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.
54 lines
1.6 KiB
Python
54 lines
1.6 KiB
Python
from app.ml.features.lag_features import add_lag_features
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from app.ml.features.rolling_features import add_rolling_features
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from app.ml.features.time_features import add_time_features
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from app.ml.features.regional_features import add_regional_features
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from app.ml.features.battery_features import add_battery_features
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from typing import List, Optional
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import pandas as pd
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def build_price_features(
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df: pd.DataFrame,
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price_col: str = "real_time_price",
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lags: Optional[List[int]] = None,
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windows: Optional[List[int]] = None,
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regions: Optional[List[str]] = None,
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include_time: bool = True,
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include_regional: bool = True,
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) -> pd.DataFrame:
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if lags is None:
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lags = [1, 5, 10, 15, 30, 60]
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if windows is None:
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windows = [5, 10, 15, 30, 60]
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result = df.copy()
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if price_col in result.columns:
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result = add_lag_features(result, price_col, lags)
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result = add_rolling_features(result, price_col, windows)
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if include_time and "timestamp" in result.columns:
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result = add_time_features(result)
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if include_regional and regions:
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result = add_regional_features(result, regions)
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return result
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def build_battery_features(
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df: pd.DataFrame,
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price_df: pd.DataFrame,
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battery_col: str = "charge_level_mwh",
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capacity_col: str = "capacity_mwh",
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timestamp_col: str = "timestamp",
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battery_id_col: str = "battery_id",
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) -> pd.DataFrame:
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result = df.copy()
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result = add_battery_features(result, price_df, battery_col, capacity_col, timestamp_col, battery_id_col)
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return result
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__all__ = ["build_price_features", "build_battery_features"]
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