Breaking the Pseudo-Multi-Agent Illusion: A Parasite–Immune Multi-Agent Trading Framework

Large language model (LLM) based multi-agent systems offer a new paradigm for collaborative decision-making in complex financial environments. However, existing systems often exhibit a ”pseudo-multi-agent illusion”: despite differing role labels, prompts, or names, agents rely on nearly identical information perspectives and produce highly correlated strategies. To address this issue, we propose a parasite-immune multi-agent trading (PI-MAT) framework. PI-MAT organizes trend-following, mean-reversion, and event-driven alpha agents into a heterogeneous cognitive ecosystem. Agents generate complementary signals from dedicated information perspectives, while parasitic replication, controlled mutation, and symbiotic complementarity expand strategic diversity. An online-performance-feedback-driven meta-learning arbiter forms the framework's cognitive immune layer, dynamically suppressing low-quality signals and reallocating capital to enable adaptive strategy integration in non-stationary markets. In a preregistered out-of-sample evaluation covering 2020–2023, PI-MAT achieved an annualized net return of \(18.72%\), a Sharpe ratio of \(0.729\), and a maximum drawdown of \(28.61%\). Relative to a price-only baseline, PI-MAT improved annualized net returns by \(3.52\) percentage points, with a paired \(95%\) confidence interval of \([0.51, 6.89]\) percentage points. The code for PI-MAT is publicly available at https://github.com/cxw0928/parasite-immune-framework-corecode.