NEAT Algorithm-based Stock Trading Strategy with Multiple Technical Indicators Resonance
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arXiv
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| _version_ | 1866915120734535680 |
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| author | Huang, Li-Chun |
| author_facet | Huang, Li-Chun |
| contents | In this study, we applied the NEAT (NeuroEvolution of Augmenting Topologies) algorithm to stock trading using multiple technical indicators. Our approach focused on maximizing earning, avoiding risk, and outperforming the Buy & Hold strategy. We used progressive training data and a multi-objective fitness function to guide the evolution of the population towards these objectives. The results of our study showed that the NEAT model achieved similar returns to the Buy & Hold strategy, but with lower risk exposure and greater stability. We also identified some challenges in the training process, including the presence of a large number of unused nodes and connections in the model architecture. In future work, it may be worthwhile to explore ways to improve the NEAT algorithm and apply it to shorter interval data in order to assess the potential impact on performance. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_14736 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | NEAT Algorithm-based Stock Trading Strategy with Multiple Technical Indicators Resonance Huang, Li-Chun Neural and Evolutionary Computing Machine Learning Portfolio Management In this study, we applied the NEAT (NeuroEvolution of Augmenting Topologies) algorithm to stock trading using multiple technical indicators. Our approach focused on maximizing earning, avoiding risk, and outperforming the Buy & Hold strategy. We used progressive training data and a multi-objective fitness function to guide the evolution of the population towards these objectives. The results of our study showed that the NEAT model achieved similar returns to the Buy & Hold strategy, but with lower risk exposure and greater stability. We also identified some challenges in the training process, including the presence of a large number of unused nodes and connections in the model architecture. In future work, it may be worthwhile to explore ways to improve the NEAT algorithm and apply it to shorter interval data in order to assess the potential impact on performance. |
| title | NEAT Algorithm-based Stock Trading Strategy with Multiple Technical Indicators Resonance |
| topic | Neural and Evolutionary Computing Machine Learning Portfolio Management |
| url | https://arxiv.org/abs/2501.14736 |