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Home > Data > Data Analysis > Python for Finance
Python for Finance
Python for Finance

Python for Finance: Apply powerful finance models and quantitative analysis with Python , Second Edition

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Python for Finance

Chapter 2. Introduction to Python Modules

In this chapter, we will discuss the most important issues related to Python modules, which are packages written by experts or any individual to serve a special purpose. In this book, we will use about a dozen modules in total. Thus, knowledge related to modules is critical in our understanding of Python and its application to finance. In particular, in this chapter, we will cover the following topics:

  • Introduction to Python modules
  • Introduction to NumPy
  • Introduction to SciPy
  • Introduction to matplotlib
  • Introduction to statsmodels
  • Introduction to pandas
  • Python modules related to finance
  • Introduction to the pandas_reader module
  • Two financial calculators written in Python
  • How to install a Python module
  • Module dependency

What is a Python module?

A module is a package or group of programs that is written by an expert, user, or even a beginner who is usually very good in a specific area, to serve a specific purpose.

For example, a Python module called quant is for quantitative financial analysis. quant combines two modules of SciPy and DomainModel. The module contains a domain model that has exchanges, symbols, markets, and historical prices, among other things. Modules are very important in Python. In this book, we will discuss about a dozen modules implicitly or explicitly. In particular, we will explain five modules in detail: NumPy, SciPy, matplotlib, statsmodels, and Pandas.

Note

As of November 16, 2016, there are 92,872 Python modules (packages) with different areas available according to the Python Package Index.

For the financial and insurance industries, there are 384 modules currently available.

Assume that we want to estimate the square root of 3 by using the sqrt() function. However, after issuing...

Introduction to NumPy

In the following examples, the np.size() function from NumPy shows the number of data items of an array, and the np.std() function is used to calculate standard deviation:

>>>import numpy as np
>>>x= np.array([[1,2,3],[3,4,6]])     # 2 by 3 matrix
>>>np.size(x)                         # number of data items
6
>>>np.size(x,1)                       # show number of columns
3
>>>np.std(x)
1.5723301886761005
>>>np.std(x,1)
Array([ 0.81649658, 1.24721913]
>>>total=x.sum()                      # attention to the format
>>>z=np.random.rand(50)               #50 random obs from [0.0, 1)
>>>y=np.random.normal(size=100)       # from standard normal
>>>r=np.array(range(0,100),float)/100 # from 0, .01,to .99

Compared with a Python array, a NumPy array is a contiguous piece of memory that is passed directly to LAPACK, which is a software library for numerical linear algebra under the hood, so...

Introduction to SciPy

The following are a few examples based on the functions enclosed in the SciPy module. The sp.npv() function estimates the present values for a given set of cash flows with the first cash flow happening at time zero. The first input value is the discount rate, and the second input is an array of all cash flows.

The following is one example. Note that the sp.npv() function is different from the Excel npv() function. We will explain why this is so in Chapter 3, Time Value of Money:

>>>import scipy as sp
>>>cashflows=[-100,50,40,20,10,50]
>>>x=sp.npv(0.1,cashflows)
>>>round(x,2)
>>>31.41

The sp.pmt() function is used to answer the following question.

What is the monthly cash flow to pay off a mortgage of $250,000 over 30 years with an annual percentage rate (APR) of 4.5 percent, compounded monthly? The following code shows the answer:

>>>payment=sp.pmt(0.045/12,30*12,250000)
>>>round(payment,2)
-1266.71

Based on...

Introduction to matplotlib

Graphs and other visual representations have become more important in explaining many complex financial concepts, trading strategies, and formulas.

In this section, we discuss the matplotlib module, which is used to create various types of graphs. In addition, the module will be used intensively in Chapter 10, Options and Futures, when we discuss the famous Black-Scholes-Merton option model and various trading strategies. The matplotlib module is designed to produce publication-quality figures and graphs. The matplotlib module depends on NumPy and SciPy, which were discussed in the previous sections. To save generated graphs, there are several output formats available, such as PDF, Postscript, SVG, and PNG.

How to install matplotlib

If Python was installed by using the Anaconda super package, then matplotlib is preinstalled already. After launching Spyder, type the following line to test. If there is no error, it means that we have imported/uploaded the module successfully...

