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.

翻译：传统金融策略评估依赖于静态环境中的孤立回测。此类评估独立检验每个策略，忽略了相关性及交互作用，无法解释策略在演化市场中最终存续或消亡的原因。我们转向生态学视角，将交易策略建模为在共享市场中交互学习的自适应智能体。本文不提出新策略，而是构建FinEvo——一种用于研究多智能体金融策略演化动态的生态博弈形式化框架。在个体层面，异质化的基于机器学习的交易者（规则驱动型、深度学习型、强化学习型及大语言模型智能体）利用历史价格与外部新闻等信号进行自适应调整。在群体层面，策略分布通过三种设计机制——选择、创新与环境扰动——实现演化，捕捉真实市场的动态驱动力。这两个适应层级的结合将演化博弈论与现代学习动态相衔接，为研究策略行为提供了原则性环境。通过外部冲击和真实新闻流的实验表明，FinEvo既具备保证可复现性的稳定性，又能有效揭示情境依赖的结果。策略可能主导市场、崩溃或形成联盟，具体取决于其竞争者——这些模式在静态回测中无法显现。通过将策略评估重构为生态博弈形式化框架，FinEvo为分析多智能体金融市场的稳健性、适应性与涌现动态提供了统一的机制层面研究范式，并可能为探索宏观经济政策与金融监管对价格演化及均衡的潜在影响提供途径。