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"""
Intraday Momentum Index Trading Signal Generator
日中の価格動向から短期モメンタムを測定
"""
import pandas as pd
import numpy as np


def calculate_imi_signals(df: pd.DataFrame,
                         period: int = 14,
                         oversold: float = 30,
                         overbought: float = 70) -> pd.DataFrame:
    """
    Intraday Momentum Index戦略のシグナル生成
    
    Parameters:
    -----------
    df : pd.DataFrame
        OHLCVデータ
    period : int
        IMI計算期間（デフォルト: 14）
    oversold : float
        売られ過ぎライン（デフォルト: 30）
    overbought : float
        買われ過ぎライン（デフォルト: 70）
    
    Returns:
    --------
    pd.DataFrame
        シグナルが追加されたDataFrame
    """
    df = df.copy()
    
    # 日中の価格変動
    df['intraday_change'] = df['close'] - df['open']
    
    # 上昇・下降の分離
    df['gains'] = np.where(df['intraday_change'] > 0, df['intraday_change'], 0)
    df['losses'] = np.where(df['intraday_change'] < 0, -df['intraday_change'], 0)
    
    # 期間内の合計
    df['sum_gains'] = df['gains'].rolling(window=period).sum()
    df['sum_losses'] = df['losses'].rolling(window=period).sum()
    
    # IMI計算
    df['imi'] = 100 * df['sum_gains'] / (df['sum_gains'] + df['sum_losses'] + 0.0001)
    
    # IMIの移動平均（スムージング）
    df['imi_ma'] = df['imi'].rolling(window=3).mean()
    
    # ダイバージェンス検出用
    df['price_ma'] = df['close'].rolling(window=period).mean()
    df['price_higher'] = df['close'] > df['close'].shift(period)
    df['imi_lower'] = df['imi_ma'] < df['imi_ma'].shift(period)
    df['bearish_div'] = df['price_higher'] & df['imi_lower']
    
    df['price_lower'] = df['close'] < df['close'].shift(period)
    df['imi_higher'] = df['imi_ma'] > df['imi_ma'].shift(period)
    df['bullish_div'] = df['price_lower'] & df['imi_higher']
    
    # シグナル生成
    df['imi_prev'] = df['imi_ma'].shift(1)
    df['is_buy'] = (
        ((df['imi_ma'] > oversold) & (df['imi_prev'] <= oversold)) |
        (df['bullish_div'] & (df['imi_ma'] < 50))
    ) & df['imi_ma'].notna()
    df['is_sell'] = (
        ((df['imi_ma'] < overbought) & (df['imi_prev'] >= overbought)) |
        (df['bearish_div'] & (df['imi_ma'] > 50))
    ) & df['imi_ma'].notna()
    
    # 不要カラム削除
    df.drop(['intraday_change', 'gains', 'losses', 'sum_gains', 'sum_losses',
             'price_ma', 'price_higher', 'imi_lower', 'bearish_div',
             'price_lower', 'imi_higher', 'bullish_div', 'imi_prev'], axis=1, inplace=True, errors='ignore')
    
    return df