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

Separating signal from noise with Alphalens

Quantopian has open sourced the Python Alphalens library for the performance analysis of predictive stock factors. It integrates well with the Zipline backtesting library and the portfolio performance and risk analysis library pyfolio, which we will explore in the next chapter.

Alphalens facilitates the analysis of the predictive power of alpha factors concerning the:

Correlation of the signals with subsequent returns
Profitability of an equal or factor-weighted portfolio based on a (subset of) the signals
Turnover of factors to indicate the potential trading costs
Factor performance during specific events
Breakdowns of the preceding by sector

The analysis can be conducted using tearsheets or individual computations and plots. The tearsheets are illustrated in the online repository to save some space.

Creating forward returns and factor quantiles

To utilize Alphalens, we need...

The rest of the chapter is locked

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

Separating signal from noise with Alphalens

Creating forward returns and factor quantiles

Authors (1)

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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

Separating signal from noise with Alphalens

Creating forward returns and factor quantiles

Unlock this book and the full library FREE for 7 days

Authors (1)

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