Machine Learning for Algorithmic Trading: Predictive models to extract signals from market and alternative data for systematic trading strategies with Python, 2nd Edition

4.5 out of 5 stars 167 ratings
ISBN-13: 978-1839217715
ISBN-10: 1839217715
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From the Publisher

Editorial Reviews

Review

"Algorithmic Trading is about timing the market using data and algorithms in order to improve your own trading performance, outcomes, and earnings. The wealth of techniques, algorithms, and models that are used for those purposes are presented comprehensively in this giant book and are also applicable to countless other predictive modeling applications and diverse use cases. That makes this an excellent machine learning book for all learners and users of predictive algorithms in data science and analytics."

--

Dr Kirk Borne, Principal Data Scientist, Data Science Fellow, and Executive Advisor at Booz Allen Hamilton, and co-author of Ten Signs of Data Science Maturity



"Stock markets are one of the most uncertain sectors, where decision making is often more an art than a science. Machine Learning is one of the best resources to analyze a large amount of data and make the most reasonable predictions. In his book, Stefan Jansen describes all cutting-edge methods, starting from the basic concepts concerning the dynamics of a stock market and going deeper and deeper into the application of robust algorithms to implement predictive analytics. With a clear, concise, and effective style, the author guides the reader on a journey to discover time-series analysis, regression methods, Bayesian algorithms, NLP, and GANs. All algorithms are provided with financial explanations and practical examples to help the reader start making rational and intelligent investments!"

--

Giuseppe Bonaccorso, Global Head of Innovative Data Science at Bayer Pharmaceuticals, and author of Mastering Machine Learning Algorithms Second Edition



"If you have done a finance module before, you will know that data and mathematics comes together very well in the world of trading. This idea is further reinforced in the book "The Man who Solved the Market" by Gregory Zuckerman. As the world of data grows in the 4 Vs dimension, namely Volume, Variety, Velocity, and Veracity, the circumstances present many opportunities for data to be used in algorithmic trading. Stefan covers the topic of algorithmic trading comprehensively, from selecting features and portfolio management to using text mining to spot trading opportunities. You will be able to find lots of possible use cases for Machine Learning in your trading! Together with the tools stated in the book which are open-source (no license fees!), your entry into the algorithmic trading world will be easier."

--

Koo Ping Shung, Co-founder & Practicum Director at Data Science Rex, Co-founder of DataScience SG, and LinkedIn Top Voice 2020

About the Author

Stefan is the founder and CEO of Applied AI. He advises Fortune 500 companies, investment firms, and startups across industries on data & AI strategy, building data science teams, and developing end-to-end machine learning solutions for a broad range of business problems.

Before his current venture, he was a partner and managing director at an international investment firm, where he built the predictive analytics and investment research practice. He was also a senior executive at a global fintech company with operations in 15 markets, advised Central Banks in emerging markets, and consulted for the World Bank.

He holds Master's degrees in Computer Science from Georgia Tech and in Economics from Harvard and Free University Berlin, and a CFA Charter. He has worked in six languages across Europe, Asia, and the Americas and taught data science at Datacamp and General Assembly.

Product details

  • Publisher ‏ : ‎ Packt Publishing (July 31, 2020)
  • Language ‏ : ‎ English
  • Paperback ‏ : ‎ 820 pages
  • ISBN-10 ‏ : ‎ 1839217715
  • ISBN-13 ‏ : ‎ 978-1839217715
  • Item Weight ‏ : ‎ 3.05 pounds
  • Dimensions ‏ : ‎ 7.5 x 1.85 x 9.25 inches
  • Customer Reviews:
    4.5 out of 5 stars 167 ratings

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Stefan is the founder and CEO of Applied AI. He advises Fortune 500 companies, investment firms, and startups across industries on data &amp; AI strategy, building data science teams, and developing end-to-end machine learning solutions.

Before his current venture, he was a partner and managing director at an international investment firm, where he built the predictive analytics and investment research practice. He was also a senior executive at a global fintech company with operations in 15 markets, advised Central Banks in emerging markets, and consulted for the World Bank.

He holds Master's degrees in Computer Science from Georgia Tech and in Economics from Harvard and Free University Berlin, and is a CFA Charterholder. He has worked in six languages across Europe, Asia, and the Americas and taught data science at Data camp and General Assembly.

Customer reviews

4.5 out of 5 stars
4.5 out of 5
167 global ratings

Top reviews from the United States

Reviewed in the United States on September 20, 2020
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Reviewed in the United States on October 27, 2020
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Reviewed in the United States on October 3, 2020
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5.0 out of 5 stars Incredibly complete book
By dan resnic on October 3, 2020
This book is painstakingly detailed, there is definitely something here for anyone interested in the subject.

For anyone that wants this book you should understand that this book is what you make of it. If you want to understand the topics you can skim through the pages skipping the details, but if you want to know every tiny aspect of what makes these algorithms run then you'll find them here as well.

Absolutely recommended
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20 people found this helpful
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Reviewed in the United States on April 9, 2021
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5.0 out of 5 stars Most books talk about theory. This one gives you the hands on labs.
By Jeremy R. Whittaker on April 9, 2021
I've never read a book as challenging as this book. But I honestly didn't expect anything less. This is hands down the best book I've read on algorithmic trading. Further, the author goes out of his way to respond to your issues personally on GitHub when they arise. I would highly recommend this book for anyone trying to crack into algorithmic trading.
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5 people found this helpful
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Reviewed in the United States on September 10, 2021
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One person found this helpful
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Reviewed in the United States on October 30, 2020
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4 people found this helpful
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Reviewed in the United States on March 20, 2021
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Reviewed in the United States on February 19, 2021
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Top reviews from other countries

Exiled-Edik
4.0 out of 5 stars A superb educator and reference book
Reviewed in the United Kingdom on January 26, 2021
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7 people found this helpful
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ML expert
3.0 out of 5 stars Overhyped book
Reviewed in India on September 17, 2020
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5 people found this helpful
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martin
3.0 out of 5 stars Does give an Introduction to the literature but some things are just plane wrong
Reviewed in Germany on June 19, 2021
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2 people found this helpful
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Jing Zhang
5.0 out of 5 stars The most thorough and the most practical book on Machine Learning for trading
Reviewed in Canada on January 31, 2021
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Dr. Marek Kolman
4.0 out of 5 stars Good but many typos
Reviewed in the Netherlands on September 21, 2021
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