data science, machine learning and algorithmic trading Chen, Jun 1990 February 16- creator author. Tsang, Edward author. text bibliography flu 2021 2021 First edition. monographic eng 1 online resource (xxvi, 138 pages) : illustrations (some color) "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 summarizing 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 ("zigzag"). By sampling data in a different way, the 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"-- Background and literature survey -- Regime change detection using directional change indicators -- Classification of normal and abnormal regimes in financial markets -- Tracking regime changes using directional change indicators -- Algorithmic trading based on regime change tracking. Jun Chen, Edward P K Tsang. Financial engineering Methodology Finance Mathematical models Stocks Prices Mathematical models Hidden Markov models Expectation-maximization algorithms MATHEMATICS / Arithmetic COMPUTERS / Machine Theory HG176.7 .C44 2021 332.01/511352 9781003087595 1003087590 9781000220162 1000220168 9781000220360 1000220362 9781000220261 1000220265 https://www.taylorfrancis.com/books/9781003087595 http://www.oclc.org/content/dam/oclc/forms/terms/vbrl-201703.pdf https://www.taylorfrancis.com/books/9781003087595 http://www.oclc.org/content/dam/oclc/forms/terms/vbrl-201703.pdf OCoLC-P 200620 20260210180807.0 9781003087595 eng