A real-time adaptive trading system using genetic programming

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@Article{Dempster:2000:QF,

• author = "M. A. H. Dempster and C. M. Jones",
• title = "A real-time adaptive trading system using genetic programming",
• journal = "Quantitative Finance",
• year = "2001",
• volume = "1",
• number = "4",
• pages = "397--413",
• keywords = "genetic algorithms, genetic programming",
• publisher = "Routledge",
• URL = "http://mahd-pc.jbs.cam.ac.uk/archive/PAPERS/2000/geneticprogramming.pdf",
• URL = "http://citeseer.ist.psu.edu/dempster01realtime.html",
• DOI = "doi:10.1088/1469-7688/1/4/301",
• size = "17 pages",
• abstract = "Technical analysis indicators are widely used by traders in financial and commodity markets to predict future price levels and enhance trading profitability. We have previously shown a number of popular indicator-based trading rules to be loss-making when applied individually in a systematic manner. However, technical traders typically use combinations of a broad range of technical indicators. Moreover, successful traders tend to adapt to market conditions by dropping trading rules as soon as they become loss-making or when more profitable rules are found. In this paper we try to emulate such traders by developing a trading system consisting of rules based on combinations of different indicators at different frequencies and lags. An initial portfolio of such rules is selected by a genetic algorithm applied to a number of indicators calculated on a set of US Dollar/British Pound spot foreign exchange tick data from 1994 to 1997 aggregated to various intraday frequencies. The genetic algorithm is subsequently used at regular intervals on out-of-sample data to provide new rules and a feedback system is used to rebalance the rule portfolio, thus creating two levels of adaptivity. Despite the individual indicators being generally loss-making over the data period, the best rule found by the developed system is found to be modestly, but significantly, profitable in the presence of realistic transaction costs.",
• notes = "INSTITUTE OF PHYSICS PUBLISHING quant.iop.org",

}

Genetic Programming entries for Michael Dempster Chris M Jones

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