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.
36 lines
978 B
Python
36 lines
978 B
Python
import pandas as pd
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def add_time_features(df: pd.DataFrame, timestamp_col: str = "timestamp") -> pd.DataFrame:
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result = df.copy()
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if timestamp_col not in result.columns:
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return result
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result[timestamp_col] = pd.to_datetime(result[timestamp_col])
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result["hour"] = result[timestamp_col].dt.hour
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result["day_of_week"] = result[timestamp_col].dt.dayofweek
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result["day_of_month"] = result[timestamp_col].dt.day
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result["month"] = result[timestamp_col].dt.month
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result["hour_sin"] = _sin_encode(result["hour"], 24)
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result["hour_cos"] = _cos_encode(result["hour"], 24)
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result["day_sin"] = _sin_encode(result["day_of_week"], 7)
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result["day_cos"] = _cos_encode(result["day_of_week"], 7)
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return result
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def _sin_encode(x, period):
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import numpy as np
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return np.sin(2 * np.pi * x / period)
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def _cos_encode(x, period):
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import numpy as np
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return np.cos(2 * np.pi * x / period)
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__all__ = ["add_time_features"]
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