Neuro-evolutionary system for FOREX trading

Jacek Mańdziuk , Piotr Rajkiewicz

Abstract

This paper proposes a neuro-genetic system for trading on Forex market. The main idea is to apply evolutionary methods for selection of the most suitable, in a given macroeconomic context, set of variables, whose usefulness is subsequently validated based on a short and simplified training of several perceptron-type networks. Once the indicative training for this selected input data proves effective, the final, more sophisticated and more detailed training is performed on the ensemble of neural predictors. The proposed method was tested on 170 five-day investment periods (spanning over 3 years) with very promising results of both average and worst case performance. Furthermore, an investigation into the way the evolutionary component of the system selects input variables in subsequent trading periods has been performed, leading to interesting observation about the usefulness of particular data sources, as well as their repeatability across independent runs of the system.
Author Jacek Mańdziuk ZSIMO
Jacek Mańdziuk,,
- Department of Artificial Intelligence and Computational Methods
, Piotr Rajkiewicz WMiNI
Piotr Rajkiewicz,,
- Faculty of Mathematics and Information Science
Pages4654-4661
Publication size in sheets0.5
Book 2016 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC), 2016, IEEE, ISBN 978-1-5090-0623-6
Keywords in Englishneuro-evolutionary systems, financial prediction, financial time series, FOREX trading
Abstract in PolishPraca przedstawia hybrydowe neuro-genetyczne podejscie do budowy systemu przeprowadzajacego transakcje na rynku FOREX. W skrocie, system wykorzystuje metody ewolucyjne do wyboru najbardziej pozadanych zmiennych wejsciowych (w danej sytuacji makroekonomicznej), ktore sa nastepnie wykorzystywane jako wektory treningowe do uczenia jednokierunkowej sieci neuronowej (perceptronu wielowarstwowego).
DOIDOI:10.1109/CEC.2016.7744384
URL http://ieeexplore.ieee.org/document/7744384/
Languageen angielski
Score (nominal)15
ScoreMinisterial score = 15.0, 27-06-2017, BookChapterMatConf
Ministerial score (2013-2016) = 15.0, 27-06-2017, BookChapterMatConf
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