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

Long-short signals for Japanese stocks

In Chapter 9, Time-Series Models for Volatility Forecasts and Statistical Arbitrage, we used cointegration tests to identify pairs of stocks with a long-term equilibrium relationship in the form of a common trend to which their prices revert.

In this chapter, we will use the predictions of a machine learning model to identify assets that are likely to go up or down so we can enter market-neutral long and short positions, accordingly. The approach is similar to our initial trading strategy that used linear regression in Chapter 7, Linear Models – From Risk Factors to Return Forecasts, and Chapter 8, The ML4T Workflow – From Model to Strategy Backtesting.

Instead of the scikit-learn random forest implementation, we will use the LightGBM package, which has been primarily designed for gradient boosting. One of several advantages is LightGBM's ability to efficiently encode categorical variables as numeric features rather...