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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.

By Yuxing Yan
$28.99 $9.99
Book Apr 2014 408 pages 1st Edition
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Product Details


Publication date : Apr 2, 2014
Length 408 pages
Edition : 1st Edition
Language : English
ISBN-13 : 9781783284375
Category :
Table of content icon View table of contents Preview book icon Preview Book

Python for Finance

Chapter 1. Introduction and Installation of Python

In this chapter, first we offer a short introduction on why we adopt Python as our computational tool and what the advantages are of using Python. Then, we discuss how to install Python and other related issues, such as how to start and quit Python, whether Python is case sensitive, and a few simple examples.

In particular, we will cover the following topics:

  • Introduction to Python

  • Installing Python

  • Which version of Python should we use and what is the version of our installed Python?

  • Ways to launch and quit Python

  • Error messages

  • Python is case sensitive

  • Initializing the variables

  • Finding help, manuals, and tutorials

  • Finding the Python versions

Introduction to Python


Our society entered the information era many years ago. Actually, we are drowning in a sea of information, such as too many e-mails to read or too many web pages we could possibly explore. With the Internet, we could find a huge amount of information about almost everything such as important events or how to learn Python. We could find information for a specific firm by searching online. For instance, if we want to collect financial information associated with International Business Machines (IBM), we could use Yahoo! Finance, Google Finance, Securities and Exchange Commission (SEC) filings, and the company's web pages. Since we are confronted with a lot of publicly available information, investors, professionals, and researchers need a tool to process such a huge amount of information. In addition, our society would move towards a more open and transparent society. In finance, a new concept of open source finance has merged recently. Dane and Masters (2009) suggest three components for open source finance: open software, open data, and open codes. For the first component of open software, Python is one of the best choices. An equally popular open source software is R. In the next section, we summarize the advantages of learning and applying Python to finance.

Firstly, Python is free in terms of license. Being free has many benefits. Let's perform a simple experiment here. Let's assume readers know nothing about Python and they have no knowledge about option theory. How long do you think it would take them to run a Python program to price a Black-Scholes call option? Less than 2 hours? Here is what they could do; they could download and install Python after reading the Installing Python section of this chapter, and it would take less than 10 minutes. Spend another 10 minutes to launch and quit Python and also try a few examples. Then, read the first page of Chapter 4, 13 Lines of Python to Price a Call Option, which contains the code for the famous Black-Scholes call option model. In total, the program has 13 lines. The reader could spend the next 40 minutes typing, correcting typos, and retyping those 13 lines. With less than 2 hours, they should be able to run the program to price a call option. The cost of adopting a new computer language includes many aspects such as annual license cost, maintenance costs, available packages, and support.

Another example is related to an SEC proposal. In 2010, the SEC proposed that all financial institutions are to accompany their new Asset-Backed Security (ABS) with a computer program showing the contractual cash flows of the securities (www.sec.gov/rules/proposed/2010/33-9117.pdf). The proposed computer language is Python. Obviously, any investor can access Python because it is free.

For bond analytics or credit risks, Roger Ehrenberg (2007) argues for an open source approach. Whether or not ratings should be required for institutional investors to buy certain securities is not the issue; the essential point is getting better transparency and analysis of instruments constituting the investable universe. Just imagine what the impact would be if many financial institutions adopt the open source initiative by storing their own debt ratings models into the public domain and allowing others to contribute to its development! To contribute to such an open source approach, Python (or R, free software as well) would be ideal in terms of computational tools.

Secondly, Python is powerful, flexible, and easy to learn. It is capable of solving almost all our financial and economic estimations. Python is available for all major operating systems such as Windows, Linux/Unix, OS/2, Mac, and Amiga, among others.

Thirdly, Python is suitable for Big Data. Dasgupta (2013) argues that R and Python are two of the most popular open source programming languages for data analysis. Compared with R, Python is generally a better overall language, especially when you consider its blend of functional programming with object orientation. Combined with Scipy/Numpy, Matplotlib, and Statsmodel, it provides a powerful tool. In this book, we will discuss a module called Pandas when we deal with financial data.

