# FinEvo: From Isolated Backtests to Ecological Market Games for Multi-Agent Financial Strategy Evolution arXiv: https://arxiv.org/abs/2602.00948 ยท HTML: https://mlsanity.com/paper/2602.00948/ ## Key facts - Cited by 2 papers in the map (data through 14 Oct 2026). - Placed in Financial trading and forecasting agents, one of 16 directions in LLM agents and tool use. - Builds on 30 of its 65 references that are in the map. - First posted on arXiv 1 Feb 2026. ## Placed in - LLM agents and tool use / [Financial trading and forecasting agents](https://mlsanity.com/agents/financial-trading-and-forecasting-agents/) ## Builds on (most cited first, 30 of 30) - [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017) - [Bootstrap your own latent: A new approach to self-supervised Learning](https://mlsanity.com/paper/2006.07733/) (2020) - [GPT-4o System Card](https://mlsanity.com/paper/2410.21276/) (2024) - [Deep reinforcement learning from human preferences](https://mlsanity.com/paper/1706.03741/) (2017) - [Asynchronous Methods for Deep Reinforcement Learning](https://mlsanity.com/paper/1602.01783/) (2016) - [Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting](https://mlsanity.com/paper/2012.07436/) (2020) - [Conservative Q-Learning for Offline Reinforcement Learning](https://mlsanity.com/paper/2006.04779/) (2020) - [Deep Reinforcement Learning with Double Q-learning](https://mlsanity.com/paper/1509.06461/) (2015) - [Decision Transformer: Reinforcement Learning via Sequence Modeling](https://mlsanity.com/paper/2106.01345/) (2021) - [FEDformer: Frequency Enhanced Decomposed Transformer for Long-term Series Forecasting](https://mlsanity.com/paper/2201.12740/) (2022) - [Chronos: Learning the Language of Time Series](https://mlsanity.com/paper/2403.07815/) (2024) - [N-BEATS: Neural basis expansion analysis for interpretable time series forecasting](https://mlsanity.com/paper/1905.10437/) (2019) - [Reinforcement Learning and Control as Probabilistic Inference: Tutorial and Review](https://mlsanity.com/paper/1805.00909/) (2018) - [Benchmarking Deep Reinforcement Learning for Continuous Control](https://mlsanity.com/paper/1604.06778/) (2016) - [Population Based Training of Neural Networks](https://mlsanity.com/paper/1711.09846/) (2017) - [TimeMixer: Decomposable Multiscale Mixing for Time Series Forecasting](https://mlsanity.com/paper/2405.14616/) (2024) - [TradingAgents: Multi-Agents LLM Financial Trading Framework](https://mlsanity.com/paper/2412.20138/) (2024) - [FinMem: A Performance-Enhanced LLM Trading Agent with Layered Memory and Character Design](https://mlsanity.com/paper/2311.13743/) (2023) - [TimeGPT-1](https://mlsanity.com/paper/2310.03589/) (2023) - [Minimax Weight and Q-Function Learning for Off-Policy Evaluation](https://mlsanity.com/paper/1910.12809/) (2019) - [Risk-sensitive Reinforcement Learning](https://mlsanity.com/paper/1311.2097/) (2013) - [FinCon: A Synthesized LLM Multi-Agent System with Conceptual Verbal Reinforcement for Enhanced Financial Decision Making](https://mlsanity.com/paper/2407.06567/) (2024) - [FinGPT: Democratizing Internet-scale Data for Financial Large Language Models](https://mlsanity.com/paper/2307.10485/) (2023) - [Autocurricula and the Emergence of Innovation from Social Interaction: A Manifesto for Multi-Agent Intelligence Research](https://mlsanity.com/paper/1903.00742/) (2019) - [ABIDES-Gym: Gym Environments for Multi-Agent Discrete Event Simulation and Application to Financial Markets](https://mlsanity.com/paper/2110.14771/) (2021) - [FinMME: Benchmark Dataset for Financial Multi-Modal Reasoning Evaluation](https://mlsanity.com/paper/2505.24714/) (2025) - [MASTER: A Multi-Agent System with LLM Specialized MCTS](https://mlsanity.com/paper/2501.14304/) (2025) - [JAX-LOB: A GPU-Accelerated limit order book simulator to unlock large scale reinforcement learning for trading](https://mlsanity.com/paper/2308.13289/) (2023) - [Fast Agent-Based Simulation Framework with Applications to Reinforcement Learning and the Study of Trading Latency Effects](https://mlsanity.com/paper/2008.07871/) (2020) - [TraderTalk: An LLM Behavioural ABM applied to Simulating Human Bilateral Trading Interactions](https://mlsanity.com/paper/2410.21280/) (2024) ## Built on by (most cited first, 2 of 2) - [Agentic Trading: When LLM Agents Meet Financial Markets](https://mlsanity.com/paper/2605.19337/) (2026) - [FinPersona-Bench: A Benchmark for Longitudinal Psychometric Stability of Autonomous Financial Agents](https://mlsanity.com/paper/2606.31522/) (2026) ## Abstract (arXiv) 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.