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

页码：71 / 86

页数：16

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