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
##雖然標記一下讀過 但是其實隻是跳著看瞭看。裏麵大量內容都十分專業 不自己做過相關內容的話估計都沒啥體會。感覺這本書是給從業者/想開對衝基金的人的參考書 不適閤自己投資的散戶讀...
##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
神作，需要N刷。核心是討論一般機器學習方法在金融時間序列這種特定數據類型上應用的一些問題，比如交叉驗證、迴測過擬閤等等。不是講策略開發或者投資方法的書。大部分內容作者都發錶過，可以看作者主頁http://www.quantresearch.info/或者SSRN。
##雖然標記一下讀過 但是其實隻是跳著看瞭看。裏麵大量內容都十分專業 不自己做過相關內容的話估計都沒啥體會。感覺這本書是給從業者/想開對衝基金的人的參考書 不適閤自己投資的散戶讀...
##我就是這條gai最量的仔
##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
##除瞭HPC的內容都看瞭，對於金融任務的特定理解非常值得學習！感覺先看這本書可以少踩很多彎路瞭。
##除瞭HPC的內容都看瞭，對於金融任務的特定理解非常值得學習！感覺先看這本書可以少踩很多彎路瞭。
##提到的分析都很實際， 雖然理論部分有難度，但是僅僅思路就很值得藉鑒