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

Machine Learning for Algorithmic Trading

Second Edition

Predictive models to extract signals from market and alternative data for systematic trading strategies with Python

Stefan Jansen

BIRMINGHAM - MUMBAI

Machine Learning for Algorithmic Trading

Second Edition

Copyright 2020 Packt Publishing

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Producer: Tushar Gupta

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Content Development Editor: Chris Nelson

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First published: December 2018

Second edition: July 2020

Production reference: 1300720

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ISBN 978-1-83921-771-5

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Stefan Jansen is the founder and CEO of Applied AI. He advises Fortune 500 companies, investment firms, and start-ups across industries on data and 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.

This thorough revision of the first edition was only possible with the collaboration and support of my family, friends, and colleagues. I want to thank the team at Packt for responding to reader feedback and taking the project from start to finish. Chris Nelson was a thorough editor and provided constructive advice. I want to thank my clients for the opportunity to do such exciting work that often offered valuable inspiration for this book.

Most important, however, has been the unrelenting patience and support of Mariana. To her and Bastian, who make it all worthwhile, I dedicate this book.

Prem Jebaseelan has about 20 years of experience in handling different financial data and enjoys the challenge of organizing, storing, retrieving, and analyzing large volumes of data. Prem has designed and implemented several enterprise-level solutions for front office trading strategies, middle office, and back office applications for funds, and has good experience in applying machine learning and AI-based solutions. Prem has an engineering degree.

Prem is currently the co-founder and CEO of Zentropy Technologies, a fintech company that specializes in creating machine learning based solutions in the financial domain. Prior to this, Prem worked in one of the leading hedge funds as a technology solutions provider.

I would like to thank all my previous employers who have helped me in developing real-world solutions that bring technology and finance together. I would specifically like to thank Dr Yves Hilpisch for our work together in the application of machine learning to real-world trading strategies.

Ramanathan Ramakrishnamoorthy is one of the co founders and directors at Zentropy Technologies. Ramanathan started his professional career with a leading hedge fund and in his latest position, he worked as a project manager responsible for building tools and technologies required by the middle and back office. At Zentropy, Ramanathan is primarily responsible for better understanding project requirements and converting them to technical specs. alongside executing them. Having a keen eye for subtle data patterns, Ramanathan also has a good understanding of the machine learning and data science domain, particularly with expertise in the time series analysis domain. Ramanathan's experience has primarily been around building trading systems, quant warehouses, and backtesting engines for capital markets.

Ramanathan is also an active core group member in the Hyderabad Python group. He leads some of the most important activities of the community, like organizing conferences, monthly meetups, and conducting Python sessions at colleges.