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. |
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| Authors: | Long, Xinpeng1 (AUTHOR) xl19586@essex.ac.uk, Kampouridis, Michael1 (AUTHOR) mkampo@essex.ac.uk, Papastylianou, Tasos1 (AUTHOR) tasos.papastylianou@essex.ac.uk |
| Source: | Artificial Intelligence Review. Feb2026, Vol. 59 Issue 2, p1-45. 45p. |
| Subjects: | 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] |
| Copyright of Artificial Intelligence Review is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Header | DbId: egs DbLabel: Engineering Source An: 190749846 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Multi-objective genetic programming-based algorithmic trading, using directional changes and a modified sharpe ratio score for identifying optimal trading strategies. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Long%2C+Xinpeng%22">Long, Xinpeng</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> xl19586@essex.ac.uk</i><br /><searchLink fieldCode="AR" term="%22Kampouridis%2C+Michael%22">Kampouridis, Michael</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> mkampo@essex.ac.uk</i><br /><searchLink fieldCode="AR" term="%22Papastylianou%2C+Tasos%22">Papastylianou, Tasos</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> tasos.papastylianou@essex.ac.uk</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Artificial+Intelligence+Review%22">Artificial Intelligence Review</searchLink>. Feb2026, Vol. 59 Issue 2, p1-45. 45p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Multi-objective+optimization%22">Multi-objective optimization</searchLink><br /><searchLink fieldCode="DE" term="%22Genetic+programming%22">Genetic programming</searchLink><br /><searchLink fieldCode="DE" term="%22Risk+management+in+business%22">Risk management in business</searchLink><br /><searchLink fieldCode="DE" term="%22Price+fluctuations%22">Price fluctuations</searchLink><br /><searchLink fieldCode="DE" term="%22Algorithmic+trading+%28Securities%29%22">Algorithmic trading (Securities)</searchLink><br /><searchLink fieldCode="DE" term="%22Profit+maximization%22">Profit maximization</searchLink><br /><searchLink fieldCode="DE" term="%22Financial+markets%22">Financial markets</searchLink><br /><searchLink fieldCode="DE" term="%22Sharpe+ratio%22">Sharpe ratio</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: 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] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Artificial Intelligence Review is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1007/s10462-025-11390-9 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 45 StartPage: 1 Subjects: – SubjectFull: Multi-objective optimization Type: general – SubjectFull: Genetic programming Type: general – SubjectFull: Risk management in business Type: general – SubjectFull: Price fluctuations Type: general – SubjectFull: Algorithmic trading (Securities) Type: general – SubjectFull: Profit maximization Type: general – SubjectFull: Financial markets Type: general – SubjectFull: Sharpe ratio Type: general Titles: – TitleFull: Multi-objective genetic programming-based algorithmic trading, using directional changes and a modified sharpe ratio score for identifying optimal trading strategies. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Long, Xinpeng – PersonEntity: Name: NameFull: Kampouridis, Michael – PersonEntity: Name: NameFull: Papastylianou, Tasos IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 02 Text: Feb2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 02692821 Numbering: – Type: volume Value: 59 – Type: issue Value: 2 Titles: – TitleFull: Artificial Intelligence Review Type: main |
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