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Home > Data > Financial Technology > Python for Finance
Python for Finance
Python for Finance

Python for Finance: If your interest is finance and trading, then using Python to build a financial calculator makes absolute sense. As does this book which is a hands-on guide covering everything from option theory to time series.

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Python for Finance

Chapter 2. Using Python as an Ordinary Calculator

In this chapter, we will learn some basic concepts and several frequently used built-in functions of Python, such as basic assignment, precision, addition, subtraction, division, power function, and square root function. In short, we demonstrate how to use Python as an ordinary calculator to solve many finance-related problems.

In this chapter, we will cover the following topics:

  • Assigning values to variables
  • Displaying the value of a variable
  • Exploring error messages
  • Understanding why we can't call a variable without assignment
  • Choosing meaningful variable names
  • Using dir() to find variables and functions
  • Deleting or unsigning a variable
  • Learning basic math operations—addition, subtraction, multiplication, and division
  • Learning about the power function, floor, and remainder
  • Choosing appropriate precision
  • Finding out more information about a specific built-in function
  • Importing the math module
  • The pi, e, log, and exponential functions...

Assigning values to variables

To assign a value to a variable is simple because unlike many other languages such as C++ or FORTRAN, in Python, we don't need to define a variable before we assign a value to it.

>>>pv=22
>>>pv+2
24

We could assign the same value to different variables simultaneously. In the following example, we assign 100 to the three variables x, y, and z at once:

>>>x=y=z=100

Displaying the value of a variable

To find out the value of a variable, just type its name as shown in the following code:

>>>pv=100
>>>pv
100
>>>R=0.1
>>>R
0.1

Error messages

Assuming that we issue the sqrt(3) command to estimate the square root of three, we would get the following error message:

>>>sqrt(3)
Traceback (most recent call last):
  File "<pyshell#17>", line 1, in <module>
    sqrt(3)
NameError: name 'sqrt' is not defined

The last line of the error message tells us that the sqrt() function is not defined. Later in the chapter, we learn that the sqrt() function is included in a module called math and that we have to load (import) the module before we can call the functions contained in it. A module is a package that contains a set of functions around a specific subject.

Can't call a variable without assignment

Assuming that we never assign a value to the abcde variable, after typing abcde, we would get the following error message:

>>>abcde
Traceback (most recent call last):
  File "<pyshell#0>", line 1, in <module>
    abcde
NameError: name 'abcde' is...

Choosing meaningful names

A perpetuity describes the situations where equivalent periodic cash flows happen in the future and last forever. For example, we receive $5 at the end of each year forever. A real-world example is the UK government bond, called consol, that pays fixed coupons. To estimate the present value of a perpetuity, we use the following formula if the first cash flow occurs at the end of the first period:

Choosing meaningful names

Here, PV is the present value, C is a perpetual periodic cash flow that happens at a fixed interval, and R is the periodic discount rate. Here C and R should be consistent. For example, if C is annual (monthly) cash flow, then R must be an annual (monthly) discount rate. This is true for other frequencies too. Assume that a constant annual cash flow is $10, with the first cash flow at the end of the first year, and that the annual discount rate is 10 percent. Compare the following two ways to name the C and R variables:

>>>x=10       # bad way for variable names...

Using dir() to find variables and functions

After assigning values to a few variables, we could use the dir() function to show their existence. In the following example, variables n, pv, and r are shown among other names. At the moment, just ignore the first five objects in the following code, which start and end with two underscores:

>>>pv=100
>>>r=0.1
>>>n=5
>>>dir()
['__builtins__', '__doc__', '__loader__', '__name__', '__package__', 'n', 'pv', 'r']

Deleting or unsigning a variable

Sometimes, when we write our programs, it might be a good idea to delete those variables that we no longer need. In this case, we could use the del() function to remove or unsign a variable. In the following example, we assign a value to rate, show its value, delete it, and type the variable name trying to retrieve its value again:

>>>rate=0.075
>>>rate
0.075

The value 0.075 seen...

Basic math operations – addition, subtraction, multiplication, and division

For basic math operations in Python, we use the conventional mathematical operators +, -, *, and /. These operators represent plus, minus, multiplication, and division operations respectively. All these operators are embedded in the following line of code:

>>>3.09+2.1*5.2-3/0.56
8.652857142857144

Although we use integer division less frequently in finance, a user might type the division sign twice (//) accidentally to get a weird result. The integer division is done with double slash //, which would return an integer value that is the largest integer than the final output. The result of 7 divided by 3 is 2.33, and 2 will be the largest integer smaller than 2.33. This example is shown in the following code:

>>>7/3
2.3333333333333335

For Python 2.x versions, 7/3 could be 2 instead of 2.333. Thus, we have to be careful. In order to avoid an integer division, we could use 7/2 or 7/2., that is...

