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>
55 lines
1.1 KiB
YAML
55 lines
1.1 KiB
YAML
name: trading-ml-engine
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channels:
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- pytorch
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- conda-forge
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- defaults
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dependencies:
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- python=3.11
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- pip>=23.0
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# Core ML and Deep Learning
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- pytorch>=2.0.0
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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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# API Framework
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- fastapi>=0.104.0
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- uvicorn>=0.24.0
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# Database
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- sqlalchemy>=2.0.0
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- redis-py>=5.0.0
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# Data visualization (for development)
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- matplotlib>=3.7.0
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- seaborn>=0.12.0
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# Development and code quality
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- pytest>=7.4.0
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- pytest-asyncio>=0.21.0
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- pytest-cov>=4.1.0
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- black>=23.0.0
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- isort>=5.12.0
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- flake8>=6.1.0
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- mypy>=1.5.0
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- ipython>=8.0.0
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- jupyter>=1.0.0
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# Additional dependencies via pip
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- pip:
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- pydantic>=2.0.0
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- pydantic-settings>=2.0.0
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- psycopg2-binary>=2.9.0
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- aiohttp>=3.9.0
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- requests>=2.31.0
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- xgboost>=2.0.0
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- joblib>=1.3.0
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- ta>=0.11.0
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- loguru>=0.7.0
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- pyyaml>=6.0.0
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- python-dotenv>=1.0.0
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# TA-Lib requires system installation first:
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# conda install -c conda-forge ta-lib
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# or from source with proper dependencies
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