A Streamlit web application powered by AutoGen agents that generates comprehensive financial reports for requested stock symbols, including historical performance, key financial ratios, recent news analysis, and a normalized price chart.
- AI-Powered Reports: Utilizes AutoGen agents to perform financial analysis, research news, and generate a cohesive report.
- Multi-Stock Analysis: Analyze multiple stock symbols simultaneously by entering a comma-separated list.
- Key Financial Metrics: Includes important ratios like P/E, Forward P/E, Dividends, Price to Book, Debt/Equity, and ROE.
- Historical Performance: Reports on the percentage change in stock price over the past 6 months.
- Normalized Price Chart: Generates and displays a normalized price chart for easy comparison.
- News Analysis: Gathers recent news headlines to provide context for stock performance.
- Markdown Report Output: The final report is presented in a clear, readable Markdown format.
- Automated Export: Automatically saves the final report to a Markdown file.
Before running the application, ensure you have the necessary dependencies installed.
git clone https://github.com/AhmedZeyadTareq/AutoStockReport.git
cd AutoStockReport
python -m venv venv
source venv/bin/activate # Linux/Mac
.\venv\Scripts\activate # Windows
pip install -r requirements.txtcd AutoStockReportstreamlit run AutoStockReport.py(Note: You will need to create a requirements.txt file containing the required libraries: streamlit, autogen, python-dotenv, pandas, yfinance, matplotlib - based on the code's likely dependencies for fetching/plotting stock data, although not all are explicitly imported in the provided snippet, they are necessary for the tasks described).
Create a .env file in the root directory of the project and add your OpenAI API key:
Code snippet
OPENAI_API_KEY="your_openai_api_key"
Replace "your_openai_api_key" with your actual OpenAI API key.
The plot is generated by the Financial_Assistant agent using Python code executed internally The application uses a multi-agent system orchestrated by AutoGen to perform the financial analysis and reporting:
User Input: The user enters stock symbols into the Streamlit interface. Initialization: Upon clicking "Start Analysis", the application initializes various AutoGen agents with specific roles. Financial Analysis: A Financial_Assistant agent fetches current stock prices, calculates historical performance, retrieves key financial ratios (P/E, Forward P/E, etc.), and generates a normalized price chart. Research: A Researcher agent gathers recent news headlines related to the specified stocks using search tools (simulated via prompts in this code). Writing: A Writer agent, acting as a professional financial report writer, compiles all the gathered data and news into a comprehensive report in Markdown format. This agent is guided by a Critic and a team of reviewers (Legal_Reviewer, Consistency_Reviewer, Text_Alignment_Reviewer, Completion_Reviewer, Meta_Reviewer) to ensure the report's quality and accuracy. Export: An Exporter agent saves the final Markdown report to a file. Display: The Streamlit application displays the normalized price chart and the final generated report to the user.
Bash
AutoStockReport/
├── .env # Environment variables (for API keys)
├── AutoStockReport.py # The main Streamlit application code
├── requirements.txt # Python dependencies
└── coding/ # Directory for code execution and generated files (like plots)
└── normalized_prices.png # Generated plot file (example)
Ensure you have completed the installation steps, including setting up the .env file and installing dependencies.
Run the Streamlit application from your terminal:
streamlit run AutoStockReport.pyThe application will open in your web browser. Enter the stock symbols you want to analyze in the text input field, separated by commas (e.g., AAPL, GOOGL, TSLA).
Click the "🚀 Start Analysis" button.
The application will display a spinner while the AI agents are running.
Once the analysis is complete, the normalized price chart and the generated financial report will appear on the page.
A Markdown file containing the report will also be saved in the coding/ directory.
Automated Reporting: Quickly generate detailed financial reports without manual data gathering. Holistic View: Combines historical data, key financial metrics, and recent news for a comprehensive analysis. AI-Powered Insights: Leverages the power of large language models via AutoGen for sophisticated analysis and report generation. Customizable: Easily adaptable to include more financial metrics, different time periods, or additional data sources by modifying agent tasks.
📌 Data Scientist & AI Developer | 🎓 Master of AI Engineering
This project is licensed under the MIT License - see the LICENSE file for details. (You should create a LICENSE file with the MIT license text if you don't have one).
If you find this project helpful, please consider giving it a star ⭐ on GitHub!
Contributions are welcome! If you have ideas for improvements or find issues, feel free to open a Pull Request or create an Issue on the GitHub repository.