Intelligent Algorithms for Trading the Euro-Dollar in the Foreign Exchange Market

Danilo Pelusi (), Massimo Tivegna () and Pierluigi Ippoliti ()
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Danilo Pelusi: University of Teramo, Department of Communication Science
Massimo Tivegna: University of Teramo, Department of Theories and Policies for Social Development
Pierluigi Ippoliti: University of Teramo, Department of Theories and Policies for Social Development

A chapter in Mathematical and Statistical Methods for Actuarial Sciences and Finance, 2014, pp 243-252 from Springer

Abstract: Abstract In order to improve the profitability of Technical Analysis (here represented by Moving Average Crossover, DMAC), suitable optimization methods are proposed. Artificial Intelligence techniques can increase the profit performance of technical systems. In this paper, two intelligent trading systems are proposed. The first one makes use of fuzzy logic techniques to enhance the power of genetic procedures. The second system attempts to improve the performances of fuzzy system through Neural Networks. The target is to obtain good profits, avoiding drawdown situations, in applications to the DMAC rule for trading the euro-dollar in the foreign exchange market. The results show that the fuzzy system gives good profits over trading periods close to training period length, but the neuro-fuzzy system achieves the best profits in the majority of cases. Both systems show an optimal robustness to drawdown and a remarkable profit performance. In principle, the algorithms, described here, could be programmed on microchips. We use an hourly time series (1999—2012) of the Euro-Dollar exchange rate.

Keywords: Fuzzy Rule; Fuzzy Logic Controller; Foreign Exchange Market; Trading Rule; Fuzzy Input (search for similar items in EconPapers)
Date: 2014
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-319-02499-8_22

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DOI: 10.1007/978-3-319-02499-8_22

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