Evolutionary Algorithm Based Approach for Modeling Autonomously Trading Agents
ABSTRACT
The autonomously trading agents described in this paper produce a decision to act such as: buy, sell or hold, based on the input data. In this work, we have simulated autonomously trading agents using the Echo State Network (ESNs) model. We generate a collection of trading agents that use different trading strategies using Evolutionary Programming (EP). The agents are tested on EUR/ USD real market data. The main goal of this study is to test the overall performance of this collection of agents when they are active simultaneously. Simulation results show that using different agents concurrently outperform a single agent acting alone.
Cite this paper
Yaman, A. , Lucci, S. and Gertner, I. (2014) Evolutionary Algorithm Based Approach for Modeling Autonomously Trading Agents. Intelligent Information Management, 6, 45-54. doi: 10.4236/iim.2014.62007.
Yaman, A. , Lucci, S. and Gertner, I. (2014) Evolutionary Algorithm Based Approach for Modeling Autonomously Trading Agents. Intelligent Information Management, 6, 45-54. doi: 10.4236/iim.2014.62007.
References
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[1] Fama, E.F. (1970) Efficient Capital Markets: A Review of Theory and Empirical Work. Journal of Finance, 25, 383 417. [2] Malkiel, B.G. (1973) A Random Walk Down Wall Street. Norton, New York, [3] Dutta, S. and Shekhar, S. (1888) Bond Rating: A Non-Conservative Application of Neural Networks. IEEE International Conference on Neural Networks, San Diego, 24-27 July 1998, 443-450. [4] Senol, D. (2008) Prediction of Stock Price Direction by Artificial Neural Network Approach. Bogazici University, Istanbul. [5] Sher, G.I. (2011) Evolving Chart Pattern Sensitive Neural Network Based Forex Trading Agents. arXiv1111.5892S. [6] White, H. (1988) Economic Prediction Using Neural Networks: The Case of IBM Daily Stock Returns. IEEE International Conference on Neural Networks, San Diago, 24-27 July 1998, 451-459. [7] Rumelhart, D.E. and McClelland, J.L. (1986) Parallel Distributed Processing: Explorations in the Microstructure of Cognition: Foundations (Volume I). MIT Press, Cambridge. [8] Rumelhart, D.E., Hinton, G.E. and Williams, R.J. (1986) Learning Internal Representations by Error Propagation. In: Rumelhart, D.E. and McClelland, J.L., Eds., Parallel Distributed Processing: Exploration in the Microstructures of Cognition, Vol. I, MIT Press, Cambridge, 318-362. [9] Chan, N.T., LeBaron, B., Lo, A.W. and Poggio, T. (1999) Agent-Based Models of Financial Markets: A Comparison with Experimental Markets. MIT Artificial Markets Project, 124. [10] Tesfatsion, L. (2002) Agent-Based Computational Economics: Growing Economies from the Bottom up. Artificial Life, 8, 55-82. http://dx.doi.org/10.1162/106454602753694765 [11] Fukumoto, R. and Kita, H. (2001) A Multi-Objective Genetic Algorithm Approach to Construction of Trading Agents for Artificial Market Study. Springer, Berlin Heidelberg. [12] Jaeger, H. (2002) A Tutorial on Training Recurrent Neural Networks. Covering BPTT, RTRL, EKF and the Echo State Network Approach. German National Research Center for Information Technology. [13] Jaeger, H. (2001) The Echo State Approach to Analysing and Training Recurrent Neural Networks. German National Research Center for Information Technology. [14] Tong, M.H., Bicket, A., Cristiansen, E. and Cottrell, G. (2007) Learning Grammatical Structure with Echo State Network. Neural Networks, 20, 424-432. http://dx.doi.org/10.1016/j.neunet.2007.04.013 [15] Holland, J.H. (1975) Adaptation in Natural and Artificial Systems. University of Michigan Press, Ann Arbor. [16] Goldberg, D.E. (1989) Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley, Boston. [17] Koza, J.R. (1992) Genetic Programming: on the Programming of Computers by Means of Natural Selection. MIT Press, Cambridge. [18] Schwefel, H. (1981) Numerical Optimization of Computer Models. John Wiley & Sons, Hoboken. [19] Rechenberg, I. (1965) Cybernetic Solution Path of an Experimental Problem. Royal Aircraft Establishment, Translation 1122. [20] Lucci, S. and Kopec, D. (2012) Artificial Intelligence in the 21st Century. Mercury Learning and Information. [21] Darwin, C. (1859) On the Origin of Species. Kindle Edition (2012). [22] Maslov, I. and Gertner, I. (2009) Evolutionary Algorithms in Digital Image Processing: A Hybrid Approach. LAP Lambert Academic Publishing, OmniScriptum GmbH & Co. KG, Saarbrücken. [23] Maslov, I. and