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>
26 lines
681 B
JSON
26 lines
681 B
JSON
[
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{
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"strategy_name": "conservative",
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"strategy_description": "Very selective - only best setups",
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"symbol": "GBPUSD",
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"period": "2024-11-04 to 2024-11-14",
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"total_signals": 285,
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"filtered_out": 239,
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"executed_trades": 46,
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"filter_rate": 0.8386,
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"wins": 18,
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"losses": 28,
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"win_rate": 0.3913,
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"total_profit_r": -7.25,
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"avg_profit_r": -0.1575,
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"expectancy": -0.1575,
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"profit_factor": 0.66,
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"max_consecutive_losses": 6,
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"max_drawdown_r": 8.45,
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"avg_attention_winners": 1.436,
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"avg_attention_losers": 1.458,
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"avg_confidence_winners": 0.74,
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"avg_confidence_losers": 0.751,
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"avg_rr_used": 2.0
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}
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] |