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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

Summary

In this chapter, we reviewed key industry trends around algorithmic trading strategies, the emergence of alternative data, and the use of ML to exploit these new sources of informational advantage. Furthermore, we introduced key elements of the ML4T workflow and outlined important use cases of ML for trading in the context of different strategies.

In the next two chapters, we will take a closer look at the oil that fuels any algorithmic trading strategy—the market, fundamental, and alternative data sources—using ML.

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Machine Learning for Algorithmic Trading - Second Edition
Published in: Jul 2020 Publisher: Packt ISBN-13: 9781839217715
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