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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
Concepts
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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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Toc

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

To get the most out of this book

All you need for this book is a basic understanding of Python and machine learning techniques.

Download the example code files

You can download the example code files for this book from your account at www.packt.com. If you purchased this book elsewhere, you can visit www.packt.com/support and register to have the files emailed directly to you.

You can download the code files by following these steps:

  1. Log in or register at www.packt.com.
  2. Select the SUPPORT tab.
  3. Click on Code Downloads & Errata.
  4. Enter the name of the book in the Search box and follow the onscreen instructions.

Once the file is downloaded, please make sure that you unzip or extract the folder using the latest version of:

  • WinRAR/7-Zip for Windows
  • Zipeg/iZip/UnRarX for Mac
  • 7-Zip/PeaZip for Linux

The code bundle for the book is also hosted on GitHub at https://github.com/PacktPublishing/Hands-On-Machine-Learning-for-Algorithmic-Trading. In case there's an update to the code, it will be updated on the existing GitHub repository.

We also have other code bundles from our rich catalog of books and videos available at https://github.com/PacktPublishing/. Check them out!

Download the color images

Conventions used

There are a number of text conventions used throughout this book.

CodeInText: Indicates code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles. Here is an example: "The algorithm continues to execute after calling the run_algorithm() function and returns the same backtest performance DataFrame."

A block of code is set as follows:

interesting_times = extract_interesting_date_ranges(returns=returns)
interesting_times['Fall2015'].to_frame('pf') \
.join(benchmark_rets) \
.add(1).cumprod().sub(1) \
.plot(lw=2, figsize=(14, 6), title='Post-Brexit Turmoil')

Bold: Indicates a new term, an important word, or words that you see onscreen.

Warnings or important notes appear like this.
Tips and tricks appear like this.
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