: CDLTHREEBLACKCROWS -> CDL3BLACKCROWS	2026-02-15 21:13:20 +01:00
Marko Djordjevic	2b86524436	fix(ml): correct CCI indicator signature to use HLC prices	2026-02-15 21:09:47 +01:00
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	16763b967e	feat(ml): implement annotation ingestion with windowed/BIO encoding and TA-Lib patterns	2026-02-15 12:28:58 +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