Adrian Flores Cortes dcfe83bb44 feat: Update data ingestion and add training reports

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>

2026-01-30 12:24:53 -06:00

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Symbol-Timeframe Model Training Report

Generated: 2026-01-25 06:10:13

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

Model Files

Models saved to: /mnt/c/Empresas/ISEM/workspace-v2/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

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

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

Report generated by Symbol-Timeframe Training Pipeline

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