# Symbol-Timeframe Model Training Report

**Generated:** 2026-01-07 03:40:26

## 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 |
|-------|--------|-----------|--------|-----|------|----|--------------| ----- | --- |
| GBPUSD_5m_high_h3 | GBPUSD | 5m | high | 0.000504 | 0.000592 | -0.6309 | 98.17% | 310314 | 54762 |
| GBPUSD_5m_low_h3 | GBPUSD | 5m | low | 0.000548 | 0.000645 | -0.6558 | 98.88% | 310314 | 54762 |
| GBPUSD_15m_high_h3 | GBPUSD | 15m | high | 0.000887 | 0.001025 | -0.6944 | 98.52% | 104191 | 18387 |
| GBPUSD_15m_low_h3 | GBPUSD | 15m | low | 0.000955 | 0.001102 | -0.7500 | 98.90% | 104191 | 18387 |

## 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

---
*Report generated by Symbol-Timeframe Training Pipeline*