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)
具体描述
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.
用户评价
神作，需要N刷。核心是讨论一般机器学习方法在金融时间序列这种特定数据类型上应用的一些问题，比如交叉验证、回测过拟合等等。不是讲策略开发或者投资方法的书。大部分内容作者都发表过，可以看作者主页http://www.quantresearch.info/或者SSRN。
评分##就刚刚入门的水平吧。。。
评分##盛名之下，难过其实，难言之隐，不如不写
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评分##很多想法还是很少见的，挺有参考价值的
评分##呵呵，基本看不懂
评分##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
评分##贵司真的就靠这本书赚到钱吗？我拭目以待
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