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Hands-On Machine Learning for Algorithmic Trading

You're reading from   Hands-On Machine Learning for Algorithmic Trading Design and implement investment strategies based on smart algorithms that learn from data using Python

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Product type Paperback
Published in Dec 2018
Publisher Packt
ISBN-13 9781789346411
Length 684 pages
Edition 1st Edition
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Authors (2):
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Jeffrey Yau Jeffrey Yau
Author Profile Icon Jeffrey Yau
Jeffrey Yau
Stefan Jansen Stefan Jansen
Author Profile Icon Stefan Jansen
Stefan Jansen
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Table of Contents (23) Chapters Close

Preface 1. Machine Learning for Trading FREE CHAPTER 2. Market and Fundamental Data 3. Alternative Data for Finance 4. Alpha Factor Research 5. Strategy Evaluation 6. The Machine Learning Process 7. Linear Models 8. Time Series Models 9. Bayesian Machine Learning 10. Decision Trees and Random Forests 11. Gradient Boosting Machines 12. Unsupervised Learning 13. Working with Text Data 14. Topic Modeling 15. Word Embeddings 16. Deep Learning 17. Convolutional Neural Networks 18. Recurrent Neural Networks 19. Autoencoders and Generative Adversarial Nets 20. Reinforcement Learning 21. Next Steps 22. Other Books You May Enjoy

Summary

In this chapter, we introduced algorithmic trading strategies and how ML has become a key ingredient for the design and combination of alpha factors, which in turn are the key drivers of portfolio performance. We covered various industry trends around algorithmic trading strategies, the emergence of alternative data, and the use of ML to exploit these new sources of informational advantages.

Furthermore, we introduced the algorithmic-trading-strategy design process, important types of alpha factors, and how we will use ML to design and execute our strategies. In the next two chapters, we will take a closer look at the oil that fuels any algorithmic trading strategy—the market, fundamental, and alternative data sources—using ML.

You have been reading a chapter from
Hands-On Machine Learning for Algorithmic Trading
Published in: Dec 2018
Publisher: Packt
ISBN-13: 9781789346411
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