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Python for Finance

By : Yuxing Yan
Book Image

Python for Finance

By: Yuxing Yan

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Python for Finance
Yuxing Yan
Apr, 2014 | 408 pages
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Table of Contents (20 chapters)
Python for Finance
Python for Finance
Credits
Credits
About the Author
About the Author
Acknowledgments
Acknowledgments
About the Reviewers
About the Reviewers
www.PacktPub.com
www.PacktPub.com
Preface
Preface
Free Chapter
1
Introduction and Installation of Python
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
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
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
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
Introduction to Modules
What is a module?
Importing a module
Module dependency
Summary
Exercises
6
Introduction to NumPy and SciPy
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
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
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
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
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
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
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
Index

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
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