Python for finance : build real-life Python applications for quantitative finance and financial engineering

Build real-life Python applications for quantitative finance and financial engineering with this book and ebook Overview Estimate market risk, form various portfolios, and estimate their variance-covariance matrixes using real-world data Explains many financial concepts and trading strategies with the help of graphs A step-by-step tutorial with many Python programs that will help you learn how to apply Python to finance In Detail Python is a free and powerful tool that can be used to build a financial calculator and price options, and can also explain many trading strategies and test various hypotheses. This book details the steps needed to retrieve time series data from different public data sources. Python for Finance explores the basics of programming in Python. It is a step-by-step tutorial that will teach you, with the help of concise, practical programs, how to run various statistic tests. This book introduces you to the basic concepts and operations related to Python. You will also learn how to estimate illiquidity, Amihud (2002), liquidity measure, Pastor and Stambaugh (2003), Roll spread (1984), spread based on high-frequency data, beta (rolling beta), draw volatility smile and skewness, and construct a binomial tree to price American options. This book is 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. What you will learn from this book 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 n-stock portfolio and estimate its variance-covariance matrix Estimate VaR (Value at Risk) for a stock or portfolio Run CAPM (Capital Asset Pricing Model) and the Fama-French 3-factor model Learn how to optimize a portfolio and draw an efficient frontier Conduct various statistic tests such as T-tests, F-tests, and normality tests Approach 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. Cover Copyright Credits About the Author Acknowledgments About the Reviewers www.PacktPub.com Table of Contents Preface Chapter 1: Introduction and Installation of Python Introduction to Python Installing Python Different versions of Python Ways to launch Python Launching Python with GUI Launching Python from the Python command line Launching Python from our own DOS window 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 Chapter 2: Using Python as an Ordinary Calculator Assigning values to variables Displaying the value of a variable Error messages Can't call a variable without assignment Choosing meaningful names Using dir() to find variables and functions Deleting or unsigning a variable Basic math operations – addition, subtraction, multiplication, and division The power function, floor, and remainder A true power function Choosing appropriate precision Finding out more information about a specific built-in function Listing all built-in functions Importing the math module The pi, e, log, and exponential functions "import math" versus "from math import *" A few frequently used functions The print() function The type() function Last expression _ (underscore) Combining two strings The upper() function The tuple data type Summary Exercises Chapter 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 first type of comment The second type of comment Finding information about our pv_f() function 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 Chapter 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 Chapter 5: Introduction to Modules What is a module? Importing a module Adopting a short name for an imported module Showing all functions in an imported module Comparing "import math" and "from math import *" Deleting an imported module Importing only a few needed functions Finding out all built-in modules Finding out all the available modules Finding the location of an imported module More information about modules Finding a specific uninstalled module Module dependency Summary Exercises Chapter 6: Introduction to NumPy and SciPy Installation of NumPy and SciPy Launching Python from Anaconda Examples of using NumPy Examples of using SciPy 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 +, -, *, / Performing plus and minus operations Performing a matrix multiplication operation Performing an item-by-item multiplication operation 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 The loadtxt() and getfromtxt() functions Installing NumPy independently Understanding the data types Summary Exercises Chapter 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 Using colors effectively Using different shapes Graphical representation of the portfolio diversification effect Number of stocks and portfolio risk Retrieving historical price data from Yahoo! Finance Histogram showing return distribution Comparing stock and market returns Understanding the time value of money Candlesticks representation of IBM's daily price Graphical representation of two-year price movement IBM's intra-day graphical representations Presenting both closing price and trading volume Adding mathematical formulae to our graph Adding simple images to our graphs Saving our figure to a file Performance comparisons among stocks Comparing return versus volatility for several stocks Finding manuals, examples, and videos Installing the matplotlib module independently Summary Exercises Chapter 8: Statistical Analysis of Time Series Installing Pandas and statsmodels Launching Python using the Anaconda command prompt Launching Python using the DOS window Launching Python using Spyder Using Pandas and statsmodels Using Pandas Examples from statsmodels Open data sources Retrieving data to our programs Inputting data from the clipboard Retrieving historical price data from Yahoo! Finance Inputting data from a text file Inputting data from an Excel file Inputting data from a CSV file Retrieving data from a web page Inputting data from a MATLAB dataset Several important functionalities Using pd.Series() to generate one-dimensional time series Using date variables Using the DataFrame Return estimation Converting daily returns to monthly returns Converting daily returns to annual returns Merging datasets by date Forming an n-stock portfolio T-test and F-test Tests of equal means and equal variances Testing the January effect Many useful applications 52-week high and low trading strategy Roll's model to estimate spread (1984) Amihud's model for illiquidity (2002) Pastor and Stambaugh (2003) liquidity measure Fama-French three-factor model Fama-MacBeth regression Estimating rolling beta Understanding VaR Constructing an efficient frontier Estimating a variance-covariance matrix Optimization – minimization Constructing an optimal portfolio Constructing an efficient frontier with n stocks Understanding the interpolation technique Outputting data to external files Outputting data to a text file Saving our data to a binary file Reading data from a binary file Python for high-frequency data Spread estimated based on high-frequency data More on using Spyder A useful dataset Summary Exercise Chapter 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 Covered call – long a stock and short a call Straddle – buy a call and a put with the same exercise prices A calendar spread Butterfly with calls 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 The binomial tree method for European options The binomial tree method for American options Hedging strategies Summary Exercises Chapter 10: Python Loops and Implied Volatility Definition of an implied volatility Understanding a for loop Estimating the implied volatility by using a for loop Implied volatility function based on a European call Implied volatility based on a put option model The enumerate() function Estimation of IRR via a for loop Estimation of multiple IRRs Understanding a while loop Using keyboard commands to stop an infinitive loop Estimating implied volatility by using a while loop Nested (multiple) for loops 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 Assignment through a for loop Looping through a dictionary Retrieving option data from CBOE Retrieving option data from Yahoo! Finance Different expiring dates from Yahoo! Finance Retrieving the current price from Yahoo! Finance The put-call ratio The put-call ratio for a short period with a trend Summary Exercises Chapter 11: Monte Carlo Simulation and Options Generating random numbers from a standard normal distribution Drawing random samples from a normal (Gaussian) distribution Generating random numbers with a seed Generating n random numbers from a normal distribution Histogram for a normal distribution Graphical presentation of a lognormal distribution 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 Bootstrapping with/without replacements Distribution of annual returns Simulation of stock price movements Graphical presentation of stock prices at options' maturity dates Finding an efficient portfolio and frontier Finding an efficient frontier based on two stocks Impact of different correlations Constructing an efficient frontier with n stocks Geometric versus arithmetic mean Long-term return forecasting Pricing a call using simulation Exotic options Using the Monte Carlo simulation to price average options Pricing barrier options using the Monte Carlo simulation Barrier in-and-out parity Graphical presentation of an up-and-out and up-and-in parity Pricing lookback options with floating strikes Using the Sobol sequence to improve the efficiency Summary Exercises Chapter 12: Volatility Measures and GARCH Conventional volatility measure – standard deviation Tests of normality Estimating fat tails 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 Graphical presentation of volatility clustering The ARCH model Simulating an ARCH (1) process The GARCH (Generalized ARCH) model Simulating a GARCH process Simulating a GARCH (p,q) process using modified garchSim() GJR_GARCH by Glosten, Jagannanthan, and Runkle (1993) Summary Exercises Index

