Gene I. Sher
ABSTRACT
Though machine learning has been applied to the foreign exchange market for algorithmic trading for quiet some time now, and neural networks(NN) have been shown to yield positive results, in most modern approaches the NN systems are optimized through traditional methods like the backpropagation algorithm for example, and their input signals are price lists, and lists composed of other technical indicator elements. The aim of this paper is twofold: the presentation and testing of the application of topology and weight evolving artificial neural network (TWEANN) systems to automated currency trading, and to demonstrate the performance when using forex chart images as input to geometrical regularity aware indirectly encoded neural network systems, enabling them to use the patterns & trends within, when trading. This paper presents the benchmark results of NN based automated currency trading systems evolved using TWEANNs, and compares the performance and generalization capabilities of these direct encoded NNs which use the standard sliding-window based price vector inputs, and the indirect (substrate) encoded NNs which use charts as input. The TWEANN algorithm I will use in this paper to evolve these currency trading agents is the memetic algorithm based TWEANN system called Deus Ex Neural Network (DXNN) platform.
- Sher I. G "DXNN Platform: Shedding the Biological Inefficiencies", arXiv:1011.6022v3 {cs.NE}Google Scholar
- Sher I. G "DXNN: Evolving Complex Organisms in Complex Environments Using a Novel TWEANN System", GECCO '11 Proceedings of the 13th annual conference companion on Genetic and evolutionary computation. Google ScholarDigital Library
- Sneha S "Applications of ANNs in Stock Market Prediction: A Survey" IJCET, 71--83. 2005Google Scholar
- Li Y. Weihua M "Applications of Artificial Neural Networks in Financial Economics: A Survey" Neural Networks: 211--214. 2010 Google ScholarDigital Library
Index Terms
- Forex trading using geometry sensitive neural networks
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