Abstract
In this contribution, we describe and compare two genetic systems which create trading strategies. The first system is based on the idea that the connection weight matrix of a neural network represents the genotype of an individual and can be changed by genetic algorithm. The second system uses genetic programming to derive trading strategies. As input data in our experiments, we used technical indicators of NASDAQ stocks. As output, the algorithms generate trading strategies, i.e. buy, hold, and sell signals. Our hypothesis that strategies obtained by genetic programming bring better results than buy-and-hold strategy has been proven as statistically significant. We discuss our results and compare them to our previous experiments with fuzzy technology, fractal approach, and with simple technical indicator strategy.
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, 245–271 (1999)
Azzini, A., Tettamanzi, A.: Evolving Neural Networks for Static Single-Position Automated Trading. Journal of Artificial Evolution and Applications, 1–17 (2008)
Brabazon, A., O’Neill, M.: Biological Inspired Algorithms for Financial Modelling. Springer (2006)
Brabazon, A., O’Neill, M., Dempsey, I.: An Introduction to Evolutionary Computation in Finance. IEEE Computational Intelligence Magazine, 42–55 (2008)
El-Henawy, I.M., Kamal, A.H., Abdelbary, H.A., Abas, A.R.: Predicting Stock Index Using Neural Network Combined with Evolutionary Computation Methods. In: The 7th International Conference on Informatics and Systems (INFOS), pp. 1–6 (2010)
Fama, E.: Efficient capital markets: A review of theory and empirical work. Journal of Finance 25, 383–417 (1970)
Kapoor, V., Dey, S., Khurana, A.P.: Genetic Algorithm: An Application to Technical Trading System Design. International Journal of Computer Applications 36(5) (2011)
Kroha, P., Lauschke, M.: Using Fuzzy and Fractal Methods for Analyzing Market Time Series. In: Proceedings of the International Conference on Fuzzy Computation and International Conference on Neural Computation ICFC 2010 and ICNC 2010, pp. 85–92 (2010)
Kwon, Y.-K., Moon, B.-R.: A Hybrid Neurogenetic Approach for Stock Forecasting. IEEE Transactions on Neural Networks 18, 851–864 (2007)
Li, R., Xiong, Z.: A Modified Genetic Fuzzy Neural Network with Application to Financial Distress Analysis. In: International Conference on Computational Intelligence for Modeling, Control and Automation and International Conference on Intelligent Agents, Web Technologies and Internet Commerce (2006)
Malkiel, B.: A Random Walk Down Wall Street. W.W. Norton, New York (1996)
Matsui, K., Sato, H.: Neighborhood Evaluation in Acquiring Stock Trading Strategy Using Genetic Algorithms. International Journal of Computer Information Systems and Industrial Management Applications 4, 366–373 (2012)
Murphy, J.J.: Technical Analysis of the Financial Markets. Prentice Hall (1999)
Skabar, A., Cloete, I.: Neural networks, Financial Trading and the Efficient Markets Hypothesis. In: Proceedings of the Twenty-Fifth Australasian Conference on Computer Science ACSC 2002, vol. 4, pp. 241–249 (2002)
Shleifer, A.: Inefficient Markets – An Introduction to Behavioral Finance. Oxford University Press (2000)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Kroha, P., Friedrich, M. (2014). Comparison of Genetic Algorithms for Trading Strategies. In: Geffert, V., Preneel, B., Rovan, B., Štuller, J., Tjoa, A.M. (eds) SOFSEM 2014: Theory and Practice of Computer Science. SOFSEM 2014. Lecture Notes in Computer Science, vol 8327. Springer, Cham. https://doi.org/10.1007/978-3-319-04298-5_34
Download citation
DOI: https://doi.org/10.1007/978-3-319-04298-5_34
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-04297-8
Online ISBN: 978-3-319-04298-5
eBook Packages: Computer ScienceComputer Science (R0)