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Python for Finance

Python for Finance

By : Yuxing Yan
3.9 (22)
Buy this Book
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Python for Finance

Python for Finance

3.9 (22)
By: Yuxing Yan
Buy this Book

Overview of this book

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.
Table of Contents (14 chapters)
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Preface
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Preface
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Why Python?
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A programming book written by a finance professor
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Small programs oriented
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Using real-world data
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What this book covers
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What could you achieve after reading this book?
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Who this book is for
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Conventions
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Two ways to use the book
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Reader feedback
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Customer support
Free Chapter
1
1. Introduction and Installation of Python
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1. Introduction and Installation of Python
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Introduction to Python
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Installing Python
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Different versions of Python
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Ways to launch Python
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Quitting Python
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Error messages
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Python language is case sensitive
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Initializing the variable
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Finding the help window
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Finding manuals and tutorials
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Finding the version of Python
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Summary
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Exercises
2
2. Using Python as an Ordinary Calculator
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2. Using Python as an Ordinary Calculator
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Assigning values to variables
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Error messages
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Choosing meaningful names
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Using dir() to find variables and functions
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Basic math operations – addition, subtraction, multiplication, and division
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The power function, floor, and remainder
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Choosing appropriate precision
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Finding out more information about a specific built-in function
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Importing the math module
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A few frequently used functions
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The tuple data type
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Summary
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Exercises
3
3. Using Python as a Financial Calculator
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3. Using Python as a Financial Calculator
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Writing a Python function without saving it
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Default input values for a function
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Indentation is critical in Python
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Checking the existence of our functions
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Defining functions from our Python editor
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Activating our function using the import function
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Debugging a program from a Python editor
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Two ways to call our pv_f() function
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Generating our own module
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Types of comments
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The if() function
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Annuity estimation
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Converting the interest rates
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Continuously compounded interest rate
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A data type – list
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Net present value and the NPV rule
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Defining the payback period and the payback period rule
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Defining IRR and the IRR rule
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Showing certain files in a specific subdirectory
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Using Python as a financial calculator
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Adding our project directory to the path
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Summary
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Exercises
4
4. 13 Lines of Python to Price a Call Option
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4. 13 Lines of Python to Price a Call Option
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Writing a program – the empty shell method
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Writing a program – the comment-all-out method
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Using and debugging other programs
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Summary
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Exercises
5
5. Introduction to Modules
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5. Introduction to Modules
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What is a module?
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Importing a module
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Module dependency
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Summary
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Exercises
6
6. Introduction to NumPy and SciPy
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6. Introduction to NumPy and SciPy
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Installation of NumPy and SciPy
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Launching Python from Anaconda
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Showing all functions in NumPy and SciPy
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More information about a specific function
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Understanding the list data type
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Working with arrays of ones, zeros, and the identity matrix
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Performing array manipulations
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Performing array operations with +, -, *, /
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The x.sum() dot function
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Looping through an array
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Using the help function related to modules
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A list of subpackages for SciPy
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Cumulative standard normal distribution
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Logic relationships related to an array
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Statistic submodule (stats) from SciPy
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Interpolation in SciPy
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Solving linear equations using SciPy
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Generating random numbers with a seed
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Finding a function from an imported module
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Understanding optimization
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Linear regression and Capital Assets Pricing Model (CAPM)
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Retrieving data from an external text file
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Installing NumPy independently
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Understanding the data types
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Summary
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Exercises
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7. Visual Finance via Matplotlib
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7. Visual Finance via Matplotlib
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Installing matplotlib via ActivePython
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Alternative installation via Anaconda
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Understanding how to use matplotlib
