. By sampling data in a different way, this book lays out concepts which enable the extraction of information that other market participants may not be able to see. About the Authors Jun Chen received his PhD in computational finance from the Centre for Computational Finance and Economic Agents, University of Essex in 2019.
. After discussing parallel computing on a theoretical level, the authors show how to avoid or ameliorate typical performance problems connected with OpenMP.
Detecting Regime Change in Computational Finance: Data Science, Machine Learning and Algorithmic Trading, Paperback - Jun Chen Based on interdisciplinary research into "Directional Change", a new data-driven approach to financial data analysis, Detecting Regime Change in Computational Finance: Data Science, Machine Learning and Algorithmic Trading applies machine learning to financial market monitoring and algorithmic trading.
Book specifications:Dimensions: 234 x 156Author: Jun ChenCover type: PaperbackPublishing Year: 2022Publishing Month: 5Pages: 138Language: EnglishPublisher: CRC PressWeight: 240 g Directional Change is a new way of summarising price changes in the market. About the Authors Jun Chen received his PhD in computational finance from the Centre for Computational Finance and Economic Agents, University of Essex in 2019. Instead of sampling prices at fixed intervals (such as daily closing in time series), it samples prices when the market changes direction ("zigzags").