python - 每日赫斯特指数

我正在尝试估计股票回报的每日赫斯特指数值（例如，每天也有赫斯特指数-类似的：https://www.quandl.com/data/PE/CKEC_HURST-Hurst-Exponent-of-Carmike-Cinemas-Inc-Common-Stock-CKEC-NASDAQ）。
我正在使用这个Python代码（取自https://www.quantstart.com/articles/Basics-of-Statistical-Mean-Reversion-Testing），但是我不知道如何将它容纳为每日Hurst值，而不仅仅是一个值：

from datetime import datetime
from pandas.io.data import DataReader
from numpy import cumsum, log, polyfit, sqrt, std, subtract
from numpy.random import randn

def hurst(ts):

    """Returns the Hurst Exponent of the time series vector ts"""
    # Create the range of lag values
    lags = range(2, 100)

    # Calculate the array of the variances of the lagged differences
    tau = [sqrt(std(subtract(ts[lag:], ts[:-lag]))) for lag in lags]

    # Use a linear fit to estimate the Hurst Exponent
    poly = polyfit(log(lags), log(tau), 1)

    # Return the Hurst exponent from the polyfit output
    return poly[0]*2.0


# Download the stock prices series from Yahoo
aapl = DataReader("AAPL", "yahoo", datetime(2012,1,1), datetime(2015,9,18))

# Call the function
hurst(aapl['Adj Close'])

最佳答案

我想你的意思是：

from datetime import timedelta
current_date = datetime(2012,1,3)
end_date = datetime(2015,9,18)
aapl = DataReader("AAPL", "yahoo", current_date, end_date)
index = 0
while index < len(aapl['Adj Close']):
    print current_date.strftime("%Y-%m-%d")
    print hurst(aapl['Adj Close'][index:index + 1])
    index += 1
    current_date += timedelta(days=1)