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A Forex trading system based on a genetic algorithm

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Abstract

In this paper, a genetic algorithm will be described that aims at optimizing a set of rules that constitute a trading system for the Forex market. Each individual in the population represents a set of ten technical trading rules (five to enter a position and five others to exit). These rules have 31 parameters in total, which correspond to the individuals’ genes. The population will evolve in a given environment, defined by a time series of a specific currency pair. The fitness of a given individual represents how well it has been able to adapt to the environment, and it is calculated by applying the corresponding rules to the time series, and then calculating the ratio between the profit and the maximum drawdown (the Stirling ratio). Two currency pairs have been used: EUR/USD and GBP/USD. Different data was used for the evolution of the population and for testing the best individuals. The results achieved by the system are discussed. The best individuals are able to achieve very good results in the training series. In the test series, the developed strategies show some difficulty in achieving positive results, if you take transaction costs into account. If you ignore transaction costs, the results are mostly positive, showing that the best individuals have some forecasting ability.

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Notes

  1. We speak of abnormal returns, or excess returns, when the returns of the investment strategy exceed those of a benchmark with a similar level of risk. The appropriate benchmarks depend on the type of investment considered. Given the type of investment we consider in this paper, we define the benchmark as the zero profit (no profit/no loss) case (as explained in Sect. 2.4).

  2. In a currency pair, the first currency is the base currency and the second is the quote currency. This paper uses the currency codes defined by ISO 4217. USD is the United States dollar, GBP is the pound sterling, EUR is the euro, DEM is the German mark and JPY is the Japanese yen.

  3. There are some slightly different definitions for the Stirling ratio. In this paper, we use the same definition as Hryshko and Downs.

  4. A pip (percentage in point) is the smallest price change that a given exchange rate can take. For instance, the EUR/USD pair is priced to four decimal places, so a pip will be a unit change in the fourth decimal place. If the exchange rate increases from 1.4500 to 1.4505, it is said to have increased by 5 pips.

  5. Data available at ratedata.gaincapital.com.

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Acknowledgements

We would like to thank the anonymous referees, whose comments helped us improve this paper.

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Authors and Affiliations

  1. Faculdade de Economia, Universidade de Coimbra, Av. Dias da Silva, 165, 3004-512, Coimbra, Portugal

    Luís Mendes

  2. Faculdade de Economia and GEMF, Universidade de Coimbra, Av. Dias da Silva, 165, 3004-512, Coimbra, Portugal

    Pedro Godinho

  3. Faculdade de Economia and Inesc-Coimbra, Universidade de Coimbra, Av. Dias da Silva, 165, 3004-512, Coimbra, Portugal

    Joana Dias

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  1. Luís Mendes
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  2. Pedro Godinho
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  3. Joana Dias
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Correspondence to Joana Dias.

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Mendes, L., Godinho, P. & Dias, J. A Forex trading system based on a genetic algorithm. J Heuristics 18, 627–656 (2012). https://doi.org/10.1007/s10732-012-9201-y

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