"""
技术指标计算模块

包含CCI、EMA、MACD等常用技术指标
"""

import pandas as pd
import numpy as np
import talib as ta


def calculate_cci(
    df: pd.DataFrame,
    period: int = 14,
    high_col: str = "high",
    low_col: str = "low",
    close_col: str = "close",
) -> pd.Series:
    """
    计算CCI指标（商品通道指数）

    Args:
        df: DataFrame with OHLC data
        period: CCI周期
        high_col: 最高价列名
        low_col: 最低价列名
        close_col: 收盘价列名

    Returns:
        Series: CCI值
    """
    return ta.CCI(
        high=df[high_col],
        low=df[low_col],
        close=df[close_col],
        timeperiod=period,
    )


def calculate_ema(
    price_series: pd.Series,
    period: int = 20,
) -> pd.Series:
    """
    计算指数移动平均线

    Args:
        price_series: 价格序列
        period: EMA周期

    Returns:
        Series: EMA值
    """
    return ta.EMA(price_series, timeperiod=period)


def calculate_macd(
    price_series: pd.Series,
    fastperiod: int = 12,
    slowperiod: int = 26,
    signalperiod: int = 9,
) -> tuple[pd.Series, pd.Series, pd.Series]:
    """
    计算MACD指标

    Args:
        price_series: 价格序列
        fastperiod: 快线周期
        slowperiod: 慢线周期
        signalperiod: 信号线周期

    Returns:
        tuple: (macd, signal, hist)
    """
    macd, signal, hist = ta.MACD(
        price_series,
        fastperiod=fastperiod,
        slowperiod=slowperiod,
        signalperiod=signalperiod,
    )
    return macd, signal, hist


def calculate_td_sequence(close_series: pd.Series) -> pd.Series:
    """
    计算TD序列（Tom DeMark Sequential）

    Args:
        close_series: 收盘价序列

    Returns:
        Series: TD序列值（正数为上涨计数，负数为下跌计数）
    """
    close = close_series.to_list()
    td = [0, 0, 0, 0]
    up = 0
    down = 0

    for i in range(4, len(close)):
        if close[i] > close[i - 4]:
            up += 1
            down = 0
            td.append(up)
        else:
            down -= 1
            up = 0
            td.append(down)

    return pd.Series(td, index=close_series.index)


def resample_to_weekly(df: pd.DataFrame) -> pd.DataFrame:
    """
    将日线数据重采样为周线数据

    Args:
        df: DataFrame with columns: date, open, high, low, close, volume

    Returns:
        DataFrame: 周线数据
    """
    df = df.copy()
    if "date" in df.columns:
        df["date"] = pd.to_datetime(df["date"])
        df.set_index("date", inplace=True)

    weekly = pd.DataFrame(
        {
            "code": df["code"].resample("W").first() if "code" in df.columns else None,
            "open": df["open"].resample("W").first(),
            "high": df["high"].resample("W").max(),
            "low": df["low"].resample("W").min(),
            "close": df["close"].resample("W").last(),
            "volume": df["volume"].resample("W").sum(),
        }
    )

    return weekly.dropna()


class TechnicalScreener:
    """技术指标筛选器基类"""

    def __init__(self, name: str):
        self.name = name

    def screen(self, df: pd.DataFrame) -> bool:
        """
        判断数据是否符合筛选条件

        Args:
            df: DataFrame with OHLCV data

        Returns:
            bool: 是否符合条件
        """
        raise NotImplementedError


class CCIScreener(TechnicalScreener):
    """CCI超卖筛选器"""

    def __init__(
        self,
        day_period: int = 14,
        week_period: int = 14,
        threshold: float = -100,
        use_weekly: bool = True,
    ):
        super().__init__("CCI超卖筛选")
        self.day_period = day_period
        self.week_period = week_period
        self.threshold = threshold
        self.use_weekly = use_weekly

    def screen(self, df: pd.DataFrame) -> dict:
        """
        筛选CCI超卖信号

        Returns:
            dict: {
                'triggered': bool,  # 是否触发信号
                'day_cci': float,   # 日线CCI值
                'week_cci': float,  # 周线CCI值（如启用）
            }
        """
        # 计算日线CCI
        day_cci = calculate_cci(df, period=self.day_period).iloc[-1]

        result = {
            "triggered": day_cci < self.threshold,
            "day_cci": day_cci,
            "week_cci": None,
        }

        # 计算周线CCI（如果启用）
        if self.use_weekly:
            weekly_df = resample_to_weekly(df)
            if len(weekly_df) >= self.week_period:
                week_cci = calculate_cci(weekly_df, period=self.week_period).iloc[-1]
                result["week_cci"] = week_cci
                # 日线或周线任一超卖即触发
                result["triggered"] = (
                    day_cci < self.threshold or week_cci < self.threshold
                )

        return result