Applications of Computational Intelligence in Data-Driven Trading - Tapa dura

Acerca del autor

CRIS DOLOC is a leading computational scientist with more than 25 years of experience in quantitative finance. He holds a PhD in Computational Physics and is currently teaching at the University of Chicago in the Financial Mathematics program. Cris is also the founder of FintelligeX, a technology platform designed to promote data-driven education, and he is very passionate about the opportunities that recent developments in Cognitive Computing and Computational Intelligence could bring to the field of Quant education.

De la contraportada

Traders have always been among the first to see and take advantage of the true potential of new technologies. From the telephone and the telegraph to computers and the Internet, trading has provided a fertile ground for the growth and development of tech innovations. Now, with the rise of data, machine learning, and artificial intelligence, quantitative analysts and trading professionals are again at the vanguard. However, unlike in times past, there is now a dizzying procession of new tools and products, and traders may have difficulty keeping up.

In Applications of Computational Intelligence in Data-Driven Trading, leading computational scientist Cris Doloc outlines a paradigm through which quantitative analysts can become astute problem solvers. Readers will learn how to effectively access, process, and interpret data, regardless of which tools they use. With this book's clear and detailed explanation of the principles behind computational finance—including time series analysis, forecasting, dynamic programming, and neural networks—readers will develop a lasting understanding that will enable them to solve financial problems in a way that transcends the latest fads.

In service of the goal of equipping students and practitioners alike with useful knowledge, Doloc has dedicated the second half of his book to in-depth case studies demonstrating how computational intelligence and data-driven trading intersect for powerful market insights. These case studies are scientifically structured, clearly demonstrating how machine learning methods are applied to specific problems, outlining the empirical results, and drawing conclusions that will help readers apply these cutting-edge techniques in their own work.

Rare is the book that actively aims to de-hype hot topics like machine learning and artificial intelligence. Applications of Computational Intelligence in Data-Driven Trading takes a refreshingly grounded approach, assessing in a balanced way how quantitative practitioners can extract the signal from the noise around artificial intelligence and permanently add valuable, data-driven problem-solving techniques to their skillsets.

De la solapa interior

Traders have always been among the first to see and take advantage of the true potential of new technologies. From the telephone and the telegraph to computers and the Internet, trading has provided a fertile ground for the growth and development of tech innovations. Now, with the rise of data, machine learning, and artificial intelligence, quantitative analysts and trading professionals are again at the vanguard. However, unlike in times past, there is now a dizzying procession of new tools and products, and traders may have difficulty keeping up.

In Applications of Computational Intelligence in Data-Driven Trading, leading computational scientist Cris Doloc outlines a paradigm through which quantitative analysts can become astute problem solvers. Readers will learn how to effectively access, process, and interpret data, regardless of which tools they use. With this book's clear and detailed explanation of the principles behind computational finance—including time series analysis, forecasting, dynamic programming, and neural networks—readers will develop a lasting understanding that will enable them to solve financial problems in a way that transcends the latest fads.

In service of the goal of equipping students and practitioners alike with useful knowledge, Doloc has dedicated the second half of his book to in-depth case studies demonstrating how computational intelligence and data-driven trading intersect for powerful market insights. These case studies are scientifically structured, clearly demonstrating how machine learning methods are applied to specific problems, outlining the empirical results, and drawing conclusions that will help readers apply these cutting-edge techniques in their own work.

Rare is the book that actively aims to de-hype hot topics like machine learning and artificial intelligence. Applications of Computational Intelligence in Data-Driven Trading takes a refreshingly grounded approach, assessing in a balanced way how quantitative practitioners can extract the signal from the noise around artificial intelligence and permanently add valuable, data-driven problem-solving techniques to their skillsets.