Introduction to statsmodels

statsmodels is a powerful Python package for many types of statistical analysis. Again, if Python was installed via Anaconda, then the module was installed at the same time. In statistics, ordinary least square (OLS) regression is a method for estimating the unknown parameters in a linear regression model. It minimizes the sum of squared vertical distances between the observed values and the values predicted by the linear approximation. The OLS method is used extensively in finance. Assume that we have the following equation, where y is an n by 1 vector (array), and x is an n by (m+1) matrix, a return matrix (n by m), plus a vector that contains 1 only. n is the number of observations, and m is the number of independent variables:

Introduction to statsmodels

In the following program, after generating the x and y vectors, we run an OLS regression (a linear regression). The x and y are artificial data. The last line prints the parameters only (the intercept is 1.28571420 and the slope is...

Introduction to pandas

The pandas module is a powerful tool used to process various types of data, including economics, financial, and accounting data. If Python was installed on your machine via Anaconda, then the pandas module was installed already. If you issue the following command without any error, it indicates that the pandas module was installed:

>>>import pandas as pd

In the following example, we generate two time series starting from January 1, 2013. The names of those two time series (columns) are A and B:

import numpy as np
import pandas as pd
dates=pd.date_range('20160101',periods=5)
np.random.seed(12345)
x=pd.DataFrame(np.random.rand(5,2),index=dates,columns=('A','B'))

First, we import both NumPy and pandas modules. The pd.date_range() function is used to generate an index array. The x variable is a pandas DataFrame with dates as its index. Later in this chapter, we will discuss the pd.DataFrame() function. The columns() function defines...

Python modules related to finance

Since this book is applying Python to finance, the modules (packages) related to finance will be our first priority.

The following table presents about a dozen Python modules or submodules related to finance:

Name

Description

Numpy.lib.financial

Many functions for corporate finance and financial management.

pandas_datareader

Retrieves data from Google, Yahoo! Finance, FRED, Fama-French factors.

googlefinance

Python module to get real-time (no delay) stock data from Google Finance API.

yahoo-finance

Python module to get stock data from Yahoo! Finance.

Python_finance

Download and analyze Yahoo! Finance data and develop trading strategies.

tstockquote

Retrieves stock quote data from Yahoo! Finance.

finance

Financial risk calculations. Optimized for ease of use through class construction and operator overload.

quant

Enterprise architecture for quantitative analysis in finance.

tradingmachine

A backtester for financial...

Introduction to the pandas_reader module

Via this module, users can download various economics and financial via Yahoo! Finance, Google Finance, Federal Reserve Economics Data (FRED), and Fama-French factors.

Assume that the pandas_reader module is installed. For detail on how to install this module, see the How to install a Python module section. First, let's look at the simplest example, just two lines to get IBM's trading data; see the following:

import pandas_datareader.data as web
df=web.get_data_google("ibm")

We could use a dot head and dot tail to show part of the results; see the following code:

>>> df.head()
>>> 
                  Open        High         Low       Close   Volume  
Date                                                                  
2010-01-04  131.179993  132.970001  130.850006  132.449997  6155300   
2010-01-05  131.679993  131.850006  130.100006  130.850006  6841400   
2010-01-06  130.679993  131.490005  129.809998  130.000000...

Two financial calculators

In the next chapter, many basic financial concepts and formulas will be introduced and discussed. Usually, when taking corporate finance or financial management, students rely on either Excel or a financial calculator to conduct their estimations. Since Python is the computational tool, a financial calculator written in Python would definitely enhance our understanding of both finance and Python.

Here is the first financial calculator, written in Python, from Numpy.lib.financial; see the following code:

>>> import numpy.lib.financial as fin
>>> dir(fin)
['__all__', '__builtins__', '__cached__', '__doc__', '__file__', '__loader__', '__name__', '__package__', '__spec__', '_convert_when', '_g_div_gp', '_rbl', '_when_to_num', 'absolute_import', 'division', 'fv', 'ipmt', 'irr&apos...

How to install a Python module

If Python was installed via Anaconda, there is a good chance that many of the modules discussed in this book have been installed together with Python. If Python was installed independently, users could use PyPi to install or update.

For example, we are interested in installing NumPy. On Windows, we have the following code:

python -m pip install -U pip numpy

If Python.exe is on the path, we could open a DOS window first, then issue the preceding line. If Python.exe is not on the path, we open a DOS window, then move to the location of the Python.exe file; for an example, see the following screenshot:

How to install a Python module

For a Mac, we have the following codes. Sometimes, after running the preceding command, you might receive the following message asking for an update of PiP:

How to install a Python module

The command line to update pip is given here:

python –m pip install –upgrade pip

See the result shown in the following screenshot:

How to install a Python module

To install NumPy independently, on Linux or OS X, we issue the following...