The power function, floor, and remainder

For our FV=PV(1+R)^n, we use a power function. The floor function would give the largest integer smaller than the current value. The remainder is the value that remains after an integer division. Given a positive discount rate, the present value of a future cash flow is always smaller than its corresponding future value.

The following formula specifies the relationship between a present value and its future value:

The power function, floor, and remainder

In this formula, PV is the present value, FV is the future value, R is the cost of capital (discount rate) per period, and n is the number of periods. Assume that we would receive $100 payment in two years and that the annual discount rate is 10 percent. What is the equivalent value today that we are willing to accept?

>>>100/(1+0.1)**2
82.64462809917354

Here, ** is used to perform a power function. The % operator is used to calculate the remainder. Refer to the following example for the implementation of these operators:

>>...

Choosing appropriate precision

The default precision for Python has 16 decimal places as shown in the following example. This is good enough for most finance-related problems or research:

>>>7/3
2.3333333333333335

We could use the round() function to change the precision as follows:

>>>payment1=3/7
>>>payment1
0.42857142857142855
>>>payment2=round(y,5)
>>>payment2
0.42857

Assume that the units for both payment1 and payment2 are in millions. The difference could be huge after we apply the round() function with just two decimal places! If we use one dollar as our unit, the exact payment is $428,571. However, if we use millions instead and apply two decimal places, we end up with 430,000, which is shown in the following example. The difference is $1,429:

>>>payment1*10**6
428571.4285714285
>>>payment2=round(payment1,2)
>>>payment2
0.43
>>>payment2*10**6
430000.0

Finding out more information about a specific built-in function

To understand each math function, we apply the help() function, such as help(round), as shown in the following example:

>>>help(round)
Help on built-in function round in module builtins:
round(...)
    round(number[, ndigits]) -> number
Round a number to a given precision in decimal  
digits (default 0 digits).This returns an int when 
called with one argument, otherwise the same type as 
the number. ndigits may be negative.

Listing all built-in functions

To find out all built-in functions, we perform the following two-step approach. First, we issue dir() to find the default name that contains all default functions. When typing its name, be aware that there are two underscores before and another two underscores after the letters of builtins, that is, __builtins__:

>>>dir()
['__builtins__', '__doc__', '__loader__', '__name__', '__package__', 'x']...

Importing the math module

When learning finance with real-world data, we deal with many issues such as downloading data from Yahoo! finance, choosing an optimal portfolio, estimating volatility for individual stocks or for a portfolio, and constructing an efficient frontier. For each subject (topic), experts develop a specific module (package). To use them, we have to import them. For example, we can use import math to import all basic math functions. In the following codes, we calculate the square root of a value:

>>>import math
>>>math.sqrt(3)
1.732050807568772

To find out all functions contained in the math module, we call the dir() function again as follows:

>>>import math
>>>dir(math)
['__doc__', '__loader__', '__name__', '__package__', 'acos', 'acosh', 'asin', 'asinh', 'atan', 'atan2', 'atanh', 'ceil', 'copysign', &apos...

A few frequently used functions

There are several functions we are going to use quite frequently. In this section, we will discuss them briefly. The functions are print(), type(), upper(), strip(), and last expression _. We will also learn how to merge two string variables. The true power function pow() discussed earlier belongs to this category as well.

The print() function

Occasionally, we need to print something on screen. One way to do so is to apply the print() function as shown in the following example:

>>>import math            
>>>print('pi=',math.pi)
pi= 3.141592653589793

At this stage, a new user just applies this format without going into more detail about the print() function.

The type() function

In Python, the type() function can be used to find out the type of a variable as follows:

>>>pv=100.23
>>>type(pv)
<class 'float'>
>>>n=10
>>>type(n)
<class 'int'>
>>>

From these results...

Assigning values to variables

To assign a value to a variable is simple because unlike many other languages such as C++ or FORTRAN, in Python, we don't need to define a variable before we assign a value to it.