Fourthly, there are many useful modules for Python, such as toolboxes in MATLAB and packages in R. Each model is developed for a special purpose. Later in the book, we will touch base with about a dozen modules. However, we will pay special attention to five of the most useful modules in finance: NumPy, SciPy, Matplotlib, Statsmodels, and Pandas. The first two modules are associated with mathematical estimations, formulae, matrices and their manipulation, data formats, and data manipulations. Matplotlib is for visual presentations such as graphs. In Chapter 8, Introduction to the Black-Scholes Option Model, we use this module intensively to explain visually different payoff functions and profit/loss functions for various trading strategies. The Statsmodels module deals with econometrics such as T-test, F-test, and GARCH models. Again, the Pandas module is used for financial data analysis.

We should mention some disadvantages of Python as well. The most important shortcoming is the lack of support because it is free. Some experts argue that the Python community needs to grow and should include more statisticians and mathematicians.

Installing Python


To install Python, perform the following two steps:

  1. Go to http://www.python.org/download.

  2. Depending on your computer, choose the appropriate package, for example, Python 3.3.2 Windows x86 MSI Installer (Windows binary -- does not include source).

At this stage, a new user could install the latest Python version. In other words, they could simply ignore the next section related to the version and go directly to the How to launch Python section.

Generally speaking, the following are the three ways to launch Python:

  • From Python IDLE (GUI)

  • From the Python command line

  • From your command-line window

The three ways will be introduced in the How to launch Python?, Launch Python from Python command line, and The third way to launch Python sections.

Different versions of Python


One of the most frequently asked questions related to Python's installation is which version we should download. At this stage, any latest version would be fine, that is, the version does not matter. There are three reasons behind this statement:

  • The contents of the first four chapters are compatible with any version

  • Removing and downloading Python is trivial

  • Different versions could coexist

Later in the book, we will explain the module dependency which is associated with a Python version. A module is a collection of many Python programs, written by one or a group of experts, to serve a special purpose. For example, we will discuss a module called Statsmodels, which is related to statistical and econometric models, linear regression and the like. Generally speaking, we have built-in modules, standard modules, third-party modules, and modules built by ourselves. We will spend several chapters on this important topic.

In this book, we will mention about two dozen modules. In particular, we will discuss in detail the NumPy, SciPy, Matplotlib, Pandas, and Statsmodels modules. The NumPy, Matplotlib, and Statsmodels modules depend on Python 2.7 or above. All these packages have different versions for Python 2.x (2.5-2.6 and above, depending on the case).

Ways to launch Python


There are three ways to launch Python and they are explained in the following sections.

Launching Python with GUI

To launch Python, perform the following steps:

  1. Click on Start and then on All Programs.

  2. Find Python 3.3.

  3. Click on IDLE (Python GUI) as shown in the following screenshot:

  4. After Python starts, the following window appears:

Assume that an estimate of $100 is expected to be received in one year with an annual discount rate of 10 percent. The present value of one future cash flow is as follows:

In this equation, PV is the present value, FV is the future value, R is the discount rate, and n is the number of periods. According to the preceding formula, we could manually type those values to get the present value of this one future cash flow. Assume that we would receive $100 in one year. If the annual discount rate is 10 percent, what is the present value of this $100? For this, let's take a look at the following lines of code:

>>>100/(1+0.1)
90.9090909090909
>>>

Note

The triple larger than signs (>>>) is the Python prompt.

It is a good idea to create a Python icon on your desktops for your convenience. In addition to the preceding method, there are other methods to launch Python; see the next two sections: Launching Python from the Python command line and Launching Python from our own DOS window.

Launching Python from the Python command line

A new user could skip this section and go to the Quitting Python section because learning how to launch Python with GUI is more than enough. There are two reasons for this. The first is because we know how to launch Python by using Python IDLE or by clicking on the Python icon on our desktops, and the second reason is that we could save and run our Python programs easily using Python IDLE.

To launch Python from the Python command line, we have to perform the following steps:

  1. Click on Start and then on All Programs.

  2. Find Python 3.3.

  3. Click on Python (command line) as shown in the following screenshot:

  4. After we click on Python (command line), we will see the following window:

Launching Python from our own DOS window

We could generate our own DOS window, and then launch Python from there. In addition, we could navigate to the subdirectory, which contains our Python programs. In order to this, perform the following steps:

  1. Open a Window command line by clicking on Start and then enter cmd in the run window as shown in the following screenshot:

  2. Type cd c:\python33 to move to the appropriate directory.

  3. Type python to run the software as shown in the following screenshot:

If we want to launch Python anywhere else, we have to include the path of our Python directory. Assume that after installation we have python33 in C:. Replace step 2 with the following DOS command:

set path=%path%;C:\python33

Quitting Python


Usually, we have several ways to quit Python, which are as follows:

  • The first way to quit Python is to use Ctrl + D

  • The second way to quit is Ctrl + Q

  • The third way to quit is to click on File and then on Exit

  • The fourth way is to click on X at the top-right corner of the window (that is, close the window)

Later in the book, we will explain how to embed certain codes to quit Python when a currently running program is finished.

