LLMs in Finance:Sentiment-Analysis
LLMs in Finance:Sentiment-Analysis
A Hands-On Guide to Using Large Language Models for Market Insights and Trading
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
Table of Contents
Table of Contents
- Preface
- Who This Book Is For
- What You Will Learn
- Structure of This Book
- How to Use This Book
- Prerequisites
- Getting Started
- Chapter 1: Understanding Financial Sentiment Analysis
- Overview
- What is Sentiment Analysis?
- Why sentiment analysis is important
- How Sentiment Analysis Works
- Applications in Finance
- Key Concepts and Challenges
- Running Example Flow
- Summary and Next Steps
- Chapter 2: Introduction to LLMs in Finance
- Impact on Different Domains
- Classical NLP Techniques
- Deep Learning and Transformer Models
- Large Language Models Overview
- Comparison of Techniques
- Applications in Finance
- Chapter 3: Data Collection and Preprocessing
- Sources of Financial Sentiment Data
- Using APIs: NewsAPI, Reddit, SEC
- Web Scraping and Preprocessing
- Troubleshooting and Pagination
- Structuring Data for LLMs
- Chapter 4: Applying LLMs to Financial Sentiment Analysis
- Prompt Engineering
- RAG Techniques
- LangChain Framework
- Sentiment Analysis Implementation
- Case Study: Market Sentiment
- Chapter 5: Sentiment-Based Trading Strategies
- Using Sentiment Scores
- Confusion Matrix
- Backtesting and Evaluation
- Chapter 6: Challenges & Best Practices
- Bias, Hallucinations, and Compliance
- Data Verification and Risk Management
- Real-World Case Studies
- Case Studies
- Reddit-Based Sentiment Pipeline
- RAG-Based SEC Filings Analysis
- Appendix
- Installation Guide
- Detailed News API Results
- Detailed SEC API Results
- References
- About the Author
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