Search icon
Arrow left icon
All Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Newsletters
Free Learning
Arrow right icon
Machine Learning for Algorithmic Trading - Second Edition

You're reading from  Machine Learning for Algorithmic Trading - Second Edition

Product type Book
Published in Jul 2020
Publisher Packt
ISBN-13 9781839217715
Pages 822 pages
Edition 2nd Edition
Languages
Author (1):
Stefan Jansen Stefan Jansen
Profile icon Stefan Jansen

Table of Contents (27) Chapters

Preface 1. Machine Learning for Trading – From Idea to Execution 2. Market and Fundamental Data – Sources and Techniques 3. Alternative Data for Finance – Categories and Use Cases 4. Financial Feature Engineering – How to Research Alpha Factors 5. Portfolio Optimization and Performance Evaluation 6. The Machine Learning Process 7. Linear Models – From Risk Factors to Return Forecasts 8. The ML4T Workflow – From Model to Strategy Backtesting 9. Time-Series Models for Volatility Forecasts and Statistical Arbitrage 10. Bayesian ML – Dynamic Sharpe Ratios and Pairs Trading 11. Random Forests – A Long-Short Strategy for Japanese Stocks 12. Boosting Your Trading Strategy 13. Data-Driven Risk Factors and Asset Allocation with Unsupervised Learning 14. Text Data for Trading – Sentiment Analysis 15. Topic Modeling – Summarizing Financial News 16. Word Embeddings for Earnings Calls and SEC Filings 17. Deep Learning for Trading 18. CNNs for Financial Time Series and Satellite Images 19. RNNs for Multivariate Time Series and Sentiment Analysis 20. Autoencoders for Conditional Risk Factors and Asset Pricing 21. Generative Adversarial Networks for Synthetic Time-Series Data 22. Deep Reinforcement Learning – Building a Trading Agent 23. Conclusions and Next Steps 24. References
25. Index
Appendix: Alpha Factor Library

What to expect

This book aims to equip you with a strategic perspective, conceptual understanding, and practical tools to add value when applying ML to the trading and investment process. To this end, we cover ML as a key element in a process rather than a standalone exercise. Most importantly, we introduce an end-to-end ML for trading (ML4T) workflow that we apply to numerous use cases with relevant data and code examples.

The ML4T workflow starts with generating ideas and sourcing data and continues to extracting features, tuning ML models, and designing trading strategies that act on the models' predictive signals. It also includes simulating strategies on historical data using a backtesting engine and evaluating their performance.

First and foremost, the book demonstrates how you can extract signals from a diverse set of data sources and design trading strategies for different asset classes using a broad range of supervised, unsupervised, and reinforcement learning algorithms. In addition, it provides relevant mathematical and statistical background to facilitate tuning an algorithm and interpreting the results. Finally, it includes financial background to enable you to work with market and fundamental data, extract informative features, and manage the performance of a trading strategy.

The book emphasizes that investors can gain at least as much value from third-party data as other industries. As a consequence, it covers not only how to work with market and fundamental data but also how to source, evaluate, process, and model alternative data sources such as unstructured text and image data.

It should not be a surprise that this book does not provide investment advice or ready-made trading algorithms. On the contrary, it intends to communicate that ML faces many additional challenges in the trading domain, ranging from lower signal content to shorter time series that often make it harder to achieve robust results. In fact, we have included several examples that do not yield great results to avoid exaggerating the benefits of ML or understating the effort it takes to have a good idea, obtain the right data, engineer ingenious features, and design an effective strategy (with potentially attractive rewards).

Instead, you should find the book most useful as a guide to leveraging key ML algorithms to inform a trading strategy using a systematic workflow. To this end, we present a framework that guides you through the ML4T process of the following:

  1. Sourcing, evaluating, and combining data for any investment objective
  2. Designing and tuning ML models that extract predictive signals from the data
  3. Developing and evaluating trading strategies based on the results

After reading this book, you will be able to begin designing and evaluating your own ML-based strategies and might want to consider participating in competitions or connecting to the API of an online broker and begin trading in the real world.

lock icon The rest of the chapter is locked
Next Chapter arrow right
Register for a free Packt account to unlock a world of extra content!
A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.
Unlock this book and the full library FREE for 7 days
Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of
Renews at $15.99/month. Cancel anytime}