>>>pv=22
>>>pv+2
24

We could assign the same value to different variables simultaneously. In the following example, we assign 100 to the three variables x, y, and z at once:

>>>x=y=z=100

Displaying the value of a variable

To find out the value of a variable, just type its name as shown in the following code:

>>>pv=100
>>>pv
100
>>>R=0.1
>>>R
0.1

Error messages

Assuming that we issue the sqrt(3) command to estimate the square root of three, we would get the following error message:

>>>sqrt(3)
Traceback (most recent call last):
  File "<pyshell#17>", line 1, in <module>
    sqrt(3)
NameError: name 'sqrt' is not defined

The last line of the error message tells us that the sqrt() function is not defined. Later in the chapter, we learn that the sqrt() function is included in a module called math and that we have to load (import) the module before we can call the functions contained in it. A module is a package that contains a set of functions around a specific subject.

Can't call a variable without assignment

Assuming that we never assign a value to the abcde variable, after typing abcde, we would get the following error message:

>>>abcde
Traceback (most recent call last):
  File "<pyshell#0>", line 1, in <module>
    abcde
NameError: name 'abcde' is not defined
>>>

The last line tells...

Choosing meaningful names

A perpetuity describes the situations where equivalent periodic cash flows happen in the future and last forever. For example, we receive $5 at the end of each year forever. A real-world example is the UK government bond, called consol, that pays fixed coupons. To estimate the present value of a perpetuity, we use the following formula if the first cash flow occurs at the end of the first period:

Here, PV is the present value, C is a perpetual periodic cash flow that happens at a fixed interval, and R is the periodic discount rate. Here C and R should be consistent. For example, if C is annual (monthly) cash flow, then R must be an annual (monthly) discount rate. This is true for other frequencies too. Assume that a constant annual cash flow is $10, with the first cash flow at the end of the first year, and that the annual discount rate is 10 percent. Compare the following two ways to name the C and R variables:

>>>x=10       # bad way for variable names...
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Description

A hands-on guide with easy-to-follow examples to help you learn about option theory, quantitative finance, financial modeling, and time series using Python. Python for Finance is perfect for graduate students, practitioners, and application developers who wish to learn how to utilize Python to handle their financial needs. Basic knowledge of Python will be helpful but knowledge of programming is necessary.

What you will learn

  • Build a financial calculator based on Python
  • Learn how to price various types of options such as European, American, average, lookback, and barrier options
  • Write Python programs to download data from Yahoo! Finance
  • Estimate returns and convert daily returns into monthly or annual returns
  • Form an nstock portfolio and estimate its variancecovariance matrix
  • Estimate VaR (Value at Risk) for a stock or portfolio
  • Run CAPM (Capital Asset Pricing Model) and the FamaFrench 3factor model
  • Learn how to optimize a portfolio and draw an efficient frontier
  • Conduct various statistic tests such as Ttests, Ftests, and normality tests
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Publication date : Apr 02, 2014
Length: 408 pages
Edition : 1st
Language : English
ISBN-13 : 9781783284375
Category :
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Languages :
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Concepts :
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Tools :
NumPy

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Length: 408 pages
Edition : 1st
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ISBN-13 : 9781783284375
Category :
Data
Languages :
Python
Concepts :
Financial Technology
Tools :
NumPy

