Abstract
In this paper we consider a simple trading system (TS) based on a set of Technical Analysis (TA) indicators. Their peculiarity is the dependence on the time-window widths used to calculate them. To attempt to improve the performances of the TS, we optimize these parameters (that is the time-window widths) by the Particle Swarm Optimization (PSO), which is a metaheuristic used to solve global optimization problems. The use of PSO is necessary since the involved optimization problem is nonlinear, nondifferentiable and integer: in summary, it is complex. In such a case, the use of exact solution methods would be excessively time-consuming, in particular for practical purposes. The proposed TS is tested using the daily closing prices from January 2, 2001, to June 30, 2016, of eight Italian stocks of different economic sectors. As benchmark, we consider the same TS but with standard time-window lengths. Irrespective of their signs, both in-sample and out-of-sample performances achieved by the TS with optimized parameters are better than those achieved by the benchmark, highlighting that parameter optimization can play an important role in TA-based TSs.
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
Notes
- 1.
The first 52 prices need to calculate the starting values of indicators.
References
Allen, F., Karjalainen, R.: Using genetic algorithms to find technical trading rules. J. Financ. Econ. 51, 245–271 (1999)
Corazza, M., Fasano, G., Gusso R.: Particle Swarm Optimization with no-smooth penalty reformulation, for a complex portfolio selection problem. Appl. Math. Comput. 224, 611–624 (2013)
Dash, R., Dash, P.K.: A hybrid stock trading framework integrating technical analysis with machine learning techniques. J. Finance Data Sci. 2, 42–57 (2016)
Fletcher, R.: Practical Methods of Optimization. Wiley, Glichester (1991)
Hu, Y., Liu, K., Zhang, X., Su, L., Ngai, E.W.T., Liu, M.: Application of evolutionary computation for rule discovery in stock algorithmic trading: a literature review. Appl. Soft Comput. 36, 534–551 (2015)
Kennedy, J., Eberhart, R.C.: Particle Swarm Optimization. In: Proceedings of the 1995 IEEE International Conference on Neural Networks, vol. IV, pp. 1942–1948 (1995)
Laskari, E.C., Parsopoulos, K., Vrahatis, M.N.: Particle swarm optimization for integer programming. In: Proceedings of the 2002 Congress on Evolutionary Computation, vol. 2, pp. 1582–1587 (2002)
Murphy, J.J.: Technical Analysis of the Financial Markets. A Comprehensive Guide to Trading Methods and Applications. New York Institute of Finance, New York (1999)
Ni, H., Yin, H.: Exchange rate prediction using hybrid neural networks and trading indicators. Neurocomputing 72, 2815–2823 (2009)
Poli, R., Kennedy, J., Blackwell, T.: Particle swarm optimisation: an overview. Swarm Intell. J. 1, 33–57 (2007)
Wu, J., Yu, L., Chang, P.: An intelligent stock trading system using comprehensive features. Appl. Soft Comput. 23, 39–50 (2014)
Yao, J.T., Tan, C.L.: A case study on using neural networks to perform technical forecasting of forex. Neurocomputing 34, 79–98 (2000)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this chapter
Cite this chapter
Corazza, M., Parpinel, F., Pizzi, C. (2017). An Evolutionary Approach to Improve a Simple Trading System. In: Corazza, M., Legros, F., Perna, C., Sibillo, M. (eds) Mathematical and Statistical Methods for Actuarial Sciences and Finance . Springer, Cham. https://doi.org/10.1007/978-3-319-50234-2_7
Download citation
DOI: https://doi.org/10.1007/978-3-319-50234-2_7
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-50233-5
Online ISBN: 978-3-319-50234-2
eBook Packages: Mathematics and StatisticsMathematics and Statistics (R0)