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

Conclusion

We started by highlighting the explosion of digital data and the emergence of ML as a strategic capability for investment and trading strategies. This dynamic reflects global business and technology trends beyond finance and is much more likely to continue than to stall or reverse. Many investment firms are just getting started to leverage the range of artificial intelligence tools, just as individuals are acquiring the relevant skills and business processes are adapting to these new opportunities for value creation, as outlined in the introductory chapter.

There are also numerous exciting developments for the application of ML to trading on the horizon that are likely to propel the current momentum. They are likely to become relevant in the coming years and include the automation of the ML process, the generation of synthetic training data, and the emergence of quantum computing. The extraordinary vibrancy of the field implies that this alone could fill a book and...

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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

Table of Contents (27) Chapters

Conclusion

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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

Table of Contents (27) Chapters

Conclusion

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