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
· · · · · · (收起)
具體描述
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.
用戶評價
##提到的分析都很實際， 雖然理論部分有難度，但是僅僅思路就很值得藉鑒
評分##翻過一點點。主要是講量化
評分##神書，有很多學術文章，其他書籍裏見不到的方法手段，即使不做machine learning，裏麵研究的方法也很有可藉鑒的地方
評分##嗬嗬，基本看不懂
評分##神書，有很多學術文章，其他書籍裏見不到的方法手段，即使不做machine learning，裏麵研究的方法也很有可藉鑒的地方
評分##比較失望，不過之前聽同事說起一些也算有心理準備瞭。
評分##二刷，大有成為未來quant必備書籍的潛質，作者寫這本書的時候還沒進AQR，後來就成為瞭AQR的head（現在是Bryan Kelly）
評分##翻過一點點。主要是講量化
評分##盛名之下，難過其實，難言之隱，不如不寫
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