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Polish Information Processing Society
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Annals of Computer Science and Information Systems, Volume 11

Proceedings of the 2017 Federated Conference on Computer Science and Information Systems

Deep Learning for Financial Time Series Forecasting in A-Trader System


DOI: http://dx.doi.org/10.15439/2017F449

Citation: Proceedings of the 2017 Federated Conference on Computer Science and Information Systems, M. Ganzha, L. Maciaszek, M. Paprzycki (eds). ACSIS, Vol. 11, pages 905912 ()

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Abstract. The paper presents aspects related to developing methods for financial time series forecasting using deep learning in relation to multi-agent stock trading system, called A-Trader. On the basis of this model, an investment strategies in A-Trader system can be build. The first part of the paper briefly discusses a problem of financial time series on FOREX market. Classical neural networks and deep learning models are outlined, their performances are analyzed. The final part presents deployment and evaluation of a deep learning model implemented using H20 library as an agent of A-Trader system.


  1. L. Mendes, P. Godinho and J. Dias, “A Forex trading system based on a genetic algorithm”, Journal of Heuristics 18 (4), pp. 627-656, 2012.
  2. F. H. Westerhoff, “Multi-Asset Market Dynamics”, Macroeconomic Dynamics, 8/2011, pp. 596—616, 2011.
  3. J.R. Thompson, J.R. Wilson and E. P. Fitts, “Analysis of market returns using multifractal time series and agent-based simulation”, in Proceedings of the Winter Simulation Conference (WSC '12). Winter Simulation Conference, Article 323, 2012.
  4. C. D. Kirkpatric and J. Dahlquist, Technical Analysis: The Complete Resource for Financial Market Technicians, Financial Times Press, 2006.
  5. C. Lento, “A Combined Signal Approach to Technical Analysis on the S&P 500”, Journal of Business & Economics Research, 6 (8), pp. 41–51, 2008.
  6. O. Badawy and A. Almotwaly, "Combining neural network knowledge in a mobile collaborating multi-agent system", Electrical, Electronic and Computer Engineering, ICEEC '04, pp. 325, 328, 2004, http://dx.doi.org/10.1109/ICEEC.2004.1374457.
  7. P. R. Kaltwasser, “Uncertainty about fundamentals and herding behavior in the FOREX market”, Physica A: Statistical Mechanics and its Applications, 389 (6), pp. 1215-1222, March 2010.
  8. H. C. Aladag, U. Yolco and E. Egrioglu, “A new time invariant fuzzy time series forecasting model based on particle swarm optimization”, Applied Soft Computing, 12 (10), pp. 3291-3299, 2012.
  9. P. Singh and B. Borah, “Forecasting stock index price based on M-factors fuzzy time series and particle swarm optimization”, International Journal of Approximate Reasoning, 55 (3), pp. 812-833, 2014.
  10. M. Aloud, E.P.K. Tsang and R. Olsen, “Modelling the FX Market Traders' Behaviour: An Agent-based Approach”, in Simulation in Computational Finance and Economics: Tools and Emerging Applications, B. Alexandrova-Kabadjova, S. Martinez-Jaramillo, A. L. Garcia-Almanza and E. Tsang (eds.), IGI Global, 2012, pp. 202-228.
  11. J. B. Glattfelder, A. Dupuis and R. Olsen, “Patterns in high-frequency FX data: Discovery of 12 empirical scaling laws”, Quantitative Finance, 11 (4), pp. 599-614, 2011.
  12. R.P. Barbosa and O. Belo, “Multi-Agent Forex Trading System”, in Agent and Multi-agent Technology for Internet and Enterprise Systems, Studies in Computational Intelligence, vol. 289, 2010, pp. 91-118.
  13. G. Batres-Estrada, Deep Learning for Multivariate Financial Time Series, Thesis of KTH Royal Institute of Technology, Stockholm, 2015.
  14. E. Busseti, I. Osband and S. Wong, Deep Learning for Time Series Modeling, http://cs229.stanford.edu/proj2012/BussetiOsbandWong-DeepLearningForTimeSeriesModeling.pdf, 2012.
