Journal of Multimedia Information System

Korea Multimedia Society (한국멀티미디어학회)
권호 표지
DOI QR코드

DOI QR Code

FOREX Web-Based Trading Platform with E-Learning Features

  • Yong, Yoke Leng (Department of Computing & Information Systems, Sunway University) ;
  • Lieu, Shang Qin (Department of Computing & Information Systems, Sunway University) ;
  • Ngo, David (Department of Computing & Information Systems, Sunway University) ;
  • Lee, Yunli (Department of Computing & Information Systems, Sunway University)
  • Received : 2017.08.30
  • Accepted : 2017.09.21
  • Published : 2017.12.31
Download PDF
⟨ Previous Next ⟩

Abstract

There has been an influx of traders and researchers eager to gain a better understanding of the market due to the rapid growth of the FOREX market. Traders with varying degree of experience are also often inundated with information, analysis methods as well as trading rules when making a trading decision on buying/selling a currency exchange pair. Thus, this paper reviews the current computational tools and analysis methods used within the FOREX trading community and proposes the development of a web-based trading platform with e-learning features to support beginners. Novice traders could also benefit from the use of the proposed e-learning trading platform as it helps them gain valuable knowledge and navigate the FOREX market in real-time. Even experienced traders would find it useful as the platform could be used for actual trading and acts as a reference point to understand the reasoning behind the certain technical analysis implementation that are still unclear to them.

