Enhancing the context-aware FOREX market simulation using a parallel elastic network model

  • Antonio V. Contreras
  • Antonio LlanesEmail author
  • Francisco J. Herrera
  • Sergio Navarro
  • Jose J. López-Espín
  • José M. Cecilia


Foreign exchange (FOREX) market is a decentralized global marketplace in which different participants, such as international banks, companies or investors, can buy, sell, exchange and speculate on currencies. This market is considered to be the largest financial market in the world in terms of trading volume. Indeed, the just-in-time price prediction for a currency pair exchange rate (e.g., EUR/USD) provides valuable information for companies and investors as they can take different actions to improve their business. The trading volume in the FOREX market is huge, disperses, in continuous operations (24 h except weekends), and the context significantly affects the exchange rates. This paper introduces a context-aware algorithm to model the behavior of the FOREX Market, called parallel elastic network model (PENM). This algorithm is inspired by natural procedures like the behavior of macromolecules in dissolution. The main results of this work include the possibility to represent the market evolution of up to 21 currency pair, being all connected, thus emulating the real-world FOREX market behavior. Moreover, because the computational needs required are highly costly as the number of currency pairs increases, a hybrid parallelization using several shared memory and message passing algorithms studied on distributed cluster is evaluated to achieve a high-throughput algorithm that answers the real-time constraints of the FOREX market. The PENM is also compared with a vector autoregressive (VAR) model using both a classical statistical measure and a profitability measure. Specifically, the results indicate that PENM outperforms VAR models in terms of quality, achieving up to \(930\times\) speed-up factor compared to traditional R codes using in this field.


FOREX simulation Trading Context-aware Big data Bioinspired computing Parallel computing 



This work was jointly supported by the Fundación Séneca (Agencia Regional de Ciencia y Tecnología, Región de Murcia) under Grant 20813/PI/18 and by the Spanish MEC and European Commission FEDER under Grants TIN2016-78799-P and TIN2016-80565-R (AEI/FEDER, UE).


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Authors and Affiliations

  1. 1.Artificial Intelligence Talentum, S.L., Edificio CEEIMCampus Universitario de EspinardoMurciaSpain
  2. 2.Center of Operations ResearchMiguel Hernández UniversityElche, AlicanteSpain
  3. 3.Polytechnic SchoolCatholic University of San Antonio of Murcia (UCAM)MurciaSpain

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