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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2510.07943 |
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| _version_ | 1866912638511874048 |
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| author | Tian, Qiushi Liang, Churong Hong, Kairan Li, Runnan |
| author_facet | Tian, Qiushi Liang, Churong Hong, Kairan Li, Runnan |
| contents | Cryptocurrency markets present formidable challenges for trading strategy optimization due to extreme volatility, non-stationary dynamics, and complex microstructure patterns that render conventional parameter optimization methods fundamentally inadequate. We introduce Cypto Genetic Algorithm Agent (CGA-Agent), a pioneering hybrid framework that synergistically integrates genetic algorithms with intelligent multi-agent coordination mechanisms for adaptive trading strategy parameter optimization in dynamic financial environments. The framework uniquely incorporates real-time market microstructure intelligence and adaptive strategy performance feedback through intelligent mechanisms that dynamically guide evolutionary processes, transcending the limitations of static optimization approaches. Comprehensive empirical evaluation across three cryptocurrencies demonstrates systematic and statistically significant performance improvements on both total returns and risk-adjusted metrics. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_07943 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Agent-Based Genetic Algorithm for Crypto Trading Strategy Optimization Tian, Qiushi Liang, Churong Hong, Kairan Li, Runnan Artificial Intelligence Cryptocurrency markets present formidable challenges for trading strategy optimization due to extreme volatility, non-stationary dynamics, and complex microstructure patterns that render conventional parameter optimization methods fundamentally inadequate. We introduce Cypto Genetic Algorithm Agent (CGA-Agent), a pioneering hybrid framework that synergistically integrates genetic algorithms with intelligent multi-agent coordination mechanisms for adaptive trading strategy parameter optimization in dynamic financial environments. The framework uniquely incorporates real-time market microstructure intelligence and adaptive strategy performance feedback through intelligent mechanisms that dynamically guide evolutionary processes, transcending the limitations of static optimization approaches. Comprehensive empirical evaluation across three cryptocurrencies demonstrates systematic and statistically significant performance improvements on both total returns and risk-adjusted metrics. |
| title | Agent-Based Genetic Algorithm for Crypto Trading Strategy Optimization |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2510.07943 |