Collaborative Multiobjective Evolutionary Algorithms in the Search of Better Pareto Fronts: An Application to Trading Systems
Technical indicators use graphic representations of datasets by applying various mathematical formulas to financial time series of prices. These formulas comprise a set of rules and parameters whose values are not necessarily known and depend on many factors, such as the market in which they operate...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-11-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/13/22/12485 |
| _version_ | 1797460269099646976 |
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| author | Francisco J. Soltero Pablo Fernández-Blanco J. Ignacio Hidalgo |
| author_facet | Francisco J. Soltero Pablo Fernández-Blanco J. Ignacio Hidalgo |
| author_sort | Francisco J. Soltero |
| collection | DOAJ |
| description | Technical indicators use graphic representations of datasets by applying various mathematical formulas to financial time series of prices. These formulas comprise a set of rules and parameters whose values are not necessarily known and depend on many factors, such as the market in which they operate, the size of the time window, and so on. This paper focuses on the real-time optimization of the parameters applied for analyzing time series of data. In particular, we optimize the parameters of some technical financial indicators. We propose the combination of several Multiobjective Evolutionary Algorithms. Unlike other approaches, this paper applies a set of different Multiobjective Evolutionary Algorithms, collaborating to construct a global Pareto Set of solutions. Solutions for financial problems seek high returns with minimal risk. The optimization process is continuous and occurs at the same frequency as the investment time interval. This technique permits the application of the non-dominated solutions obtained with different MOEAs at the same time. Experimental results show that Collaborative Multiobjective Evolutionary Algorithms obtain up to 22% of profit and increase the returns of the commonly used Buy and Hold strategy and other multi-objective strategies, even for daily operations. |
| first_indexed | 2024-03-09T17:02:44Z |
| format | Article |
| id | doaj.art-a3b7c3f167804986b1c4a4e5e5cff406 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-09T17:02:44Z |
| publishDate | 2023-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-a3b7c3f167804986b1c4a4e5e5cff4062023-11-24T14:28:06ZengMDPI AGApplied Sciences2076-34172023-11-0113221248510.3390/app132212485Collaborative Multiobjective Evolutionary Algorithms in the Search of Better Pareto Fronts: An Application to Trading SystemsFrancisco J. Soltero0Pablo Fernández-Blanco1J. Ignacio Hidalgo2Facultad de Ciencias de la Economía y de la Empresa, Universidad Rey Juan Carlos, 28032 Madrid, SpainEscuela Politécnica Superior, Universidad Francisco de Vitoria, 28223 Pozuelo de Alarcón, SpainDepartment of Computer Architecture and Automatics, Faculty of Computer Science, Universidad Complutense de Madrid, 28040 Madrid, SpainTechnical indicators use graphic representations of datasets by applying various mathematical formulas to financial time series of prices. These formulas comprise a set of rules and parameters whose values are not necessarily known and depend on many factors, such as the market in which they operate, the size of the time window, and so on. This paper focuses on the real-time optimization of the parameters applied for analyzing time series of data. In particular, we optimize the parameters of some technical financial indicators. We propose the combination of several Multiobjective Evolutionary Algorithms. Unlike other approaches, this paper applies a set of different Multiobjective Evolutionary Algorithms, collaborating to construct a global Pareto Set of solutions. Solutions for financial problems seek high returns with minimal risk. The optimization process is continuous and occurs at the same frequency as the investment time interval. This technique permits the application of the non-dominated solutions obtained with different MOEAs at the same time. Experimental results show that Collaborative Multiobjective Evolutionary Algorithms obtain up to 22% of profit and increase the returns of the commonly used Buy and Hold strategy and other multi-objective strategies, even for daily operations.https://www.mdpi.com/2076-3417/13/22/12485machine learningtrading systemsmultiobjective optimizationevolutionary algorithms |
| spellingShingle | Francisco J. Soltero Pablo Fernández-Blanco J. Ignacio Hidalgo Collaborative Multiobjective Evolutionary Algorithms in the Search of Better Pareto Fronts: An Application to Trading Systems Applied Sciences machine learning trading systems multiobjective optimization evolutionary algorithms |
| title | Collaborative Multiobjective Evolutionary Algorithms in the Search of Better Pareto Fronts: An Application to Trading Systems |
| title_full | Collaborative Multiobjective Evolutionary Algorithms in the Search of Better Pareto Fronts: An Application to Trading Systems |
| title_fullStr | Collaborative Multiobjective Evolutionary Algorithms in the Search of Better Pareto Fronts: An Application to Trading Systems |
| title_full_unstemmed | Collaborative Multiobjective Evolutionary Algorithms in the Search of Better Pareto Fronts: An Application to Trading Systems |
| title_short | Collaborative Multiobjective Evolutionary Algorithms in the Search of Better Pareto Fronts: An Application to Trading Systems |
| title_sort | collaborative multiobjective evolutionary algorithms in the search of better pareto fronts an application to trading systems |
| topic | machine learning trading systems multiobjective optimization evolutionary algorithms |
| url | https://www.mdpi.com/2076-3417/13/22/12485 |
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