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

Yelleti Vivek (), P. Shanmukh Kali Prasad (), Vadlamani Madhav (), Ramanuj Lal () and Vadlamani Ravi ()
Additional contact information
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

Computational Economics, 2025, vol. 66, issue 1, No 23, 757-807

Abstract: 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.

Keywords: NSGA-II; MOEA/D; AGE-MOEA; Sharpe ratio; Maximum drawdown; Multi objective optimization; Trading strategy (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s10614-024-10701-6 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:kap:compec:v:66:y:2025:i:1:d:10.1007_s10614-024-10701-6

Ordering information: This journal article can be ordered from
http://www.springer. ... ry/journal/10614/PS2

DOI: 10.1007/s10614-024-10701-6

Access Statistics for this article

Computational Economics is currently edited by Hans Amman

More articles in Computational Economics from Springer, Society for Computational Economics Contact information at EDIRC.
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().