みんなの「買いたい！」気持ちがわかる？「需要指数」で仮想通貨に挑戦！

"""
Demand Index Trading Signal Generator
価格と出来高の関係から需要を測定
"""
import pandas as pd
import numpy as np


def calculate_demand_index_signals(df: pd.DataFrame,
                                  period: int = 5) -> pd.DataFrame:
    """
    Demand Index戦略のシグナル生成
    
    Parameters:
    -----------
    df : pd.DataFrame
        OHLCVデータ
    period : int
        移動平均期間（デフォルト: 5）
    
    Returns:
    --------
    pd.DataFrame
        シグナルが追加されたDataFrame
    """
    df = df.copy()
    
    # 価格変化率
    df['price_change'] = df['close'] / df['close'].shift(1) - 1
    
    # ボリューム変化率
    df['volume_change'] = df['volume'] / df['volume'].shift(1) - 1
    
    # Buying Pressure（買い圧力）
    df['bp'] = df['volume'] * np.where(df['close'] > df['open'], 
                                       (df['close'] - df['low']) / (df['high'] - df['low'] + 0.0001), 
                                       0)
    
    # Selling Pressure（売り圧力）
    df['sp'] = df['volume'] * np.where(df['close'] < df['open'],
                                       (df['high'] - df['close']) / (df['high'] - df['low'] + 0.0001),
                                       0)
    
    # Demand Index
    df['di_raw'] = (df['bp'] - df['sp']) / (df['bp'] + df['sp'] + 0.0001)
    
    # スムージング
    df['di'] = df['di_raw'].rolling(window=period).mean()
    
    # DI変化率
    df['di_change'] = df['di'] - df['di'].shift(1)
    
    # 移動平均
    df['di_ma'] = df['di'].rolling(window=period * 2).mean()
    
    # ダイバージェンス検出
    price_ma = df['close'].rolling(window=period).mean()
    df['price_trend'] = (price_ma - price_ma.shift(period)) / price_ma.shift(period)
    df['di_trend'] = (df['di'] - df['di'].shift(period)) / (np.abs(df['di'].shift(period)) + 0.0001)
    df['divergence'] = df['price_trend'] * df['di_trend']
    
    # シグナル生成
    df['di_prev'] = df['di'].shift(1)
    df['is_buy'] = (
        ((df['di'] > 0) & (df['di_prev'] <= 0)) |  # ゼロラインクロス
        ((df['di'] > df['di_ma']) & (df['divergence'] < -0.1))  # 強気ダイバージェンス
    ) & df['di'].notna()
    df['is_sell'] = (
        ((df['di'] < 0) & (df['di_prev'] >= 0)) |  # ゼロラインクロス
        ((df['di'] < df['di_ma']) & (df['divergence'] < -0.1))  # 弱気ダイバージェンス
    ) & df['di'].notna()
    
    # 不要カラム削除
    df.drop(['price_change', 'volume_change', 'bp', 'sp', 'di_raw', 'di_change',
             'di_ma', 'price_trend', 'di_trend', 'divergence', 'di_prev'], axis=1, inplace=True, errors='ignore')
    
    return df