ビットコイン、「人気すぎ？」「不人気すぎ？」な時に反対をねらう作戦！その結果はどうだった？

#!/usr/bin/env python3
"""
MFI（Money Flow Index）逆張り戦略
出来高を考慮したRSI、資金流入・流出を測定
"""
import pandas as pd
import numpy as np


def calculate_mfi_signals(df: pd.DataFrame, mfi_period: int = 14, 
                         oversold: float = 20, overbought: float = 80) -> pd.DataFrame:
    """
    MFI逆張りシグナルを生成
    
    Parameters:
    -----------
    df : pd.DataFrame
        OHLCVデータ
    mfi_period : int
        MFI計算期間（デフォルト: 14）
    oversold : float
        売られすぎ水準（デフォルト: 20）
    overbought : float
        買われすぎ水準（デフォルト: 80）
    
    Returns:
    --------
    pd.DataFrame
        シグナル列が追加されたDataFrame
    """
    df = df.copy()
    
    # Typical Price
    df['typical_price'] = (df['high'] + df['low'] + df['close']) / 3
    
    # Raw Money Flow
    df['raw_money_flow'] = df['typical_price'] * df['volume']
    
    # Positive and Negative Money Flow
    df['money_flow_positive'] = np.where(
        df['typical_price'] > df['typical_price'].shift(1),
        df['raw_money_flow'],
        0
    )
    df['money_flow_negative'] = np.where(
        df['typical_price'] < df['typical_price'].shift(1),
        df['raw_money_flow'],
        0
    )
    
    # Money Flow Ratio
    positive_flow = df['money_flow_positive'].rolling(window=mfi_period).sum()
    negative_flow = df['money_flow_negative'].rolling(window=mfi_period).sum()
    df['money_flow_ratio'] = positive_flow / negative_flow
    
    # MFI計算
    df['mfi'] = 100 - (100 / (1 + df['money_flow_ratio']))
    
    # シグナル生成
    df['is_buy'] = (
        (df['mfi'] < oversold) & 
        (df['mfi'].shift(1) >= oversold)
    )
    
    df['is_sell'] = (
        (df['mfi'] > overbought) & 
        (df['mfi'].shift(1) <= overbought)
    )
    
    # NaN値をFalseに置換
    df['is_buy'] = df['is_buy'].fillna(False)
    df['is_sell'] = df['is_sell'].fillna(False)
    
    print(f"MFI逆張り: 期間={mfi_period}, 売られすぎ={oversold}, 買われすぎ={overbought}")
    print(f"買いシグナル数: {df['is_buy'].sum()}")
    print(f"売りシグナル数: {df['is_sell'].sum()}")
    
    return df