Daily Hurst Exponent
Question: I am trying to estimate daily Hurst exponent values of a stock returns (e.g.
for each day to have also Hurst exponent - something like that:
<https://www.quandl.com/data/PE/CKEC_HURST-Hurst-Exponent-of-Carmike-Cinemas-
Inc-Common-Stock-CKEC-NASDAQ>).

I am using this Python code (taken from
<https://www.quantstart.com/articles/Basics-of-Statistical-Mean-Reversion-
Testing>), but I do not know how to accommodate it for daily Hurst values
instead of just one value:

    
    
    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'])
    


Answer: I guess you mean:

    
    
    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)
    

