AGENTIC FINANCE: DEPLOYING AUTONOMOUS LLM AGENTS FOR REAL-TIME PORTFOLIO OPTIMIZATION AND STRATEGIC TRADING IN VOLATILE MARKETS GENERATIVE DATA FRAMEWORK FOR SYNTHETIC MARKET SIMULATION, RISK TRANSFER MODELING, AND DECISION-AUGMENTATION IN FINANCIAL

The integration of autonomous large language model (LLM) agents into real-time financial decision-making marks a paradigm shift in portfolio management and trading strategy, especially in increasingly volatile market environments. This paper proposes a hybrid architecture for Agentic Finance, combining generative synthetic market simulation with reinforcement learning-augmented portfolio optimization and real-time trade execution. Leveraging a generative data framework, we simulate market stress scenarios, support risk transfer modeling, and augment decision-making in financial AI systems. Through backtesting on volatile historical data and simulation environments, we demonstrate how LLM agents outperform traditional algorithmic trading systems in both adaptability and strategic foresight. The framework offers significant implications for hedge funds, robo-advisors, and central banks exploring autonomous financial systems.