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A Genetic Programming Approach for EUR/USD Exchange Rate Forecasting and Trading

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Abstract

The purpose of this article is to present a novel genetic programming trading technique in the task of forecasting the next day returns when trading the EUR/USD exchange rate based on the exchange rates of historical data. Aiming at testing its effectiveness, we benchmark the forecasting performance of our genetic programming implementation with three traditional strategies (naive strategy, MACD, and a buy & hold strategy) plus a hybrid evolutionary artificial neural network approach. The proposed genetic programming technique was found to demonstrate the highest trading performance in terms of annualized return and information ratio when compared to all other strategies which have been used. When more elaborate trading techniques, such as leverage, were combined with the examined models, the genetic programming approach still presented the highest trading performance. To the best of our knowledge, this is the first time that genetic programming is applied in the problem of effectively modeling and trading with the EUR/USD exchange rate. Our application now offers practitioners with an effective and extremely promising set of results when forecasting in the foreign exchange market. The developed genetic programming environment is implemented using the C++ programming language and includes a variation of the genetic programming algorithm with tournament selection.

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Authors and Affiliations

  1. Pattern Recognition Laboratory, Department of Computer Engineering & Informatics, University of Patras, 26500, Patras, Greece

    Georgios A. Vasilakis, Konstantinos A. Theofilatos & Spiros D. Likothanassis

  2. School of Agricultural Technology, Technological Institute of Kalamata, 24100, Kalamata, Greece

    Efstratios F. Georgopoulos

  3. London Metropolitan University, 166-220 Holloway Road, London, N7 8DB, UK

    Andreas Karathanasopoulos

Authors
  1. Georgios A. Vasilakis
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  2. Konstantinos A. Theofilatos
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  3. Efstratios F. Georgopoulos
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  4. Andreas Karathanasopoulos
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  5. Spiros D. Likothanassis
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Corresponding author

Correspondence to Konstantinos A. Theofilatos.

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Vasilakis, G.A., Theofilatos, K.A., Georgopoulos, E.F. et al. A Genetic Programming Approach for EUR/USD Exchange Rate Forecasting and Trading. Comput Econ 42, 415–431 (2013). https://doi.org/10.1007/s10614-012-9345-8

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  • DOI: https://doi.org/10.1007/s10614-012-9345-8

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