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

Designing an NN

DL relies on NNs, which consist of a few key building blocks, which in turn can be configured in a multitude of ways. In this section, we introduce how NNs work and illustrate their most important components used to design different architectures.

(Artificial) NNs were originally inspired by biological models of learning like the human brain, either in an attempt to mimic how it works and achieve similar success, or to gain a better understanding through simulation. Current NN research draws less on neuroscience, not least since our understanding of the brain has not yet reached a sufficient level of granularity. Another constraint is overall size: even if the number of neurons used in NNs continued to double every year since their inception in the 1950s, they would only reach the scale of the human brain around 2050.

We will also explain how backpropagation, often simply called backprop, uses gradient information (the value of the partial derivative of the...