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# Function to calculate MAMA and FAMA
def mesa_adaptive_moving_average(close_prices, fast_limit=0.25, slow_limit=0.05):
    n = len(close_prices)
    mama = np.zeros(n)
    fama = np.zeros(n)
    
    smooth = np.zeros(n)
    detrender = np.zeros(n)
    I1 = np.zeros(n)
    Q1 = np.zeros(n)
    I2 = np.zeros(n)
    Q2 = np.zeros(n)
    Re = np.zeros(n)
    Im = np.zeros(n)
    period = np.zeros(n)
    phase = np.zeros(n)
    delta_phase = np.zeros(n)
    alpha = np.zeros(n)
    
    for i in range(6, n):
        smooth[i] = (4 * close_prices[i] + 3 * close_prices[i - 1] + 2 * close_prices[i - 2] + close_prices[i - 3]) / 10
        detrender[i] = (0.0962 * smooth[i] + 0.5769 * smooth[i - 2] - 0.5769 * smooth[i - 4] - 0.0962 * smooth[i - 6]) * (0.075 * period[i - 1] + 0.54)
        I1[i] = detrender[i] * 0.5 + 0.5 * detrender[i - 1]
        Q1[i] = (0.0962 * I1[i] + 0.5769 * I1[i - 2] - 0.5769 * I1[i - 4] - 0.0962 * I1[i - 6]) * (0.075 * period[i - 1] + 0.54)
        I2[i] = I1[i - 3]
        Q2[i] = Q1[i - 3]
        
        Re[i] = 0.2 * (I2[i] * I1[i] + Q2[i] * Q1[i]) + 0.8 * Re[i - 1]
        Im[i] = 0.2 * (I2[i] * Q1[i] - Q2[i] * I1[i]) + 0.8 * Im[i - 1]
        period[i] = period[i - 1] if (Im[i] == 0 and Re[i] == 0) else max(slow_limit, min(fast_limit, 6.2832 / np.arctan(Im[i] / Re[i])))
        phase[i] = np.arctan(Q1[i] / I1[i])
        delta_phase[i] = phase[i - 1] - phase[i]
        
        if delta_phase[i] < 0:
            delta_phase[i] += 1.0
        
        alpha[i] = (fast_limi Write your response with at least 1000 words.
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