The Rise of LLM Agents in Algorithmic Trading

The financial world is on the cusp of a paradigm shift, driven by the convergence of Large Language Models (LLMs) and autonomous agentic systems. Traditional algorithmic trading, while sophisticated, often relies on structured numerical data and predefined rules. LLM agents, however, can process and reason over vast amounts of unstructured text data—news articles, social media, research reports, and regulatory filings—to make more nuanced and context-aware trading decisions.

This article explores the architecture of such an agent, detailing the pipeline from data ingestion and real-time analysis to signal generation and execution. We discuss the critical role of Retrieval-Augmented Generation (RAG) in grounding the model's decisions in factual, timely information, mitigating the risk of hallucinations. Furthermore, we delve into the challenges of latency, cost, and ensuring the agent's actions align with predefined risk parameters. The future of trading isn't just automated; it's cognitive.