romeo-v7 / README.md

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metadata

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: 2.1
            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

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.