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>
31 lines
559 B
JSON
31 lines
559 B
JSON
{
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"symbol": "XAUUSD",
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"timeframe": "15m",
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"period": {
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"start": "2025-01-06",
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"end": "2025-01-12"
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},
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"data_points": 1281,
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"models_loaded": {
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"range_predictor": true,
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"movement_predictor": true,
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"amd_detector": true,
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"tpsl_classifier": false
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},
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"predictions_generated": {
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"range": [
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"delta_low",
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"delta_high",
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"direction"
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],
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"movement": [
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"high_usd",
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"low_usd",
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"direction",
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"asymmetry",
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"confidence"
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],
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"amd": []
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},
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"output_path": "charts/XAUUSD"
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} |