Research on Automated Forex Trading System Based on BP Neural Network

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This paper reports on an expert advisor for forex trading based on Back Propagation Neural Network (BPNN) in MetaTrader4 platform. A single hidden layer feedforward network was established for foreign exchange rate prediction. Trading rules based on the prediction results was designed and realized. Finally, we optimized the parameters according to the profitability performed on EUR/USD, GBP/USD currency pairs separately. The optimized results are able to achieve good results in the training series. In the test series, the strategies are consistently profitable for at least the first twenty days. It is concluded that the BPNN based model do have the ability to make profits from the experimental currency pairs for the period investigated.

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Periodical:

Advanced Materials Research (Volumes 753-755)

Edited by:

Xiaoming Sang and Yun-Hae Kim

Pages:

3080-3083

Citation:

L. Meng and Y. Sun, "Research on Automated Forex Trading System Based on BP Neural Network", Advanced Materials Research, Vols. 753-755, pp. 3080-3083, 2013

Online since:

August 2013

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$38.00

[1] C. Dunis, Williams, Derivatives Use, Trading and Regulation, 8(2002): 211-239.

[2] S. K. Sharma, V. Sharma, International Journal of Computer Applications, 43(2012): 26-28.

[3] F. Fernández-Rodrı́guez, C. González-Martel, and S. Sosvilla-Rivero, Economics Letters, 69(2000): 89-94.

DOI: https://doi.org/10.1016/s0165-1765(00)00270-6

[4] K. M . Kiani, T. Kastens, Computational Economics, 32(2000): 383-406.

[5] A. H. S. M. Nor, B. Gharleghi, K. Omar, and T. Sarmidi, A Novel Artificial Neural Network Model for Exchange Rate Forecast., presented at the 2nd International Conference on Business and Economic Research (2nd ICBER 2011) PROCEEDING, 2011: 99-108.

[6] A. A. Philip, A. A. Taofiki, A. A. Bidemi, Artificial Neural Network Model for Forecast Foreign Exchange Rate., Presented at the World of Computer Science and Information Technology Journal (WCSIT), 1(2011): 110-118.

[7] M. Mohapatra, B. Majhi, M. Rout, A Robust Technique for Exchange Rate Prediction Using Wilcoxon Norm., Paper presented at the International Conference On Advances In Engineering, Science And Management (ICAESM -2012), Nagapattinam, Tamil Nadu, March 30-31, (2012).

[8] T. Chenoweth, Z. Obradovic, S. S. Lee, Applied Artificial Intelligence, 10(1996): 523-541.

[9] Y. Bengio, Using a Financial Training Criterion Rather than a Prediction Criterion., (1998).

[10] R. Gencay, Economics Letters, 59(1998): 249-254.

[11] H. Chou, Y. Chang, Visual Development Platform for White-Box Algorithmic Trading., presented at the Sixth International Conference on Intelligent Information Hiding and Multimedia Signal Processing, Darmstadt, Germany, October 15-17, (2010).

DOI: https://doi.org/10.1109/iihmsp.2010.180

[12] A. S. Spritzer, C. M. D. S. Freitas, A Visual Tool to Support Technical Analysis of Stock Market Data., Proceedings of the working conference on Advanced visual interfaces, 512-515.

DOI: https://doi.org/10.1145/1133265.1133372

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