| { | |
| "model_id": "JonusNattapong/romeo-v6", | |
| "owner": "JonusNattapong", | |
| "license": "mit", | |
| "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", | |
| "keras", | |
| "tensorflow" | |
| ], | |
| "artifacts": [ | |
| "trading_model_romeo_daily.pkl", | |
| "romeo_keras_daily.keras" | |
| ], | |
| "metrics": { | |
| "initial_capital": 100.0, | |
| "final_capital": 162.31426062637883, | |
| "total_return_pct": 62.314260626378825, | |
| "total_trades": 207, | |
| "win_trades": 102, | |
| "win_rate": 0.4927536231884058, | |
| "avg_pnl": 0.30103507548975383 | |
| }, | |
| "feature_list": "artifact['features']", | |
| "usage": "Load artifact with joblib.load(). Align data to artifact['features'], fill missing with 0. Predict with ensemble weights. Use v6/backtest_v6.py with risk_per_trade=0.25 and threshold=0.8 for high-risk backtesting. Tested under normal (commission 0.02%, slippage 0.5pips) and difficult conditions (commission 0.05%, slippage 1.0pips).", | |
| "training_data": "Yahoo Finance GC=F 15m intraday data with SMC and technical features", | |
| "evaluation_data": "Unseen fresh 15m intraday data", | |
| "frameworks": ["scikit-learn", "xgboost", "lightgbm", "tensorflow"], | |
| "python_version": "3.8+", | |
| "dependencies": ["joblib", "pandas", "numpy", "scipy"], | |
| "caveats": "High-risk position sizing (25% risk per trade); higher volatility and drawdown; historical backtests only; not financial advice" | |
| } |