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

TL;DR

FinEvo introduces an ecological game framework for modeling and analyzing the evolutionary dynamics of multi-agent financial strategies, incorporating adaptive learning and market interactions to better understand strategy success and market behavior.

Contribution

It presents a novel ecological game formalism that models strategy evolution in financial markets, integrating learning dynamics and market interactions for comprehensive analysis.

Findings

Strategies can dominate, collapse, or form coalitions depending on competitors.

FiniteEvo is stable and reproducible in experiments.

Reveals context-dependent outcomes unseen in static backtests.

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.