Module dependency

At the very beginning of this book, we argued that one of the advantages of using Python is that it is a rich source of hundreds of special packages called modules.

To avoid duplicated efforts and to save time in developing new modules, later modules choose to use functions developed on early modules; that is, they depend on early modules.

The advantage is obvious because developers can save lots of time and effort when building and testing a new module. However, one disadvantage is that installation becomes difficult.

There are two competing approaches:

  • The first approach is to bundle everything together and make sure that all parts play together nicely, thus avoiding the pain of installing n packages independently. This is wonderful, assuming that it works. A potential issue is that the updating of individual modules might not be reflected in the super package.
  • The second approach is to use minimal dependencies. It causes fewer headaches for the package maintainer, but for...

Exercises

  1. Do we have to install NumPy independently if our Python was installed via Anaconda?
  2. What are the advantages of using a super package to install many modules simultaneously?
  3. How do you find all the functions contained in NumPy or SciPy?
  4. How many ways are there to import a specific function contained in SciPy?
  5. What is wrong with the following operation?
    >>>x=[1,2,3]
>>>x.sum()
  6. How can we print all the data items for a given array?
  7. What is wrong with the following lines of code?
    >>>import np
>>>x=np.array([True,false,true,false],bool)
  8. Find out the meaning of skewtest included in the stats submodule (SciPy), and give an example of using this function.
  9. What is the difference between an arithmetic mean and a geometric mean?
  10. Debug the following lines of code, which are used to estimate a geometric mean for a given set of returns:
    >>>import scipy as sp
>>>ret=np.array([0.05,0.11,-0.03])
>>>pow(np.prod(ret+1),1/len(ret))-1
  11. Write a Python...

Summary

In this chapter, we have discussed one of the most important properties of Python: modules. A module is a package written by an expert or any individual to serve a special purpose. The knowledge related to modules is essential in our understanding of Python and its application to finance. In particular, we have introduced and discussed the most important modules, such as NumPy, SciPy, matplotlib, statsmodels, pandas, and pandas_reader. In addition, we have briefly mentioned module dependency and other issues. Two financial calculators written in Python were also presented. In Chapter 3, Time Value of Money, we will discuss many basic concepts associated with finance, such as the present value of one future cash flow, present value of perpetuity, present value of growing perpetuity, present value of annuity, and formulas related to future values. In addition, we will discuss definitions of Net Present Value (NPV), Internal Rate of Return (IRR), and Payback period. After that, several...

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Key benefits

  • Understand the fundamentals of Python data structures and work with time-series data
  • Implement key concepts in quantitative finance using popular Python libraries such as NumPy, SciPy, and matplotlib
  • A step-by-step tutorial packed with many Python programs that will help you learn how to apply Python to finance

Description

This book uses Python as its computational tool. Since Python is free, any school or organization can download and use it. This book is organized according to various finance subjects. In other words, the first edition focuses more on Python, while the second edition is truly trying to apply Python to finance. The book starts by explaining topics exclusively related to Python. Then we deal with critical parts of Python, explaining concepts such as time value of money stock and bond evaluations, capital asset pricing model, multi-factor models, time series analysis, portfolio theory, options and futures. This book will help us to learn or review the basics of quantitative finance and apply Python to solve various problems, such as estimating IBM’s market risk, running a Fama-French 3-factor, 5-factor, or Fama-French-Carhart 4 factor model, estimating the VaR of a 5-stock portfolio, estimating the optimal portfolio, and constructing the efficient frontier for a 20-stock portfolio with real-world stock, and with Monte Carlo Simulation. Later, we will also learn how to replicate the famous Black-Scholes-Merton option model and how to price exotic options such as the average price call option.

Who is this book for?

This book assumes that the readers have some basic knowledge related to Python. However, he/she has no knowledge of quantitative finance. In addition, he/she has no knowledge about financial data.

