I found some beta calculation code what do you think is the method best used to develop low beta algos?

import scipy.stats as stats  
from scipy  import  polyfit, polyval  
import datetime  
import pytz  
import pandas as pd  
import numpy as np  
import re  
from pandas import DataFrame,Series  
from zipline.utils.tradingcalendar import get_early_closes  
from zipline.utils import tradingcalendar  
from datetime import timedelta  
import operator  
from functools import partial

def estimateBeta(priceY,priceX,algo = 'standard'):

    #estimate stock Y vs stock X beta using iterative linear  
   # regression. Outliers outside 3 sigma boundary are filtered out

   # Parameters  
   # --------  
    #priceX : price series of x (usually market)  
   # priceY : price series of y (estimate beta of this price)

   # Returns  
  #  --------  
    #beta : stockY beta relative to stock X  
    X = pd.DataFrame({'x':priceX,'y':priceY})

    if algo=='returns':  
        ret = (X/X.shift(1)-1).dropna().values  
        x = ret[:,0]  
        y = ret[:,1]  
        # filter high values  
        low = np.percentile(x,20)  
        high = np.percentile(x,80)  
        iValid = (x>low) & (x<high)  
        x = x[iValid]  
        y = y[iValid]  
        iteration = 1  
        nrOutliers = 1  
        while iteration < 10 and nrOutliers > 0 :  
            (a,b) = polyfit(x,y,1)  
            yf = polyval([a,b],x)  
            #plot(x,y,'x',x,yf,'r-')  
            err = yf-y  
            idxOutlier = abs(err) > 3*np.std(err)  
            nrOutliers =sum(idxOutlier)  
            beta = a  
            #print 'Iteration: %i beta: %.2f outliers: %i' % (iteration,beta, nrOutliers)  
            x = x[~idxOutlier]  
            y = y[~idxOutlier]  
            iteration += 1  
    elif algo=='log':  
        x = np.log(X['x'])  
        y = np.log(X['y'])  
        (a,b) = polyfit(x,y,1)  
        beta = a  
    elif algo=='standard':  
        ret =np.log(X).diff().dropna()  
        beta = ret['x'].cov(ret['y'])/ret['x'].var()  
    else:  
        raise TypeError("unknown Beta algorithm type, use 'standard', 'log' or 'returns'")

    return beta