Romeo V6 is a high-risk ensemble machine learning model designed for predicting price movements in XAUUSD (Gold vs US Dollar) futures on 15-minute intraday data. 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 with aggressive settings for higher returns and volatility.
1import joblib
2artifact = joblib.load('trading_model_romeo_daily.pkl')
3features = artifact['features'] # Canonical feature list
4models = artifact['models'] # Dict of XGBoost/LightGBM models
5weights = artifact['weights'] # Ensemble weights
1import pandas as pd
2# Prepare df with features matching artifact['features']
3X = df[features].fillna(0) # Fill missing features with 0
4probabilities = sum(weight * model.predict_proba(X)[:, 1] for model, weight in zip(models.values(), weights.values())) / sum(weights.values())
5signals = (probabilities > threshold).astype(int) # threshold e.g. 0.5
