FinEvo: From Isolated Backtests to Ecological Market Games for Multi-Agent Financial Strategy Evolution

2026-02-01

AI Summary

This study addresses the limitations of traditional financial strategy evaluation, which relies on static backtesting and overlooks strategic interactions and the dynamic evolution of markets, thereby failing to explain the rise and fall of strategies in real-world settings. To bridge this gap, the authors propose FinEvo, an ecological game-theoretic framework that integrates evolutionary game theory with multi-agent learning for the first time. FinEvo features a three-layer evolutionary mechanism encompassing selection, innovation, and environmental perturbation. It embeds rule-based systems, deep learning, reinforcement learning, and large language model agents within a shared market environment, leveraging historical price data and real-time news streams to simulate adaptive competition and cooperation among strategies. Experiments demonstrate that FinEvo reliably reproduces market dynamics, revealing that strategy performance is highly context-dependent and capable of spontaneously generating coalitions, dominance hierarchies, or systemic collapses—offering a novel paradigm for financial regulation and macroeconomic policy analysis.

Technology Category

• Game Theory and Economic Paradigms: Mechanism Design
• Multiagent Systems: Mechanism Design
• Machine Learning: Evolutionary Learning

Application Category

• Economics, Online Markets and Human Computation: Uses of LLMs and GenAI for marketplace design, bidding, and strategic interactions
• Web Mining and Content Analysis: Models for Web evolution
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

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.

Problem

• financial strategy evaluation
• ecological market dynamics
• multi-agent interaction
• evolutionary dynamics
• static backtesting

Innovation

• ecological market games
• multi-agent financial strategies
• evolutionary dynamics
• adaptive trading agents
• FinEvo