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Optimal Technical Indicator Based Trading Strategies Using Evolutionary Multi Objective Optimization Algorithms

Author

Listed:
  • Yelleti Vivek

    (Institute for Development and Research in Banking Technology)

  • P. Shanmukh Kali Prasad

    (Cornell University)

  • Vadlamani Madhav

    (IIT Bombay)

  • Ramanuj Lal

    (Stanford University)

  • Vadlamani Ravi

    (Institute for Development and Research in Banking Technology)

Abstract

This paper proposes a bi-objective evolutionary approach to perform technical indicator-based stock trading. The objective is to find the optimal combinations of technical indicators in order to generate buy and sell strategies such that the objective functions, namely, Sharpe ratio and Maximum Drawdown, are maximized and minimized, respectively. In this study, Adaptive geometry-based MOEA (AGE-MOEA) and AGE-MOEA2 are proposed to accomplish the optimization owing to their popularity and power. This study incorporates a rolling-window-based approach (two years of training followed by a year for testing), and thus, the results of the approach seem to be considerably better in stable periods without major economic fluctuations. For the baseline comparison purpose, we employ Non-dominated sorting genetic algorithm-II (NSGA-II), Multi-objective evolutionary algorithm based on decomposition (MOEA/D) too for the problem. Further, we incorporate the transaction cost and domain expertise in the whole modeling approach. It is observed that AGE-MOEA turned out to be the best in 6 out of 11 time horizons by devising a better optimal strategy. However, MOEA/D selected less number of indicators in most of the buy strategy cases and stood first in terms of interpretability. The same observation is noticed with AGE-MOEA in sell strategy cases.

Suggested Citation

  • Yelleti Vivek & P. Shanmukh Kali Prasad & Vadlamani Madhav & Ramanuj Lal & Vadlamani Ravi, 2025. "Optimal Technical Indicator Based Trading Strategies Using Evolutionary Multi Objective Optimization Algorithms," Computational Economics, Springer;Society for Computational Economics, vol. 66(1), pages 757-807, July.
    • Handle: RePEc:kap:compec:v:66:y:2025:i:1:d:10.1007_s10614-024-10701-6
    DOI: 10.1007/s10614-024-10701-6
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