romeo-v5 / metadata.json
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{
"model_id": "JonusNattapong/romeo-v5",
"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": 484.8199412897085,
"cagr": 0.044435345346789834,
"annual_volatility": 0.4118163868756299,
"sharpe": 0.31192432046397695,
"max_drawdown": -0.47656310794093215,
"total_trades": 3610,
"win_trades": 1786,
"win_rate": 0.49473684210526314,
"avg_pnl": 0.10659832168689985
},
"feature_list": "artifact['features']",
"usage": "Load artifact with joblib.load(). Align data to artifact['features'], fill missing with 0. Predict with ensemble weights. Use v5/backtest_v5.py for backtesting.",
"training_data": "Yahoo Finance GC=F historical data with SMC and technical features",
"evaluation_data": "Unseen fresh daily data",
"frameworks": ["scikit-learn", "xgboost", "lightgbm", "tensorflow"],
"python_version": "3.8+",
"dependencies": ["joblib", "pandas", "numpy", "scipy"],
"caveats": "Simplified position sizing; historical backtests only; not financial advice"
}