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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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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

Creating synthetic data with GANs

This book mostly focuses on supervised learning algorithms that receive input data and predict an outcome, which we can compare to the ground truth to evaluate their performance. Such algorithms are also called discriminative models because they learn to differentiate between different output values.

GANs are an instance of generative models like the variational autoencoder we encountered in the previous chapter. As described there, a generative model takes a training set with samples drawn from some distribution pdata and learns to represent an estimate pmodel of that data-generating distribution.

As mentioned in the introduction, GANs are considered one of the most exciting recent machine learning innovations because they appear capable of generating high-quality samples that faithfully mimic a range of input data. This is very attractive given the absence or high cost of labeled data required for supervised learning.

GANs have triggered...

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