Evolutionary Computation for Learning Rules to Trade Financial Markets

Evolutionary Computation for Learning Rules to Trade Financial Markets

Abstract

Evolutionary Computation and other biologically inspired learning algorithms are particularly suitable for the task of modelling financial markets. Financial markets are complex, ever changing environments with no fixed set of driving factors; but rather there is a varying set of interacting factors with dynamic effects over time. The adaptive search capabilities of evolutionary algorithms, in defining flexible and changing models, have been used in both real world forecasting applications and research to develop insight into the nature of financial markets. In this talk we discuss an application of Evolutionary Computation to learning trading rules together with some initial results of the system describing performance in deriving risk-adjusted returns from stocks in the Morgan Stanley Capital International (MSCI) Europe Index.