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About the Book

LLMs in Finance: Sentiment Analysis is a practical guide for beginners and intermediate readers who want to explore how Large Language Models (LLMs) can be applied to financial text data to uncover market sentiment, inform trading strategies, and drive decision-making.

This book introduces the fundamentals of sentiment analysis and walks you through the process of collecting, analyzing, and leveraging financial data using modern AI techniques. With a hands-on focus, you’ll implement real-world examples using Python, LangChain, OpenAI, the SEC API, NewsAPI, and Reddit’s PRAW API.

You’ll also explore case studies that demonstrate powerful applications of Retrieval-Augmented Generation (RAG) and sentiment-aware trading models—bridging the gap between AI research and practical finance.

Whether you're a finance professional looking to modernize your toolkit, a data scientist stepping into the world of finance, or a developer curious about LLMs in action, this book provides a clear path forward—without overwhelming jargon or academic complexity.

Key Features:

• Beginner-friendly explanations of NLP and LLM concepts
• Data collection from financial news, filings, and social platforms
• Step-by-step implementation of sentiment pipelines using modern Python tools
• Case studies including Reddit sentiment analysis and SEC 10-Q filing insights
• Code walkthroughs for building trading strategies informed by sentiment
• Installation and environment setup guide included in the appendix

This book is part of an evolving project. Readers can expect ongoing updates and refinements. Your feedback is welcome and helps shape future releases.

About the Author

Pankaj Kumar

@pankajkt83

Pankaj Kumar is a technologist with over a decade of industry experience in software engineering, data architecture, and digital transformation. His professional journey has spanned roles in corporate transformation, financial technology, and applied AI, where he has consistently focused on building scalable, data-driven solutions.

He has worked on large-scale system modernization projects and is passionate about exploring how cutting-edge technologies like Large Language Models (LLMs) and NLP can be applied to financial workflows and market insights. His work blends practical engineering with curiosity-driven learning — often outside the academic sphere.

This book reflects his hands-on, systems-thinking approach. It is written for developers, finance professionals, and AI practitioners who want to connect the dots between language models, sentiment, and trading — without unnecessary complexity.

When not experimenting with APIs and LLMs, he enjoys simplifying difficult ideas, mentoring peers, and staying grounded in practical impact.