Integrated model of multiple kernel learning and differential evolution for EUR/USD trading

Shangkun Deng, Akito Sakurai

Research output: Contribution to journalArticle

4 Citations (Scopus)

Abstract

Currency trading is an important area for individual investors, government policy decisions, and organization investments. In this study, we propose a hybrid approach referred to as MKL-DE, which combines multiple kernel learning (MKL) with differential evolution (DE) for trading a currency pair. MKL is used to learn a model that predicts changes in the target currency pair, whereas DE is used to generate the buy and sell signals for the target currency pair based on the relative strength index (RSI), while it is also combined with MKL as a trading signal. The new hybrid implementation is applied to EUR/USD trading, which is the most traded foreign exchange (FX) currency pair. MKL is essential for utilizing information from multiple information sources and DE is essential for formulating a trading rule based on a mixture of discrete structures and continuous parameters. Initially, the prediction model optimized by MKL predicts the returns based on a technical indicator called the moving average convergence and divergence. Next, a combined trading signal is optimized by DE using the inputs from the prediction model and technical indicator RSI obtained from multiple timeframes. The experimental results showed that trading using the prediction learned by MKL yielded consistent profits.

Original languageEnglish
Article number914641
JournalScientific World Journal
Volume2014
DOIs
Publication statusPublished - 2014

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currency
learning
Learning
Profitability
prediction
divergence
Organizations
index
indicator

ASJC Scopus subject areas

  • Biochemistry, Genetics and Molecular Biology(all)
  • Environmental Science(all)
  • Medicine(all)

Cite this

Integrated model of multiple kernel learning and differential evolution for EUR/USD trading. / Deng, Shangkun; Sakurai, Akito.

In: Scientific World Journal, Vol. 2014, 914641, 2014.

Research output: Contribution to journalArticle

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