Ensemble LLM Consensus Architectures for Deterministic Trade Signal Generation: An Empirical Study of Multi-Persona AI Panels in Retail FinTech Applications

Michael D. Anton, Jean T. Wells

Abstract

This paper presents an empirical study of a multi-persona large language model (LLM) consensus architecture deployed in a live retail FinTech platform for equity and options trade signal generation. Grounded in a prior quantitative trading framework (Anton, 2025) and building upon academic antecedents in multi-agent LLM systems (Xiao et al., 2024), the Edge Stack Trading platform deploys a six-persona AI analyst panel producing structured, constrained verdicts across a real-time multi-signal scoring architecture. Analysis of nine live trade reports generated between April and May 2026 reports a preliminary 87.5% directional accuracy rate across eight assessable signals in an exploratory proof-of-concept evaluation, with unanimous panel consensus strongly correlated with system confidence extremes. Inter-model disagreement functions as an auditable risk signal, and model API failures produce identifiable, recoverable degradation patterns consistent with graceful degradation requirements for financial AI systems. The architecture addresses the 2026 conference theme of Reliable and Deterministic AI for Financial Systems through structured verdict formats, role-constrained persona prompting, and explicit multi-signal deterministic scoring as mechanisms for AI governance in retail financial decision support. One incorrect signal (Palantir) reveals a boundary condition in which extreme valuation risk overrides technical alignment, informing a specific architectural recommendation. Implications for HBCU financial technology education, democratized institutional-grade AI analysis, and regulatory auditability are discussed.

Source: semanticscholar · PDF

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