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

Deng S, Sakurai A - ScientificWorldJournal (2014)

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: Graduate School of Science and Technology, Keio University, 3-14-1 Hiyoshi, Kohoku-ku, Yokohama 223-8522, Japan.

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.

Show MeSH
Structure of the proposed method.
© Copyright Policy - open-access
Related In: Results  -  Collection


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fig2: Structure of the proposed method.

Mentions: Figure 2 shows the structure of the proposed method. First, the proposed method uses a MKL framework to predict directional changes in the currency rate based on the MACD of three currency pairs. The RSI signals are generated using multiple timeframe features of EUR/USD by considering the MKL trading signals. Finally, the MKL signal and RSIs signal are combined to produce a final decision, that is, the trading signal.


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

Deng S, Sakurai A - ScientificWorldJournal (2014)

Structure of the proposed method.
© Copyright Policy - open-access
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4109363&req=5

fig2: Structure of the proposed method.
Mentions: Figure 2 shows the structure of the proposed method. First, the proposed method uses a MKL framework to predict directional changes in the currency rate based on the MACD of three currency pairs. The RSI signals are generated using multiple timeframe features of EUR/USD by considering the MKL trading signals. Finally, the MKL signal and RSIs signal are combined to produce a final decision, that is, the trading signal.

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: Graduate School of Science and Technology, Keio University, 3-14-1 Hiyoshi, Kohoku-ku, Yokohama 223-8522, Japan.

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.

Show MeSH