def hurst (input_ts, lags_to_test=[2,201]):
    # interpretation of return vale
    # hurst < 0.5 - input ts is mean reverting
    # hurst = 0.5 - input ts is effectively random/geometric brownian motion
    # hurst > 0.5 - input ts is trending

    tau = []
    lagvec = []
    # Step through the different lags
    for lag in range(lags_to_test[0], lags_to_test[1]):
        # produce time series difference with lag
        pp = np.subtract(input_ts[lag:], input_ts[:-lag])
        # Write the different lags into a vector 
        lagvec.append(lag)
        # Calculate the variance of the difference vector 
        tau.append(np.std(pp))
    # linear fit to double-log graph (gives power)
    m = np.polyfit(np.log10(lagvec), np.log10(tau), 1)
    # hurst exponent is the slope of the line of best fit
    hurst = m[0]
    return hurst