Abstract
Research and development of automatic trading systems are becoming more frequent, as they can reach a high potential for predicting market movements. The use of these systems allows to manage a huge amount of data related to the factors that affect investment performance (macroeconomic variables, company information, industry indicators, market variables, etc.), while avoiding psychological reactions of traders when investing in financial markets.Movements in stock markets are continuous throughout each day, which requires trading systems must be supported by more powerful engines, since the amount of data to process grows, while the response time required to support operations is shortened. In this chapter we present two parallel implementations of a GA based trading system. The first uses a Grid Volunteer System based on BOINC and the second one takes advantage of a Graphic Processing Unit implementation.
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
Preview
Unable to display preview. Download preview PDF.
References
Allen, F., Karjalainen, R.: Using genetic algorithms to find technical trading rules. Journal of Financial Economics 51(2), 245–271 (1999)
Bali, T.G., Demirtas, O., Tehranian, H.: Aggregate earnings, firm-level earnings, and expected stock returns. JFQA 43(3), 657–684 (2008)
Banzhaf, W., Harding, S., Langdon, W.B., Wilson, G.: Accelerating genetic programming through graphics processing units. In: Genetic Programming Theory and Practice VI, pp. 1–19. Springer, Heidelberg (2009)
Basu, S.: The investment performance of common stocks in relation to their price-earnings ratios: A test of the efficient market hypothesis. Journal of Finance 32, 663–682 (1977)
Bodas-Sagi, D.J., Fernández, P., Hidalgo, J.I., Soltero, F.J., Risco-Martín, J.L.: Multiobjective optimization of technical market indicators. In: Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers, GECCO 2009, pp. 1999–2004. ACM, New York (2009)
Campbell, J.Y., Yogo, M.: Efficient tests of stock return predictability. Journal of Financial Economics 81, 27–60 (2006)
Cantú-Paz, E.: Efficient and Accurate Parallel Genetic Algorithms. Kluwer Academic Publishers, Norwell (2000)
Chan, L.K.C., Hamao, Y., Lakonishok, R.: Fundamentals and Stock Returns in Japan, 1739–1764 (December 1991)
Cole, N., Desell, T., Lombraña González, D., Fernández de Vega, F., Magdon-Ismail, M., Newberg, H., Szymanski, B., Varela, C.: Evolutionary Algorithms on Volunteer Computing Platforms: The milkyWay@Home Project. In: Fernández de Vega, F., Cantú-Paz, E. (eds.) Parallel and Distributed Computational Intelligence. SCI, vol. 269, pp. 63–90. Springer, Heidelberg (2010)
Contreras, I., Jiang, Y., Hidalgo, J.I., Núñez-Letamendia, L.: Using a gpu-cpu architecture to speed up a ga-based real-time system for trading the stock market. Soft Computing - A Fusion of Foundations, Methodologies and Applications, 1–13 (2011)
Ellenby, J.: (1979), http://www.thegridsystems.org/
Fama, E.F., French, K.R.: Business conditions and expected returns on stocks and bonds. Journal of Financial Economics 25, 23–49 (1989)
Fama, E.F., French, K.R.: The cross-section of expected stock returns. Journal of Finance 47(2), 427–465 (1992)
Fernández-Blanco, P., Bodas-Sagi, D.J., Soltero, F.J., Hidalgo, J.I.: Technical market indicators optimization using evolutionary algorithms. In: Ryan, C., Keijzer, M. (eds.) GECCO (Companion), pp. 1851–1858. ACM (2008)
Goldberg, D.E.: Genetic Algorithms in Search, Optimization and Machine Learning, 1st edn. Addison-Wesley Longman Publishing Co., Boston (1989)
