Integrated Model of Multiple Kernel Learning and Differential Evolution for EUR/USD Trading
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. MK...
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| Format: | Article |
| Language: | English |
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Wiley
2014-01-01
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| Series: | The Scientific World Journal |
| Online Access: | http://dx.doi.org/10.1155/2014/914641 |
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| author | Shangkun Deng Akito Sakurai |
| author_facet | Shangkun Deng Akito Sakurai |
| author_sort | Shangkun Deng |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-4341c774a43f4f26ae560813419897b2 |
| institution | Kabale University |
| issn | 2356-6140 1537-744X |
| language | English |
| publishDate | 2014-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | The Scientific World Journal |
| spelling | doaj-art-4341c774a43f4f26ae560813419897b22025-02-03T01:01:51ZengWileyThe Scientific World Journal2356-61401537-744X2014-01-01201410.1155/2014/914641914641Integrated Model of Multiple Kernel Learning and Differential Evolution for EUR/USD TradingShangkun Deng0Akito Sakurai1Graduate School of Science and Technology, Keio University, 3-14-1 Hiyoshi, Kohoku-ku, Yokohama 223-8522, JapanGraduate School of Science and Technology, Keio University, 3-14-1 Hiyoshi, Kohoku-ku, Yokohama 223-8522, JapanCurrency 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.http://dx.doi.org/10.1155/2014/914641 |
| spellingShingle | Shangkun Deng Akito Sakurai Integrated Model of Multiple Kernel Learning and Differential Evolution for EUR/USD Trading The Scientific World Journal |
| title | Integrated Model of Multiple Kernel Learning and Differential Evolution for EUR/USD Trading |
| title_full | Integrated Model of Multiple Kernel Learning and Differential Evolution for EUR/USD Trading |
| title_fullStr | Integrated Model of Multiple Kernel Learning and Differential Evolution for EUR/USD Trading |
| title_full_unstemmed | Integrated Model of Multiple Kernel Learning and Differential Evolution for EUR/USD Trading |
| title_short | Integrated Model of Multiple Kernel Learning and Differential Evolution for EUR/USD Trading |
| title_sort | integrated model of multiple kernel learning and differential evolution for eur usd trading |
| url | http://dx.doi.org/10.1155/2014/914641 |
| work_keys_str_mv | AT shangkundeng integratedmodelofmultiplekernellearninganddifferentialevolutionforeurusdtrading AT akitosakurai integratedmodelofmultiplekernellearninganddifferentialevolutionforeurusdtrading |