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Many factors could directly or indirectly affect the future price of an investment. Such factors are often inter-related. The space of models explaining the interaction between these factors is huge, and growing exponentially with the number of factors given. Genetic Programming offers an efficient search engine for the space of possible models and generates rules that the user can interpret. In collaboration with the Computational Finance Research Group at University of Essex we de- scribed the applications of an interactive decision tools, EDDIE, in making financial decision, in- cluding applications to share prices and indices forecasting and arbitrage. EDDIE searches the space of models using genetic programming. A candidate solution is represented by a genetic decision tree (GDT). The basic elements of GDTs are rules and forecast values. For a GP to work, one must be able to evaluate each GDT, and assign to it a fitness value, which reflects the quality of the GDT. EDDIE maintains a set of GDTs called a population and works in iterations. In each itera- tion, GDTs are picked from the population: the fitter a GDT is, the greater chance it has of being picked. The set of all GDTs thus picked form a mating pool from which pairs of GDTs, which are referred to as parents are picked. A branch in each parent is picked at random. The parents then exchange the subtrees under those branches. Offspring are mutated occasionally, which is done by replacing random elements of the GDT by random values. The possibly mutated offspring will then replace the old GDTs to form the new population. There are many variations in the way that the population is updated by new offspring, the way that the initial population is generated, the way that parents are picked, the way that crossover and mutation are done, etc. The typical problem EDDIE try to solve is to make investment recommendation. One of the aim of this reserach is to increase precision, or reduce the rate of failure when EDDIE recommends investing. The ob ject of another exercise is to develop and implement EDDIE on intra daily tick data for stock index options and futures arbitrage in a manner that is suitable for online trading when windows of profitable arbitrage opportunities exist for short periods from one minute to under ten minutes.