Detecting regime change in computational finance : data science, machine learning and algorithmic trading / Jun Chen, Edward P K Tsang.
Material type:
TextPublisher: Boca Raton : CRC Press, Taylor & Francis Group, 2021Copyright date: ©2021Edition: First editionDescription: 1 online resource (xxvi, 138 pages) : illustrations (some color)Content type: text Media type: computer Carrier type: online resourceISBN: 9781003087595; 1003087590; 9781000220162; 1000220168; 9781000220360; 1000220362; 9781000220261; 1000220265Subject(s): Financial engineering -- Methodology | Finance -- Mathematical models | Stocks -- Prices -- Mathematical models | Hidden Markov models | Expectation-maximization algorithms | MATHEMATICS / Arithmetic | COMPUTERS / Machine TheoryDDC classification: 332.01/511352 LOC classification: HG176.7 | .C44 2021Online resources: Taylor & Francis | OCLC metadata license agreement 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.
"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"-- Provided by publisher.
OCLC-licensed vendor bibliographic record.