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. 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.

[1]  Chukiat Worasucheep.  A new self adaptive differential evolution: Its application in forecasting the index of Stock Exchange of Thailand , 2007, 2007 IEEE Congress on Evolutionary Computation.

[2]  John Shawe-Taylor,et al.  Multiple Kernel Learning on the Limit Order Book , 2010, WAPA.

[3]  Alexandre d'Aspremont,et al.  Predicting abnormal returns from news using text classification , 2008, 0809.2792.

[4]  Shangkun Deng,et al.  Stock Price Change Rate Prediction by Utilizing Social Network Activities , 2014, TheScientificWorldJournal.

[5]  Shangkun Deng,et al.  Hybrid Method of Multiple Kernel Learning and Genetic Algorithm for Forecasting Short-Term Foreign Exchange Rates , 2015 .

[6]  Shie-Jue Lee,et al.  A multiple-kernel support vector regression approach for stock market price forecasting , 2011, Expert Syst. Appl..

[7]  Xiaodong Li,et al.  Time series forecasting by evolving artificial neural networks using genetic algorithms and differential evolution , 2010, The 2010 International Joint Conference on Neural Networks (IJCNN).

[8]  Michael I. Jordan,et al.  Multiple kernel learning, conic duality, and the SMO algorithm , 2004, ICML.

[9]  F. Fabozzi,et al.  Streetwise : the best of the Journal of portfolio management , 1998 .

[10]  Shian-Chang Huang,et al.  Integrating GA-based time-scale feature extractions with SVMs for stock index forecasting , 2008, Expert Syst. Appl..

[11]  Hitoshi Iba,et al.  Optimization of the trading rule in foreign exchange using genetic algorithm , 2009, GECCO.

[12]  Shangkun Deng,et al.  Prediction of Foreign Exchange Market States with Support Vector Machine , 2011, 2011 10th International Conference on Machine Learning and Applications and Workshops.

[13]  Chien-Feng Huang,et al.  A hybrid stock selection model using genetic algorithms and support vector regression , 2012, Appl. Soft Comput..

[14]  Iftekhar Ahmad,et al.  SVM based models for predicting foreign currency exchange rates , 2003, Third IEEE International Conference on Data Mining.

[15]  Yen-Liang Chen,et al.  Mining associative classification rules with stock trading data - A GA-based method , 2010, Knowl. Based Syst..

[16]  Joe Celko,et al.  A moving average , 2006 .

[17]  Rainer Storn,et al.  Differential Evolution – A Simple and Efficient Heuristic for global Optimization over Continuous Spaces , 1997, J. Glob. Optim..

[18]  A. Sakurai,et al.  Crude Oil Spot Price Forecasting Based on Multiple Crude Oil Markets and Timeframes , 2014 .

[19]  Shangkun Deng,et al.  Foreign Exchange Trading Rules Using a Single Technical Indicator from Multiple Timeframes , 2013, 2013 27th International Conference on Advanced Information Networking and Applications Workshops.

[20]  Keiji Yanai,et al.  A food image recognition system with Multiple Kernel Learning , 2009, 2009 16th IEEE International Conference on Image Processing (ICIP).

[21]  Jacek Mandziuk,et al.  One Day Prediction of NIKKEI Index Considering Information from Other Stock Markets , 2004, ICAISC.

[22]  Terence Tai-Leung Chong,et al.  Technical analysis and the London stock exchange: testing the MACD and RSI rules using the FT30 , 2008 .

[23]  Akbar Esfahanipour,et al.  A genetic programming model to generate risk-adjusted technical trading rules in stock markets , 2011, Expert Syst. Appl..

[24]  Gunnar Rätsch,et al.  The SHOGUN Machine Learning Toolbox , 2010, J. Mach. Learn. Res..

[25]  Tai-Liang Chen,et al.  A hybrid model based on adaptive-network-based fuzzy inference system to forecast Taiwan stock market , 2011, Expert Syst. Appl..

[26]  Robin L. Tarpley,et al.  Contextual Fundamental Analysis Through the Prediction of Extreme Returns , 2001 .

[27]  A. P. Millar,et al.  RSI , 1986, The Medical journal of Australia.

[28]  Mikhail F. Kanevski,et al.  Multiple Kernel Learning of Environmental Data. Case Study: Analysis and Mapping of Wind Fields , 2009, ICANN.