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

Product type Paperback

Published in Jul 2020

Publisher Packt

ISBN-13 9781839217715

Length 820 pages

Edition 2nd Edition

Languages

Python

Tools

TensorFlow

Concepts

Machine Learning

Author (1):

Stefan Jansen

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Table of Contents (27) Chapters

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

Deep RL for trading with the OpenAI Gym

In the previous section, we saw how Q-learning allows us to learn the optimal state-action value function q* in an environment with discrete states and discrete actions using iterative updates based on the Bellman equation.

In this section, we will take RL one step closer to the real world and upgrade the algorithm to continuous states (while keeping actions discrete). This implies that we can no longer use a tabular solution that simply fills an array with state-action values. Instead, we will see how to approximate q* using a neural network (NN), which results in a deep Q-network. We will first discuss how deep learning integrates with RL before presenting the deep Q-learning algorithm, as well as various refinements that accelerate its convergence and make it more robust.

Continuous states also imply a more complex environment. We will demonstrate how to work with OpenAI Gym, a toolkit for designing and comparing RL algorithms. First...