Calculate Larry Connors RSI (CRSI) 3-Component Mean Reversion Indicator in Python

import pandas as pd
import numpy as np

def rsi_wilder(series, period):
    delta = series.diff()
    gain = delta.clip(lower=0).ewm(alpha=1/period, adjust=False).mean()
    loss = (-delta.clip(upper=0)).ewm(alpha=1/period, adjust=False).mean()
    rs = gain / loss.replace(0, np.nan)
    return (100 - (100 / (1 + rs))).fillna(50)

def calculate_connors_rsi(df, rsi_len=3, streak_rsi_len=2, rank_len=100):
    df = df.copy()
    c_rsi = rsi_wilder(df['Close'], rsi_len)
    
    # Compute consecutive up/down close streak
    diff = df['Close'].diff()
    streak = np.zeros(len(df))
    for i in range(1, len(df)):
        if diff.iloc[i] > 0:
            streak[i] = streak[i-1] + 1 if streak[i-1] > 0 else 1
        elif diff.iloc[i] < 0:
            streak[i] = streak[i-1] - 1 if streak[i-1] < 0 else -1
        else:
            streak[i] = 0
    s_rsi = rsi_wilder(pd.Series(streak, index=df.index), streak_rsi_len)
    
    ret = df['Close'].pct_change()
    pct_rank = ret.rolling(rank_len).apply(lambda x: (np.sum(x[:-1] < x[-1]) / (len(x) - 1)) * 100, raw=True)
    df['CRSI'] = ((c_rsi + s_rsi + pct_rank) / 3.0).round(2)
    return df