📈 Financial Agent

🌟 Overview

The Financial Agent is an AI-powered tool designed to assist users with financial research and analysis. It helps users gather real-time stock data, market insights, and financial news using advanced AI models.

🚀 Features

• 🔍 Web Search – Retrieves the latest financial news from the web.
• 📊 Finance Analysis – Provides stock price trends, analyst recommendations, and financial data.
• 🖥️ User-Friendly Interface – Built using Streamlit for an interactive experience.
• 📚 Multi-Agent System – Uses specialized AI agents for different tasks to improve accuracy.
• ⚠️ Robust Error Handling – Logs issues using Python’s logging module and provides user-friendly warnings.

---

❓ Why Multi-Agent?

Traditional financial research requires navigating multiple sources, which can be time-consuming. This project automates financial insights using an AI-driven multi-agent system:

• ✅ Automates Web Search → Finds relevant financial news.
• ✅ Performs Stock Analysis → Retrieves real-time stock data and trends.
• ✅ AI Coordination → Combines insights into structured, easy-to-read reports.

Multi-Agent System Breakdown

• 🔍 Web_Search_Agent → Searches the latest finance news.
• 📊 Finance_Analysis_Agent → Fetches stock data and fundamentals.
• 🏆 Finance_Team_Agent → Combines results and presents a final, clear answer.

---

Example Workflow

A user asks: "What is the latest update on Tesla stock?"

🚀 Financial Agent:
1️⃣ Fetches Tesla’s latest stock price & trends 📈
2️⃣ Searches for recent Tesla-related financial news 🔍
3️⃣ Summarizes insights with AI 🏆

---

🛠️ Technologies Used

• Phidata – Advanced AI modeling
• DuckDuckGo API – Web search for finance news
• YFinanceTools – Stock market data
• Streamlit – Interactive UI
• Python-dotenv – Environment variable management
• Logging – Built-in Python logging for error handling and debugging

---

🏗️ System Architecture

This diagram shows how the Financial Agent processes user queries using multiple AI agents.

flowchart TD
    A[User] -->|Enters Query| B[Streamlit Web UI]
    B -->|Sends Query| C[Main AI Coordinator]

    subgraph TEAM[Main AI Coordinator]
      C -->|Delegates to| D[Web Search Agent 🔍]
      C -->|Delegates to| E[Finance Analysis Agent 📊]
    end

    D -->|Finds Latest Financial News & Data| C
    E -->|Analyzes Stock Trends & Insights| C

    C -->|Combines Data & Generates Report| F[Response Processor 📝]
    F -->|Sends Processed Insights to User| B
    B -->|Displays Results| A

Loading

Installation

1. Clone the Repository:

   git clone https://github.com/deepakmalikk/Financial_Agent.git
   cd Financial_Agent

2. Set Up Environment Variables:

   GROQ_API_KEY=your_groq_api_key_here
   GOOGLE_API_KEY=your_google_api_key_here
   

3. Install Dependencies:

   pip install -r requirements.txt
   

4. Run the Application:

   streamlit run src/app.py
   

🖥️ Usage

• Open the app in your browser.
• Enter a financial question (e.g., "Latest news on Tesla stock").
• Click “Get Financial Insights” – The AI will fetch results.
• View insights including stock prices, news, and analysis.

🚀 Deployment

🔗 Live Demo: [https://financialagent01.streamlit.app/]

🤝 Contributing

🙌 Contributions are welcome! Follow these steps to contribute:

1. Fork the repository
2. Create a new branch:

git checkout -b feature-branch

3. Make your changes
4. Commit your changes:

git commit -m "Added a new feature"

5. Push to GitHub:

  git push origin feature-branch

6. Create a pull request 🚀

📜 License

This project is licensed under the MIT License – see the LICENSE file for details.