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
##很多想法還是很少見的，挺有參考價值的
評分##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...
評分##AQR的head of ml
評分##神書，有很多學術文章，其他書籍裏見不到的方法手段，即使不做machine learning，裏麵研究的方法也很有可藉鑒的地方
評分##盛名之下，難過其實，難言之隱，不如不寫
評分##以自己從事相關工作雖不短仍淺薄的經驗，這是一本在量化投資有框架有總結有細節有誠意的書。作者並沒有在最top的公司（AQR雖在中國有名聲，但並不是這行業最前沿的地方)有過成功實戰經驗，即使有他也不會寫齣書來，卻有實踐結閤理論的認知。不要期待在書裏找到策略最核心的東西，但是框架和應有的態度執行力已經很重要。其他在於悟性努力，平颱，和運氣。 誰不期待年少成名，難的是在領域高峰之時，能堅持不停止好奇心求知欲。與其用某些方法取得他人的策略迴到國內賺錢，不如紮實去理解一個領域裏的核心和漸進過程。由out smart他人到out smart狹隘的自己。
評分##就剛剛入門的水平吧。。。
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##我就是這條gai最量的仔
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