Calculation of Beta of Stocks Using Python Libraries (Stock Risk Analysis)

As an example, let us consider Coca Cola (NYSE:KO). Historical Coca Cola stock data can be downloaded from Google Finance:
Historical NYSE:KO Data

Suppose we consider NYSE:SPY as the market indicator/index in calculating beta. Historical stock data of NYSE:SPY can be downloaded from Google Finance:
Historical NYSE:SPY Data

The following python script finds the beta value for market indicator and symbol historical data file names passed as command line parameters:

	import numpy as np
	from sklearn import datasets, linear_model
	import sys
	fh = open(sys.argv[1], 'r')
	lines = fh.readlines()
	fh.close()
	market_x = []
	for i in range(len(lines)-1):
	if i==0:
	continue
	line_i = lines[i].strip().split(',')[4]
	line_i_1 = lines[i+1].strip().split(',')[4]
	rate = (float(line_i) - float(line_i_1))/(float(line_i_1))
	market_x.append([rate])
	fh = open(sys.argv[2], 'r')
	lines = fh.readlines()
	fh.close()
	stock_y = []
	for i in range(len(lines)-1):
	if i==0:
	continue
	line_i = lines[i].strip().split(',')[4]
	line_i_1 = lines[i+1].strip().split(',')[4]
	rate = (float(line_i) - float(line_i_1))/(float(line_i_1))
	stock_y.append([rate])
	regr = linear_model.LinearRegression()
	regr.fit(market_x, stock_y)
	print 'Beta: %s' % regr.coef_[0][0]

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