Detecting Regime Change in Computational Finance: Data Science, Machine Learning and Algorithmic Trading

This is the first book of its kind to build on the framework of Directional Change. The concept of Directional Change opens a whole new area of research. - From the Foreword by Dr Richard Olsen, Founder and CEO of Lykke, Co-founder of OANDA and pioneer in high-frequency finance and fintech

A creative start at a novel and difficult problem for investors large and small. - Professor M. A. H. Dempster, University of Cambridge and Cambridge Systems Associates Limited

Financial markets technology and the practice of trading are in a state of constant change. A book that details a completely new concept in trading, however, is very rare. This is one such book, and the authors should be applauded for producing this exciting new work. The concept and framework of Directional Change in prices is an area of research with much promise! - Dr David Norman, Founder of TTC Institute and author

This book shows how AI could be a game-changer in finance. - Dr Amadeo Alentorn, Head of Research/Fund Manager at Merian Global Investors

Based on interdisciplinary research into Directional Change, a new data-driven approach to financial data analysis, Detecting Regime Change in Computational Data Science, Machine Learning and Algorithmic Trading applies machine learning to financial market monitoring and algorithmic trading. Directional Change is a new way of summarising price changes in the market. Instead of sampling prices at fixed intervals (such as daily closing in time series), it samples prices when the market changes direction (zigzags). 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. The book includes a Foreword by Richard Olsen and explores the following



Data as an alternative to time series, price movements in a market can be summarised as directional changes



Machine learning for regime change historical regime changes in a market can be discovered by a Hidden Markov Model



Regime normal and abnormal regimes in historical data can be characterised using indicators defined under Directional Change



Market by using historical characteristics of normal and abnormal regimes, one can monitor the market to detect whether the market regime has changed



Algorithmic regime tracking information can help us to design trading algorithms

It will be of great interest to researchers in computational finance, machine learning and data science.

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.

Edward P K Tsang is an Emeritus Professor at the University of Essex, where he co-founded the Centre for Computational Finance and Economic Agents in 2002.