Yves Hilpisch - Financial Theory with Python: A Gentle Introduction
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Nowadays, finance, mathematics, and programming are intrinsically linked. This book provides the relevant foundations of each discipline to give you the major tools you need to get started in the world of computational finance.
Using an approach where mathematical concepts provide the common background against which financial ideas and programming techniques are learned, this practical guide teaches you the basics of financial economics. Written by the best-selling author of Python for Finance, Yves Hilpisch, Financial Theory with Python explains financial, mathematical, and Python programming concepts in an integrative manner so that the interdisciplinary concepts reinforce each other.
- Draw upon mathematics to learn the foundations of financial theory and Python programming
- Learn about financial theory, financial data modeling, and the use of Python for computational finance
- Leverage simple economic models to better understand basic notions of finance and Python programming concepts
- Use both static and dynamic financial modeling to address fundamental problems in finance, such as pricing, decision-making, equilibrium, and asset allocation
- Learn the basics of Python packages useful for financial modeling, such as NumPy, pandas, Matplotlib, and SymPy
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by Yves Hilpisch
Copyright 2022 Yves Hilpisch. All rights reserved.
Printed in the United States of America.
Published by OReilly Media, Inc. , 1005 Gravenstein Highway North, Sebastopol, CA 95472.
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- Illustrator: Kate Dullea
- October 2021: First Edition
- 2021-09-23: First 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.
978-1-098-10435-1
[LSI]
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.
Kindman 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 the 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 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 independently from one another and only later in combination with 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 nor 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. More guidance is provided in the final chapter.
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. For all of my previous books and the training classes, the book readers and training participants are expected to already have some background knowledge in both finance and Python programming or a similar language.
This book starts completely from scratch, with just the expectation that the reader has 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, an introductory mathematics book like the one by Pemberton and Rau (2016) is recommended for further details if needed.
Given this approach, this 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. Readers 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). This book also teaches, for example, how to value options and other derivatives by standard methods such as replication portfolios or risk-neutral pricing.
This book is also suitable for executives in the financial industry who want to learn about the Python programming language as applied to finance. On the other hand, it can also be read by those already proficient in Python or another programming language who want to learn more about the application of Python in finance.
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