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>
46 lines
621 B
Plaintext
46 lines
621 B
Plaintext
# Core ML dependencies
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numpy>=1.24.0
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pandas>=2.0.0
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scikit-learn>=1.3.0
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scipy>=1.11.0
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# Deep Learning
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torch>=2.0.0
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torchvision>=0.15.0
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# XGBoost with CUDA support
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xgboost>=2.0.0
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# API & Web
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fastapi>=0.104.0
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uvicorn>=0.24.0
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websockets>=12.0
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pydantic>=2.0.0
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python-multipart>=0.0.6
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# Data processing
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pyarrow>=14.0.0
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tables>=3.9.0
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# Logging & Monitoring
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loguru>=0.7.0
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python-json-logger>=2.0.7
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# Configuration
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pyyaml>=6.0
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python-dotenv>=1.0.0
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# Database
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pymongo>=4.6.0
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motor>=3.3.0
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# Utilities
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python-dateutil>=2.8.2
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tqdm>=4.66.0
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joblib>=1.3.2
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# Testing (optional)
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pytest>=7.4.0
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pytest-asyncio>=0.21.0
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httpx>=0.25.0
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