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An Evolutionary Approach to Improve a Simple Trading System

In: Mathematical and Statistical Methods for Actuarial Sciences and Finance

Author

Listed:
  • Marco Corazza

    (Ca’ Foscari University of Venice, Department of Economics)

  • Francesca Parpinel

    (Ca’ Foscari University of Venice, Department of Economics)

  • Claudio Pizzi

    (Ca’ Foscari University of Venice, Department of Economics)

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.

Suggested Citation

  • Marco Corazza & Francesca Parpinel & Claudio Pizzi, 2017. "An Evolutionary Approach to Improve a Simple Trading System," Springer Books, in: Marco Corazza & Florence Legros & Cira Perna & Marilena Sibillo (ed.), Mathematical and Statistical Methods for Actuarial Sciences and Finance, pages 83-95, Springer.
  • Handle: RePEc:spr:sprchp:978-3-319-50234-2_7
    DOI: 10.1007/978-3-319-50234-2_7
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