dcfe83bb44
Scripts: - Update ingest_ohlcv_polygon.py for improved data processing Reports: - Add attention model training reports (2x) - Add standard training reports (2x) Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
47 lines
1.4 KiB
Markdown
47 lines
1.4 KiB
Markdown
# Symbol-Timeframe Model Training Report
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**Generated:** 2026-01-25 06:10:13
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## Configuration
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- **Training Data Cutoff:** 2024-12-31 (excluding 2025 for backtesting)
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- **Dynamic Factor Weighting:** Enabled
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- **Sample Weight Method:** Softplus with beta=4.0, w_max=3.0
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## Training Results Summary
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| Model | Symbol | Timeframe | Target | MAE | RMSE | R2 | Dir Accuracy | Train | Val |
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|-------|--------|-----------|--------|-----|------|----|--------------| ----- | --- |
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## Model Files
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Models saved to: `/mnt/c/Empresas/ISEM/workspace-v2/projects/trading-platform/apps/ml-engine/models/symbol_timeframe_models`
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### Model Naming Convention
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- `{symbol}_{timeframe}_high_h{horizon}.joblib` - High range predictor
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- `{symbol}_{timeframe}_low_h{horizon}.joblib` - Low range predictor
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## Usage Example
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```python
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from training.symbol_timeframe_trainer import SymbolTimeframeTrainer
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# Load trained models
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trainer = SymbolTimeframeTrainer()
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trainer.load('models/symbol_timeframe_models/')
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# Predict for XAUUSD 15m
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predictions = trainer.predict(features, 'XAUUSD', '15m')
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print(f"Predicted High: {predictions['high']}")
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print(f"Predicted Low: {predictions['low']}")
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```
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## Notes
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1. Models exclude 2025 data for out-of-sample backtesting
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2. Dynamic factor weighting emphasizes high-movement samples
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3. Separate models for HIGH and LOW predictions per symbol/timeframe
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---
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*Report generated by Symbol-Timeframe Training Pipeline*
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