# Symbol-Timeframe Model Training Report

**Generated:** 2026-01-05 02:28:25

## Configuration

- **Training Data Cutoff:** 2024-12-31 (excluding 2025 for backtesting)
- **Dynamic Factor Weighting:** Enabled
- **Sample Weight Method:** Softplus with beta=4.0, w_max=3.0

## Training Results Summary

| Model | Symbol | Timeframe | Target | MAE | RMSE | R2 | Dir Accuracy | Train | Val |
|-------|--------|-----------|--------|-----|------|----|--------------| ----- | --- |
| XAUUSD_5m_high_h3 | XAUUSD | 5m | high | 0.759862 | 1.228181 | 0.0840 | 90.76% | 288285 | 50874 |
| XAUUSD_5m_low_h3 | XAUUSD | 5m | low | 0.761146 | 1.123620 | 0.0730 | 93.92% | 288285 | 50874 |
| XAUUSD_15m_high_h3 | XAUUSD | 15m | high | 1.398330 | 2.184309 | 0.0574 | 94.25% | 96991 | 17117 |
| XAUUSD_15m_low_h3 | XAUUSD | 15m | low | 1.348695 | 1.961190 | 0.0556 | 96.30% | 96991 | 17117 |
| EURUSD_5m_high_h3 | EURUSD | 5m | high | 0.000323 | 0.000440 | -0.1931 | 97.82% | 313653 | 55351 |
| EURUSD_5m_low_h3 | EURUSD | 5m | low | 0.000316 | 0.000463 | -0.1203 | 97.66% | 313653 | 55351 |
| EURUSD_15m_high_h3 | EURUSD | 15m | high | 0.000586 | 0.000784 | -0.2201 | 98.25% | 105128 | 18552 |
| EURUSD_15m_low_h3 | EURUSD | 15m | low | 0.000588 | 0.000796 | -0.1884 | 98.32% | 105128 | 18552 |

## Model Files

Models saved to: `/home/isem/workspace-v1/projects/trading-platform/apps/ml-engine/models/symbol_timeframe_models`

### Model Naming Convention
- `{symbol}_{timeframe}_high_h{horizon}.joblib` - High range predictor
- `{symbol}_{timeframe}_low_h{horizon}.joblib` - Low range predictor

## Usage Example

```python
from training.symbol_timeframe_trainer import SymbolTimeframeTrainer

# Load trained models
trainer = SymbolTimeframeTrainer()
trainer.load('models/symbol_timeframe_models/')

# Predict for XAUUSD 15m
predictions = trainer.predict(features, 'XAUUSD', '15m')
print(f"Predicted High: {predictions['high']}")
print(f"Predicted Low: {predictions['low']}")
```

## Notes

1. Models exclude 2025 data for out-of-sample backtesting
2. Dynamic factor weighting emphasizes high-movement samples
3. Separate models for HIGH and LOW predictions per symbol/timeframe

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*Report generated by Symbol-Timeframe Training Pipeline*