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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
Languages
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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 2. Market and Fundamental Data FREE CHAPTER 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

Conclusion

We started by highlighting the explosion of digital data and the emergence of ML as a strategic capability for investment and trading strategies. This dynamic reflects global business and technology trends beyond finance and is much more likely to continue than to stall or reverse. Many investment firms are just getting started to leverage the range of artificial intelligence tools, just as individuals are acquiring the relevant skills and business processes are adapting to these new opportunities for value creation, as outlined in the introductory chapter.

There are also numerous exciting developments for the application of ML to trading on the horizon that are likely to further propel the current momentum. They are likely to become relevant in the coming years and include the automation of the ML process, the generation of synthetic training data, and the emergence...

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