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| Metric | Value |
|---|---|
| Win Rate | 68.18% |
| Profit Factor | 2.16 |
| Sharpe Ratio | 4.64 |
| Max Drawdown | 11.06% |
| Total Return | 26.81% |
| Total Trades | 66 |
Super Ensemble Pipeline:
├── Base Learners (10 algorithms)
│ ├── XGBoost, LightGBM, CatBoost
│ ├── RandomForest, ExtraTrees
│ ├── Neural Networks (Keras/TensorFlow)
│ ├── SVM, KNN, LogisticRegression, NaiveBayes
│ └── Individual training with cross-validation
├── Confidence Calibration
│ └── Isotonic regression for probability calibration
├── Stacking Meta-Learner
│ └── LogisticRegression combining base predictions
├── Dynamic Weighting
│ └── Real-time weight optimization
└── Cross-Validation Ensemble
└── Multiple CV fold combination1from v8.train_v8 import load_romeo_v8, SuperEnsemble
2
3# Load the trained model
4model = load_romeo_v8('v8/models_romeo_v8/trading_model_romeo_15m.pkl')
5
6# Make predictions
7predictions = model.predict(your_data)
8probabilities = model.predict_proba(your_data)1# Run backtest on 15m timeframe
2python v8/backtest_v8.py --timeframe 15m --initial-capital 1001# Train full model
2python v8/train_v8.py --data data_xauusd_v3/15m_data_v3.csv --timeframe 15m --mode full1@misc{jonusnattapong_romeo_v8,
2 title={Romeo V8 Super Ensemble Trading AI},
3 author={Jonus Nattapong},
4 year={2025},
5 publisher={Hugging Face},
6 url={https://huggingface.co/JonusNattapong/romeo-v8-super-ensemble-trading-ai}
7}