Gertner, I. (2006) Multi-Sensor Fusion: An Evolutionary Algorithm Approach. Information Fusion, 7, 304-330. http://dx.doi.org/10.1016/j.inffus.2005.01.001 [24] Maslov, I. and Gertner, I. (2007) Multi-Sensor Target Recognition in Image Response Space Using Evolutionary Algorithms. In: Sadjadi, Firooz, Javidi and Bahram, Eds., Physics of Automatic Target Recognition, Chapter 8, Springer, Berlin, 127-141. [25] Angeline, P.J., Saunders, G.M. and Pollack, J.B. (1994) An Evolutionary Algorithm that Constructs Recurrent Neural Networks. IEEE Transactions on Neural Networks, 5, 54-65. http://dx.doi.org/10.1109/72.265960 [26] Belew, R., McInerney, J. and Schraudolph, N.N. (1990) Evolving Networks: Using the Genetic Algorithm with Connectionist Learning. CSE Technical Report CS90-174, University of California, Berkeley. [27] Caudell, T.P. and Dolan, C.P. (1989) Parametric Connectivity: Training of Constrained Networks Using Genetic Algorithms. Proceedings of the 3rd International Conference on Genetic Algorithms, Fairfax, June 1989, 370-374. [28] Cliff, D., Harvey, I. and Husbands, P. (1993) Incremental Evolution of Neural Network Architectures for Adaptive Behaviour. Proceedings of the 1st European Symposium on Artificial Neural Networks, Brussels, 7-9 April 1993, 3944. [29] Floreano, D. and Mondada, F. (1994) Automatic Creation of an Autonomous Agent: Genetic Evolution of a Neural Network Driven Robot. 3rd International Conference on Simulation of Adaptive Behavior (SAB’1994), Brighton, 8-12 August 1994, 421-430. [30] Floreano, D., Dürr, P. and Mattiussi, C. (2008) Neuroevolution: From Architectures to Learning. Evolutionary Intelligence, 1, 47-62. http://dx.doi.org/10.1007/s12065-007-0002-4 [31] Igel, C. (2003) Neuroevolution for Reinforcement Learning Using Evolutionary Strategies. Congress on Evolutionary Strategies, 4, 2588-2595. [32] Jung, J.-Y. (2007) Evolutionary Design of Artificial Neural Networks Using a Descriptive Encoding Language. Doctoral Dissertation, University of Maryland, College Park. [33] Kenneth, O.S. and Miikkulainen, R. (2002) Efficient Evolution of Neural Network Topologies. Proceedings of the 2002 Congress on Evolutionary Computation (CEC’02). In: Langdon, W.B., Cantú-Paz, E., Mathias, K.E., Roy, R., Davis, D., Poli, R., Balakrishnan, K., Honavar, V., Rudolph, G., Wegener, J., Bull, L., Potter, M. A., Schultz, A.C., Miller, J.F., Burke, E.K. and Jonoska, N., Eds., GECCO 2002: Proceedings of the Genetic and Evolutionary Computation Conference, New York, 9-13 July 2002, 569-577. [34] Montana, D. and Davis, L. (1989) Training Feedforward Neural Networks Using Genetic Algorithms. Proceedings of the Eleventh Joint Conference on Artificial Intelligence, 1, 762-767. [35] Nolfi, S., Miglino, O. and Parisi, D. (1994) Phenotypic Plasticity in Evolving Neural Networks. Proceedings of the International Conference from Perception to Action, Lausanne, 5-9 September 1994, 146-157.
http://dx.doi.org/10.1109/FPA.1994.636092 [36] Saravanan, N. and Fogel, D.B. (1995) Evolving Neural Control Systems. IEEE Expert, 10, 23-27.
http://dx.doi.org/10.1109/64.393139 [37] Whitley, D., Starkweather, T. and Bogart, C. (1990) Genetic Algorithms and Neural Networks: Optimizaing Connections and Connectivity. Parallel Computing, 14, 347-361. http://dx.doi.org/10.1016/0167-8191(90)90086-O [38] Yao, X. (1999) Evolving Artificial Neural Networks. Proceedings of the IEEE, 87, 1423-1447.
http://dx.doi.org/10.1109/5.784219 [39] Wieland, A.P. (1990) Evolving Neural Network Controllers for Unstable Systems. IEEE International Joint Conference on Neural Networks, II, 667-673. [40] Kitano, H. (1990) Designing Neural Networks by Genetic Algorithms Using Graph Generation System. Complex Systems, 4, 461-476. [41] Fogel, L.J., Owens, A.J. and Walsh, M.J. (1966) Artificial Intelligence through Simulated Evolution. John Wiley & Sons, Hoboken. [42] Fogel, D.B. (1991) System Identification through Simulated Evolution: A Machine Learning Approach to Modeling. Ginn, Needham Heights. [43] Bäck, T. and Schwefel, H.-P. (1993) An Overview of Evolutionary Algorithms for Parameter Optimization. Evolutionary Computation, 1, 1-24. http://dx.doi.org/10.1162/evco.1993.1.1.1 [44] Fogel, D.B. (1992) Evolving Artificial Intelligence. Doctoral Dissertation, University of California, San Diego, La Jolla. [45] de Castro, N.L. (2011) Fundamentals of Natural Computing. Chapman and Hall/CRC, Boca Raton. [46] Pring, M.J. (1991) Technical Analysis Explained. McGraw-Hill, New York.