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Table of Contents

13 Chapters
1. Introduction and Installation of Python Chevron down icon Chevron up icon
1. Introduction and Installation of Python
Introduction to Python
Installing Python
Different versions of Python
Ways to launch Python
Quitting Python
Error messages
Python language is case sensitive
Initializing the variable
Finding the help window
Finding manuals and tutorials
Finding the version of Python
Summary
Exercises
2. Using Python as an Ordinary Calculator Chevron down icon Chevron up icon
2. Using Python as an Ordinary Calculator
Assigning values to variables
Error messages
Choosing meaningful names
Using dir() to find variables and functions
Basic math operations – addition, subtraction, multiplication, and division
The power function, floor, and remainder
Choosing appropriate precision
Finding out more information about a specific built-in function
Importing the math module
A few frequently used functions
The tuple data type
Summary
Exercises
3. Using Python as a Financial Calculator Chevron down icon Chevron up icon
3. Using Python as a Financial Calculator
Writing a Python function without saving it
Default input values for a function
Indentation is critical in Python
Checking the existence of our functions
Defining functions from our Python editor
Activating our function using the import function
Debugging a program from a Python editor
Two ways to call our pv_f() function
Generating our own module
Types of comments
The if() function
Annuity estimation
Converting the interest rates
Continuously compounded interest rate
A data type – list
Net present value and the NPV rule
Defining the payback period and the payback period rule
Defining IRR and the IRR rule
Showing certain files in a specific subdirectory
Using Python as a financial calculator
Adding our project directory to the path
Summary
Exercises
4. 13 Lines of Python to Price a Call Option Chevron down icon Chevron up icon
4. 13 Lines of Python to Price a Call Option
Writing a program – the empty shell method
Writing a program – the comment-all-out method
Using and debugging other programs
Summary
Exercises
5. Introduction to Modules Chevron down icon Chevron up icon
5. Introduction to Modules
What is a module?
Importing a module
Module dependency
Summary
Exercises
6. Introduction to NumPy and SciPy Chevron down icon Chevron up icon
6. Introduction to NumPy and SciPy
Installation of NumPy and SciPy
Launching Python from Anaconda
Showing all functions in NumPy and SciPy
More information about a specific function
Understanding the list data type
Working with arrays of ones, zeros, and the identity matrix
Performing array manipulations
Performing array operations with +, -, *, /
The x.sum() dot function
Looping through an array
Using the help function related to modules
A list of subpackages for SciPy
Cumulative standard normal distribution
Logic relationships related to an array
Statistic submodule (stats) from SciPy
Interpolation in SciPy
Solving linear equations using SciPy
Generating random numbers with a seed
Finding a function from an imported module
Understanding optimization
Linear regression and Capital Assets Pricing Model (CAPM)
Retrieving data from an external text file
Installing NumPy independently
Understanding the data types
Summary
Exercises
7. Visual Finance via Matplotlib Chevron down icon Chevron up icon
7. Visual Finance via Matplotlib
Installing matplotlib via ActivePython
Alternative installation via Anaconda
Understanding how to use matplotlib
Understanding simple and compounded interest rates
Adding texts to our graph
Working with DuPont identity
Understanding the Net Present Value (NPV) profile
Graphical representation of the portfolio diversification effect
Retrieving historical price data from Yahoo! Finance
Understanding the time value of money
Candlesticks representation of IBM's daily price
IBM's intra-day graphical representations
Presenting both closing price and trading volume
Performance comparisons among stocks
Comparing return versus volatility for several stocks
Finding manuals, examples, and videos
Installing the matplotlib module independently
Summary
Exercises
8. Statistical Analysis of Time Series Chevron down icon Chevron up icon
8. Statistical Analysis of Time Series
Installing Pandas and statsmodels
Using Pandas and statsmodels
Open data sources
Retrieving data to our programs
Several important functionalities
Return estimation
Merging datasets by date
T-test and F-test
Many useful applications
Constructing an efficient frontier
Understanding the interpolation technique
Outputting data to external files
Python for high-frequency data
More on using Spyder
A useful dataset
Summary
Exercise
9. The Black-Scholes-Merton Option Model Chevron down icon Chevron up icon
9. The Black-Scholes-Merton Option Model
Payoff and profit/loss functions for the call and put options
European versus American options
Cash flows, types of options, a right, and an obligation
Normal distribution, standard normal distribution, and cumulative standard normal distribution
The Black-Scholes-Merton option model on non-dividend paying stocks
The p4f module for options
European options with known dividends
Various trading strategies
Relationship between input values and option values
Greek letters for options
The put-call parity and its graphical representation
Binomial tree (the CRR method) and its graphical representation
Hedging strategies
Summary
Exercises
10. Python Loops and Implied Volatility Chevron down icon Chevron up icon
10. Python Loops and Implied Volatility
Definition of an implied volatility
Understanding a for loop
Estimation of IRR via a for loop
Understanding a while loop
Using keyboard commands to stop an infinitive loop
Estimating implied volatility by using an American call
Measuring efficiency by time spent in finishing a program
The mechanism of a binary search
Sequential versus random access
Looping through an array/DataFrame
Retrieving option data from CBOE
Retrieving option data from Yahoo! Finance
The put-call ratio
Summary
Exercises
11. Monte Carlo Simulation and Options Chevron down icon Chevron up icon
11. Monte Carlo Simulation and Options
Generating random numbers from a standard normal distribution
Generating random numbers from a uniform distribution
Using simulation to estimate the pi value
Generating random numbers from a Poisson distribution
Bootstrapping with/without replacements
Distribution of annual returns
Simulation of stock price movements
Finding an efficient portfolio and frontier
Geometric versus arithmetic mean
Long-term return forecasting
Pricing a call using simulation
Exotic options
Barrier in-and-out parity
Pricing lookback options with floating strikes
Using the Sobol sequence to improve the efficiency
Summary
Exercises
12. Volatility Measures and GARCH Chevron down icon Chevron up icon
12. Volatility Measures and GARCH
Conventional volatility measure – standard deviation
Tests of normality
Lower partial standard deviation
Test of equivalency of volatility over two periods
Test of heteroskedasticity, Breusch, and Pagan (1979)
Retrieving option data from Yahoo! Finance
Volatility smile and skewness
The ARCH model
The GARCH (Generalized ARCH) model
Summary
Exercises
Index Chevron down icon Chevron up icon
Index

