FinEvo: 从孤立回测到多智能体金融策略进化的生态市场博弈
FinEvo: From Isolated Backtests to Ecological Market Games for Multi-Agent Financial Strategy Evolution
AI总结：
FinEvo通过生态博弈形式化方法研究多智能体金融策略的进化动态，揭示策略在动态市场中的适应与演化机制。
摘要：
传统金融策略评估依赖于静态环境中的孤立回测。此类评估单独评估每个策略，忽视相关性和互动，并未能解释为什么策略最终在演变市场中持续或消失。我们转向生态视角，将交易策略建模为适应性智能体，在共享市场中互动和学习。不同于提出新策略，我们提出FinEvo，一种生态博弈形式化方法，用于研究多智能体金融策略的进化动态。在个体层面，异质的基于机器学习的交易者、基于规则的深度学习、强化学习和大语言模型（LLM）智能体利用历史价格和外部新闻等信号进行适应。在种群层面，策略分布通过三种设计机制——选择、创新和环境扰动——演变，捕捉真实市场的动态力量。这两层适应将进化博弈论与现代学习动态相结合，提供了一个原理性的环境来研究战略行为。使用外部冲击和真实世界新闻流的实验表明，FinEvo在可重复性和揭示上下文依赖性结果方面都具有稳定性。策略可能主导、崩溃或形成联盟，这在静态回测中是看不见的。通过将策略评估重新构造成生态博弈形式化方法，FinEvo提供了一个统一的、机制层面的协议，用于分析多智能体金融市场中的稳健性、适应性和涌现动态，并可能为探索宏观经济政策和金融监管对价格演变和均衡的潜在影响提供途径。
英文摘要：
Conventional financial strategy evaluation relies on isolated backtests in static environments. Such evaluations assess each policy independently, overlook correlations and interactions, and fail to explain why strategies ultimately persist or vanish in evolving markets. We shift to an ecological perspective, where trading strategies are modeled as adaptive agents that interact and learn within a shared market. Instead of proposing a new strategy, we present FinEvo, an ecological game formalism for studying the evolutionary dynamics of multi-agent financial strategies. At the individual level, heterogeneous ML-based traders-rule-based, deep learning, reinforcement learning, and large language model (LLM) agents-adapt using signals such as historical prices and external news. At the population level, strategy distributions evolve through three designed mechanisms-selection, innovation, and environmental perturbation-capturing the dynamic forces of real markets. Together, these two layers of adaptation link evolutionary game theory with modern learning dynamics, providing a principled environment for studying strategic behavior. Experiments with external shocks and real-world news streams show that FinEvo is both stable for reproducibility and expressive in revealing context-dependent outcomes. Strategies may dominate, collapse, or form coalitions depending on their competitors-patterns invisible to static backtests. By reframing strategy evaluation as an ecological game formalism, FinEvo provides a unified, mechanism-level protocol for analyzing robustness, adaptation, and emergent dynamics in multi-agent financial markets, and may offer a means to explore the potential impact of macroeconomic policies and financial regulations on price evolution and equilibrium.
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