import pandas as pd

def Stop_and_Reversal(high: pd.Series, low: pd.Series, af: float = 0.02, amax: float = 0.2) -> pd.Series:
    """
    Calculates the Stop and Reverse (SAR) indicator from the given price data.

    The SAR (Stop and Reverse) indicator is used to identify potential reversals in the price direction of an asset.

    Parameters:
    high (pd.DataFrame): DataFrame containing the high price data.
    low (pd.DataFrame): DataFrame containing the low price data.
    af (float): Acceleration factor used in the SAR calculation.
    amax (float): Maximum acceleration factor.

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

    Example:
    # Assuming 'high' and 'low' are DataFrames with corresponding price data
    sar = Stop_and_Reverse(high, low, af=0.02, amax=0.2)
    """

    # Initialize SAR values
    sar = low.copy()
    sar.iloc[0] = high.iloc[0]  # Set initial SAR value to the first high price

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

    # 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_current = af  # Reset acceleration factor
            else:
                sar.iloc[i] = sar.iloc[i - 1] + af_current * (high.iloc[i - 1] - sar.iloc[i - 1])  # Calculate SAR for uptrend
                af_current = min(af_current + af, amax)  # 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_current = af  # Reset acceleration factor
            else:
                sar.iloc[i] = sar.iloc[i - 1] + af_current * (low.iloc[i - 1] - sar.iloc[i - 1])  # Calculate SAR for downtrend
                af_current = min(af_current + af, amax)  # Increase acceleration factor up to the maximum

    return sar