train_ml_validator.py

#!/usr/bin/env python3
"""Script to train the ML Strategy Validator"""

import logging

from core.quant.validators.ml_validator import MLStrategyValidator


def train_ml_validator(data_days: int = 60, symbol: str = "XAUUSD") -> bool:
    """Train the ML validator model.

    Args:
        data_days: Number of days of historical data to use for training.
        symbol: Trading symbol to train on.

    Returns:
        True if training completes successfully, False otherwise.
    """

    print("🤖 Training ML Strategy Validator")
    print("=" * 50)
    print(f"📊 Using {data_days} days of {symbol} data")
    print()

    try:
        return _extracted_from_train_ml_validator_17(symbol, data_days)
    except Exception as e:
        print(f"❌ Error during training: {e}")
        logging.exception("Training error")
        return False


# TODO Rename this here and in `train_ml_validator`
def _extracted_from_train_ml_validator_17(symbol, data_days):
    # Initialize validator
    validator = MLStrategyValidator()

    # Train model
    print("Training model with historical data...")
    results = validator.train_from_history(symbol=symbol, n_days=data_days)

    if "error" in results:
        print(f"❌ Training failed: {results['error']}")
        return False

    # Display results
    print("✅ Training completed successfully!")
    print("📊 Training Results:")
    print(f"   - Training samples: {results['training_samples']}")
    print(f"   - Test samples: {results['test_samples']}")
    print(f"   - Training accuracy: {results['train_accuracy']:.3f}")
    print(f"   - Test accuracy: {results['test_accuracy']:.3f}")

    print("\n📈 Classification Report:")
    report = results['classification_report']
    for class_label, metrics in report.items():
        if isinstance(metrics, dict):
            precision = metrics.get('precision', 0)
            recall = metrics.get('recall', 0)
            f1 = metrics.get('f1-score', 0)
            support = metrics.get('support', 0)
            print(f"   {class_label}: Precision={precision:.3f}, "
                  f"Recall={recall:.3f}, F1={f1:.3f}, Support={support}")

    print("\n🎯 Feature Importances:")
    importances = results['feature_importances']
    sorted_features = sorted(importances.items(), key=lambda x: x[1], reverse=True)
    for feature, importance in sorted_features:
        print(f"   {feature}: {importance:.3f}")

    print(f"\n💾 Model saved to: {validator.model_path}")

    return True

if __name__ == "__main__":
    import argparse

    logging.basicConfig(level=logging.INFO)

    parser = argparse.ArgumentParser(description="Train ML Strategy Validator")
    parser.add_argument(
        "--days",
        type=int,
        default=60,
        help="Number of days of historical data (default: 60)",
    )
    parser.add_argument(
        "--symbol",
        type=str,
        default="XAUUSD",
        help="Trading symbol (default: XAUUSD)",
    )

    args = parser.parse_args()

    success = train_ml_validator(data_days=args.days, symbol=args.symbol)
    exit(0 if success else 1)