Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Bridging the gap between business objectives and parameters of data mining algorithms
Decision Support Systems - Special issue: knowledge discovery and its applications to business decision making
Data mining in finance: advances in relational and hybrid methods
Data mining in finance: advances in relational and hybrid methods
Mining the stock market (extended abstract): which measure is best?
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Trading on the Edge: Neural, Genetic, and Fuzzy Systems for Chaotic Financial Markets
Trading on the Edge: Neural, Genetic, and Fuzzy Systems for Chaotic Financial Markets
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Introduction to Multiagent Systems
Introduction to Multiagent Systems
Genetic Algorithms and Genetic Programming in Computational Finance
Genetic Algorithms and Genetic Programming in Computational Finance
Developing multiagent systems: The Gaia methodology
ACM Transactions on Software Engineering and Methodology (TOSEM)
Service-Oriented Architecture: A Field Guide to Integrating XML and Web Services
Service-Oriented Architecture: A Field Guide to Integrating XML and Web Services
Finding Trading Patterns in Stock Market Data
IEEE Computer Graphics and Applications
A critical review of multi-objective optimization in data mining: a position paper
ACM SIGKDD Explorations Newsletter
Mining in-depth patterns in stock market
International Journal of Intelligent Systems Technologies and Applications
Agent Services-Oriented Architectural Design of a Framework for Artificial Stock Markets
Proceedings of the 2006 conference on Advances in Intelligent IT: Active Media Technology 2006
Discovering golden nuggets: data mining in financial application
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Particle swarm optimiser with hybrid multi-parent crossover and discrete recombination
International Journal of Intelligent Information and Database Systems
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The backtesting and optimisation of trading strategies has emerged as an interesting research and experimental problem in both finance and Information Technology (IT) fields. However, it is a non-trivial task to effectively and efficiently optimise trading strategies, not to mention the optimisation in the real-world situations. This paper discusses the application of evolutionary technologies (genetic algorithm in particular) to the optimisation of trading strategies. Experimental results show that this approach is promising. Due to the complexity involved in the optimisation process, we further present an agent-based system that can help users easily specify and execute optimisation jobs to their advantages.