Error messages


For the previous example, if we enter 100/(1+0.1)^2 instead of 100/(1+0.1), we will see the following error message, which tells us that ^ is not supported:

>>>100/(1+0.1)^2
Traceback (most recent call last):
File "<psyhell#1>, line 1, in <module>
100/(1+0.1)^2
TypeError: unsupported operand type(s) for ^: 'float' and 'int'
>>>

Tip

Downloading the example code

You can download the example code files for all Packt books you have purchased from your account at http://www.packtpub.com. If you purchased this book elsewhere, you can visit http://www.packtpub.com/support and register to have the files e-mailed directly to you.

At this stage, a new user needs to pay attention to the last sentence of the error message. Obviously, the last line tells us that ^ is not supported. Again, for a power function, we should use double multiplications, **, instead of a karat, ^. In Chapter 2, Using Python as an Ordinary Calculator, we will show that a true power function, pow(), is available.

Python language is case sensitive


Case sensitive means that x is different from X. The variable of John is different from the variable of john. If we assume a value for x (lowercase x) and then call X (uppercase X), we will see the following error message:

>>>x=2
>>>X
Traceback (most recent call last):
       File "<pyshell#1>", line 1, in <module>
           X
       NameError: name 'X' is not defined
>>>

In the preceding example, X is not assigned any value. Thus, when we call it by typing X, we will receive an error message. Note that the last line mentions NameError instead of TypeError. In Python, we use name for variables.

Initializing the variable


From the previous example, we know that after we assign a value to x, we can use x, which means that x is now defined in the sense of other computer languages such as FORTRAN and C/C++. The opposite is also true, that we could not use X if it is not assigned a value in Python. In other words, when we assign a value to X, we have to define it first. Compared to languages such as C++ or FORTRAN, we don't have to define x as an integer before we assign 10 to it.

Another advantage is that we could change the data type of a variable easily. For the FOTRAN language, if we have defined x as an integer, we cannot assign a string to it. Since there is only assignment in Python, we could assign any value to a variable. For example, we could assign 10 to x. It is legal to assign a string, such as Hello World, to x in the next minute. However, we should not be confused with the data type conversion, such as converting an integer to a string or vice versa. Conversion between different data types will be discussed in the later chapters.

Finding the help window


After we launch Python, typing help() would initiate the help window (as shown in the following lines of code). The prompt of the help window is help>. To quit the help window, we simply press the Enter key once or type quit. After we quit the help window, the Python prompt of >>> would reappear. Now, we launch the help window as shown in the following lines of code:

>>>help()
Welcome to Python 3.3!  This is the interactive help utility.
If this is your first time using Python, you should definitely check out the tutorial on the Internet at http://docs.python.org/3.3/tutorial/.
Enter the name of any module, keyword, or topic to get help on writing Python programs and using Python modules.  To quit this help utility and return to the interpreter, just type "quit".
To get a list of available modules, keywords, or topics, type "modules", "keywords", or "topics".  Each module also comes with a one-line summary of what it does; to list the modules whose summaries contain a given word such as "spam", type "modules spam".
help>

After typing keywords, we will have the following information:

>>>help> keywords
Here is a list of the Python keywords.  Enter any keyword to get more help.
False       def         if               raise
None        del         import           return
True        elif        in               try
and         else        is               while
as          except     lambda            with
assert      finally    nonlocal          yield
break       for        not                 
class       from        or                  
continue    global      pass                

help>

On the other hand, after typing topics, we will see what is shown in the following screenshot:

At the moment, a new user doesn't need to understand those topics. Just remember that we have a command to show us all the topics we could use.

