"""
PE / PB 估值因子。

基于日线收盘价 + 财务数据（EPS/每股净资产）计算。
"""

import pandas as pd

from factors.base import BaseFactor
from factors.fundamental._mapping import map_fundamental_to_daily


class PEFactor(BaseFactor):
    """
    市盈率因子 = close / eps。

    eps 来自财务数据中的 'eps' 列或 TTM EPS。
    因子值越大表示估值越贵。
    """

    category = "fundamental"

    def __init__(self, financial_df: pd.DataFrame | None = None):
        """
        参数:
            financial_df: 含 'end_date' 和 'eps' 的 DataFrame。
        """
        self._financial_df = financial_df
        self.name = "pe"

    def calculate(self, df: pd.DataFrame) -> pd.Series:
        if self._financial_df is None or self._financial_df.empty:
            return pd.Series(float("nan"), index=df.index)

        eps_series = map_fundamental_to_daily(df, self._financial_df, "eps")
        close = df["close"]
        return close / eps_series.replace(0, float("nan"))

    def get_required_columns(self) -> list[str]:
        return ["close"]


class PBFactor(BaseFactor):
    """
    市净率因子 = close / bvps（每股净资产）。

    因子值越大表示估值越贵。
    """

    category = "fundamental"

    def __init__(self, financial_df: pd.DataFrame | None = None):
        self._financial_df = financial_df
        self.name = "pb"

    def calculate(self, df: pd.DataFrame) -> pd.Series:
        if self._financial_df is None or self._financial_df.empty:
            return pd.Series(float("nan"), index=df.index)

        bvps_series = map_fundamental_to_daily(df, self._financial_df, "bvps")
        return df["close"] / bvps_series.replace(0, float("nan"))

    def get_required_columns(self) -> list[str]:
        return ["close"]


class EPFactor(BaseFactor):
    """
    盈利收益率因子 = eps / close = 1 / PE。

    值越大表示估值越便宜，适合与动量等因子同向排序。
    """

    category = "fundamental"

    def __init__(self, financial_df: pd.DataFrame | None = None):
        self._financial_df = financial_df
        self.name = "ep"

    def calculate(self, df: pd.DataFrame) -> pd.Series:
        if self._financial_df is None or self._financial_df.empty:
            return pd.Series(float("nan"), index=df.index)

        eps_series = map_fundamental_to_daily(df, self._financial_df, "eps")
        return eps_series / df["close"].replace(0, float("nan")) * 100

    def get_required_columns(self) -> list[str]:
        return ["close"]