Advances in Financial Machine Learning

作者：MarcosLopezdePrado

出版社：JohnWiley&Sons

出版年：2018-2-22

页数：400

定价：USD50.00

装帧：Hardcover

ISBN：9781119482086

内容简介
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Machine learning (ML) is changing virtually every aspect of our lives. Today ML algorithms accomplish tasks that until recently only expert humans could perform. As it relates to finance, this is the most exciting time to adopt a disruptive technology that will transform how everyone invests for generations. Readers will learn how to structure Big data in a way that is amenable to ML algorithms; how to conduct research with ML algorithms on that data; how to use supercomputing methods; how to backtest your discoveries while avoiding false positives. The book addresses real-life problems faced by practitioners on a daily basis, and explains scientifically sound solutions using math, supported by code and examples. Readers become active users who can test the proposed solutions in their particular setting. Written by a recognized expert and portfolio manager, this book will equip investment professionals with the groundbreaking tools needed to succeed in modern finance.

作者简介
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DR. MARCOS LÓPEZ DE PRADO manages several multibillion-dollar funds for institutional investors using ML algorithms. Marcos is also a research fellow at Lawrence Berkeley National Laboratory (U.S. Department of Energy, Office of Science). One of the top-10 most read authors in finance (SSRN's rankings), he has published dozens of scientific articles on ML in the leading academic journals, and he holds multiple international patent applications on algorithmic trading. Marcos earned a PhD in Financial Economics (2003), a second PhD in Mathematical Finance (2011) from Universidad Complutense de Madrid, and is a recipient of Spain's National Award for Academic Excellence (1999). He completed his post-doctoral research at Harvard University and Cornell University, where he teaches a Financial ML course at the School of Engineering. Marcos has an Erdös #2 and an Einstein #4 according to the American Mathematical Society.

目录
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About the Author

Preamble

1. Financial Machine Learning as a Distinct Subject

Part 1: Data Analysis

2. Financial Data Structures

3. Labeling

4. Sample Weights

5. Fractionally Differentiated Features

Part 2: Modelling

6. Ensemble Methods

7. Cross-validation in Finance

8. Feature Importance

9. Hyper-parameter Tuning with Cross-Validation

Part 3: Backtesting

10. Bet Sizing

11. The Dangers of Backtesting

12. Backtesting through Cross-Validation

13. Backtesting on Synthetic Data

14. Backtest Statistics

15. Understanding Strategy Risk

16. Machine Learning Asset Allocation

Part 4: Useful Financial Features

17. Structural Breaks

18. Entropy Features

19. Microstructural Features

Part 5: High-Performance Computing Recipes

20. Multiprocessing and Vectorization

21. Brute Force and Quantum Computers

22. High-Performance Computational Intelligence and Forecasting Technologies

Dr. Kesheng Wu and Dr. Horst Simon

Index

评论 ······

提到的分析都很实际， 虽然理论部分有难度，但是仅仅思路就很值得借鉴

神书，有很多学术文章，其他书籍里见不到的方法手段，即使不做machine learning，里面研究的方法也很有可借鉴的地方

贵司真的就靠这本书赚到钱吗？我拭目以待

呵呵，基本看不懂

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