AUTOMATING PORTFOLIO MANAGEMENT USING MULTI-AGENT SYSTEM WITH DYNAMIC PROMPT OPTIMISATION AND FEEDBACK LOOPS
TL;DR
A closed-loop multi-agent decision framework that introduces prompt-level learning as a scalable alternative to full model retraining and highlights the potential of prompt-level adaptation for building robust and autonomous financial decision systems.
Abstract
Traditional portfolio management systems often rely on static rules or fixed prompts, which limits their ability to adapt to changing market conditions. This paper proposes a closed-loop multi-agent decision framework that introduces prompt-level learning as a scalable alternative to full model retraining. The architecture comprises specialised agents for market signal extraction, sentiment analysis, macroeconomic interpretation, risk control, and portfolio construction, all coordinated through the DSPy framework and powered by Llama 3.1 8B.A key contribution is a feedback-driven optimisation mechanism that refines agent prompts using realised trading outcomes without human intervention. Moderate drawdowns trigger incremental prompt updates, while severe drawdowns activate full prompt reconfiguration. Empirical evaluation on a six-year dataset (2015–2020) shows that the system achieves cumulative returns above 80% with improved risk-adjusted performance (Sharpe > 1.5), outperforming a SPY buy-and-hold benchmark, including during the COVID-19 market disruption. Overall, the results highlight the potential of prompt-level adaptation for building robust and autonomous financial decision systems.