Metaheuristics optimization of financial trading strategies for single asset trading
| dc.contributor.author | Derdour, Abdeslem | |
| dc.contributor.other | Tadjine, M., Directeur de thèse | |
| dc.date.accessioned | 2023-10-30T08:05:53Z | |
| dc.date.available | 2023-10-30T08:05:53Z | |
| dc.date.issued | 2023 | |
| dc.description | Mémoire de Projet de Fin d’Études : Automatique: Alger, École Nationale Polytechnique : 2023 | fr_FR |
| dc.description.abstract | The study focused on optimizing technical trading systems using metaheuristic techniques, such as Particle Swarm Optimization and Multi-Objective Evolutionary Algorithms. The results indicate that these methods significantly enhance the performance and robustness of the trading systems. Traditional training approaches were found to be susceptible to overfitting, a concern that was mitigated through WalkForward Optimization. The choice of the objective function was highlighted as crucial in improving system robustness. It is recommended that practitioners carefully select the objective functionand optimization method for designing and evaluating technical trading systems. | fr_FR |
| dc.identifier.other | EP00697 | |
| dc.identifier.uri | https://repository.enp.edu.dz/handle/123456789/10943 | |
| dc.language.iso | en | fr_FR |
| dc.subject | Low Frequency Technical Trading Systems (LFTTS) | fr_FR |
| dc.subject | Walk Forward Optimization (WFO) | fr_FR |
| dc.subject | Crypto-currencies | fr_FR |
| dc.subject | Particle Swarm Optimizer (PSO) | fr_FR |
| dc.subject | Multi-Objective Evolutionary Algorithm based on Decomposition (MOEA/D) | fr_FR |
| dc.title | Metaheuristics optimization of financial trading strategies for single asset trading | fr_FR |
| dc.type | Thesis | fr_FR |