Python for Finance电子书

Python for Finance

Table of Contents

Python for Finance

Credits

About the Author

Acknowledgments

About the Reviewers

www.PacktPub.com

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Preface

Why Python?

A programming book written by a finance professor

Small programs oriented

Using real-world data

What this book covers

What could you achieve after reading this book?

Who this book is for

Conventions

Two ways to use the book

Reader feedback

Customer support

Downloading the example code

Downloading the color images of this book

Errata

Piracy

Questions

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

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

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

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

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

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

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

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

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

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

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

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