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Evolving Trading Signals at Foreign Exchange Market

  • Svitlana Galeshchuk
  • Sumitra Mukherjee
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 722)

Abstract

Paper examines the merit of evolutionary algorithms to generate trading signals for trading decisions at financial markets. We focus on foreign-exchange market. It is among the largest financial markets. “Technical” traders base their decisions on a set of technical rules evolved from past market activity. We employ a genetic algorithm to learn a set of profitable trading rules considering transaction costs; each rule generates a ‘buy’, ‘hold’, or ‘sell’ signal using moving average technical rule. We empirically evaluate our approach using exchange rates of four major currency pairs over the period 2000 to 2015. Performance evaluation on out-of-sample data indicates that our approach is able to provide acceptably high returns on investment. Comparison with exhaustive search proves convincing performance of our approach.

Keywords

Trading rules Forex market Excess returns Evolutionary algorithms 

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Faculty of Accounting and AuditTernopil National Economic UniversityTernopilUkraine
  2. 2.Laboratoire d’Informatique de GrenobleUniversité Grenoble AlpesGrenobleFrance
  3. 3.Nova Southeastern UniversityFort LauderdaleUSA

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