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.
18 lines
601 B
Python
18 lines
601 B
Python
from typing import List
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import pandas as pd
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def add_rolling_features(df: pd.DataFrame, col: str, windows: List[int]) -> pd.DataFrame:
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result = df.copy()
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for window in windows:
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result[f"{col}_rolling_mean_{window}"] = result[col].rolling(window=window).mean()
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result[f"{col}_rolling_std_{window}"] = result[col].rolling(window=window).std()
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result[f"{col}_rolling_min_{window}"] = result[col].rolling(window=window).min()
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result[f"{col}_rolling_max_{window}"] = result[col].rolling(window=window).max()
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return result
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__all__ = ["add_rolling_features"]
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