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Machine Learning for Algorithmic Trading

You're reading from   Machine Learning for Algorithmic Trading Predictive models to extract signals from market and alternative data for systematic trading strategies with Python

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Product type Paperback
Published in Jul 2020
Publisher Packt
ISBN-13 9781839217715
Length 820 pages
Edition 2nd Edition
Languages
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Author (1):
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Stefan Jansen Stefan Jansen
Author Profile Icon Stefan Jansen
Stefan Jansen
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Toc

Table of Contents (27) Chapters Close

Preface 1. Machine Learning for Trading – From Idea to Execution 2. Market and Fundamental Data – Sources and Techniques FREE CHAPTER 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 explored the use of topic modeling to gain insights into the content of a large collection of documents. We covered latent semantic indexing that uses dimensionality reduction of the DTM to project documents into a latent topic space. While effective in addressing the curse of dimensionality caused by high-dimensional word vectors, it does not capture much semantic information. Probabilistic models make explicit assumptions about the interplay of documents, topics, and words that allow algorithms to reverse engineer the document generation process and evaluate the model fit on new documents. We learned that LDA is capable of extracting plausible topics that allow us to gain a high-level understanding of large amounts of text in an automated way, while also identifying relevant documents in a targeted way.

In the next chapter, we will learn how to train neural networks that embed individual words in a high-dimensional vector space that captures important...

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