This paper presents how buy and sell trading rules are generated using gene expression programming with special setup. Market concepts are presented and market analysis is discussed with emphasis on technical analysis and quantitative methods. The use of genetic algorithms in deriving trading rules is presented. Gene expression programming is applied in a form where multiple types of operators and operands are used. This gives birth to multiple gene contexts and references between genes in order to keep the linear structure of the gene expression programming chromosome. The setup of multiple gene contexts is presented. The case study shows how to use the proposed gene setup to derive trading rules encoded by Boolean expressions, using a dataset with the reference exchange rates between the Euro and the Romanian leu. The conclusions highlight the positive results obtained in deriving useful trading rules.
[1]
L. Lin,et al.
The Applications Of Genetic Algorithms InStock Market Data Mining Optimisation
,
2004
.
[2]
Frank Westerhoff,et al.
Modeling Exchange Rate Behavior with a Genetic Algorithm
,
2003
.
[3]
Adrian Visoiu.
Structure Refinement for Vulnerability Estimation Models using Genetic Algorithm Based Model Generators
,
2009
.
[4]
P ? ? ? ? ? ? ? % ? ? ? ?
,
1991
.
[5]
Franklin Allen,et al.
Using genetic algorithms to find technical trading rules
,
1999
.
[6]
Jean-Yves Potvin,et al.
Generating trading rules on the stock markets with genetic programming
,
2004,
Comput. Oper. Res..
[7]
Shu-Heng Chen,et al.
Genetic Algorithms and Genetic Programming in Computational Finance
,
2002
.