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Though machine learning has been applied to the foreign exchange market foralgorithmic trading for quiet some time now, and neural networks(NN) have beenshown to yield positive results, in most modern approaches the NN systems areoptimized through traditional methods like the backpropagation algorithm forexample, and their input signals are price lists, and lists composed of othertechnical indicator elements. The aim of this paper is twofold: thepresentation and testing of the application of topology and weight evolvingartificial neural network (TWEANN) systems to automated currency trading, andto demonstrate the performance when using Forex chart images as input togeometrical regularity aware indirectly encoded neural network systems,enabling them to use the patterns & trends within, when trading. This paperpresents the benchmark results of NN based automated currency trading systemsevolved using TWEANNs, and compares the performance and generalizationcapabilities of these direct encoded NNs which use the standard sliding-windowbased price vector inputs, and the indirect (substrate) encoded NNs which usecharts as input. The TWEANN algorithm I will use in this paper to evolve thesecurrency trading agents is the memetic algorithm based TWEANN system calledDeus Ex Neural Network (DXNN) platform.

Evolving Chart Pattern Sensitive Neural Network Based Forex Trading Agents