Finding manuals and tutorials


There are many ways to find Python manuals and other related materials online. We just mentioned two ways: from your computer and from the Python home. These two ways are explained in details as follows:

To implement the first method (to have it manually installed on your computer), we need to perform the following steps:

  1. Click on Start and then on All Programs.

  2. Find Python 3.3.

  3. Click on Python Manuals as shown in the following screenshot:

  4. After we click on Python Manuals, we will see the following window:

From the Python home, the following documents can be downloaded:

For new Python learners, the following are the web pages where they could find many tutorial materials related to Python learning:

Finding the version of Python


When Python is launched, the first line will show our current version. Another way is to issue the following two lines of Python code after we launch Python:

>>>import sys
>>>sys.version
'3.3.2 (v3.3.2:d047928ae3f6, May 16 2013, 00:03:43) [MSC v.1600 32 bit (Intel)]'
>>>

The first line of command imports a module called sys. A module is a collection of many Python programs serving a special purpose. Understanding a module is critical in learning Python. We will discuss this in more detail in Chapter 5, Introduction to Modules; Chapter 6, Introduction to NumPy and SciPy; Chapter 7, Visual Finance via Matplotlib; and Chapter 8, Statistical Analysis of Time Series.

Summary


In this chapter, we learned how to install Python and other related issues, such as how to launch and quit Python, whether Python is case sensitive, and a few simple examples. Since it's a simple and straightforward explanation, any reader who is new to Python could easily download and install Python in a few minutes. After that, they could try a few given examples. We also offered a brief introduction as to why we adopt Python as our computational tool, and what the advantages and disadvantages are of using Python.

In the next chapter, you will learn some basic concepts and several frequently used Python built-in functions. We will demonstrate how to use Python as an ordinary calculator to solve many finance-related problems. For example, we could estimate the present value of one future cash flow, the future value of one cash flow today, the present value of a perpetuity, or the present value of a growing perpetuity. In addition, we will discuss the dir(), type(), floor(), round(), and help() functions.

Exercises


1. Use a few sentences to describe the Python software.

2. What are the advantages and disadvantages of using Python as our computational tool?

3. Where can we download and install Python?

4. Is Python case sensitive? What is the basic rule to define various variables (names)?

5. Can we use a variable without defining it first?

6. Is it possible that we use a variable before we assign a value to it?

7. Is the version of Python important at this stage? Is the version of Python important later in the book?

8. In how many ways can we launch Python?

9. Where can we find videos on how to install Python?

10. What is the URL for Python's homepage?

11. Estimate the area of a circle if the diameter is 10 using Python.

12. How do you assign a value to a new variable?

13. How can you find some sample examples related to Python?

14. How do you launch Python's help function?

15. Where is the location of Python on your PC (Mac)? How do we find the path?

16. What is the difference between defining a variable and assigning a value to it?

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Key benefits

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

What do you get with eBook?

Product feature icon Instant access to your Digital eBook purchase
Product feature icon Download this book in EPUB and PDF formats
Product feature icon Access this title in our online reader with advanced features
Product feature icon DRM FREE - Read whenever, wherever and however you want
Buy Now

Product Details


Publication date : Apr 2, 2014
Length 408 pages
Edition : 1st Edition
Language : English
ISBN-13 : 9781783284375
Category :

Table of Contents

20 Chapters
Python for Finance Chevron down icon Chevron up icon
Credits Chevron down icon Chevron up icon
About the Author Chevron down icon Chevron up icon
Acknowledgments Chevron down icon Chevron up icon
About the Reviewers Chevron down icon Chevron up icon
www.PacktPub.com Chevron down icon Chevron up icon
Preface Chevron down icon Chevron up icon
Introduction and Installation of Python Chevron down icon Chevron up icon
Using Python as an Ordinary Calculator Chevron down icon Chevron up icon
Using Python as a Financial Calculator Chevron down icon Chevron up icon
13 Lines of Python to Price a Call Option Chevron down icon Chevron up icon
Introduction to Modules Chevron down icon Chevron up icon
Introduction to NumPy and SciPy Chevron down icon Chevron up icon
Visual Finance via Matplotlib Chevron down icon Chevron up icon
Statistical Analysis of Time Series Chevron down icon Chevron up icon
The Black-Scholes-Merton Option Model Chevron down icon Chevron up icon
Python Loops and Implied Volatility Chevron down icon Chevron up icon
Monte Carlo Simulation and Options Chevron down icon Chevron up icon
Volatility Measures and GARCH Chevron down icon Chevron up icon
Index Chevron down icon Chevron up icon

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