Yves Hilpisch - Financial Theory with Python - Early Release
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by Yves Hilpisch
Copyright 2021 Yves Hilpisch. All rights reserved.
Printed in the United States of America.
Published by OReilly Media, Inc. , 1005 Gravenstein Highway North, Sebastopol, CA 95472.
OReilly books may be purchased for educational, business, or sales promotional use. Online editions are also available for most titles (http://oreilly.com). For more information, contact our corporate/institutional sales department: 800-998-9938 or corporate@oreilly.com .
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- November 2021: First Edition
- 2021-06-24: First Release
- 2021-08-23: Second Release
See http://oreilly.com/catalog/errata.csp?isbn=9781098104351 for release details.
The OReilly logo is a registered trademark of OReilly Media, Inc. Financial Theory with Python, the cover image, and related trade dress are trademarks of OReilly Media, Inc.
The views expressed in this work are those of the author, and do not represent the publishers views. While the publisher and the author have used good faith efforts to ensure that the information and instructions contained in this work are accurate, the publisher and the author disclaim all responsibility for errors or omissions, including without limitation responsibility for damages resulting from the use of or reliance on this work. Use of the information and instructions contained in this work is at your own risk. If any code samples or other technology this work contains or describes is subject to open source licenses or the intellectual property rights of others, it is your responsibility to ensure that your use thereof complies with such licenses and/or rights. This book is not intended as financial advice. Please consult a qualified professional if you require financial advice.
978-1-098-10428-3
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If you have comments about how we might improve the content and/or examples in this book, or if you notice missing material within this chapter, please reach out to the editor at .
Python was quickly becoming the de-facto language for data science, machine learning and natural language processing; it would unlock new sources of innovation. Python would allow us to engage with its sizeable open source community, bringing state-of-the-art technology in-house quickly, while allowing for customization.
Kindmann and Taylor (2021)
Technological trends like online trading platforms, open source software, and open financial data have significantly lowered or even completely removed the barriers of entry to the global financial markets. Individuals with only limited amounts of cash at their free disposal can get started, for example, with algorithmic trading within hours. Students and academics in financial disciplines with a little bit of background knowledge in programming can easily apply cutting edge innovations in machine and deep learning to financial dataon the notebooks they bring to their finance classes. On the hardware side, cloud providers offer professional compute and data processing capabilities starting at 5 USD per month, billed by the hour and with almost unlimited scalability. So far, academic and professional finance education has only partly reacted to these trends.
This book teaches both finance and the Python programming language from ground up. Nowadays, finance and programming in general are closely intertwined disciplines, with Python being one of the most widely used programming languages in the financial industry. The book presents all relevant foundationsfrom mathematics, finance, and programmingin an integrated but not too technical fashion. Traditionally, theoretical finance and computational finance have been more or less separate disciplines. The fact that programming classes (for example, in Python but also in C++) have become an integral part of Master of Financial Engineering and similar university programs shows how important programming skills have become in the field.
However, mathematical foundations, theoretical finance, and basic programming techniques are still quite often taught independent from each other and only later on combined to computational finance. This book takes a different approach in that the mathematical conceptsfor example, from linear algebra and probability theoryprovide the common background against which financial ideas and programming techniques alike are introduced. Abstract mathematical concepts are thereby motivated from two different angles: finance and programming. In addition, this approach allows for a new learning experience since both mathematical and financial concepts can directly be translated into executable code that can then be explored interactively.
Several readers of one of my other books, Python for Finance (2nd ed., 2018, OReilly), pointed out that it teaches neither finance or Python from the ground up. Indeed, the reader of that book is expected to have at least some experience in both finance and (Python) programming. Financial Theory with Python closes this gap in that it focuses on more fundamental concepts from both finance and Python programming. In that sense, readers who finish this book can naturally progress to Python for Finance to further build and improve their Python skills as applied to finance.
I have written a number of books about Python applied to finance. My company, The Python Quants offers a number of live and online training classes in Python for finance. All my previous books and the training classes expect the book readers and training participants to have already some background knowledge in both finance and Python programming or a similar language.
This book starts completely from scratch, just expecting some basic knowledge in mathematics, in particular from calculus, linear algebra, and probability theory. Although the book material is almost self-contained with regard to the mathematical concepts introduced, it is recommended to use an introductory mathematics book like the one by Pemberton and Rau (2016) for further details if needed.
Given this approach, the book targets students, academics, and professionals alike who want to learn about financial theory, financial data modeling, and the use of Python for computational finance. It is a systematic introduction to the field on which to build through more advanced books or training programs. Reader with a formal financial background will find the mathematical and financial elements of the book rather simple and straightforward. On the other hand, readers with a stronger programming background will find the Python elements rather simple and easy to understand.
Even if the reader does not intend to move on to more advanced topics in computational finance, algorithmic trading, or asset management, the Python and finance skills acquired through this book can be applied beneficially to standard problems in finance, such as the composition of investment portfolios according to Modern Portfolio Theory (MPT). The book also teaches, for example, how to value options and other derivatives by standard methods such as replication portfolios or risk-neutral pricing.
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