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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 introduced a different class of machine learning problems that focus on automating decisions by agents that interact with an environment. We covered the key features required to define an RL problem and various solution methods.

We saw how to frame and analyze an RL problem as a finite Markov decision problem, as well as how to compute a solution using value and policy iteration. We then moved on to more realistic situations, where the transition probabilities and rewards are unknown to the agent, and saw how Q-learning builds on the key recursive relationship defined by the Bellman optimality equation in the MDP case. We saw how to solve RL problems using Python for simple MDPs and more complex environments with Q-learning.

We then expanded our scope to continuous states and applied the Deep Q-learning algorithm to the more complex Lunar Lander environment. Finally, we designed a simple trading environment using the OpenAI Gym platform, and also...

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