rckrdmrd
75c4d07690
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>
25 lines
546 B
JSON
25 lines
546 B
JSON
{
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"symbol": "XAUUSD",
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"period": "2025-01-01 to 2025-03-18",
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"initial_capital": 1000.0,
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"final_capital": 1058.49,
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"total_return_pct": 5.85,
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"total_trades": 60,
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"winning_trades": 20,
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"losing_trades": 40,
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"win_rate": 33.3,
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"profit_factor": 1.07,
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"max_drawdown_pct": 15.12,
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"avg_winner": 42.75,
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"avg_loser": -19.91,
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"best_trade": 57.6,
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"worst_trade": -24.93,
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"long_trades": 0,
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"long_wins": 0,
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"long_wr": 0,
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"short_trades": 60,
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"short_wins": 20,
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"short_wr": 33.3,
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"total_weeks": 3,
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"profitable_weeks": 2
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} |