Scikit-learn
English
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
Eval Results
Romeo V7 β 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
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.
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Evaluation results
- Win Rate on Gold Futures (GC=F)self-reported57.100
- Profit Factor on Gold Futures (GC=F)self-reported2.100
- Max Drawdown on Gold Futures (GC=F)self-reported8.200
- Capital Preservation Score on Gold Futures (GC=F)self-reported28.400