def estimateAnnualVolatility(close_prices, open_prices=None, high_prices=None, low_prices=None, N=10, algo='YangZhang'):  
    """  
    Volatility estimation  
    Possible algorithms: ['YangZhang', 'CC', 'Parkinson','Kurtosis','Skew']  
    """  
    cc = np.log(close_prices/close_prices.shift(1))  
    if algo == 'YangZhang': # Yang-zhang volatility  
        ho = np.log(high_prices/open_prices)  
        lo = np.log(low_prices/open_prices)  
        co = np.log(close_prices/open_prices)  
        oc = np.log(open_prices/close_prices.shift(1))  
        oc_sq = oc**2  
        cc_sq = cc**2  
        rs = ho*(ho-co)+lo*(lo-co)  
        close_vol = pd.rolling_sum(cc_sq, window=N) * (1.0 / (N - 1.0))  
        open_vol = pd.rolling_sum(oc_sq, window=N) * (1.0 / (N - 1.0))  
        window_rs = pd.rolling_sum(rs, window=N) * (1.0 / (N - 1.0))  
        result = (open_vol + 0.164333 * close_vol + 0.835667 * window_rs).apply(np.sqrt) * np.sqrt(252)  
    elif algo == 'CC': # standard close-close estimator  
        result = np.sqrt(252)*np.sqrt(((pd.rolling_sum(cc**2,N))/N))  
    elif algo == 'Parkinson':  
        result =  np.sqrt(252)*(1 / (4 * math.log(2))) * ((high_prices.tail(N) / low_prices.tail(N)).apply(np.log))**2

    elif algo == 'Skew':  
        result = pd.rolling_skew(cc, window=N)  
    elif algo == 'Kurtosis':  
        result = pd.rolling_kurt(cc, window=N)-3.0  
    else:  
        raise ValueError('Unknown algo type.')  
    result[:N-1] = np.nan  
    return result  

Hi, Peter, now working well, thank a lot!!

Thanks Bro, very well!