Yuxing Yan - Python for Finance
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- Book:Python for Finance
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- Year:2017
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Abstract: Learn and implement various Quantitative Finance concepts using the popular Python librariesAbout This Book* Understand the fundamentals of Python data structures and work with time-series data* Implement key concepts in quantitative finance using popular Python libraries such as NumPy, SciPy, and matplotlib* A step-by-step tutorial packed with many Python programs that will help you learn how to apply Python to financeWho This Book Is ForThis book assumes that the readers have some basic knowledge related to Python. However, he/she has no knowledge of quantitative finance. In addition, he/she has no knowledge about financial data. What You Will Learn* Become acquainted with Python in the first two chapters* Run CAPM, Fama-French 3-factor, and Fama-French-Carhart 4-factor models* Learn how to price a call, put, and several exotic options* Understand Monte Carlo simulation, how to write a Python program to replicate the Black-Scholes-Merton options model, and how to price a few exotic options* Understand the concept of volatility and how to test the hypothesis that volatility changes over the years* Understand the ARCH and GARCH processes and how to write related Python programsIn DetailThis book uses Python as its computational tool. Since Python is free, any school or organization can download and use it. This book is organized according to various finance subjects. In other words, the first edition focuses more on Python, while the second edition is truly trying to apply Python to finance. The book starts by explaining topics exclusively related to Python. Then we deal with critical parts of Python, explaining concepts such as time value of money stock and bond evaluations, capital asset pricing model, multi-factor models, time series analysis, portfolio theory, options and futures. This book will help us to learn or review the basics of quantitative finance and apply Python to solve various problems, such as estimating IBMs market risk, running a Fama-French 3-factor, 5-factor, or Fama-French-Carhart 4 factor model, estimating the VaR of a 5-stock portfolio, estimating the optimal portfolio, and constructing the efficient frontier for a 20-stock portfolio with real-world stock, and with Monte Carlo Simulation. Later, we will also learn how to replicate the famous Black-Scholes-Merton option model and how to price exotic options such as the average price call option. Style and approachThis book takes a step-by-step approach in explaining the libraries and modules in Python, and how they can be used to implement various aspects of quantitative finance. Each concept is explained in depth and supplemented with code examples for better understanding
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Copyright 2017 Packt Publishing
All rights reserved. No part of this book may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, without the prior written permission of the publisher, except in the case of brief quotations embedded in critical articles or reviews.
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Packt Publishing has endeavored to provide trademark information about all of the companies and products mentioned in this book by the appropriate use of capitals. However, Packt Publishing cannot guarantee the accuracy of this information.
First published: April 2014
Second edition: June 2017
Production reference: 1270617
Published by Packt Publishing Ltd.
Livery Place
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Birmingham B3 2PB, UK.
ISBN 978-1-78712-569-8
www.packtpub.com
Author
Yuxing Yan
Reviewers
Dr. Param Jeet
Nabih Ibrahim Bawazir, M.Sc.
Joran Beasley
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Cover Work
Nilesh Mohite
Yuxing Yan graduated from McGill University with a PhD in finance. Over the years, he has been teaching various finance courses at eight universities: McGill University and Wilfrid Laurier University (in Canada), Nanyang Technological University (in Singapore), Loyola University of Maryland, UMUC, Hofstra University, University at Buffalo, and Canisius College (in the US).
His research and teaching areas include: market microstructure, open-source finance and financial data analytics. He has 22 publications including papers published in the Journal of Accounting and Finance, Journal of Banking and Finance, Journal of Empirical Finance, Real Estate Review, Pacific Basin Finance Journal, Applied Financial Economics, and Annals of Operations Research.
He is good at several computer languages, such as SAS, R, Python, Matlab, and C.
His four books are related to applying two pieces of open-source software to finance: Python for Finance (2014), Python for Finance (2nd ed., expected 2017), Python for Finance (Chinese version, expected 2017), and Financial Modeling Using R (2016).
In addition, he is an expert on data, especially on financial databases. From 2003 to 2010, he worked at Wharton School as a consultant, helping researchers with their programs and data issues. In 2007, he published a book titled Financial Databases (with S.W. Zhu). This book is written in Chinese.
Currently, he is writing a new book called Financial Modeling Using Excel in an R-Assisted Learning Environment. The phrase "R-Assisted" distinguishes it from other similar books related to Excel and financial modeling. New features include using a huge amount of public data related to economics, finance, and accounting; an efficient way to retrieve data: 3 seconds for each time series; a free financial calculator, showing 50 financial formulas instantly, 300 websites, 100 YouTube videos, 80 references, paperless for homework, midterms, and final exams; easy to extend for instructors; and especially, no need to learn R.
I would like to thank Ben Amoako-Adu, Brian Smith (who taught me the first two finance courses and offered unstinting support for many years after my graduation), George Athanassakos (one of his assignments "forced" me to learn C), and Jin-Chun Duan.
I would also like to thank Wei-Hung Mao, Jerome Detemple, Bill Sealey, Chris Jacobs, Mo Chaudhury, Summon Mazumdar (my former professors at McGill), and Lawrence Kryzanowski. (His wonderful teaching inspired me to concentrate on empirical finance and he edited my doctoral thesis word by word even though he was not my supervisor!). There is no doubt that my experience at Wharton has shaped my thinking and enhanced my skill sets. I thank Chris Schull and Michael Boldin for offering me the job; Mark Keintz, Dong Xu, Steven Crispi, and Dave Robinson, my former colleagues, who helped me greatly during my first two years at Wharton; and Eric Zhu, Paul Ratnaraj, Premal Vora, Shuguang Zhang, Michelle Duan, Nicholle Mcniece, Russ Ney, Robin Nussbaum-Gold, and Mireia Gine for all their help. In addition, I'd like to thank Shaobo Ji, Tong Yu, Shaoming Huang, Xing Zhang.
Dr. Param Jeet has a Ph.D. in mathematics from one of India's leading engineering institutes, IIT Madras. Dr. Param Jeet has a decade of experience in the data analytics industry. He started his career with Bank of America and since then worked with a few companies as a data scientist. He has also worked across domains such as capital market, education, telecommunication and healthcare. Dr. Param Jeet has expertise in Quantitative finance, Data analytics, machine learning, R, Python, Matlab, SQL, and big data technologies. He has also published a few research papers in reputed international journals, published and reviewed books, and has worked on Learning Quantitative Finance with R .
Nabih Ibrahim Bawazir, M.Sc. is a data scientist at an Indonesian financial technology start-up backed by Digital Alpha Group, Pte Ltd., Singapore. Most of his work is research on the development phase, from financial modeling to data-driven underwriting. Previously, he worked as actuary in CIGNA. He holds M.Sc in Financial Mathematics from Gadjah Mada University, Indonesia.
Joran Beasley received his degree in computer science from the University of Idaho. He works has been programming desktop applications in wxPython professionally for monitoring large scale sensor networks for use in agriculture for the last 7 years. He currently lives in Moscow Idaho, and works at Decagon Devices Inc. as a software engineer.
I would like to thank my wife Nicole, for putting up with my long hours hunched over a keyboard, and her constant support and help in raising our two wonderful children.
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