000			02827nam a22002417a 4500
999			_c4505 _d4505
005			20230117120118.0
008			230117b ||||| |||| 00| 0 eng d
020			_a9780367540951
082			_a332.01511352 _bCHE
100			_aChen, Jun _910515
245			_aDetecting regime change in computational finance: _bdata science, machine learning and algorithmic trading
260			_bCRC Press _aBoco Raton _c2021
300			_axxvi, 138 p.
365			_aGBP _b41.99
504			_aTable of Contents 1. Introduction. 2. Background and Literature Survey. 3. Regime Change Detection using Directional Change Indicators. 4. Classification of Normal and Abnormal Regimes in Financial Markets. 5. Tracking Regime Changes using Directional Change Indicators. 6. Algorithmic Trading based on Regime Change Tracking. 7. Conclusion. Appendix A. A Formal Definition of Directional Change. Appendix B. Extended Results of Chapter. 3 Appendix C. Experiment Summary of Chapter. 4 Appendix D. Detected Regime Changes in Chapter.
520			_aBased 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.
650			_aStocks--Prices--Mathematical models _911360
650			_aHidden Markov models _911361
650			_aExpectation-maximization algorithms _911362
650			_aFinance--Mathematical models _9180
700			_aTsang, Edward P K _911363
942			_2ddc _cBK