def SQZMIval(data, depth = 20):
    new = []
    high = data.High
    low = data.Low
    close = data.Close

    for x in range(1, len(data)):
        if abs(-x-depth) < len(data):
            a = max(data.iloc[-x-depth:-x]['High'])
            b = min(data.iloc[-x-depth:-x]['Low'])
            average1 = (a + b)/2

            c = sum(data.iloc[-x-depth:-x]['Close'])/depth

            average2 = (average1 + c)/2

            final = data.iloc[-x]['Close'] - average2

            new.insert(0, final)
        else:
            new.insert(0, 0)
    new.insert(0,0)

    data['Average1'] = pd.Series(new)

    z = []
    for y in range(1,(depth+1)):
        z.append(y)

    for x in range(1, len(data)):
        if abs(-x-depth) < len(data):
            final = data.iloc[-x-depth:-x]['Average1']

            X = np.array(z).reshape(-1, 1)
            Y = final.values
            linear_regressor = LinearRegression()
            linear_regressor.fit(X, Y)
            Y_pred = linear_regressor.predict(X)

            new.insert(0, Y_pred[-1])
        else:
            new.insert(0, 0)
    new.insert(0,0)


    return pd.Series(new)