import pandas as pd

def Parabolic_Stop_and_Reversal(close: pd.Series, high: pd.Series, low: pd.Series, iaf: float = 0.02, maxaf: float = 0.2) -> pd.Series:
    """
    Calculates the Parabolic Stop and Reverse (SAR) indicator from the given price data.

    The Parabolic SAR indicator, developed by J. Welles Wilder, is used to determine potential reversals in the price direction of an asset.

    Parameters:
    close (pd.DataFrame): DataFrame containing the close price data.
    high (pd.DataFrame): DataFrame containing the high price data.
    low (pd.DataFrame): DataFrame containing the low price data.
    iaf (float): Initial acceleration factor used in the SAR calculation.
    maxaf (float): Maximum acceleration factor.

    Returns:
    pd.DataFrame: A DataFrame containing the Parabolic Stop and Reverse (SAR) values.

    Example:
    # Assuming 'close', 'high', and 'low' are DataFrames with corresponding price data
    sar = Parabolic_Stop_and_Reverse(close, high, low, iaf=0.02, maxaf=0.2)
    """

    # Initialize SAR values
    sar = pd.Series(index=close.index)
    sar.iloc[0] = low.iloc[0]  # Set initial SAR value to the first low price

    # Initialize trend and acceleration factor
    trend = 1  # Initial trend is up
    af = iaf

    # Calculate SAR values
    for i in range(1, len(sar)):
        if trend == 1:  # If current trend is up
            if low.iloc[i] < sar.iloc[i - 1]:  # If low price penetrates previous SAR
                sar.iloc[i] = high.iloc[i - 1]  # Reverse trend and set SAR to previous high
                trend = -1  # Change trend to down
                af = iaf  # Reset acceleration factor
            else:
                sar.iloc[i] = sar.iloc[i - 1] + af * (high.iloc[i - 1] - sar.iloc[i - 1])  # Calculate SAR for uptrend
                af = min(af + iaf, maxaf)  # Increase acceleration factor up to the maximum
        else:  # If current trend is down
            if high.iloc[i] > sar.iloc[i - 1]:  # If high price penetrates previous SAR
                sar.iloc[i] = low.iloc[i - 1]  # Reverse trend and set SAR to previous low
                trend = 1  # Change trend to up
                af = iaf  # Reset acceleration factor
            else:
                sar.iloc[i] = sar.iloc[i - 1] + af * (low.iloc[i - 1] - sar.iloc[i - 1])  # Calculate SAR for downtrend
                af = min(af + iaf, maxaf)  # Increase acceleration factor up to the maximum

    return sar