  15. Y. Bengio, “Learning Deep Architectures for AI", Foundations and Trends in Machine Learning, 2 (1), 2009.
  16. L. Takeuchi and L.Y. Ying, Applying Deep Learning to Enhance Momentum Trading Strategies in Stocks http://cs229.stanford.edu/proj2013/TakeuchiLee-ApplyingDeepLearningToEnhanceMomentumTradingStrategiesInStocks.pdf, 2013.
  17. G. E. Hinton., S. Osindero and Y. W. Teh, “A fast learning algorithm for deep belief nets”, Neural computation, 18(7), pp. 1527-1554.
  18. T. Kuremoto, S. Kimura, K. Kobayashi and M. Obayashi, “Time Series Forecasting Using a Deep Belief Network with Restricted Boltzmann Machines” Neurocomputing 137 (2014), pp. 47-56, 2014.
  19. J. Korczak, M. Hernes and M. Bac, “Fuzzy Logic as Agents’ Knowledge Representation in A-Trader System”, in E. Ziemba (ed.), Information Technology for Management, Lecture Notes in Business Information Processing, vol. 243, Springer International Publishing, 2016, pp. 109-124.
  20. J. Korczak, M. Hernes and M. Bac, “Performance evaluation of decision-making agents’ in the multi-agent system”, in Proceedings of Federated Conference Computer Science and Information Systems (FedCSIS), Warszawa, 2014, pp. 1171 – 1180. http://dx.doi.org/10.15439/2014F188.
  21. J. Korczak, M. Hernes and M. Bac, “Fundamental analysis in the multi-agent trading system”, in Proceedings of Federated Conference Computer Science and Information Systems (FedCSIS), Gdańsk, 2016, pp. 1171 – 1180. http://dx.doi.org/10.15439/2014F188.
  22. M. Hernes and N.T. Nguyen, “Deriving Consensus for Hierarchical Incomplete Ordered Partitions and Coverings”, Journal of Universal Computer Science 13 (2), pp. 317-328, 2007.
  23. M. Hernes and J. Sobieska-Karpińska , “Application of the consensus method in a multi-agent financial decision support system”, Information Systems and e-Business Management 14 (1), Springer Berlin Heidelberg, 2016, http://dx.doi.org/10.1007/s10257-015-0280-9.
  24. P. D. McNelis, “Neural Networks in Finance: Gaining Predictive Edge in the Market”, Academic Press Advanced Finance Series, Academic Press, Inc., Orlando, 2004.
  25. V.V. Kondratenko and Y. Kuperin, Using Recurrent Neural Networks To Forecasting of Forex, https://arxiv.org/abs/cond-mat/0304469 [cond-mat.disnn], 2003.
  26. L. Di Persio and O. Honchar, “Artificial neural networks approach to the forecast of stock market price movements”, International Journal of Economics and Management Systems, Volume 1, pp. 158-162, 2016.
  27. L. Arnold, S. Rebecchi, S. Chevallier and H. Paugam-Moisy, An Introduction to Deep Learning. ESANN, 2011.
  28. X. Glorot and Y. Bengio, “Understanding the difficulty of training deep feedforward neural networks”, Proceedings of the 13th International Conference on Artificial Intelligence and Statistics, 9, 2010 pp. 249– 256.
  29. H. Lu, B. Li, J. Zhu, Y. Li, Y. Li, X. Xu, L. He, X. Li, J. Li and S. Serikawa, “Wound intensity correction and segmentation with convolutional neural networks”, Concurrency and Computation: Practice and Experience 29, 2017.
  30. Y.J. Cha, W. Choi and O. Büyüköztürk, “Deep Learning-Based Crack Damage Detection Using Convolutional Neural Networks”, Computer-Aided Civil and Infrastructure Engineering, 32, pp. 361–378, 2017, http://dx.doi.org/10.1111/mice.12263.
  31. H. Larochelle and Y. Bengio, “Exploring strategies for training deep neural networks”, Journal of Machine Learning Research, 1, pp. 1–40, 2009.
  32. P. Hall, “How is deep learning different from multilayer perceptron?”, https://www.quora.com/How-is-deep-learning-different-from-multilayer-perceptron, [access: 01.05.2017].
  33. https://github.com/h2oai [access: 01.05.2017].
  34. A. Candel, V. Parmar, E. LeDell, and A. Arora, “Deep learning with h2o, 2015, https://h2o-release.s3.amazonaws.com/h2o/rel-slater/9/docs-website/h2o-docs/booklets/DeepLearning_Vignette.pdf.