Scikit-learn
English
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
capital-preservation
risk-management
recovery-mechanisms
Eval Results (legacy)
| { | |
| "model_id": "JonusNattapong/romeo-v7", | |
| "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", | |
| "capital-preservation", | |
| "risk-management", | |
| "recovery-mechanisms", | |
| "dynamic-position-sizing" | |
| ], | |
| "artifacts": [ | |
| "trading_model_romeo_15m.pkl" | |
| ], | |
| "metrics": { | |
| "initial_capital": 100.0, | |
| "final_capital": 144.24, | |
| "total_return_pct": 44.24, | |
| "max_drawdown_pct": 8.2, | |
| "total_trades": 133, | |
| "win_trades": 76, | |
| "win_rate": 0.571, | |
| "profit_factor": 2.10, | |
| "sharpe_ratio": 4.37, | |
| "capital_preservation_score": 28.4, | |
| "recovery_effectiveness": 1.00, | |
| "risk_adjusted_return": 5.38, | |
| "avg_trade": 0.33, | |
| "high_confidence_trades": 98, | |
| "recovery_mode_trades": 0 | |
| }, | |
| "feature_list": "artifact['features']", | |
| "usage": "Load artifact with joblib.load(). Use v7/backtest_v7.py with CapitalPreservationBacktester for capital-preserving backtesting. Key settings: risk_per_trade=0.10, confidence_threshold=0.65, dynamic_position_sizing=True. Includes recovery mechanisms and volatility adjustment.", | |
| "training_data": "Yahoo Finance GC=F 15m intraday data with enhanced capital preservation features", | |
| "evaluation_data": "Unseen fresh 15m intraday data with capital preservation backtesting", | |
| "frameworks": ["scikit-learn", "xgboost", "lightgbm"], | |
| "python_version": "3.8+", | |
| "dependencies": ["joblib", "pandas", "numpy", "scipy"], | |
| "capital_preservation_features": { | |
| "dynamic_position_sizing": true, | |
| "confidence_filtering": true, | |
| "recovery_mechanisms": true, | |
| "volatility_adjustment": true, | |
| "max_risk_per_trade": 0.15, | |
| "recovery_mode_threshold": 0.85, | |
| "confidence_threshold": 0.65 | |
| }, | |
| "caveats": "Capital preservation focused with dynamic risk management. Includes recovery mechanisms for drawdown periods. Historical backtests only; not financial advice. Monitor capital preservation metrics in live trading." | |
| } |