Assume that we are interested in estimating the market risk (beta) for IBM using daily data. The following is the program we can use to download IBM's price, market return, and risk-free interest rate since we need them to run a capital asset pricing model (CAPM):
from matplotlib.finance import quotes_historical_yahoo import numpy as np import pandas as pd ticker='IBM' begdate=(2013,10,1) enddate=(2013,11,9) x = quotes_historical_yahoo(ticker, begdate, enddate,asobject=True, adjusted=True) k=x.date date=[] for i in range(0,size(x)): date.append(''.join([k[i].strftime("%Y"),k[i].strftime("%m"),k[i].strftime("%d")])) x2=pd.DataFrame(x['aclose'],np.array(date,dtype=int64),columns=[ticker+'_adjClose']) ff=load('c:/temp/ffDaily.pickle') final=pd.merge(x2,ff,left_index=True,right_index=True)
A part of the output is given as follows:
In the preceding output, there are two types of data for the five columns: price and returns. The first column is price while the rest...