In Detail

Python is a free and powerful tool that can be used to build a financial calculator and price options, and can also explain many trading strategies and test various hypotheses. This book details the steps needed to retrieve time series data from different public data sources.

Python for Finance explores the basics of programming in Python. It is a step-by-step tutorial that will teach you, with the help of concise, practical programs, how to run various statistic tests. This book introduces you to the basic concepts and operations related to Python. You will also learn how to estimate illiquidity, Amihud (2002), liquidity measure, Pastor and Stambaugh (2003), Roll spread (1984), spread based on high-frequency data, beta (rolling beta), draw volatility smile and skewness, and construct a binomial tree to price American options.

This book is 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.

Approach

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.

Who this book is for

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 programming knowledge is helpful, but not necessary.

Python is a free and powerful tool which can be used to build a financial calculator and price options, and can also explain many trading strategies and test various hypotheses. In addition to that, real-world data can be used to run CAPM (Capital Asset Pricing Model), the Fama-French 3-factor model, estimate VaR (Value at Risk), and estimate spread, illiquidity, and liquidity. This book explores the basics of programming in Python. It is a step-by-step tutorial that will teach you, with the help of concise, practical programs, how to run various statistic tests. With this book, you will learn "Explores the basics of programming in Python, [providing] a tutorial that will teach you ... how to run various statistic tests. ... You will also learn how to estimate illiquidity, Amihud (2002), liquidity measure, Pastor and Stambaugh (2003), Roll spread (1984), spread based on high-frequency data, beta (rolling beta), draw volatility smile and skewness, and construct a binomial tree to price American options"--Amazon.com. 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.