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
##贵司真的就靠这本书赚到钱吗？我拭目以待
评分##购买链接：https://item.taobao.com/item.htm?spm=0.7095261.0.0.71a11debf7UsVf&id=568847882964
评分##盛名之下，难过其实，难言之隐，不如不写
评分##盛名之下，难过其实，难言之隐，不如不写
评分##1）启发性的话题给的多，但是解决问题的方法给了一半，浅尝辄止 2）符号标注或者解释不清晰，举例也不清楚，本身一个实例就可以解释清楚的，但是没有。 优点就是，此类书很少，他提到的很多点给我以启发。总体上我觉得这本书值得一读的。 2018-01-05想读
评分##1）启发性的话题给的多，但是解决问题的方法给了一半，浅尝辄止 2）符号标注或者解释不清晰，举例也不清楚，本身一个实例就可以解释清楚的，但是没有。 优点就是，此类书很少，他提到的很多点给我以启发。总体上我觉得这本书值得一读的。 2018-01-05想读
评分##二刷，大有成为未来quant必备书籍的潜质，作者写这本书的时候还没进AQR，后来就成为了AQR的head（现在是Bryan Kelly）
评分##贵司真的就靠这本书赚到钱吗？我拭目以待
评分##比较失望，不过之前听同事说起一些也算有心理准备了。
