AI Agents for Stock Analysis: Using LLM's to Analyze Financial Documents

Описание: This tutorial shows you how to extract meaningful insights for stock market analysis from financial documents like company annual reports (Form 10-K) using AI agents with Python, OpenAI API, and Llama Index. By combining Retrieval-Augmented Generation (RAG) with AI Agents, you’ll learn how to automate fundamental analysis. This is just starter code to get you up and running with this technology: It is up to you to customize the analysis to your specific use case and market thesis.

What’s Covered:
PDF Upload using Llama Index
Vector Embedding: Preprocess financial reports for efficient data retrieval.
Using Agentic RAG and GPT-4o-mini: Analyze large datasets to identify and summarize risks.
Building an AI Risk Analysis Agent: Configure tools, set up prompts, and run autonomous analyses.

Whether you’re a Financial Analyst, Machine Learning Engineer, Data Scientist, or Student, this tutorial provides a practical framework to automate finance insights of the stock market.

***Important Note: This video is not financial or investing advice. Everything shown is for educational purposes only, highlighting how to leverage cutting-edge AI and LLM techniques. Always apply critical thinking and incorporate subject matter expertise into your decision-making process—LLMs are experimental and can produce inaccurate results.***

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*Full Code*
https://deepcharts.substack.com/p/ai-...

*Additional Resources*
OpenAI Developer Platform for API: https://platform.openai.com/docs/over...
SEC Edgar Financial Documents Search: https://www.sec.gov/search-filings
10-K data used in analysis: https://ir.netflix.net/financials/ann...

*Chapters*
0:00 Intro to AI Agents and Retrieval Augmented Generation (RAG), Use Cases, and Workflow
2:00 Setting Up Python Environment
2:40 Creating Vector Embeddings of a PDF file
3:27 How to do Agentic RAG
5:18 Bonus: AI Agent Loop through multiple PDF documents