用智能代理协同遗传算法优化加密货币交易策略，提升收益与风控表现。
Agent-Based Genetic Algorithm for Crypto Trading Strategy Optimization
- 结合遗传算法与多智能体协作，动态优化交易参数
- 在三种加密货币上实现收益与风险调整后指标显著提升
- 实时捕捉市场微观结构，适应非平稳市场变化
加密货币市场因极端波动性、非平稳动态和复杂微观结构模式，使传统参数优化方法难以奏效。我们提出Cypto遗传算法代理（CGA-Agent），一种融合遗传算法与智能多代理协同机制的创新框架，用于动态金融环境中交易策略参数的自适应优化。该框架通过智能机制实时融入市场微观结构信息，并基于策略绩效反馈动态引导演化过程，突破了静态优化方法的局限。在三种加密货币上的全面实证评估表明，该方法在总回报率和风险调整指标上均实现系统性且统计显著的提升。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。