Python for Finance - Second Edition电子书

Python for Finance Second Edition

Table of Contents

Python for Finance Second Edition

Credits

About the Author

About the Reviewers

www.PacktPub.com

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Why subscribe?

Customer Feedback

Preface

A few words for the second edition

Why Python?

A programming book written by a finance professor

What this book covers

Small-program oriented

Using real-world data

What you need for this book

Who this book is for

Conventions

Reader feedback

Customer support

Downloading the example code

Errata

Piracy

Questions

1. Python Basics

Python installation

Installation of Python via Anaconda

Launching Python via Spyder

Direct installation of Python

Variable assignment, empty space, and writing our own programs

Writing a Python function

Python loops

Python loops, if...else conditions

Data input

Data manipulation

Data output

Exercises

Summary

2. Introduction to Python Modules

What is a Python module?

Introduction to NumPy

Introduction to SciPy

Introduction to matplotlib

How to install matplotlib

Several graphical presentations using matplotlib

Introduction to statsmodels

Introduction to pandas

Python modules related to finance

Introduction to the pandas_reader module

Two financial calculators

How to install a Python module

Module dependency

Exercises

Summary

3. Time Value of Money

Introduction to time value of money

Writing a financial calculator in Python

Definition of NPV and NPV rule

Definition of IRR and IRR rule

Definition of payback period and payback period rule

Writing your own financial calculator in Python

Two general formulae for many functions

Appendix A – Installation of Python, NumPy, and SciPy

Appendix B – visual presentation of time value of money

Appendix C – Derivation of present value of annuity from present value of one future cash flow and present value of perpetuity

Appendix D – How to download a free financial calculat

Appendix E – The graphical presentation of the relationship between NPV and R

Appendix F – graphical presentation of NPV profile with two IRRs

Appendix G – Writing your own financial calculator in Python

Exercises

Summary

4. Sources of Data

Diving into deeper concepts

Retrieving data from Yahoo!Finance

Retrieving data from Google Finance

Retrieving data from FRED

Retrieving data from Prof. French's data library

Retrieving data from the Census Bureau, Treasury, and BLS

Generating two dozen datasets

Several datasets related to CRSP and Compustat

Appendix A – Python program for return distribution versus a normal distribution

Appendix B – Python program to a draw candle-stick picture

Appendix C – Python program for price movement

Appendix D – Python program to show a picture of a stock's intra-day movement

Appendix E –properties for a pandas DataFrame

Appendix F –how to generate a Python dataset with an extension of .pkl or .pickle

Appendix G – data case #1 -generating several Python datasets

Exercises

Summary

5. Bond and Stock Valuation

Introduction to interest rates

Term structure of interest rates

Bond evaluation

Stock valuation

A new data type – dictionary

Appendix A – simple interest rate versus compounding interest rate

Appendix B – several Python functions related to interest conversion

Appendix C – Python program for rateYan.py

Appendix D – Python program to estimate stock price based on an n-period model

Appendix E – Python program to estimate the duration for a bond

Appendix F – data case #2 – fund raised from a new bond issue

Summary

6. Capital Asset Pricing Model

Introduction to CAPM

Moving beta

Adjusted beta

Scholes and William adjusted beta

Extracting output data

Outputting data to text files

Saving our data to a .csv file

Saving our data to an Excel file

Saving our data to a pickle dataset

Saving our data to a binary file

Reading data from a binary file

Simple string manipulation

Python via Canopy

References

Exercises

Summary

7. Multifactor Models and Performance Measures

Introduction to the Fama-French three-factor model

Fama-French three-factor model

Fama-French-Carhart four-factor model and Fama-French five-factor model

Implementation of Dimson (1979) adjustment for beta

Performance measures

How to merge different datasets

Appendix A – list of related Python datasets

Appendix B – Python program to generate ffMonthly.pkl

Appendix C – Python program for Sharpe ratio

Appendix D – data case #4 – which model is the best, CAPM, FF3, FFC4, or FF5, or others?