What you will learn

  • Become acquainted with Python in the first two chapters
  • Run CAPM, Fama-French 3-factor, and Fama-French-Carhart 4-factor models
  • Learn how to price a call, put, and several exotic options
  • Understand Monte Carlo simulation, how to write a Python program to
  • replicate the Black-Scholes-Merton options model, and how to price a few
  • exotic options
  • Understand the concept of volatility and how to test the hypothesis that
  • volatility changes over the years
  • Understand the ARCH and GARCH processes and how to write related
  • Python programs
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Data Analysis
Tools :
Matplotlib

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Table of Contents

16 Chapters
1. Python Basics Chevron down icon Chevron up icon
1. Python Basics
Python installation
Variable assignment, empty space, and writing our own programs
Writing a Python function
Python loops
Data input
Data manipulation
Data output
Exercises
Summary
2. Introduction to Python Modules Chevron down icon Chevron up icon
2. Introduction to Python Modules
What is a Python module?
Introduction to NumPy
Introduction to SciPy
Introduction to matplotlib
Introduction to statsmodels
Introduction to pandas
Python modules related to finance
Introduction to the pandas_reader module
Two financial calculators
How to install a Python module
Module dependency
Exercises
Summary
3. Time Value of Money Chevron down icon Chevron up icon
3. Time Value of Money
Introduction to time value of money
Writing a financial calculator in Python
Definition of NPV and NPV rule
Definition of IRR and IRR rule
Definition of payback period and payback period rule
Writing your own financial calculator in Python
Two general formulae for many functions
Exercises
Summary
4. Sources of Data Chevron down icon Chevron up icon
4. Sources of Data
Diving into deeper concepts
5. Bond and Stock Valuation Chevron down icon Chevron up icon
5. Bond and Stock Valuation
Introduction to interest rates
Term structure of interest rates
Bond evaluation
Stock valuation
A new data type – dictionary
Summary
6. Capital Asset Pricing Model Chevron down icon Chevron up icon
6. Capital Asset Pricing Model
Introduction to CAPM
Moving beta
Adjusted beta
Extracting output data
Simple string manipulation
Python via Canopy
References
Exercises
Summary
7. Multifactor Models and Performance Measures Chevron down icon Chevron up icon
7. Multifactor Models and Performance Measures
Introduction to the Fama-French three-factor model
Fama-French three-factor model
Fama-French-Carhart four-factor model and Fama-French five-factor model
Implementation of Dimson (1979) adjustment for beta
Performance measures
How to merge different datasets
References
Exercises
Summary
8. Time-Series Analysis Chevron down icon Chevron up icon
8. Time-Series Analysis
Introduction to time-series analysis
Merging datasets based on a date variable
Understanding the interpolation technique
Tests of normality
52-week high and low trading strategy
Estimating Roll's spread
Estimating Amihud's illiquidity
Estimating Pastor and Stambaugh (2003) liquidity measure
Fama-MacBeth regression
Durbin-Watson
Python for high-frequency data
Spread estimated based on high-frequency data
Introduction to CRSP
References
Exercises
Summary
9. Portfolio Theory Chevron down icon Chevron up icon
9. Portfolio Theory
Introduction to portfolio theory
A 2-stock portfolio
Optimization – minimization
Forming an n-stock portfolio
Constructing an optimal portfolio
Constructing an efficient frontier with n stocks
References
Exercises
Summary
10. Options and Futures Chevron down icon Chevron up icon
10. Options and Futures
Introducing futures
Payoff and profit/loss functions for call and put options
European versus American options
Black-Scholes-Merton option model on non-dividend paying stocks
Generating our own module p4f
European options with known dividends
Various trading strategies
Put-call parity and its graphic presentation
Binomial tree and its graphic presentation
Hedging strategies
Implied volatility
Binary-search
Retrieving option data from Yahoo! Finance
Volatility smile and skewness
References
Exercises
Summary
11. Value at Risk Chevron down icon Chevron up icon
11. Value at Risk
Introduction to VaR
Normality tests
Skewness and kurtosis
Modified VaR
VaR based on sorted historical returns
Simulation and VaR
VaR for portfolios
Backtesting and stress testing
Expected shortfall
References
Exercises
Summary
12. Monte Carlo Simulation Chevron down icon Chevron up icon
12. Monte Carlo Simulation
Importance of Monte Carlo Simulation
Generating random numbers from a standard normal distribution
Generating random numbers with a seed
Generating random numbers from a uniform distribution
Using simulation to estimate the pi value
Generating random numbers from a Poisson distribution
Selecting m stocks randomly from n given stocks
With/without replacements
Distribution of annual returns
Simulation of stock price movements
Graphical presentation of stock prices at options' maturity dates
Replicating a Black-Scholes-Merton call using simulation
Liking two methods for VaR using simulation
Capital budgeting with Monte Carlo Simulation
Python SimPy module
Comparison between two social policies – basic income and basic job
Finding an efficient frontier based on two stocks by using simulation
Constructing an efficient frontier with n stocks
Long-term return forecasting
Efficiency, Quasi-Monte Carlo, and Sobol sequences
References
Exercises
Summary
13. Credit Risk Analysis Chevron down icon Chevron up icon
13. Credit Risk Analysis
Introduction to credit risk analysis
Credit rating
Credit spread
YIELD of AAA-rated bond, Altman Z-score
Using the KMV model to estimate the market value of total assets and its volatility
Term structure of interest rate
Distance to default
Credit default swap
References
Exercises
Summary
14. Exotic Options Chevron down icon Chevron up icon
14. Exotic Options
European, American, and Bermuda options
Chooser options
Shout options
Binary options
Rainbow options
Pricing average options
Pricing barrier options
Barrier in-and-out parity
Graph of up-and-out and up-and-in parity
Pricing lookback options with floating strikes
References
Exercises
Summary
15. Volatility, Implied Volatility, ARCH, and GARCH Chevron down icon Chevron up icon
15. Volatility, Implied Volatility, ARCH, and GARCH
Conventional volatility measure – standard deviation
Tests of normality
Estimating fat tails
Lower partial standard deviation and Sortino ratio
Test of equivalency of volatility over two periods
Test of heteroskedasticity, Breusch, and Pagan
Volatility smile and skewness
Graphical presentation of volatility clustering
The ARCH model
Simulating an ARCH (1) process
The GARCH model
Simulating a GARCH process
Simulating a GARCH (p,q) process using modified garchSim()
GJR_GARCH by Glosten, Jagannanthan, and Runkle
References
Exercises
Summary
Index Chevron down icon Chevron up icon
Index

