"""Daily summary data derived from raw minute bars."""

import numpy as np
import pandas as pd

from pipeline.derived.base import BaseDerivedData
from pipeline.derived.registry import register_derived


@register_derived
class MinuteDailySummaryDerived(BaseDerivedData):
    """Aggregate intraday bars into daily summary columns."""

    name = "minute_daily_summary"

    def compute(
        self,
        daily: pd.DataFrame | None = None,
        minute: pd.DataFrame | None = None,
    ) -> pd.DataFrame:
        if minute is None:
            raise ValueError("minute_daily_summary requires minute input")

        minute = minute.copy()
        minute["date"] = pd.to_datetime(minute["date"]).dt.normalize()
        sort_cols = ["symbol_id", "date"]
        if "datetime" in minute.columns:
            minute["datetime"] = pd.to_datetime(minute["datetime"])
            sort_cols.append("datetime")
        elif "time" in minute.columns:
            sort_cols.append("time")
        minute = minute.sort_values(sort_cols)

        grouped = minute.groupby(["symbol_id", "date"], sort=True)
        summary = grouped.agg(
            minute_bar_count=("close", "count"),
            first_open=("open", "first"),
            last_close=("close", "last"),
            high=("high", "max"),
            low=("low", "min"),
            volume_sum=("volume", "sum"),
            amount_sum=("amount", "sum"),
        )
        summary["minute_intraday_return"] = (
            summary["last_close"] / summary["first_open"] - 1.0
        )
        summary["minute_intraday_range"] = summary["high"] / summary["low"] - 1.0
        summary["minute_vwap"] = (
            summary["amount_sum"] / summary["volume_sum"].where(summary["volume_sum"] > 0)
        )
        summary = summary.reset_index()

        if daily is not None:
            daily_keys = daily[["symbol_id", "date"]].copy()
            daily_keys["date"] = pd.to_datetime(daily_keys["date"]).dt.normalize()
            daily_keys = daily_keys.drop_duplicates(["symbol_id", "date"])
            result = daily_keys.merge(summary, on=["symbol_id", "date"], how="left")
            if "close" in daily.columns:
                daily_close = daily[["symbol_id", "date", "close"]].copy()
                daily_close["date"] = pd.to_datetime(daily_close["date"]).dt.normalize()
                daily_close = daily_close.drop_duplicates(["symbol_id", "date"])
                result = result.merge(
                    daily_close.rename(columns={"close": "daily_close"}),
                    on=["symbol_id", "date"],
                    how="left",
                )
                reference_close = result["daily_close"].fillna(result["last_close"])
            else:
                reference_close = result["last_close"]
        else:
            result = summary
            reference_close = result["last_close"]

        result["minute_vwap_deviation"] = (
            result["minute_vwap"] / reference_close.replace(0.0, np.nan) - 1.0
        )
        return result[
            [
                "symbol_id",
                "date",
                "minute_bar_count",
                "minute_intraday_return",
                "minute_intraday_range",
                "minute_vwap",
                "minute_vwap_deviation",
            ]
        ]