Python for Finance

Capital Asset Pricing Model
Moving beta
Sometimes, researchers need to generate a beta time series based on, for example,
a three-year moving window. In such cases, we could write a loop or double loops.
Let's look at a simpler case: estimating the annual beta for IBM over several years.
First, let's look at two ways of getting years from a date variable:
import datetime
today=datetime.date.today()
year=today.year
print(year)
2017
print(today.strftime("%Y"))
'2017'
[ 192 ]
# Method I
# Method II
The Python program used to estimate the annual beta is shown here:
import numpy as np
import scipy as sp
import pandas as pd
from scipy import stats
from matplotlib.finance import quotes_historical_yahoo_ochl
def ret_f(ticker,begdate, enddate):
p = quotes_historical_yahoo_ochl(ticker, begdate,
enddate,asobject=True,adjusted=True)
return((p.aclose[1:] - p.aclose[:-1])/p.aclose[:-1])
#
begdate=(2010,1,1)
enddate=(2016,12,31)
#
y0=pd.Series(ret_f('IBM',begdate,enddate))
x0=pd.Series(ret_f('^GSPC',begdate,enddate))
#
d=quotes_historical_yahoo_ochl('^GSPC', begdate, enddate,asobject=True
,adjusted=True).date[0:-1]
lag_year=d[0].strftime("%Y")
y1=[]
x1=[]
beta=[]
index0=[]
for i in sp.arange(1,len(d)):
year=d[i].strftime("%Y")
if(year==lag_year):
x1.append(x0[i])
y1.append(y0[i])
else: