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Multi-objective genetic programming-based algorithmic trading, using directional changes and a modified sharpe ratio score for identifying optimal trading strategies.

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Title: Multi-objective genetic programming-based algorithmic trading, using directional changes and a modified sharpe ratio score for identifying optimal trading strategies.
Authors: Long, Xinpeng, Kampouridis, Michael, Papastylianou, Tasos
Source: Artificial Intelligence Review; Feb2026, Vol. 59 Issue 2, p1-45, 45p
Subject Terms: MULTI-objective optimization, GENETIC programming, RISK management in business, PRICE fluctuations, ALGORITHMIC trading (Securities), PROFIT maximization, FINANCIAL markets, SHARPE ratio
Abstract: This study explores the integration of directional changes (DC), genetic programming (GP), and multi-objective optimisation (MOO) to develop advanced algorithmic trading strategies. Directional changes offer a dynamic, event-based approach to market analysis, identifying significant price movements and trends. Genetic programming evolves trading rules to discover effective and profitable strategies. However, financial trading presents a multi-objective challenge, balancing conflicting objectives such as returns and risk. We propose a novel algorithmic trading framework, termed MOO3, which integrates genetic programming with the NSGA-II multi-objective optimisation algorithm to optimise three fitness functions: total return, expected rate of return, and risk. While the use of NSGA-II itself is well-established, our contribution lies in how we apply it within a trading context that combines (i) directional changes, (ii) genetic programming with both DC-based and physical-time indicators, and (iii) a modified Sharpe Ratio for post-optimisation strategy selection based on trader preferences. Utilising indicators from both paradigms allows the GP algorithm to create profitable trading strategies, while the multi-objective fitness function allows it to simultaneously optimise for risk. A definitive strategy is chosen from Pareto-optimal solutions using the modified Sharpe Ratio, allowing traders to prioritise multiple objectives. Our methodology is tested on 110 stock datasets from 10 international markets, aiming to demonstrate that the multi-objective framework can yield superior trading strategies with lower risk. Results indicate that the MOO3 algorithm consistently and significantly outperforms single-objective optimisation (SOO) methods, even when the same SOO criterion is employed for choosing a single, definitive investment strategy from the Pareto front. [ABSTRACT FROM AUTHOR]
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Abstract:This study explores the integration of directional changes (DC), genetic programming (GP), and multi-objective optimisation (MOO) to develop advanced algorithmic trading strategies. Directional changes offer a dynamic, event-based approach to market analysis, identifying significant price movements and trends. Genetic programming evolves trading rules to discover effective and profitable strategies. However, financial trading presents a multi-objective challenge, balancing conflicting objectives such as returns and risk. We propose a novel algorithmic trading framework, termed MOO3, which integrates genetic programming with the NSGA-II multi-objective optimisation algorithm to optimise three fitness functions: total return, expected rate of return, and risk. While the use of NSGA-II itself is well-established, our contribution lies in how we apply it within a trading context that combines (i) directional changes, (ii) genetic programming with both DC-based and physical-time indicators, and (iii) a modified Sharpe Ratio for post-optimisation strategy selection based on trader preferences. Utilising indicators from both paradigms allows the GP algorithm to create profitable trading strategies, while the multi-objective fitness function allows it to simultaneously optimise for risk. A definitive strategy is chosen from Pareto-optimal solutions using the modified Sharpe Ratio, allowing traders to prioritise multiple objectives. Our methodology is tested on 110 stock datasets from 10 international markets, aiming to demonstrate that the multi-objective framework can yield superior trading strategies with lower risk. Results indicate that the MOO3 algorithm consistently and significantly outperforms single-objective optimisation (SOO) methods, even when the same SOO criterion is employed for choosing a single, definitive investment strategy from the Pareto front. [ABSTRACT FROM AUTHOR]
ISSN:02692821
DOI:10.1007/s10462-025-11390-9