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
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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##虽然标记一下读过 但是其实只是跳着看了看。里面大量内容都十分专业 不自己做过相关内容的话估计都没啥体会。感觉这本书是给从业者/想开对冲基金的人的参考书 不适合自己投资的散户读...
评分##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
评分##AQR的head of ml
评分##翻过一点点。主要是讲量化
评分##贵司真的就靠这本书赚到钱吗？我拭目以待
评分##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
评分##翻过一点点。主要是讲量化
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