version: '3.8'

services:
  afml-trainer:
    build:
      context: .
      dockerfile: Dockerfile
    container_name: afml_dual_trainer
    volumes:
      # Mount the local Models folder so the generated ONNX models and reports are available on the host machine
      - ./Models:/app/Models
      # Mount the Cache directory to speed up subsequent runs
      - ./afml/Cache:/app/afml/Cache
    environment:
      - PYTHONUNBUFFERED=1
      # Note: For production, you may want to set MT5 authentication credentials here if accessing a remote bridge
    # Override the command to test different assets or timeframes
    command: python scripts/run_dual_model_production.py --symbols BTCUSD XAUUSD --timeframe M15 --days 90
    restart: "no"