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Customer reviews

Top Reviews
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  • Most Recent
Rating distribution
Full star icon Full star icon Full star icon Half star icon Empty star icon 3.9
(22 Ratings)
5 star 50%
4 star 18.2%
3 star 13.6%
2 star 4.5%
1 star 13.6%
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Amazon Customer Aug 26, 2014
Full star icon Full star icon Full star icon Full star icon Full star icon 5
This book is a five star. It is a good book, it explains a lot about the concept in finance and python. If you are into learning python and finance, this is a good book for you. This book is more for beginners or intermediate. If you know a lot about about python or about finance, you should not get this book. I would recommend this book for finance major and people that want to learn about python. Since I have finish reading this book, I was able to learn how to code on my own. This book had help some of my finance classes.
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
Luis Cuellar Jun 02, 2014
Full star icon Full star icon Full star icon Full star icon Full star icon 5
I am a systems engineer but have not program for a long time, and I am very interested in finance so I have been studying finance for the past months.For me this book is perfect, it teaches you python witch to me is a very powerful language witch you can learn fast and do very sophisticated things. and it teaches it with a very specific focus witch is applying it to Financial problems.The examples of the book are very well done, and helps ground your financial knowledge by programming the examples.The book takes you from installing the language to complicated graphical and mathematical solutions. The examples are well written and to the point.The only negative I find is the book assumes you know finance, so it is a programming book more that a financial book. But for me that was just fine.
Amazon Verified review Amazon
Red'D, Vijayawada Sep 16, 2018
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Good book
Amazon Verified review Amazon
Nemo Aug 10, 2014
Full star icon Full star icon Full star icon Full star icon Full star icon 5
First of all this is not an in-depth python book or an in-depth finance book. So if you are looking for something with a strong technical bent on python or finance this is not it.It is, however, a great read if you are familiar with doing a lot of finance in another language like R and want to transition to python. With bloomberg providing a python API and C++ still being a real pain the rear this is a good way for more "analyst" types to become much more fluent and competent using a vastly more flexible language. It is not mega detailed - basically a "crash through" approach to doing a bit of python and doing it quickly. This is by no means a standalone solution to anything.The best use of this book is in conjunction with something more rigorous for finance and python. Aside from that it could be put to good use in an undergrad finance class so that instead of messing around in excel people actually learn a bit of code that they can build on later.
Amazon Verified review Amazon

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About the author

Profile icon Yuxing Yan
Yuxing Yan
LinkedIn icon Github icon
Yuxing Yan graduated from McGill University with a PhD in finance. Over the years, he has been teaching various finance courses at eight universities: McGill University and Wilfrid Laurier University (in Canada), Nanyang Technological University (in Singapore), Loyola University of Maryland, UMUC, Hofstra University, University at Buffalo, and Canisius College (in the US). His research and teaching areas include: market microstructure, open-source finance and financial data analytics. He has 22 publications including papers published in the Journal of Accounting and Finance, Journal of Banking and Finance, Journal of Empirical Finance, Real Estate Review, Pacific Basin Finance Journal, Applied Financial Economics, and Annals of Operations Research. He is good at several computer languages, such as SAS, R, Python, Matlab, and C. His four books are related to applying two pieces of open-source software to finance: Python for Finance (2014), Python for Finance (2nd ed., expected 2017), Python for Finance (Chinese version, expected 2017), and Financial Modeling Using R (2016). In addition, he is an expert on data, especially on financial databases. From 2003 to 2010, he worked at Wharton School as a consultant, helping researchers with their programs and data issues. In 2007, he published a book titled Financial Databases (with S.W. Zhu). This book is written in Chinese. Currently, he is writing a new book called Financial Modeling Using Excel — in an R-Assisted Learning Environment. The phrase "R-Assisted" distinguishes it from other similar books related to Excel and financial modeling. New features include using a huge amount of public data related to economics, finance, and accounting; an efficient way to retrieve data: 3 seconds for each time series; a free financial calculator, showing 50 financial formulas instantly, 300 websites, 100 YouTube videos, 80 references, paperless for homework, midterms, and final exams; easy to extend for instructors; and especially, no need to learn R.
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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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