Machine Learning In Computational Finance : Practical algorithms for building artificial intelligence applications

Victor Boyarshinov

ISBN 10: 3659118893 / ISBN 13: 9783659118890

Published by LAP LAMBERT Academic Publishing, 2012

About this Item

nach der Bestellung gedruckt Neuware - Printed after ordering - In the first part of the book practical algorithms for building optimal trading strategies are constructed. Both non-restricted and risk-adjusted (Sterling ratio and Sharp ratio) trading strategies are considered. Constructed optimal trading strategies can be used as training dataset for the AI application. In the next part of the book one particular type of Machine Learning - finding optimal linear separators - is considered, and combinatorial deterministic algorithm for computing minimum linear separator set in 2 dimensions is given. In the last part of the book presented efficient algorithms for preventing overfitting. Shape constrained regression is an accepted methodology to deal with overfitting. Algorithms for nonparametric shape constrained regression in the form of isotonic and unimodal regressions are given.

Seller Inventory # 9783659118890

Bibliographic Details

• Title: Machine Learning In Computational Finance : Practical algorithms for building artificial intelligence applications
• Publisher: LAP LAMBERT Academic Publishing
• Publication Date: 2012
• Language: English
• ISBN 10: 3659118893
• ISBN 13: 9783659118890
• Binding: Taschenbuch
• Condition: Neu

About this title

Synopsis

In the first part of the book practical algorithms for building optimal trading strategies are constructed. Both non-restricted and risk-adjusted (Sterling ratio and Sharp ratio) trading strategies are considered. Constructed optimal trading strategies can be used as training dataset for the AI application. In the next part of the book one particular type of Machine Learning - finding optimal linear separators - is considered, and combinatorial deterministic algorithm for computing minimum linear separator set in 2 dimensions is given. In the last part of the book presented efficient algorithms for preventing overfitting. Shape constrained regression is an accepted methodology to deal with overfitting. Algorithms for nonparametric shape constrained regression in the form of isotonic and unimodal regressions are given.

About the Author

Victor Boyarshinov was born in the family of Russian military pilot. Victor grew up in a small village on the shore of Okhotsk Sea. After finishing high school he went to Novosibirsk State University to get MS degree in Math. Later he graduated from Rensselaer Polytechnic Institute with Ph.D. in Computer Science. Currently he lives in Vancouver.