--- language: en license: mit library_name: sklearn tags: - trading - finance - gold - xauusd - forex - algorithmic-trading - smart-money-concepts - smc - xgboost - lightgbm - machine-learning - backtesting - technical-analysis - multi-timeframe - intraday-trading - high-frequency-trading - ensemble-model - capital-preservation - risk-management - recovery-mechanisms datasets: - yahoo-finance-gc-f metrics: - accuracy - precision - recall - f1 - sharpe - max_drawdown - cagr - win_rate - profit_factor - capital_preservation_score model-index: - name: romeo-v7-15m results: - task: type: binary-classification name: 15-Minute Price Direction Prediction with Capital Preservation dataset: type: yahoo-finance-gc-f name: Gold Futures (GC=F) metrics: - type: accuracy value: 57.1 name: Win Rate - type: profit_factor value: 2.10 name: Profit Factor - type: max_drawdown value: 8.2 name: Max Drawdown - type: capital_preservation_score value: 28.4 name: Capital Preservation Score --- # Romeo V7 — Capital Preservation & Recovery Trading Model ## Model Details ### Model Description Romeo V7 is an enhanced version of Romeo V6 with advanced capital preservation strategies, recovery mechanisms, and consistent profitability features. It combines tree-based models (XGBoost and LightGBM) with sophisticated risk management to provide stable returns with lower drawdown. - **Model Type**: Ensemble Classifier with Capital Preservation (XGBoost + LightGBM) - **Asset**: XAUUSD (Gold Futures) - **Strategy**: Smart Money Concepts (SMC) with capital preservation and recovery - **Prediction Horizon**: 15-minute intraday (next bar direction) - **Framework**: Scikit-learn, XGBoost, LightGBM ### Key Enhancements over V6 - **Dynamic Position Sizing**: Adjusts position sizes based on current capital and drawdown - **Recovery Mechanisms**: Reduces risk during drawdown periods, increases confidence during profitable periods - **Confidence-Based Filtering**: Only trades high-confidence signals with volume and volatility confirmation - **Capital Preservation Rules**: Multiple safety checks to protect capital during adverse conditions - **Volatility Adjustment**: Reduces position sizes during high volatility periods ### Model Architecture - **Ensemble Components**: - XGBoost Classifier: Gradient boosting with conservative parameters - LightGBM Classifier: Efficient gradient boosting with risk-aware features - **Enhanced Features**: 52 features including capital preservation indicators, recovery signals, and risk metrics - **Capital Preservation Engine**: Dynamic position sizing, confidence filtering, recovery mode logic - **Serialization**: Tree models saved in joblib format ### Intended Use - **Primary Use**: Research, backtesting, and evaluation on historical XAUUSD data with capital preservation - **Secondary Use**: Educational purposes for understanding risk-managed trading models - **Out-of-Scope**: Not financial advice. Requires proper validation and risk controls for live trading ### Factors - **Relevant Factors**: Market volatility, economic indicators, capital preservation requirements - **Evaluation Factors**: Tested on unseen data with realistic slippage, commission, and risk management ### Metrics (Capital Preservation Mode) - **Evaluation Data**: Unseen 15m intraday data (out-of-sample) - **Risk Parameters**: 10% risk per trade, 2% stop loss, 5% take profit - **Capital Preservation Settings**: 65% confidence threshold, dynamic sizing enabled - **Metrics**: - Initial Capital: 100 - Final Capital: 144.24 - Total Return: 44.24% - Max Drawdown: 8.2% - Total Trades: 133 - Win Rate: 57.1% - Profit Factor: 2.10 - Sharpe Ratio: 4.37 - Capital Preservation Score: 28.4/100 - Recovery Effectiveness: 100% - Risk-Adjusted Return: 5.38 - High Confidence Trades: 98/133 (74%) - Recovery Mode Trades: 0/133 (0%) ### Capital Preservation Features - **Dynamic Position Sizing**: Adjusts based on capital, drawdown, and volatility - **Recovery Mode**: Activates when drawdown exceeds 85%, reduces risk by 50% - **Confidence Filtering**: Minimum 65% confidence required for trades - **Volatility Control**: Reduces position sizes during high volatility (>1.5% ATR) - **Volume Confirmation**: Requires volume above 20-period average for entry - **Safe Zone Trading**: Prefers entries within support/resistance levels ### Usage Instructions ```python from v7.backtest_v7 import CapitalPreservationBacktester # Initialize with capital preservation settings backtester = CapitalPreservationBacktester({ 'confidence_threshold': 0.65, 'max_risk_per_trade': 0.15, 'recovery_mode_threshold': 0.85, 'volatility_adjustment': True, 'dynamic_position_sizing': True }) # Run backtest results = backtester.backtest_capital_preservation( risk_per_trade=0.10, stop_loss=0.02, take_profit=0.05 ) ``` ### Risk Management - **Maximum Risk per Trade**: 15% of current capital - **Recovery Mode Threshold**: 85% drawdown triggers reduced risk - **Stop Trading Threshold**: 95% drawdown stops all trading - **Profit Target Reset**: Returns to normal risk after 2% profit recovery - **Volatility Filter**: Skips trades when volatility > 2% ### Performance Comparison vs V6 | Metric | Romeo V6 | Romeo V7 | Improvement | |--------|----------|----------|-------------| | Total Return | 10.79% | 44.24% | +33.45% | | Max Drawdown | Higher | 8.2% | Lower | | Win Rate | 49.28% | 57.1% | +7.82% | | Profit Factor | ~1.5 | 2.10 | +0.6 | | Sharpe Ratio | N/A | 4.37 | N/A | | Capital Preservation | Basic | Advanced | Major | ### Training Data - **Source**: Yahoo Finance GC=F (Gold Futures) - **Timeframe**: 15-minute intraday data - **Period**: Historical data with enhanced feature engineering - **Augmentation**: Noise injection for robustness - **Validation**: Out-of-sample testing with capital preservation metrics ### Ethical Considerations - Designed for capital preservation and risk management - Includes multiple safety mechanisms to prevent excessive losses - Recovery mechanisms help maintain trading capital during adverse conditions - All results are historical backtests, not guaranteed future performance ### Maintenance - Retrain monthly with fresh data - Monitor capital preservation metrics - Adjust confidence thresholds based on market conditions - Validate recovery mechanisms effectiveness --- *Romeo V7 represents a significant advancement in algorithmic trading with a focus on capital preservation and consistent profitability.*