Lombraña Gonzalez, D., Ferná de Vega, F., Trujillo, L., Olague, G., Araujo, L., Castillo, P.A., Merelo Guervós, J.J., Sharman, K.: Increasing gp computing power for free via desktop grid computing and virtualization. In: El Baz, D., Spies, F., Gross, T. (eds.) PDP, pp. 419–423. IEEE Computer Society (2009)
Jiang, Y., Núñez, L.: Efficient market hypothesis or adaptive market hypothesis? a test with the combination of technical and fundamental analysis. In: Proceedings of the 15th International Conference on Computing in Economics and Finance, The Society for Computational Economics, University of Technology, Sydney, Australia (2009)
Krüger, F., Maitre, O., Jiménez, S., Baumes, L., Collet, P.: Speedups Between ×70 and ×120 for a Generic Local Search (Memetic) Algorithm on a Single GPGPU Chip. In: Di Chio, C., Cagnoni, S., Cotta, C., Ebner, M., Ekárt, A., Esparcia-Alcazar, A.I., Goh, C.-K., Merelo, J.J., Neri, F., Preuß, M., Togelius, J., Yannakakis, G.N. (eds.) EvoApplicatons 2010. LNCS, vol. 6024, pp. 501–511. Springer, Heidelberg (2010)
Langdon, W.B.: A fast high quality pseudo random number generator for nvidia cuda. In: Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers, GECCO 2009, pp. 2511–2514. ACM, New York (2009)
Maitre, O., Baumes, L., Lachiche, N., Corma, A., Collet, P.: Coarse grain parallelization of evolutionary algorithms on gpgpu cards with easea. In: Proceedings of the 11th Annual Conference on Genetic and Evolutionary Computation, GECCO 2009, pp. 1403–1410. ACM, New York (2009)
Miller, B.L., Goldberg, D.E.: Genetic algorithms, tournament selection, and the effects of noise. Complex Systems 9, 193–212 (1995)
Munawar, A., Wahib, M., Munetomo, M., Akama, K.: Hybrid of genetic algorithm and local search to solve max-sat problem using nvidia cuda framework. Genetic Programming and Evolvable Machines 10, 391–415 (2009)
Núñez, L.: Trading systems designed by genetic algorithms. Managerial Finance 28, 87–106 (2002)
Núñez, L.: Fitting the control parameters of a genetic algorithm: an application to technical trading systems design. European Journal of Operational Research 179, 847–868 (2007)
Núñez, L., Pacheco, J., Casado, S.: Applying genetic algorithms to wall street. Int. J. Data Mining, Modelling and Management (2011) (forthcoming: in press)
Pospichal, P., Jaros, J., Schwarz, J.: Parallel Genetic Algorithm on the CUDA Architecture. In: Di Chio, C., Cagnoni, S., Cotta, C., Ebner, M., Ekárt, A., Esparcia-Alcazar, A.I., Goh, C.-K., Merelo, J.J., Neri, F., Preuß, M., Togelius, J., Yannakakis, G.N. (eds.) EvoApplicatons 2010 Part I. LNCS, vol. 6024, pp. 442–451. Springer, Heidelberg (2010)
Reinganum, M.: Selecting superior securities charlottesville. the tesearch foundation of the institute of chartered financial analysts. Technical report, The Tesearch foundation of the institute of Chartered Financial Analysts (1988)
Ritter, J.R.: Behavioral finance. Pacific-Basin Finance Journal 11(4), 429–437 (2003)
Zhang, S., He, Z.: Implementation of Parallel Genetic Algorithm Based on Cuda. In: Cai, Z., Li, Z., Kang, Z., Liu, Y. (eds.) ISICA 2009. LNCS, vol. 5821, pp. 24–30. Springer, Heidelberg (2009)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer Berlin Heidelberg
About this chapter
Cite this chapter
Contreras, I., Hidalgo, J.I., Nuñez-Letamendía, L., Jiang, Y. (2012). Parallel Architectures for Improving the Performance of a GA Based Trading System. In: Fernández de Vega, F., Hidalgo Pérez, J., Lanchares, J. (eds) Parallel Architectures and Bioinspired Algorithms. Studies in Computational Intelligence, vol 415. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28789-3_9
Download citation
DOI: https://doi.org/10.1007/978-3-642-28789-3_9
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-28788-6
Online ISBN: 978-3-642-28789-3
eBook Packages: EngineeringEngineering (R0)