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Understanding simple and compounded interest rates
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Adding texts to our graph
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Working with DuPont identity
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Understanding the Net Present Value (NPV) profile
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Graphical representation of the portfolio diversification effect
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Retrieving historical price data from Yahoo! Finance
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Understanding the time value of money
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Candlesticks representation of IBM's daily price
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IBM's intra-day graphical representations
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Presenting both closing price and trading volume
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Performance comparisons among stocks
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Comparing return versus volatility for several stocks
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Finding manuals, examples, and videos
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Installing the matplotlib module independently
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Summary
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Exercises
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8. Statistical Analysis of Time Series
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8. Statistical Analysis of Time Series
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Installing Pandas and statsmodels
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Using Pandas and statsmodels
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Open data sources
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Retrieving data to our programs
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Several important functionalities
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Return estimation
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Merging datasets by date
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T-test and F-test
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Many useful applications
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Constructing an efficient frontier
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Understanding the interpolation technique
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Outputting data to external files
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Python for high-frequency data
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More on using Spyder
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A useful dataset
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Summary
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Exercise
9
9. The Black-Scholes-Merton Option Model
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9. The Black-Scholes-Merton Option Model
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Payoff and profit/loss functions for the call and put options
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European versus American options
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Cash flows, types of options, a right, and an obligation
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Normal distribution, standard normal distribution, and cumulative standard normal distribution
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The Black-Scholes-Merton option model on non-dividend paying stocks
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The p4f module for options
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European options with known dividends
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Various trading strategies
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Relationship between input values and option values
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Greek letters for options
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The put-call parity and its graphical representation
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Binomial tree (the CRR method) and its graphical representation
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Hedging strategies
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Summary
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Exercises
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10. Python Loops and Implied Volatility
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10. Python Loops and Implied Volatility
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Definition of an implied volatility
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Understanding a for loop
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Estimation of IRR via a for loop
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Understanding a while loop
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Using keyboard commands to stop an infinitive loop
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Estimating implied volatility by using an American call
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Measuring efficiency by time spent in finishing a program
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The mechanism of a binary search
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Sequential versus random access
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Looping through an array/DataFrame
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Retrieving option data from CBOE
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Retrieving option data from Yahoo! Finance
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The put-call ratio
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Summary
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Exercises
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11. Monte Carlo Simulation and Options
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11. Monte Carlo Simulation and Options
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Generating random numbers from a standard normal distribution
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Generating random numbers from a uniform distribution
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Using simulation to estimate the pi value
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Generating random numbers from a Poisson distribution
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Bootstrapping with/without replacements
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Distribution of annual returns
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Simulation of stock price movements
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Finding an efficient portfolio and frontier
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Geometric versus arithmetic mean
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Long-term return forecasting
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Pricing a call using simulation
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Exotic options
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Barrier in-and-out parity
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Pricing lookback options with floating strikes
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Using the Sobol sequence to improve the efficiency
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Summary
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Exercises
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12. Volatility Measures and GARCH
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12. Volatility Measures and GARCH
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Conventional volatility measure – standard deviation
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Tests of normality
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Lower partial standard deviation
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Test of equivalency of volatility over two periods
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Test of heteroskedasticity, Breusch, and Pagan (1979)
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Retrieving option data from Yahoo! Finance
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Volatility smile and skewness
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The ARCH model
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The GARCH (Generalized ARCH) model
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Summary
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Exercises
13
Index
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Index

Relationship between input values and option values

When the volatility of an underlying stock increases, both its call and put values increase. The logic is that when a stock becomes more volatile, we have a better chance to observe extreme values, that is, we have a better chance to exercise our option. The following Python program shows this relationship:

import numpy as np
import p4f as pf
s0=30;T0=0.5;sigma0=0.2;r0=0.05;x0=30
sigma=np.arange(0.05,0.8,0.05)
T=np.arange(0.5,2.0,0.5)
call_0=pf.bs_call(s0,x0,T0,r0,sigma0)
call_sigma=pf.bs_call(s0,x0,T0,r0,sigma)
call_T=pf.bs_call(s0,x0,T,r0,sigma0)
plot(sigma,call_sigma,'b')
plot(T,call_T)

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