Abstract
The application of Neural Networks to financial analysis in general, and currency trading in particular, has been explored for a number of years. The most commonly [2,3,4,5] used NN training algorithm in this application is the backpropagation. The application of TWEANN systems to the same field is only now starting to emerge, and is showing a significant amount of potential. In this chapter we create a Forex simulator, and then use our neuroevolutionary system to evolve automated currency trading agents. For this application we will utilize not only the standard sliding window approach when feeding the sensory signals to the neural encoded agents, but also the sliding chart window, where we feed the evolved substrate encoded agents the actual candle-stick price charts, and then compare the performance of the two approaches. As of this writing, the use of geometrical pattern sensitive NN based agents in the analysis of financial instrument charts has not yet been explored in any other paper, to this author’s knowledge. Thus in this chapter we pioneer this approach, and explore its performance and properties.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Chapter-19 Supplementary material: www.DXNNResearch.com/NeuroevolutionThroughErlang/Chapter19
Halliday R (2004) Equity Trend Prediction With Neural Networks. Res. Lett. Inf. Math. Sci., Vol. 6, pp 15-29.
Mendelsohn L (1993) Using Neural Networks For Financial Forecasting. Stocks & Commodities. Volume 11:12, October. p.518-521.
Min Qi, Peter GZ (2008) Trend Time-Series Modeling and Forecasting With Neural Networks. IEEE Transactions on neural networks, Vol. 19, no. 5.
Lowe David (1994) Novel Exploitation of Neural Network Methods in Financial Markets. Proceedings of the 3rd IEE International Conference on Artificial Neural Networks, IEE Publications, Aston, United Kingdom.
Jung H, Jia Y, et al (2010) Stock Market Trend Prediction Using ARIMA-Based Neural Networks. 2008 Proceedings of 17th International Conference on Computer Communications and Networks 4, 1-5.
Versace M, Bhatt R, Hinds O, Shiffer M (2004) Predicting The Exchange Traded Fund DIA With a Combination of Genetic Algorithms and Neural Networks. Expert Systems with Applications 27, 417-425.
Yao J, Poh HL (1995) Forecasting the KLSE Index Using Neural Networks. IEEE International Conference on Artificial neural networks.
Kimoto T, Asakawa K, Yoda M, Takeoka M (1990) Stock Market Prediction System With Modular Neural Networks. International Joint Conference on Neural Networks 1, 1-6.
Hutchinson JM, Lo AW, Poggio T (1994) A Nonparametric Approach to Pricing and Hedging Derivative Securities Via Learning Networks. Journal of Finance 49, 851-889.
Refenes AN, Bentz Y, Bunn DW, Burgess AN, Zapranis AD (1997) Financial Time Series Modelling With Discounted Least Squares Backpropagation. Science 14, 123- 138.
Li Y, Ma W (2010) Applications of Artificial Neural Networks in Financial Economics: A Survey. 2010 International Symposium on Computational Intelligence and Design, 211-214.
Rong L, Zhi X (2005) Prediction Stock Market With Fuzzy Neural Networks. Proceedings of the Fourth International Conference on Machine Learning and Cybernetics, Guangzhou, 18-21.
Maridziuk J, Jaruszewicz M (2007) Neuro-Evolutionary Approach to Stock Market Prediction. 2007 International Joint Conference on Neural Networks, 2515-2520.
Soni S (2005) Applications of ANNs in Stock Market Prediction: A Survey. ijcsetcom 2, 71-83.
White H, Diego S (1988) Economic Prediction Using Neural Networks: The Case of IBM Daily Stock Returns. Neural Networks 1988 IEEE International Conference on, 451–458.
Dogac S (2008) Prediction of stock price direction by artificial neural network approach. Master thesis, Bogazici University.
Yamashita T, Hirasawa K, Hu J (2005) Application of Multi-Branch Neural Networks to Stock Market Prediction. English, 2544-2548.
Quiyong Z, Xiaoyu Z, Fu D (2009) Prediction Model of Stock Prices Based on Correlative Analysis and Neural Networks. Second International Conference on Information and Computing Science, pp: 189-192 , IEEE.
Risi S, Stanley KO (2010) Indirectly Encoding Neural Plasticity as a Pattern of Local Rules. Neural Plasticity 6226, 1-11.
Author information
Authors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer Science+Business Media New York
About this chapter
Cite this chapter
Sher, G.I. (2013). Evolving Currency Trading Agents. In: Handbook of Neuroevolution Through Erlang. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-4463-3_19
Download citation
DOI: https://doi.org/10.1007/978-1-4614-4463-3_19
Published:
Publisher Name: Springer, New York, NY
Print ISBN: 978-1-4614-4462-6
Online ISBN: 978-1-4614-4463-3
eBook Packages: Computer ScienceComputer Science (R0)