{
  "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"
}