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<p>Most portfolio optimization frameworks assume static objectives and constraints, making them fragile under regime shifts, transaction frictions, and evolving information. Existing LLM-based methods focus on signal generation without governing execution or constraint elasticity, while reinforcement learning approaches often lack transparency and cost discipline. This lack of a unified, interpretable architecture hinders adaptability and accountability during live rebalancing. We address this gap by introducing an agentic portfolio optimization framework that integrates regime-aware convex optimization, LLM-derived sentiment and uncertainty features, and a constrained reinforcement learning controller in a closed loop. The agent senses market and news data, infers regimes, and dynamically adjusts objectives, risk budgets, and position limits, while enforcing friction-aware execution through Sharpe-gated trade activation, partial rebalancing, and turnover budgeting. In a 50-stock S &amp; P 500 portfolio tested under walk-forward evaluation (2021–2025 Q1), agentic portfolios consistently outperform non-agentic benchmarks, with Sharpe ratio gains of up to +0.373 (NSGA-3), persisting net of transaction costs and alongside lower turnover. These results highlight the value of combining forward-looking signals, regime-conditioned intent, and disciplined execution within a unified transparent, adaptive agentic architecture. Future research should explore multi-asset extensions, richer textual and alternative data for regime inference, explainability metrics for LLM-driven signals, and hybrid architectures blending deterministic control with selective policy learning for extreme market conditions.</p>

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Toward a unified agentic framework for regime-aware portfolio optimization with LLM signals

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年3月9日

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  • Lamukanyani Alson Mantshimuli,
  • John Weirstrass Muteba Mwamba

摘要

Most portfolio optimization frameworks assume static objectives and constraints, making them fragile under regime shifts, transaction frictions, and evolving information. Existing LLM-based methods focus on signal generation without governing execution or constraint elasticity, while reinforcement learning approaches often lack transparency and cost discipline. This lack of a unified, interpretable architecture hinders adaptability and accountability during live rebalancing. We address this gap by introducing an agentic portfolio optimization framework that integrates regime-aware convex optimization, LLM-derived sentiment and uncertainty features, and a constrained reinforcement learning controller in a closed loop. The agent senses market and news data, infers regimes, and dynamically adjusts objectives, risk budgets, and position limits, while enforcing friction-aware execution through Sharpe-gated trade activation, partial rebalancing, and turnover budgeting. In a 50-stock S & P 500 portfolio tested under walk-forward evaluation (2021–2025 Q1), agentic portfolios consistently outperform non-agentic benchmarks, with Sharpe ratio gains of up to +0.373 (NSGA-3), persisting net of transaction costs and alongside lower turnover. These results highlight the value of combining forward-looking signals, regime-conditioned intent, and disciplined execution within a unified transparent, adaptive agentic architecture. Future research should explore multi-asset extensions, richer textual and alternative data for regime inference, explainability metrics for LLM-driven signals, and hybrid architectures blending deterministic control with selective policy learning for extreme market conditions.