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
##购买链接：https://item.taobao.com/item.htm?spm=0.7095261.0.0.71a11debf7UsVf&id=568847882964
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##我就是这条gai最量的仔
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。
评分##AQR的head of ml
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##很多想法还是很少见的，挺有参考价值的
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##翻过一点点。主要是讲量化
相关图书
本站所有内容均为互联网搜索引擎提供的公开搜索信息，本站不存储任何数据与内容，任何内容与数据均与本站无关，如有需要请联系相关搜索引擎包括但不限于百度，google,bing,sogou 等，本站所有链接都为正版商品购买链接。
© 2025 windowsfront.com All Rights Reserved. 静流书站 版权所有