A real-time adaptive trading system using genetic programming
Created by W.Langdon from
gp-bibliography.bib Revision:1.7325
- @Article{Dempster:2000:QF,
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author = "M. A. H. Dempster and C. M. Jones",
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title = "A real-time adaptive trading system using genetic
programming",
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journal = "Quantitative Finance",
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year = "2001",
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volume = "1",
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number = "4",
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pages = "397--413",
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keywords = "genetic algorithms, genetic programming",
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publisher = "Routledge",
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URL = "
http://mahd-pc.jbs.cam.ac.uk/archive/PAPERS/2000/geneticprogramming.pdf",
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URL = "
http://citeseer.ist.psu.edu/dempster01realtime.html",
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DOI = "
doi:10.1088/1469-7688/1/4/301",
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size = "17 pages",
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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.",
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notes = "INSTITUTE OF PHYSICS PUBLISHING quant.iop.org",
- }
Genetic Programming entries for
Michael Dempster
Chris M Jones
Citations