Python for Finance

Python Basics
To retrieve the file, we have the following code:
>>>infile=open("c:/temp/dell.txt","r")
>>>x=infile.read()
One issue is that the preceding saved text file contains many unnecessary characters,
such as [ and]. We could apply a substitution function called sub() contained in the
Python module;see the simplest example given here:
>>> import re
>>>re.sub("a","9","abc")
>>>
'9bc'
>>>
In the preceding example, we will replace the letter a with9. Interested readers could
try the following two lines of code for the preceding program:
p2= re.sub('[\(\)\{\}\.<>a-zA-Z]','', p)
outfile.write(p2)
It is a good idea to generate Python datasets with an extension of .pickle since we
can retrieve such data quite efficiently. The following is the complete Python code to
generate ffMonthly.pickle. Here, we show how to download price data and then
estimate returns:
import numpy as np
import pandas as pd
file=open("c:/temp/ffMonthly.txt","r")
data=file.readlines()
f=[]
index=[]
for i in range(1,np.size(data)):
t=data[i].split()
index.append(int(t[0]))
for j in range(1,5):
k=float(t[j])
f.append(k/100)
n=len(f)
f1=np.reshape(f,[n/4,4])
ff=pd.DataFrame(f1,index=index,columns=['Mkt_Rf','SMB','HML','Rf'])
ff.to_pickle("c:/temp/ffMonthly.pickle")
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