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##就刚刚入门的水平吧。。。
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##全书废话，而且大小错误一大把，叙事没有前因后果，读到最后完全无法相信这个人。浪费时间。
评分##全书废话，而且大小错误一大把，叙事没有前因后果，读到最后完全无法相信这个人。浪费时间。
评分##盛名之下，难过其实，难言之隐，不如不写
评分##以自己从事相关工作虽不短仍浅薄的经验，这是一本在量化投资有框架有总结有细节有诚意的书。作者并没有在最top的公司（AQR虽在中国有名声，但并不是这行业最前沿的地方)有过成功实战经验，即使有他也不会写出书来，却有实践结合理论的认知。不要期待在书里找到策略最核心的东西，但是框架和应有的态度执行力已经很重要。其他在于悟性努力，平台，和运气。 谁不期待年少成名，难的是在领域高峰之时，能坚持不停止好奇心求知欲。与其用某些方法取得他人的策略回到国内赚钱，不如扎实去理解一个领域里的核心和渐进过程。由out smart他人到out smart狭隘的自己。
评分##翻过一点点。主要是讲量化
评分##盛名之下，难过其实，难言之隐，不如不写
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