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>
37 lines
780 B
Docker
37 lines
780 B
Docker
# ML Engine Dockerfile
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# OrbiQuant IA - Trading Platform
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FROM python:3.11-slim
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WORKDIR /app
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# Instalar dependencias del sistema
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RUN apt-get update && apt-get install -y \
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build-essential \
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curl \
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libpq-dev \
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&& rm -rf /var/lib/apt/lists/*
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# Copiar requirements primero para cache de layers
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COPY requirements.txt .
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# Instalar dependencias Python
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RUN pip install --no-cache-dir -r requirements.txt
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# Copiar código fuente
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COPY . .
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# Variables de entorno
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ENV PYTHONPATH=/app
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ENV PYTHONUNBUFFERED=1
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# Puerto
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EXPOSE 8000
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# Health check
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HEALTHCHECK --interval=30s --timeout=10s --start-period=5s --retries=3 \
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CMD curl -f http://localhost:8000/health || exit 1
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# Comando de inicio
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CMD ["uvicorn", "src.api.main:app", "--host", "0.0.0.0", "--port", "8000"]
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