LLM-Augmented Trading and Decision Platforms: Bridging Generative Intelligence with Financial Decision Systems

Abstract

Financial markets generate substantial volumes of unstructured textual information influencing asset valuations and trading behaviors. Traditional algorithmic trading systems process numerical indicators effectively yet lack semantic reasoning capabilities for interpreting news articles, earnings transcripts, regulatory filings, and social media content. Large Language Models offer remarkable text comprehension abilities suitable for financial sentiment extraction. Deploying such computationally intensive models within latency-sensitive trading environments presents significant architectural challenges. The proposed framework addresses the gap between semantic understanding and high-frequency execution requirements through optimized inference pipelines and structured signal taxonomies. Multi-source information aggregation enables parallel processing of heterogeneous data streams including news feeds and corporate communications. Event extraction and entity recognition transform raw text into structured representations suitable for downstream processing. Hierarchical signal classification converts sentiment outputs into actionable trading recommendations across multiple time horizons. Deep reinforcement learning agents interface with generated signals for adaptive strategy optimization. Portfolio allocation modules integrate textual intelligence with quantitative risk constraints for comprehensive decision support. Simulation environments enable systematic evaluation of latency, accuracy, and portfolio-level performance without capital exposure. The framework bridges generative language understanding with practical trading system requirements.