Course Description

In this hands-on course, you will build a fully functional AI-powered stock market analysis agent using AutoGen and Streamlit. Starting with a deep dive into agent-based system architecture, this course guides you through creating specialized agents for financial reporting, technical analysis, and trading strategies. You’ll also design a supervisor agent for orchestration, build a responsive Streamlit-based interface, and deploy your multi-agent system for real-world use.

By the end, you’ll gain practical experience in AutoGen, multi-agent collaboration, prompt engineering, and end-to-end application deployment, making you industry-ready to create AI-driven financial analysis tools.

Course curriculum

  • 1
    Introduction to AI Agents & Stock Market Analysis
    • What Are AI Agents
    • Overview of AutoGen Framework
    • Why Use AI Agents for Stock Analysis
    • Stock Analysis Tool - Demo
    • Quiz
    • Course Handouts
  • 2
    System Architecture & Agents Planning
    • End-to-End Architecture Overview
    • Tooling & Technology Stack
    • Tools Planning
    • Planning Agent Roles
    • Quiz
  • 3
    Building AI Agents with AutoGen
    • Finance Report Analyst and Tools
    • Technical Analyst and Tools
    • Strategy Agent and Tools
    • Supervisor
    • Agent Orchestration using AutoGen
    • Quiz
  • 4
    UI Development & Agent Integration
    • Designing the Frontend UI
    • Implementing UI with Streamlit
    • Real-time Agent Integration
    • Quiz
  • 5
    Deployment & Wrap-Up
    • Packaging the Application
    • Deploying to the Cloud
    • Scaling Up Agents: Practical Tips & Next Steps
    • Quiz

Who Should Enroll

  • Developers & data scientists exploring LLM-based multi-agent applications.

  • Finance and trading professionals looking to automate analysis workflows.

  • AI enthusiasts interested in building agentic systems with real-world use cases.

  • Students & professionals aiming to showcase a high-impact portfolio project.

What you will Learn

  • How to design and implement a multi-agent system using AutoGen for financial applications.

  • Building specialized agents for financial reporting, technical analysis, and strategy recommendations.

  • Integrating a supervisor agent to orchestrate multi-agent collaboration.

  • Creating a Streamlit-based UI for intuitive interaction with your AI assistant.

  • Packaging, deploying, and scaling your application for real-world use.

  • Best practices in prompt engineering and contextual inputs for reliable agent performance.

About Instructor

Nitin Agarawal- Principal Data Scientist/ Ex-Microsoft

Nitin is a Principal AI Scientist with expertise in Generative AI, Machine Learning, Natural Language Processing, and Deep Learning. A thought leader in the data science community, with numerous accolades for innovation and excellence. At Microsoft, he had developed AI Copilots that leverage advanced AI technologies, industry best practices, and user-focused design.
About Instructor

FAQ

  • Do I need prior experience with AutoGen?

    Not at all. The course begins with an introduction to AutoGen, making it accessible for beginners with basic Python knowledge.

  • Will I build a fully functional application?

    Yes! By the end of the course, you’ll have a deployed, multi-agent stock market analysis application.

  • Is this course suitable for finance professionals?

    Absolutely. The course is tailored for both technical and finance-oriented learners interested in leveraging AI for trading and analysis.

  • What technologies will I work with in this course?

    You’ll use AutoGen, Python, Streamlit, and various financial data APIs.

  • Can I use this project in my portfolio?

    Definitely. This project is highly relevant for showcasing LLM integration, multi-agent orchestration, and real-world AI application development.