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

Python

Concepts

Machine Learning

Author (1):

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

Conclusions and Next Steps

Our goal for this book was to enable you to apply machine learning (ML) to a variety of data sources and extract signals that add value to a trading strategy. To this end, we took a more comprehensive view of the investment process, from idea generation to strategy evaluation, and introduced ML as an important element of this process in the form of the ML4T workflow.

While demonstrating the use of a broad range of ML algorithms, from the fundamental to the advanced, we saw how ML can add value at multiple steps in the process of designing, testing, and executing a strategy. For the most part, however, we focused on the core ML value proposition, which consists of the ability to extract actionable information from much larger amounts of data more systematically than human experts would ever be able to.

This value proposition has really gained currency with the explosion of digital data that made it both more promising and necessary...