Title

Forex Trading Using Geometry Sensitive Neural Networks

Keywords

DXNN; Evolutionary computation; Financial analysis; Forex; Memetic algorithm; Neural network; Neuroevolution

Abstract

When neural network based systems are used within the field of financial analysis, either as price oracles or autonomous traders, they are primarily used with a sliding price window. This paper presents a novel approach where the indirectly encoded neural network system, just like the technical analysts, looks directly at the candlestick style sliding chart instead, the actual geometrical-patterns within it, to make its predictions. The results presented demonstrate that this approach results in a higher and more consistent generalization to previously unseen financial data, while maintaining a profit level on par with the neuroevolutionary system which uses a standard sliding window. Copyright is held by the author/owner(s).

Publication Date

1-1-2012

Publication Title

GECCO'12 - Proceedings of the 14th International Conference on Genetic and Evolutionary Computation Companion

Number of Pages

1533-1534

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.1145/2330784.2331032

Socpus ID

84864974247 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/84864974247

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