「未来の値段がわかる計算式？」ビットコインの取引を試してみた結果

"""
Inverse Fisher Transform Trading Signal Generator
価格を正規分布に変換してシグナルを明確化
"""
import pandas as pd
import numpy as np


def calculate_inverse_fisher_signals(df: pd.DataFrame,
                                    period: int = 9) -> pd.DataFrame:
    """
    Inverse Fisher Transform戦略のシグナル生成
    
    Parameters:
    -----------
    df : pd.DataFrame
        OHLCVデータ
    period : int
        計算期間（デフォルト: 9）
    
    Returns:
    --------
    pd.DataFrame
        シグナルが追加されたDataFrame
    """
    df = df.copy()
    
    # RSI計算
    df['price_change'] = df['close'].diff()
    df['gain'] = df['price_change'].where(df['price_change'] > 0, 0)
    df['loss'] = -df['price_change'].where(df['price_change'] < 0, 0)
    
    df['avg_gain'] = df['gain'].rolling(window=period).mean()
    df['avg_loss'] = df['loss'].rolling(window=period).mean()
    
    df['rs'] = df['avg_gain'] / (df['avg_loss'] + 0.0001)
    df['rsi'] = 100 - (100 / (1 + df['rs']))
    
    # RSIを-1から1にスケーリング
    df['normalized'] = (df['rsi'] - 50) / 50
    
    # スムージング
    df['smooth'] = df['normalized'].ewm(span=5, adjust=False).mean()
    
    # Fisher Transform
    df['fisher'] = 0.5 * np.log((1 + df['smooth'].clip(-0.999, 0.999)) / (1 - df['smooth'].clip(-0.999, 0.999)))
    
    # Inverse Fisher Transform
    df['ift'] = (np.exp(2 * df['fisher']) - 1) / (np.exp(2 * df['fisher']) + 1)
    
    # シグナルライン
    df['ift_signal'] = df['ift'].shift(1)
    
    # シグナル生成
    df['is_buy'] = (df['ift'] > df['ift_signal']) & (df['ift'] > -0.5) & (df['ift_signal'] <= -0.5) & df['ift'].notna()
    df['is_sell'] = (df['ift'] < df['ift_signal']) & (df['ift'] < 0.5) & (df['ift_signal'] >= 0.5) & df['ift'].notna()
    
    # 不要カラム削除
    df.drop(['price_change', 'gain', 'loss', 'avg_gain', 'avg_loss', 'rs', 'rsi',
             'normalized', 'smooth', 'fisher', 'ift_signal'], axis=1, inplace=True, errors='ignore')
    
    return df