Optimizing trading strategies using genetic algorithms: A review and implementation

Christos N. Christodoulou-Volos ()

Edelweiss Applied Science and Technology, 2025, vol. 9, issue 10, 334-345

Abstract: This article explores the application of genetic algorithms (GAs) to optimize trading systems, based on both a literature review and an empirical implementation. It initially introduces the fundamentals of GAs, including selection, crossover, mutation, and fitness evaluation, and their advantages in coping with complex financial markets. The review section discusses previous work in GA-based trading models and the effectiveness of GAs in parameter optimization, rule extraction, and handling dynamic market situations. In the implementation section, a GA is applied to optimize a trading strategy for a sample financial instrument and evaluate its performance based on benchmark models. Key conclusions validate the effectiveness of GAs in maximizing profitability when overfitting and computationally intensive problems dominate. The work ends with its pragmatic implications, limitations, and directions for future research into evolutionary computing within financial markets.

Keywords: Evolutionary computing; Genetic algorithms (GAs); Machine learning in finance; Overfitting in trading Models; Trading strategy optimization. (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:ajp:edwast:v:9:y:2025:i:10:p:334-345:id:10407

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