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trading-platform-ml-engine-v2/reports/range_backtest/XAUUSD_scalping_20260104_191458.json
rckrdmrd 75c4d07690 feat: Initial commit - ML Engine codebase 
Hierarchical ML Pipeline for trading predictions:
- Level 0: Attention Models (volatility/flow classification)
- Level 1: Base Models (XGBoost per symbol/timeframe)
- Level 2: Metamodels (XGBoost Stacking + Neural Gating)

Key components:
- src/pipelines/hierarchical_pipeline.py - Main prediction pipeline
- src/models/ - All ML model classes
- src/training/ - Training utilities
- src/api/ - FastAPI endpoints
- scripts/ - Training and evaluation scripts
- config/ - YAML configurations

Note: Trained models (*.joblib, *.pt) are gitignored.
      Regenerate with training scripts.

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
2026-01-18 04:27:40 -06:00

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{
"config": {
"symbol": "XAUUSD",
"timeframe": "15m",
"horizon": "scalping",
"tp_factor": 0.6,
"sl_factor": 5.0,
"min_range_pct": 0.0001,
"direction_bias": 1.0,
"signal_every_n": 8
},
"metrics": {
"total_trades": 974,
"win_rate": 0.8449691991786448,
"n_wins": "823",
"n_losses": "117",
"n_timeouts": "34",
"total_pnl": -2692.2675394708413,
"final_capital": 7307.732460529166,
"max_drawdown": 0.31690259745691546
},
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