Upload metadata.json with huggingface_hub

metadata.json

@@ -1,27 +1,50 @@
 {
-  "model_id": "romeo-v5",
+  "model_id": "JonusNattapong/romeo-v5",
   "owner": "JonusNattapong",
   "license": "mit",
-  "tags": ["trading", "xauusd", "ensemble", "xgboost", "lightgbm", "keras"],
+  "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",
-    "MODEL_CARD.md"
+    "romeo_keras_daily.keras"
   ],
   "metrics": {
-    "evaluation_data": "data_xauusd_v3/daily_data_v3_fresh.csv",
-    "initial_capital": 100,
+    "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_rate": 0.49473684210526314
+    "win_trades": 1786,
+    "win_rate": 0.49473684210526314,
+    "avg_pnl": 0.10659832168689985
   },
-  "feature_list_path": "trading_model_romeo_daily.pkl (artifact['features'])",
-  "usage": {
-    "load": "joblib.load('trading_model_romeo_daily.pkl')",
-    "predict": "model.predict_proba(X) or keras.models.load_model('romeo_keras_daily.keras')"
-  }
-}
+  "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"
+}