"The Autonomous Analyst: Leveraging LLMs and Transformers for Market Insight" The Autonomous Analyst is a practical guide to building next-generation research workflows at the intersection of quantitative finance and modern AI. Written for portfolio managers, quantitative researchers, data scientists, and technically inclined fundamental analysts, it shows how large language models and transformer architectures can be turned into disciplined, auditable "autonomous analysts" that operate within real market, data, and regulatory constraints. Grounded in market microstructure, time-series behavior, and statistical rigor, the book walks from mathematical and machine-learning foundations through transformer internals, embeddings, and long-context modeling. It then develops full-stack, retrieval-augmented systems for ingesting filings, news, and alternative data; orchestrating prompts, tools, and agents; and translating textual insight into signals, portfolios, and executed trades. Along the way, it emphasizes leakage control, causal thinking, and robust backtesting so that apparent alpha survives contact with production. Assuming comfort with basic Python, linear algebra, and probability, the book is organized to support both end-to-end reading and selective deep dives. Its focus on system design, data engineering, and governance-rather than isolated models-makes it especially relevant for teams operating under institutional risk, compliance, and latency constraints.