Keywords

References

  1. A. Abraham, "Analysis of hybrid soft and hard computing techniques for forex monitoring systems," in Proc. 2002 IEEE Int. Conf. Fuzzy Syst., vol. 2, 2002.
  2. P. S. Gabriel, "How Newspaper-Article-Events, Other Stock Market Indices, and the Foreign Currency Rate Affect the Philippine Stock Market," Asian Economic and Financial Review, vol. 3, no. 4, pp. 423-444, 2013.
  3. A. Khadjeh Nassirtoussi, S. Aghabozorgi, Y. W. Teh, and D. C. L. Ngo, "Text mining of news-headlines for FOREX market prediction: A Multi-Layer Dimension Reduction Algorithm with Semantics and Sentiment," Expert Systems with Applications, vol. 42, no. 1, pp. 306-324, 2014. https://doi.org/10.1016/j.eswa.2014.08.004
  4. A. Khadjeh Nassirtoussi, S. Aghabozorgi, Y. W. Teh, and D. C. L Ngo, "Text mining for Market Prediction: A Systematic Review," Expert Systems with Applications, vol. 41, no. 16, pp. 7653-7670, 2014. https://doi.org/10.1016/j.eswa.2014.06.009
  5. T. Rao and S. Srivastava, "Using Twitter Sentiments and Search Volumes Index to Predict Oil, Gold, Forex and Markets Indices, in Proceedings of the Annual ACM Web Science Conference, pp. 336 - 345, 2012.
  6. A. Bagheri, H. Mohammadi Peyhani, and M. Akbari, "Financial forecasting using ANFIS networks with Quantum-behaved Particle Swarm Optimization," Expert Syst. Appl., vol. 41, no. 14, pp. 6235-6250, 2014. https://doi.org/10.1016/j.eswa.2014.04.003
  7. C. J. Neely and P. A. Weller, "Technical analysis in the foreign exchange market," Technical Report No. 2011-001B, 2011.
  8. S. Schulmeister, "Components of the Profitability of Technical Currency Trading," Applied Financial Economics, vol. 18, no. 1, pp. 917 - 930, 2008. https://doi.org/10.1080/09603100701335416
  9. S. Pang, L. Song, and N. Kasabov, "Correlation-aided support vector regression for forex time series prediction," Neural Comput. Appl., vol. 20, no. 8, pp. 1193-1203, 2011. https://doi.org/10.1007/s00521-010-0482-5
  10. P. H. Hsu, M. P. Taylor, and Z. Wang, "Technical Trading: Is it still beating the foreign exchange market?," Journal of International Economics, vol. 102, pp. 188-208, 2016. https://doi.org/10.1016/j.jinteco.2016.03.012
  11. H. T. Wong, "Exchange rate forecast: A note," J. Stock Forex Trading, vol. 2, no. 2, 2013.
  12. E. A. Gerlein, M. McGinnity, A. Belatreche, S. Coleman, "Evaluating machine learning classification for financial trading: An empirical approach," Expert Systems with Applications, vol. 54, pp.193-207, 2016. https://doi.org/10.1016/j.eswa.2016.01.018
  13. J. Yao and C. L. Tan, "A case study on using neural networks to perform technical forecasting of forex," Neurocomputing, vol. 34, no. 1, pp. 79-98, 2000. https://doi.org/10.1016/S0925-2312(00)00300-3
  14. A. Emam, "Optimal artificial neural network topology for foreign exchange forecasting," in Proc. 46th Annual Southeast Regional Conference on XX, pp. 63-68, 2008.
  15. T. Zafeiriou and D. Kalles, "Short-term trend prediction of foreign exchange rates with a Neural- Network based ensemble of financial technical indicators," Int. J. Artif. Intell. Tools, vol. 22, no. 3, p. 1350016, 2013. https://doi.org/10.1142/S0218213013500164
  16. K. Slany, "Towards the automatic evolutionary prediction of the FOREX market behaviour," in Proc. 2009 International Conference on Adaptive and Intelligent Systems, pp. 141-145, 2009.
  17. K. Theofilatos, S. Likothanassis, and A. Karathanasopoulos, "Modeling and trading the EUR/USD exchange rate using machine learning techniques," Eng. Technol. Appl. Sci. Res., vol. 2, no. 5, pp. 269-272, 2012.
  18. M. Ozturk, I. H. Toroslu, G. Fidan, "Heuristic based trading system on Forex data using technical indicator rules," Applied Soft Computing, vol. 43, pp. 170-186, 2016. https://doi.org/10.1016/j.asoc.2016.01.048
  19. S. M. Fahimifard, M. Homayounifar, M. Sabouhi, and A. R. Moghaddamnia, "Comparison of ANFIS, ANN, GARCH and ARIMA Techniques to Exchange Rate Forecasting," J. Appl. Sci., vol. 9, no. 20, pp. 3641- 3651, 2009. https://doi.org/10.3923/jas.2009.3641.3651
  20. A. A. Baasher and M. W. Fakhr, "Forex trend classification using machine learning techniques," in Proc. 11th WSEAS International Conference on Applied Computer Science, pp. 41-47, 2011.
  21. R. F. B. De Brito and A. L. I. Oliveira, "A foreign exchange market trading system by combining GHSOM and SVR," in Proc. 2012 International Joint Conference on Neural Networks (IJCNN), pp. 1-7, 2012.
  22. R. F. B. De Brito and A. L. I. Oliveira, "Comparative study of FOREX trading systems built with SVR+GHSOM and Genetic Algorithms optimization of technical indicators," in Proceedings of the 2012 IEEE 24th International Conference on Tools with Artificial Intelligence, 2012, vol. 1, pp. 351-358, 2012.
  23. C. Gallo, "The forex market in practice: A computing approach for automated trading strategies," Int. J. Econ. Manag. Sci., vol. 3, no. 1, 2014.
  24. "OANDA" https://www.oanda.com.
  25. R. M. C. Pinto and J. C. M. Silva, "Strategic methods for automated trading in forex," in Proc. 12th International Conference on Intelligent Systems Design and Applications (ISDA), pp. 34-39, 2012.
  26. "IQ option" https://iqoption.com/en.
  27. "FxPro MT4 (Metatrader4) WebTrader" http://www.fxpro.co.uk/trading/platforms/mt4/web.
  28. "CQG" http://partners.cqg.com, August, 2017.
  29. "Ensign Windows" http://www.ensignsoftware.com, Aug., 2017.
  30. "MetaStock" https://www.metastock.com.
  31. "MetaTrader" https://www.metatrader5.com/en.
  32. "MultiCharts" https://www.multicharts.com.
  33. "NeoTicker" Available: http://www.tickquest.com.
  34. "NinjaTrader" Available: http://ninjatrader.com.
  35. "SierraChart" https://www.sierrachart.com.
  36. "TradeDecision" http://www.tradecision.com.
  37. "TradeStation" http://www.tradestation.com.
  38. "Wealth-Lab" https://www.wealth-lab.com.