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>
1424 lines
37 KiB
JSON
1424 lines
37 KiB
JSON
{
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"config": {
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"symbol": "XAUUSD",
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"timeframe": "15m",
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"direction_bias": 1.2,
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"signal_every_n": 8
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},
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"metrics": {
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},
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{
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"bar": 14432,
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"time": "2025-03-17 19:10:00",
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"direction": "long",
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"entry": 3001.17,
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"tp": 3001.693542857143,
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"sl": 2998.9885714285715,
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"exit": 3001.693542857143,
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"result": "tp",
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"pnl": 24.320384756732256,
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"bars_held": 1,
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"pred_high": 0.0002907437527935444,
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"pred_low": 0.0002907437527935444
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}
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]
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} |