用大模型驱动交易策略自进化，自动适应市场变化。
AlgoEvolve: LLM-driven Meta-evolution of Algorithmic Trading Programs
- 大模型生成并迭代优化可执行的交易代码，实现自我改进。
- 系统在多轮实验中展现出自主切换交易规则的能力。
- 通过元进化优化提示词，提升搜索效率，减少无效交易。
近期研究表明，大语言模型（LLMs）可作为程序与证明演化的语义变异算子。现有应用多集中于静态编码基准。本文将该范式拓展至算法交易领域。该领域具有噪声大、非平稳性强、高度不连续等特点，挑战显著。我们提出 AlgoEvolve，一个由大模型驱动的演化框架，用于生成、评估并持续优化可执行的交易策略。策略以 Python 代码形式表达，并通过严格的测试协议进行验证。在多个实验中，系统涌现出适应不同市场状态的策略逻辑，包括自主调整交易规则。此外，我们引入元演化外循环，对内层程序合成所用提示词进行演化。该外循环发现了更优的搜索启发式，平衡了探索与利用，减少了零交易失败率。结果表明，基于大模型的语义演化为复杂环境中持续程序合成提供了可行路径。
原文摘要 · Abstract (English)
Recent work shows that Large Language Models (LLMs) can act as semantic mutation operators for the evolutionary discovery of programs and proofs. Most current applications focus on static coding benchmarks. We extend this paradigm to algorithmic trading. This domain is uniquely challenging because it is noisy, non-stationary, and highly discontinuous. We present AlgoEvolve, an LLM-driven evolutionary framework that generates, evaluates, and iteratively improves executable trading strategies. These strategies are expressed as Python code and evaluated through a rigorous testing protocol. Across multiple experiments, the system exhibits emergent regime-adaptive strategy logic, including autonomous shifts in trading rules. We further introduce a meta-evolutionary outer loop that evolves the prompts guiding program synthesis in the inner loop. This outer loop discovers improved search heuristics. These heuristics balance exploration and exploitation while reducing zero-trade failures. They consistently outperform initial human-designed instructions. The results demonstrate that LLM-based semantic evolution provides a viable approach for continual program synthesis in complex environments.
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