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)
具體描述
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.
用戶評價
##就剛剛入門的水平吧。。。
評分##全書廢話，而且大小錯誤一大把，敘事沒有前因後果，讀到最後完全無法相信這個人。浪費時間。
評分##很多想法還是很少見的，挺有參考價值的
評分##全書廢話，而且大小錯誤一大把，敘事沒有前因後果，讀到最後完全無法相信這個人。浪費時間。
評分##貴司真的就靠這本書賺到錢嗎？我拭目以待
評分##盛名之下，難過其實，難言之隱，不如不寫
評分##盛名之下，難過其實，難言之隱，不如不寫
評分##1）啓發性的話題給的多，但是解決問題的方法給瞭一半，淺嘗輒止 2）符號標注或者解釋不清晰，舉例也不清楚，本身一個實例就可以解釋清楚的，但是沒有。 優點就是，此類書很少，他提到的很多點給我以啓發。總體上我覺得這本書值得一讀的。 2018-01-05想讀
評分##很多想法還是很少見的，挺有參考價值的
相關圖書
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
© 2026 book.tinynews.org All Rights Reserved. 静思书屋 版权所有