romeo-v7

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

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

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

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Romeo V7 represents a significant advancement in algorithmic trading with a focus on capital preservation and consistent profitability.