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.
26 lines
593 B
Python
26 lines
593 B
Python
from typing import Tuple
|
|
import pandas as pd
|
|
from datetime import datetime
|
|
|
|
|
|
def time_based_split(
|
|
df: pd.DataFrame,
|
|
timestamp_col: str = "timestamp",
|
|
train_pct: float = 0.70,
|
|
val_pct: float = 0.85,
|
|
) -> Tuple[pd.DataFrame, pd.DataFrame, pd.DataFrame]:
|
|
df_sorted = df.sort_values(timestamp_col)
|
|
|
|
n_total = len(df_sorted)
|
|
n_train = int(n_total * train_pct)
|
|
n_val = int(n_total * val_pct)
|
|
|
|
train = df_sorted.iloc[:n_train]
|
|
val = df_sorted.iloc[n_train:n_val]
|
|
test = df_sorted.iloc[n_val:]
|
|
|
|
return train, val, test
|
|
|
|
|
|
__all__ = ["time_based_split"]
|