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Full star icon Full star icon Full star icon Half star icon Empty star icon 3.5
(33 Ratings)
5 star 39.4%
4 star 15.2%
3 star 15.2%
2 star 15.2%
1 star 15.2%
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N/A Feb 07, 2024
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Excellent ! Can be a good textbook.
Feefo Verified review Feefo
Daniele Feb 12, 2016
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Amazing book with many interesting examples. Completely worth it
Amazon Verified review Amazon
Ana Isabel Bezerra Cavalcanti Oct 15, 2020
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Atendeu às minhas expectativas, em termos de pesquisas atuais e futuras.
Amazon Verified review Amazon
Jamie Jul 06, 2018
Full star icon Full star icon Full star icon Full star icon Full star icon 5
I bought this eBook (with source code) from Packt. Great intro to Finance using Python. Chapter 10 and 12 are especially helpful as an addition to Hull's Options, Futures and Other Derivatives.
Amazon Verified review Amazon
Jaime May 21, 2014
Full star icon Full star icon Full star icon Full star icon Full star icon 5
For the past 3 years I have been investing part of my time directing and managing SW financial implementations in parallel of providing consulting services on several industries leveraging my expertise on Data Advisory specifically data management, governance and analytics/big data.During this tenure I have implemented diverse strategies to support my teams having a quick and solid ramp up getting hands on using in several cases Python in an specific framework either for data management or solving complex financial challenges on tricky calculations.I have found this book a great overview and hands on guidance to who is in the Python learning path and has realized to focus its functionality and powerful use on problem solving in the Finance arena or Analytics for example.Taking in count Python is powerful, flexible, ad easy to learn, the author provides a good and strong basis to go from the basic use of Python calculating net present value to more complex financial problems like valuations, pricing options, Monte Carlo series usage and regressions. Even also the author shares guidance to interact with public data sources such as Yahoo! Finance or Google Finance.Last but not least the content of this book will provide guidance to create very useful graphs by using Matlotlib in a clear way.Enjoy your reading!Note: If you have no Finance background, I recommend reading this book getting access to electronic or printed dictionary or related finance terms reference.
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Add one extra business day for deliveries to Northern Ireland and Scottish Highlands and islands

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Economy: Can deliver to P. O. Boxes and private residences.
Trackable service with delivery to addresses in Australia only.
Delivery time ranges from 7-9 business days for VIC and 8-10 business days for Interstate metro
Delivery time is up to 15 business days for remote areas of WA, NT & QLD.

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Disclaimer:
All orders received before 5 PM U.K time would start printing from the next business day. So the estimated delivery times start from the next day as well. Orders received after 5 PM U.K time (in our internal systems) on a business day or anytime on the weekend will begin printing the second to next business day. For example, an order placed at 11 AM today will begin printing tomorrow, whereas an order placed at 9 PM tonight will begin printing the day after tomorrow.


Unfortunately, due to several restrictions, we are unable to ship to the following countries:

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