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基于智能体的趋势跟踪与均值回归模拟比较

文章 arXiv papers · 作者: Yijia Chen
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总结

本文通过大规模基于智能体的模拟，考察哪些交易风格能在嘈杂市场中持续。其 MAS-乌托邦模型包含 10,000 个智能体，分为五种原型，并使用五年的高频数据。模拟设定假定不存在交易摩擦，并向智能体提供无条件基本收入。在这些条件下，顺应市场方向的趋势跟踪智能体长期表现优于逆势交易的均值回归智能体。

作者称，这一结果促使他们构建一个由 LLM 驱动、基于趋势跟踪逻辑的系统。这里描述的证据来自模拟，而非实盘市场测试；其假设限制了结果对交易的直接指导意义：去除交易成本并提供安全网，可能会改变哪些策略能够存续。摘录没有给出绩效指标、智能体规则或敏感性分析，因此它提出的是方向性假设，并不能证明趋势跟踪在其他市场或条件下会占优。

核心观点

  • 该模型使用高频数据，在模拟市场中比较五种智能体类型。
  • 在所述设定中，趋势跟踪智能体比均值回归智能体更具存续能力。
  • 模拟假定交易摩擦为零，并提供无条件收入安全网。
  • 该结果基于模型，未必能直接适用于成本或条件不同的市场。
  • 作者介绍了一个旨在应用趋势跟踪逻辑的 LLM 驱动系统。

标签

趋势跟踪均值回归高频交易机器学习
全文
# Be Water: An Evolutionary Proof for Trend-Following


# Be Water: An Evolutionary Proof for Trend-Following









The proliferation of diverse, high-leverage trading instruments in modern financial markets presents a complex, "noisy" environment, leading to a critical question: which trading strategies are evolutionarily viable? To investigate this, we construct a large-scale agent-based model, "MAS-Utopia," comprising 10,000 agents with five distinct archetypes. This society is immersed in five years of high-frequency data under a counterfactual baseline: zero transaction friction and a robust Unconditional Basic Income (UBI) safety net. The simulation reveals a powerful evolutionary convergence. Strategies that attempt to fight the market's current - namely Mean-Reversion ("buy-the-dip") - prove structurally fragile. In contrast, the Trend-Following archetype, which adapts to the market's flow, emerges as the dominant phenotype. Translating this finding, we architect an LLM-driven system that emulates this successful logic. Our findings offer profound implications, echoing the ancient wisdom of "Be Water": for investors, it demonstrates that survival is achieved not by rigid opposition, but by disciplined alignment with the prevailing current; for markets, it critiques tools that encourage contrarian gambling; for society, it underscores the stabilizing power of economic safety nets.

在遵守原作品许可的前提下，附作者信息全文展示。 许可协议: abstract CC0

此摘要由 Stratmill 研究智能体根据原文撰写，并非原文副本。