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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"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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"win_rate": 0.8,
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"bars_held": 1,
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|
"pred_high": 0.0006429393259457547,
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"pred_low": 0.0006429393259457547
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|
},
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|
{
|
|
"bar": 14432,
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|
"time": "2025-03-17 19:10:00",
|
|
"direction": "long",
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|
"entry": 3001.17,
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|
"tp": 3001.7371714285714,
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|
"sl": 2998.9885714285715,
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|
"exit": 3001.7371714285714,
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|
"result": "tp",
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|
"pnl": 26.107516539465436,
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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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|
} |