Scikit-learn
English
trading
finance
xauusd
gold
forex
machine-learning
ensemble
super-ensemble
xgboost
lightgbm
catboost
neural-network
tensorflow
Instructions to use JonusNattapong/romeo-v8-super-ensemble-trading-ai with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Scikit-learn
How to use JonusNattapong/romeo-v8-super-ensemble-trading-ai with Scikit-learn:
from huggingface_hub import hf_hub_download import joblib model = joblib.load( hf_hub_download("JonusNattapong/romeo-v8-super-ensemble-trading-ai", "sklearn_model.joblib") ) # only load pickle files from sources you trust # read more about it here https://skops.readthedocs.io/en/stable/persistence.html - Notebooks
- Google Colab
- Kaggle
| { | |
| "initial_capital": 100.0, | |
| "final_capital": 126.80919582503007, | |
| "total_return_pct": 26.809195825030073, | |
| "peak_capital": 142.58333833268898, | |
| "max_drawdown_pct": 11.063103650198721, | |
| "trades": 66, | |
| "win_rate": 0.6818181818181818, | |
| "avg_win": 1.1074004382650395, | |
| "avg_loss": -1.096372566518893, | |
| "profit_factor": 2.164411087623176, | |
| "sharpe_ratio": 4.64030876344748, | |
| "super_ensemble_metrics": { | |
| "algorithms_used": 10, | |
| "ensemble_method": "stacking", | |
| "avg_consensus_score": 1.0, | |
| "avg_ensemble_proba": 0.6687202993993976, | |
| "calibration_used": true, | |
| "cv_ensemble_used": true, | |
| "dynamic_weighting_used": true | |
| } | |
| } |