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Detecting Regime Change in Computational Finance - Data Science, Machine Learning and Algorithmic Trading (Paperback)
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Detecting Regime Change in Computational Finance - Data Science, Machine Learning and Algorithmic Trading (Paperback)
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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. 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
topics: Data science: as an alternative to time series, price
movements in a market can be summarised as directional changes
Machine learning for regime change detection: historical regime
changes in a market can be discovered by a Hidden Markov Model
Regime characterisation: normal and abnormal regimes in historical
data can be characterised using indicators defined under
Directional Change Market Monitoring: by using historical
characteristics of normal and abnormal regimes, one can monitor the
market to detect whether the market regime has changed Algorithmic
trading: 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.
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