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trading-platform-ml-engine-v2/reports/backtest_oos/BACKTEST_REPORT_20260106_232228.md
rckrdmrd 75c4d07690 feat: Initial commit - ML Engine codebase 
Hierarchical ML Pipeline for trading predictions:
- Level 0: Attention Models (volatility/flow classification)
- Level 1: Base Models (XGBoost per symbol/timeframe)
- Level 2: Metamodels (XGBoost Stacking + Neural Gating)

Key components:
- src/pipelines/hierarchical_pipeline.py - Main prediction pipeline
- src/models/ - All ML model classes
- src/training/ - Training utilities
- src/api/ - FastAPI endpoints
- scripts/ - Training and evaluation scripts
- config/ - YAML configurations

Note: Trained models (*.joblib, *.pt) are gitignored.
      Regenerate with training scripts.

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
2026-01-18 04:27:40 -06:00

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OOS Backtest Report

Generated: 2026-01-06 23:22:28

Configuration

  • OOS Period: 2024-03-01 to 2025-03-18
  • Training Data Cutoff: 2024-03-01 (excluded from training)

Summary by Symbol/Timeframe

Symbol TF Samples MAE High MAE Low Dir Acc High Dir Acc Low Signal Acc
XAUUSD 5m 73226 1.0982 1.2217 91.4% 93.2% 91.5%
XAUUSD 15m 24578 2.0019 2.3882 94.6% 95.9% 94.7%
EURUSD 5m 76858 0.0003 0.0003 98.0% 98.1% 98.0%
EURUSD 15m 25635 0.0005 0.0006 98.6% 98.8% 98.6%

R:R Analysis

Risk/Reward Performance by Symbol

XAUUSD 5m

R:R Win Rate Trades Expectancy
1.0 51.0% 45984 0.019
1.5 36.2% 35367 -0.094
2.0 22.7% 29182 -0.318
2.5 13.1% 25943 -0.543
3.0 7.4% 24352 -0.704

XAUUSD 15m

R:R Win Rate Trades Expectancy
1.0 55.4% 13514 0.107
1.5 39.1% 9905 -0.022
2.0 24.5% 7984 -0.266
2.5 14.2% 7033 -0.501
3.0 8.1% 6562 -0.676

EURUSD 5m

R:R Win Rate Trades Expectancy
1.0 44.2% 30193 -0.116
1.5 24.5% 22300 -0.388
2.0 13.9% 19565 -0.583
2.5 7.9% 18292 -0.723
3.0 4.8% 17698 -0.807

EURUSD 15m

R:R Win Rate Trades Expectancy
1.0 45.7% 9031 -0.086
1.5 27.0% 6721 -0.324
2.0 15.9% 5830 -0.523
2.5 9.1% 5396 -0.680
3.0 5.9% 5213 -0.762

Conclusions

Key Observations

  1. Directional Accuracy: The models show high directional accuracy (>90%) in predicting whether price will move up or down.

  2. Signal Quality: Signal-based accuracy helps identify when predictions are most reliable.

  3. R:R Performance: The expectancy values show the expected return per unit of risk.

    • Positive expectancy = profitable strategy
    • Expectancy > 0.5 with 2:1 R:R = strong edge

Recommendations

  1. Focus on configurations with positive expectancy
  2. Consider combining with DirectionalFilters for additional confirmation
  3. Use volume/volatility filters during low-quality periods

Report generated by OOS Backtest Pipeline