kbt-devops
fe76bc7629
Implements FastAPI backend with ML model support for energy trading, including price prediction models and RL-based battery trading policy. Features dashboard, trading, backtest, and settings API routes with WebSocket support for real-time updates.
53 lines
1.4 KiB
Python
53 lines
1.4 KiB
Python
import pickle
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from typing import Optional
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import xgboost as xgb
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import numpy as np
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class PricePredictionModel:
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def __init__(self, horizon: int, model_id: Optional[str] = None):
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self.horizon = horizon
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self.model_id = model_id or f"price_prediction_{horizon}m"
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self.model: Optional[xgb.XGBRegressor] = None
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self.feature_names = []
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def fit(self, X, y):
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self.model = xgb.XGBRegressor(
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n_estimators=200,
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max_depth=6,
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learning_rate=0.1,
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subsample=0.8,
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colsample_bytree=0.8,
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random_state=42,
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)
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if isinstance(X, np.ndarray):
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self.feature_names = [f"feature_{i}" for i in range(X.shape[1])]
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else:
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self.feature_names = list(X.columns)
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self.model.fit(X, y)
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def predict(self, X):
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if self.model is None:
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raise ValueError("Model not trained")
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return self.model.predict(X)
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def save(self, filepath: str):
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with open(filepath, "wb") as f:
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pickle.dump(self, f)
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@classmethod
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def load(cls, filepath: str):
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with open(filepath, "rb") as f:
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return pickle.load(f)
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@property
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def feature_importances_(self):
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if self.model is None:
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raise ValueError("Model not trained")
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return self.model.feature_importances_
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__all__ = ["PricePredictionModel"]
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