Detecting Regime Change in Computational Finance: Data Science, Machine Learning and Algorithmic Trading - Tapa blanda
Sinopsis
Based on interdisciplinary research into "Directional Change", a new data-driven approach to financial data analysis, this book applies machine learning to financial market monitoring and algorithmic trading.
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Acerca del autor
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. He is a Visiting Professor at University of Hong Kong.
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Detecting Regime Change in Computational Finance : Data Science, Machine Learning and Algorithmic Trading
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Chapman and Hall/CRC, 2022
ISBN 10: 0367540959
ISBN 13: 9780367540951
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Detecting Regime Change in Computational Finance
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ISBN 13: 9780367540951
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Detecting Regime Change in Computational Finance : Data Science, Machine Learning and Algorithmic Trading
Publicado por
Chapman and Hall/CRC, 2022
ISBN 10: 0367540959
ISBN 13: 9780367540951
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Detecting Regime Change in Computational Finance (Paperback)
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ISBN 10: 0367540959
ISBN 13: 9780367540951
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Paperback. Condición: new. Paperback. 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 algorithmsIt will be of great interest to researchers in computational finance, machine learning and data science.About the AuthorsJun 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. Based on interdisciplinary research into "Directional Change", a new data-driven approach to financial data analysis, this book applies machine learning to financial market monitoring and algorithmic trading. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Nº de ref. del artículo: 9780367540951
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Detecting Regime Change in Computational Finance
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ISBN 13: 9780367540951
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Detecting Regime Change in Computational Finance
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ISBN 13: 9780367540951
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Detecting Regime Change in Computational Finance
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ISBN 10: 0367540959
ISBN 13: 9780367540951
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Detecting Regime Change in Computational Finance
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ISBN 10: 0367540959
ISBN 13: 9780367540951
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Detecting Regime Change in Computational Finance
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ISBN 10: 0367540959
ISBN 13: 9780367540951
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Paperback. Condición: New. 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 algorithmsIt will be of great interest to researchers in computational finance, machine learning and data science.About the AuthorsJun 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. Nº de ref. del artículo: LU-9780367540951
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