Computational Intelligence for Evolving Trading Rules

被引：32
|
作者：
Ghandar, Adam [1 ]
Michalewicz, Zbigniew [1 ,2 ,3 ]
Schmidt, Martin [4 ]
To, Thuy-Duong [5 ]
Zurbrugg, Ralf [5 ]
机构：
[1] Univ Adelaide, Sch Comp Sci, Adelaide, SA 5005, Australia
[2] Polish Acad Sci, Inst Comp Sci, PL-01237 Warsaw, Poland
[3] Polish Japanese Inst Informat Technol, PL-02008 Warsaw, Poland
[4] SolveIT Software Pty Ltd, Adelaide, SA 5000, Australia
[5] Univ Adelaide, Sch Commerce, Adelaide, SA 5005, Australia
来源：
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION | 2009年 / 13卷 / 01期
关键词：
Evolutionary computation; fuzzy systems; portfolio management; stock market; trading systems; STATISTICAL-INFERENCE; TECHNICAL ANALYSIS; NEURAL NETWORKS; PREDICTION; TIME;
D O I：
10.1109/TEVC.2008.915992
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
This paper describes an adaptive computational intelligence system for learning trading rules. The trading rules are represented using a fuzzy logic rule base, and using an artificial evolutionary process the system learns to form rules that can perform well in dynamic market conditions. A comprehensive analysis of the results or applying the system for portfolio construction using portfolio evaluation tools widely accepted by both the financial industry and academia is provided.
引用
收藏
页码：71 / 86
页数：16
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