Search icon CANCEL
Arrow left icon
Explore Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Conferences
Free Learning
Arrow right icon
Arrow up icon
GO TO TOP
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

Arrow left icon
Product type Paperback
Published in Jul 2020
Publisher Packt
ISBN-13 9781839217715
Length 820 pages
Edition 2nd Edition
Languages
Arrow right icon
Author (1):
Arrow left icon
Stefan Jansen Stefan Jansen
Author Profile Icon Stefan Jansen
Stefan Jansen
Arrow right icon
View More author details
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 Bayesian approaches to machine learning. We saw that they have several advantages, including the ability to encode prior knowledge or opinions, deeper insights into the uncertainty surrounding model estimates and predictions, and suitability for online learning, where each training sample incrementally impacts the model's prediction.

We learned to apply the Bayesian workflow from model specification to estimation, diagnostics, and prediction using PyMC3 and explored several relevant applications. We will encounter more Bayesian models in Chapter 14, Text Data for Trading – Sentiment Analysis, where we'll discuss natural language processing and topic modeling, and in Chapter 20, Autoencoders for Conditional Risk Factors and Asset Pricing, where we'll introduce variational autoencoders.

The next chapter introduces nonlinear, tree-based models, namely decision trees, and shows how to combine multiple models into an ensemble...

lock icon The rest of the chapter is locked
Register for a free Packt account to unlock a world of extra content!
A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.
Unlock this book and the full library FREE for 7 days
Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of
Renews at €18.99/month. Cancel anytime