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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

The rise of ML in the investment industry

The investment industry has evolved dramatically over the last several decades and continues to do so amid increased competition, technological advances, and a challenging economic environment. This section reviews key trends that have shaped the overall investment environment and the context for algorithmic trading and the use of ML more specifically.

The trends that have propelled algorithmic trading and ML to their current prominence include:

  • Changes in the market microstructure, such as the spread of electronic trading and the integration of markets across asset classes and geographies
  • The development of investment strategies framed in terms of risk-factor exposure, as opposed to asset classes
  • The revolutions in computing power, data generation and management, and statistical methods, including breakthroughs in deep learning
  • The outperformance of the pioneers in algorithmic trading relative to human...
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Machine Learning for Algorithmic Trading - Second Edition
Published in: Jul 2020 Publisher: Packt ISBN-13: 9781839217715
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