Agent based Genetic Algorithm Employing Financial Technical Analysis for Making Trading Decisions Using Historical Equity Market Data

Authors:
Cyril Schoreels;Brian Logan;Jonathan M. Garibaldi
Affiliations:
University of Nottingham;University of Nottingham;University of Nottingham
Venue:
IAT '04 Proceedings of the IEEE/WIC/ACM International Conference on Intelligent Agent Technology
Year:
2004

Citing 0
Cited 7

Genetic Algorithm as a Tool for Stock Market Modelling

ICAISC '08 Proceedings of the 9th international conference on Artificial Intelligence and Soft Computing
Investigating technical trading strategy via an multi-objective evolutionary platform

Expert Systems with Applications: An International Journal
Optimization of the trading rule in foreign exchange using genetic algorithm

Proceedings of the 11th Annual conference on Genetic and evolutionary computation
A hybrid stock trading system for intelligent technical analysis-based equivolume charting

Neurocomputing
A BDI agent system for credit risk assessment based on fuzzy logic

ASC '07 Proceedings of The Eleventh IASTED International Conference on Artificial Intelligence and Soft Computing
Building Trade System by Genetic Algorithm

ISICA '09 Proceedings of the 4th International Symposium on Advances in Computation and Intelligence
Stock trading system based on the multi-objective particle swarm optimization of technical indicators on end-of-day market data

Applied Soft Computing

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper investigates the effectiveness of an agent based trading system. The system developed employs a simple genetic algorithm to evolve an optimized trading approach for every agent, with their trading decisions based on a range of technical indicators generating trading signals. Their trading pattern follows a simple fitness function of maximizing net assets for every evolutionary cycle. Their performance is analyzed compared to market movements as represented by its Index, as well as investment funds run by human professionals to establish a relative measure of success. The results show that the developed system performs comparably to its human counterparts across different market environments, despite these agents being rather primitive in nature. Future forthcoming work will refine and explore the potential of this approach further.