romeo-v7 / README.md
JonusNattapong's picture
Upload README.md with huggingface_hub
51b8c9f verified
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.