DR. MARCOS LÓPEZ DE PRADO manages several multibillion-dollar funds for institutional investors using ML algorithms. Marcos is also a research fellow at Lawrence Berkeley National Laboratory (U.S. Department of Energy, Office of Science). One of the top-10 most read authors in finance (SSRN's rankings), he has published dozens of scientific articles on ML in the leading academic journals, and he holds multiple international patent applications on algorithmic trading. Marcos earned a PhD in Financial Economics (2003), a second PhD in Mathematical Finance (2011) from Universidad Complutense de Madrid, and is a recipient of Spain's National Award for Academic Excellence (1999). He completed his post-doctoral research at Harvard University and Cornell University, where he teaches a Financial ML course at the School of Engineering. Marcos has an Erdös #2 and an Einstein #4 according to the American Mathematical Society.
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.
##就刚刚入门的水平吧。。。
评分 评分##呵呵,基本看不懂
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##就刚刚入门的水平吧。。。
评分 评分从目录上看应该是本好书,但是翻译和校对的真是太拉了,完全没用心。 简单一些例子: 56页里面的函数代码缩进是错的; 同页中的代码注释里的”清单“ 应该直接用 list 表示一种特定的代码数据结构; 很多名词首次出现时没有英文原意; 对一些概念名词翻译的并不清晰。 等等 综...
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