romeo-v5

Romeo V5 — Ensemble Trading Model for XAUUSD

Model Details

Model Description

Romeo V5 is an ensemble machine learning model designed for predicting price movements in XAUUSD (Gold vs US Dollar) futures. It combines tree-based models (XGBoost and LightGBM) with an optional Keras neural network head to generate trading signals. The model outputs a probability score for long (up) trades, and the backtester handles entry/exit logic, position sizing, and risk management.

• Model Type: Ensemble Classifier (XGBoost + LightGBM + optional Keras NN)
• Asset: XAUUSD (Gold Futures)
• Strategy: Smart Money Concepts (SMC) with technical indicators
• Prediction Horizon: Daily timeframe (5-day ahead direction)
• Framework: Scikit-learn, XGBoost, LightGBM, TensorFlow/Keras

Model Architecture

• Ensemble Components:
  • XGBoost Classifier: Gradient boosting on decision trees.
  • LightGBM Classifier: Efficient gradient boosting with leaf-wise growth.
  • Optional Keras Neural Network: Dense layers with custom SumAxis1Layer to replace anonymous Lambda for serialization.
• Features: 31 canonical features including technical indicators (SMA, EMA, RSI, Bollinger Bands) and SMC elements (order blocks, volume profiles).
• Serialization: Tree models saved in joblib .pkl format; Keras model in native .keras format.
• Weights: Ensemble weights stored in artifact for weighted probability averaging.

Intended Use

• Primary Use: Research, backtesting, and evaluation on historical XAUUSD data.
• Secondary Use: Educational purposes for understanding ensemble trading models.
• Out-of-Scope: Not financial advice. Do not use for live trading without proper validation, risk controls, and regulatory compliance.

Factors

• Relevant Factors: Market volatility, economic indicators affecting gold prices (e.g., USD strength, inflation data).
• Evaluation Factors: Tested on unseen data; robustness scanned across slippage, commission, and threshold parameters.

Metrics

• Evaluation Data: Unseen daily data (out-of-sample).
• Metrics:
  • Initial Capital: 100
  • Final Capital: 484.82
  • CAGR: 0.0444
  • Annual Volatility: 0.4118
  • Sharpe Ratio: 0.3119
  • Max Drawdown: -47.66%
  • Total Trades: 3610
  • Win Rate: 49.47%
  • Avg PnL per Trade: 0.1066

Training Data

• Source: Yahoo Finance (GC=F) historical data.
• Preprocessing: Feature engineering with technical indicators and SMC concepts.
• Split: Trained on historical data; evaluated on unseen fresh dataset.

Quantitative Analyses

• Robustness Scan: Coarse grid sweep (slippage: 0-1 pips, commission: 0-0.0005, threshold: 0.5-0.6). Best scenarios: low friction, threshold ~0.5. Worst: high commission/threshold.
• M2M Equity: Per-bar mark-to-market equity calculation for accurate risk metrics.

Ethical Considerations

• Bias: Model trained on historical data; may not account for future market changes or black swan events.
• Risk: High volatility in forex; potential for significant losses.
• Transparency: Full disclosure of assumptions, limitations, and evaluation.

Caveats and Recommendations

• Limitations: Simplified position sizing; small-account behavior may differ with margin rules. Historical backtests not indicative of future results.
• Recommendations: Use with stop-loss, diversify, and consult financial advisors. Validate on your own data before use.

Usage

Loading the Model

import joblib
artifact = joblib.load('trading_model_romeo_daily.pkl')
features = artifact['features']  # Canonical feature list
models = artifact['models']      # Dict of XGBoost/LightGBM models
weights = artifact['weights']    # Ensemble weights

Making Predictions

import pandas as pd
# Prepare df with features matching artifact['features']
X = df[features].fillna(0)  # Fill missing features with 0
probabilities = sum(weight * model.predict_proba(X)[:, 1] for model, weight in zip(models.values(), weights.values())) / sum(weights.values())
signals = (probabilities > threshold).astype(int)  # threshold e.g. 0.5

Backtesting

Use v5/backtest_v5.py with --data <path> to run on custom data. It aligns features automatically.

Requirements

• Python 3.8+
• scikit-learn, xgboost, lightgbm, tensorflow, joblib

Files

• trading_model_romeo_daily.pkl: Main artifact.
• romeo_keras_daily.keras: Optional Keras model.
• README.md: This model card.
• metadata.json: Structured metadata.

Contact

For issues or contributions: https://github.com/JonusNattapong/AITradings-samsam