I think you are referring to the hurst exponent? I remember there was a factor in this thread by Luca: https://www.quantopian.com/posts/lets-see-if-technical-analysis-works

class HurstExp(CustomFactor):  
    inputs = [USEquityPricing.open]  
    window_length = int(252*0.5)  
    def Hurst(self, ts):  
        # Create a range of lag values  
        lags=np.arange(2,20)  
        # Calculate variance array of the lagged differences  
        tau = [np.sqrt(np.std(np.subtract(ts[lag:], ts[:-lag]))) for lag in lags]

        # Use a linear fit to estimate  
        #poly = np.polyfit(np.log(lags), np.log(tau), 1)[0]

        # 1st degree polynomial approximation for speed  
        # source: http://stackoverflow.com/questions/28237428/fully-vectorise-numpy-polyfit  
        n = len(lags)  
        x = np.log(lags)  
        y = np.log(tau)  
        poly = (n*(x*y).sum() - x.sum()*y.sum()) / (n*(x*x).sum() - x.sum()*x.sum())

        # Return the Hurst exponent  
        return poly*2.0  
    def compute(self, today, assets, out,  OPEN):  
        SERIES = np.nan_to_num(OPEN)  
        hurst_exp_per_asset = map(self.Hurst, [SERIES[:,col_id].flatten() for col_id in np.arange(SERIES.shape[1])])  
        #print 'Hurst Exp:\n',hurst_exp_per_asset  
        out[:] = hurst_exp_per_asset

Thank you I didn't know about it and it seems to be what I was looking for.

@vladimir Are you having an issue with the HurstExp Custom Factor constantly timing out before trading start? I get about 10 days worth of backtested results before the time out during most random time periods.

It is a very resource intensive function. So make sure you mask the universe first for example with DollarVolume. It works for me with about 1000 stocks.

yeah this makes sense then, thanks for the feedback