---
language: en
tags:
- trading
- finance
- xauusd
- gold
- forex
- machine-learning
- ensemble
- super-ensemble
- xgboost
- lightgbm
- catboost
- neural-network
- tensorflow
- sklearn
license: mit
datasets:
- custom
metrics:
- accuracy: 0.682
- profit-factor: 2.16
- sharpe-ratio: 4.64
- max-drawdown: 0.111
---

# Romeo V8 Super Ensemble Trading AI

Romeo V8 is an advanced super ensemble trading model that combines **10+ different algorithms** working collaboratively for maximum accuracy and efficiency in XAUUSD (Gold vs US Dollar) trading.

## 🚀 Key Features

- **Super Ensemble Architecture**: 10 algorithms working together (XGBoost, LightGBM, CatBoost, RandomForest, ExtraTrees, Neural Networks, SVM, KNN, LogisticRegression, NaiveBayes)
- **Stacking Meta-Learner**: Intelligent combination of base learner predictions
- **Dynamic Weighting**: Real-time weight adjustment based on performance
- **Confidence Calibration**: Calibrated probability fusion using isotonic regression
- **Cross-Validation Ensemble**: Multiple CV folds combined for robustness
- **Advanced Risk Management**: Multi-algorithm consensus scoring with position sizing

## 📊 Performance Metrics

| 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 |

## 🏗️ Architecture

```
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 combination
```

## 📈 Advanced Features

### Technical Indicators (15+)
- Moving Averages (SMA, EMA)
- Oscillators (RSI, MACD, Stochastic)
- Volatility (Bollinger Bands, ATR)
- Volume (MFI, OBV)
- Momentum indicators

### Quantum-Inspired Features
- Entropy calculations
- Phase space analysis
- Amplitude modulation
- Wavelet energy features

### Algorithm Collaboration Features
- Trend strength indicators
- Volume confirmation signals
- Fractal dimension analysis
- Consensus scoring

## 🛠️ Usage

### Quick Start
```python
from v8.train_v8 import load_romeo_v8, SuperEnsemble

# Load the trained model
model = load_romeo_v8('v8/models_romeo_v8/trading_model_romeo_15m.pkl')

# Make predictions
predictions = model.predict(your_data)
probabilities = model.predict_proba(your_data)
```

### Backtesting
```bash
# Run backtest on 15m timeframe
python v8/backtest_v8.py --timeframe 15m --initial-capital 100
```

### Training
```bash
# Train full model
python v8/train_v8.py --data data_xauusd_v3/15m_data_v3.csv --timeframe 15m --mode full
```

## 📊 Data

The model is trained on enhanced XAUUSD data with:
- **Timeframes**: 1m, 15m, 30m, 1h, 4h, daily
- **Features**: 50+ engineered features per sample
- **Quality**: Clean, processed, and validated data
- **Period**: Multi-year historical data

## 🔬 Research & Development

This model represents the culmination of extensive research in:
- Ensemble learning for financial prediction
- Algorithm collaboration techniques
- Risk management in algorithmic trading
- Feature engineering for time series data
- Neural network integration with traditional ML

## 📝 Citation

If you use this model in your research, please cite:

```bibtex
@misc{jonusnattapong_romeo_v8,
  title={Romeo V8 Super Ensemble Trading AI},
  author={Jonus Nattapong},
  year={2025},
  publisher={Hugging Face},
  url={https://huggingface.co/JonusNattapong/romeo-v8-super-ensemble-trading-ai}
}
```

## ⚠️ Disclaimer

This model is for research and educational purposes only. Trading involves substantial risk of loss and is not suitable for all investors. Past performance does not guarantee future results. Always perform your own due diligence and risk assessment before using in live trading.

## 🤝 Contributing

Contributions are welcome! Please feel free to:
- Report issues
- Suggest improvements
- Submit pull requests
- Share your results

## 📧 Contact

For questions or collaboration opportunities:
- GitHub: [JonusNattapong](https://github.com/JonusNattapong)
- LinkedIn: [Your LinkedIn Profile]

---

*Built with ❤️ for the quantitative finance community*