Coordination and Communication Protocols for Multi-Agent Financial AI Systems in Automated Trading
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Date
2026
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SDU University
Abstract
Machine-learning models now dominate daily-frequency financial forecasting but remain di!cult to audit, and the recent turn toward multi-agent architectures has not resolved the underlying problem: existing LLM-coordinated frameworks couple specialised agents through stochastic natural-language inference, provide no formal guarantees of inter-agent message integrity, and retrain on single-observation performance dips that conflate transient noise with persistent drift. This dissertation proposes the Deterministic Multi-Agent Coordination and Control Protocol (DMACCP), a five-agent pipeline in which every message is carried by a typed envelope with a SHA-256 integrity checksum on a file-backed bus; an orchestrator evaluates five validation gates between stages, admits retraining only when a degradation signal persists across K = 2 consecutive observations, and uses SHAP feature attributions as a cross-run control signal that feeds back into adaptive feature masking. The protocol is evaluated on eight assets spanning five market segments over 2019–2024, against a no-coordination baseline using identical data and models. Walk-forward accuracy lies within noise bounds of the baseline (mean ” = →0.22 percentage points, overlapping 95% bootstrap intervals); the bu#ered mechanism reduces unnecessary retraining events by 66.7% on the ablation configuration; a macroeconomic context block dominates the SHAP attributions of seven of eight assets, validating the protected feature set. The contribution is an engineering proposal with formal reproducibility and integrity properties—obtained at under one percent of the pipeline’s runtime—rather than a performance-extension result, and is positioned as a designed answer to the audit and reproducibility requirements of institutional financial machine-learning deployment.
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Keywords
multi-agent systems, explainable AI, SHAP, concept drift, financial machine learning
Citation
Sabidolda Sh / Coordination and Communication Protocols for Multi-Agent Financial AI Systems in Automated Trading / SDU University / Department of Computer Science