from __future__ import annotations
import pandas as pd
from pathlib import Path

def load_data(start: str, end: str, timeframe_minutes: int, csv_path: Path) -> tuple[pd.DataFrame, pd.DataFrame]:
    df_1min = pd.read_csv(csv_path)
    df_1min["Timestamp"] = pd.to_datetime(df_1min["Timestamp"], unit="s", utc=True)
    df_1min = df_1min[(df_1min["Timestamp"] >= pd.Timestamp(start, tz="UTC")) &
                      (df_1min["Timestamp"] <= pd.Timestamp(end, tz="UTC"))] \
                     .sort_values("Timestamp").reset_index(drop=True)

    if timeframe_minutes != 1:
        g = df_1min.set_index("Timestamp").resample(f"{timeframe_minutes}min")
        df = pd.DataFrame({
            "Open":   g["Open"].first(),
            "High":   g["High"].max(),
            "Low":    g["Low"].min(),
            "Close":  g["Close"].last(),
            "Volume": g["Volume"].sum(),
        }).dropna().reset_index()
    else:
        df = df_1min.copy()

    return df_1min, df