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

Custom embeddings for financial news

Many tasks require embeddings of domain-specific vocabulary that models pretrained on a generic corpus may not be able to capture. Standard word2vec models are not able to assign vectors to out-of-vocabulary words and instead use a default vector that reduces their predictive value.

For example, when working with industry-specific documents, the vocabulary or its usage may change over time as new technologies or products emerge. As a result, the embeddings need to evolve as well. In addition, documents like corporate earnings releases use nuanced language that GloVe vectors pretrained on Wikipedia articles are unlikely to properly reflect.

In this section, we will train and evaluate domain-specific embeddings using financial news. We'll first show how to preprocess the data for this task, then demonstrate how the skip-gram architecture outlined in the first section works, and finally visualize the results. We also will introduce...