References

Exercises

Summary

8. Time-Series Analysis

Introduction to time-series analysis

Merging datasets based on a date variable

Using pandas.date_range() to generate one dimensional time-series

Return estimation

Converting daily returns to monthly ones

Merging datasets by date

Understanding the interpolation technique

Merging data with different frequencies

Tests of normality

Estimating fat tails

T-test and F-test

Tests of equal variances

Testing the January effect

52-week high and low trading strategy

Estimating Roll's spread

Estimating Amihud's illiquidity

Estimating Pastor and Stambaugh (2003) liquidity measure

Fama-MacBeth regression

Durbin-Watson

Python for high-frequency data

Spread estimated based on high-frequency data

Introduction to CRSP

References

Appendix A – Python program to generate GDP dataset usGDPquarterly2.pkl

Appendix B – critical values of F for the 0.05 significance level

Appendix C – data case #4 - which political party manages the economy better?

Exercises

Summary

9. Portfolio Theory

Introduction to portfolio theory

A 2-stock portfolio

Optimization – minimization

Forming an n-stock portfolio

Constructing an optimal portfolio

Constructing an efficient frontier with n stocks

References

Appendix A – data case #5 - which industry portfolio do you prefer?

Appendix B – data case #6 - replicate S&P500 monthly returns

Exercises

Summary

10. Options and Futures

Introducing futures

Payoff and profit/loss functions for call and put options

European versus American options

Understanding cash flows, types of options, rights and obligations

Black-Scholes-Merton option model on non-dividend paying stocks

Generating our own module p4f

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

Butterfly with calls

The relationship between input values and option values

Greeks

Put-call parity and its graphic presentation

The put-call ratio for a short period with a trend

Binomial tree and its graphic presentation

Binomial tree (CRR) method for European options

Binomial tree (CRR) method for American options

Hedging strategies

Implied volatility

Binary-search

Retrieving option data from Yahoo! Finance

Volatility smile and skewness

References

Appendix A – data case 6: portfolio insurance

Exercises

Summary

11. Value at Risk

Introduction to VaR

Normality tests

Skewness and kurtosis

Modified VaR

VaR based on sorted historical returns

Simulation and VaR

VaR for portfolios

Backtesting and stress testing

Expected shortfall

Appendix A – data case 7 – VaR estimation for individual stocks and a portfolio

References

Exercises

Summary

12. Monte Carlo Simulation

Importance of Monte Carlo Simulation

Generating random numbers from a standard normal distribution

Drawing random samples from a normal distribution

Generating random numbers with a seed

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

With/without replacements

Distribution of annual returns

Simulation of stock price movements

Graphical presentation of stock prices at options' maturity dates

Replicating a Black-Scholes-Merton call using simulation

Exotic option #1 – using the Monte Carlo Simulation to price average

Exotic option #2 – pricing barrier options using the Monte Carlo Simulation

Liking two methods for VaR using simulation

Capital budgeting with Monte Carlo Simulation

Python SimPy module

Comparison between two social policies – basic income and basic job

Finding an efficient frontier based on two stocks by using simulation

Constructing an efficient frontier with n stocks

Long-term return forecasting

Efficiency, Quasi-Monte Carlo, and Sobol sequences

Appendix A – data case #8 - Monte Carlo Simulation and blackjack

References

Exercises

Summary

13. Credit Risk Analysis

Introduction to credit risk analysis

Credit rating

Credit spread

YIELD of AAA-rated bond, Altman Z-score

Using the KMV model to estimate the market value of total assets and its volatility

Term structure of interest rate

Distance to default

Credit default swap

Appendix A – data case #8 - predicting bankruptcy by using Z-score

References

Exercises

Summary

14. Exotic Options

European, American, and Bermuda options

Chooser options

Shout options

Binary options

Rainbow options

Pricing average options

Pricing barrier options

Barrier in-and-out parity

Graph of up-and-out and up-and-in parity

Pricing lookback options with floating strikes

Appendix A – data case 7 – hedging crude oil

References

Exercises

Summary

15. Volatility, Implied Volatility, ARCH, and GARCH

Conventional volatility measure – standard deviation

Tests of normality

Estimating fat tails

Lower partial standard deviation and Sortino ratio

Test of equivalency of volatility over two periods

Test of heteroskedasticity, Breusch, and Pagan

Volatility smile and skewness

Graphical presentation of volatility clustering

The ARCH model

Simulating an ARCH (1) process

The GARCH model

Simulating a GARCH process

Simulating a GARCH (p,q) process using modified garchSim()

GJR_GARCH by Glosten, Jagannanthan, and Runkle

References

Appendix A – data case 8 - portfolio hedging using VIX calls

References

Appendix B – data case 8 - volatility smile and its implications

Exercises

Summary

Index