Python for Finance

Python Basics
In Chapter 3, Time Value of Money, we will discuss both means again.
We could say that NumPy is a basic module while SciPy is a more advanced one.
NumPy tries to retain all features supported by either of its predecessors, while most
new features belong in SciPy rather than NumPy. On the other hand, NumPy and
SciPy have many overlapping features in terms of functions for finance. For those
two types of definitions, see the following example:
>>> import scipy as sp
>>> ret=sp.array([0.1,0.05,-0.02])
>>>sp.mean(ret)
0.043333333333333342
>>>pow(sp.prod(ret+1),1./len(ret))-1
0.042163887067679262
Our second example is related to processing theFama-French 3 factor time series.
Since this example is more complex than the previous one, if a user feels it is difficult
to understand, he/she could simply skip this example. First, a ZIP file called F-F_
Research_Data_Factor_TXT.zip could be downloaded from Prof. French's Data
Library. After unzipping and removing the first fewlines and annual datasets, we
will have a monthly Fama-French factor time series. The first few lines and last few
lines are shown here:
DATE MKT_RFSMBHMLRF
192607 2.96 -2.30 -2.87 0.22
192608 2.64 -1.40 4.19 0.25
192609 0.36 -1.32 0.01 0.23
201607 3.95 2.90 -0.98 0.02
201608 0.49 0.94 3.18 0.02
201609 0.25 2.00 -1.34 0.02
Assume that the final file is called ffMonthly.txt under c:/temp/. The following
program is used to retrieve and process the data:
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)
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