Optimization of Algorithmic Trading and Portfolio Management

Authors

  • Adebayo Adewuyi Department of Mathematics and Statistics, Faculty of Pure and applied sciences, Kwara State University, Malete, Nigeria
  • T. O. Aliu Department of Mathematics and Statistics, Faculty of Pure and applied sciences, Kwara State University, Malete, Nigeria
  • S. F. Olaniran Department of Mathematics and Statistics, Faculty of Pure and applied sciences, Kwara State University, Malete, Nigeria
  • O. T. Wahab Department of Mathematics and Statistics, Faculty of Pure and applied sciences, Kwara State University, Malete, Nigeria

Keywords:

Optmization, Genetic Algorithm, Algorithmic Trading, Financial Market

Abstract

In algorithmic trading, computer programs are used to execute trades based on predefined logic, although their success depends critically on the choice of parameters. This study bridges the gap between theoretical models and practical trading by optimizing two technical indicators, Relative Strength Index (RSI) and Exponential Moving Average (EMA), using exhaustive grid search on historical EURUSD and GBPUSD data spanning January 2023 to June 2025. The optimization results show that optimal parameters vary across currency pairs. In addition, the study expands the optimization framework by incorporating position sizing and risk constraints to further bridge the gap between academic models and real-world trading activities. Statistical validation shows that the optimal strategies have significant mean returns (p < 0.001), while the confidence intervals of the Sharpe ratios do not include zero. These in-sample significance results are interpreted cautiously because the strategy selection process involved multiple parameter configurations.

DOI: https://doi.org/10.5281/zenodo.22167067

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Published

2026-09-06

Issue

Vol. 2 No. 3 (2026): Volume 2, Issue 3, 2026

Section

Articles