%0 Journal Article %1 Dempster:2000:QF %A Dempster, M. A. H. %A Jones, C. M. %D 2000 %J Quantitative Finance %K algorithms, genetic programming %P 397--413 %T A real-time adaptive trading system using genetic programming %U http://citeseer.ist.psu.edu/dempster01realtime.html %V 1 %X 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.

@article{Dempster:2000:QF, 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.}, added-at = {2008-06-19T17:35:00.000+0200}, author = {Dempster, M. A. H. and Jones, C. M.}, biburl = {https://www.bibsonomy.org/bibtex/2af54bdf83b460ecfd65ed43ce529cdc6/brazovayeye}, interhash = {9875fb80cac881cf322e166b45988d9d}, intrahash = {af54bdf83b460ecfd65ed43ce529cdc6}, journal = {Quantitative Finance}, keywords = {algorithms, genetic programming}, notes = {INSTITUTE OF PHYSICS PUBLISHING quant.iop.org}, pages = {397--413}, size = {17 pages}, timestamp = {2008-06-19T17:38:35.000+0200}, title = {A real-time adaptive trading system using genetic programming}, url = {http://citeseer.ist.psu.edu/dempster01realtime.html}, volume = 1, year = 2000 }