Python for Finance Cookbook
Second Edition
Over 80 powerful recipes for effective financial data analysis
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Python for Finance Cookbook
Second Edition
Copyright © 2022 Packt Publishing
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First published: Jan 2020
Second edition: Dec 2022
Production reference: 2291222
Published by Packt Publishing Ltd.
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ISBN 978-1-80324-319-1
Contributors
About the author
received his master’s degree in Quantitative Finance from Erasmus University Rotterdam. In his professional career, he has gained experience in the practical application of data science methods while working in risk management and data science departments of two “big 4” companies, a Dutch neo-broker and most recently the Netherlands’ largest online retailer.
Outside of work, he has written over a hundred articles about topics related to data science, which have been viewed more than 3 million times. In his free time, he enjoys playing video games, reading books, and traveling with his girlfriend.
Writing the second edition of my book was a unique experience. On the one hand, I knew what I should expect. On the other, it proved to be much more challenging in terms of both improving the existing content and expanding upon it. I must also admit that it was a very rewarding feeling to be contacted by readers with kind words about the first edition and valuable feedback on what to add and improve. Thanks to all of that, I have certainly learned a lot, and—in the end—I will remember those times fondly.
I would like to thank Agnieszka for her undeterred support and patience, my brother for once again being my first reader, and my mom for always having my back. I also greatly appreciated all the words of encouragement from my friends and colleagues. Without all of you, completing this book would not have been possible. Thank you.
About the reviewer
is a quantitative researcher specializing in the use of applied natural language processing and machine learning to extract equity trading signals. He is the course director and founder of Quant Guild (https://quantguild.com), an online community dedicated to education on topics pertaining to quantitative finance, data science, and software engineering. Roman is also the maintainer and sole contributor of the popular quantitative finance Python library QFin, available on GitHub and PyPi for use in the simulation of stochastic processes and various derivative pricing settings. His current research interests include natural language processing, machine learning for derivative pricing, randomized numerical linear algebra, and optimal portfolio hedging via reinforcement learning.
Thank you to my family and friends—without them none of my work would be possible.
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