Stock-Research-Assistant

The Stock Research Assistant is a Python-based Streamlit application designed to assist users in researching stocks by leveraging AI-powered tools. It integrates with Gemini AI services and FAISS for vector search to provide insightful analyses and information on various stocks.

🔧 Features

• AI-Powered Stock Analysis: Utilizes Gemini AI to fetch and analyze stock-related data.
• Efficient Vector Search: Uses FAISS for fast and scalable similarity search over stock-related embeddings.
• Interactive Streamlit Interface: Provides a user-friendly web interface for seamless interaction.
• Customizable Research Parameters: Allows users to tailor their stock research based on specific criteria.

🛠️ Requirements

• Python 3.10.16
• Required Python packages (listed in requirements.txt)
• Google API Key for accessing the Gemini AI services

🚀 Installation

1. Clone the repository:

   git clone https://github.com/Vish501/Stock-Research-Assistant.git

   cd Stock-Research-Assistant

2. Install dependencies:

   It's recommended to use a virtual environment to manage dependencies. You can create and activate one using:

   conda create -p venv python=3.10.16 -y

   conda activate venv/

   Then, install the required packages: pip install -r requirements.txt

💻 Setting Up the Google API Key

To use the Gemini API, you need to set up your GOOGLE_API_KEY:

1. Obtain your Google API Key from the Google Cloud Console.

2. Add the API key to your environment:

   • Locally (Linux/macOS): export GOOGLE_API_KEY="your-api-key-here"
   • Locally (Windows - Command Prompt): set GOOGLE_API_KEY="your-api-key-here"
   • Locally (Windows - PowerShell): $env:GOOGLE_API_KEY="your-api-key-here"

3. If you are using GitHub Codespaces, store the API key as a GitHub repository secret:

   • Go to your GitHub repository
   • Navigate to Settings > Secrets and variables > Actions
   • Click New repository secret
   • Set the name as GOOGLE_API_KEY and paste your API key as the value
   • Click Add secret

This will allow the chatbot to authenticate and communicate with the Gemini API securely.

💻 Usage

To run the application:​

streamlit run app.py

This will launch the Streamlit web interface in your default browser, where you can start researching stocks using the integrated AI tools.

📁 Project Structure

• app.py: Main application file for Streamlit.
• requirements.txt: List of required Python packages.
• setup.py: Setup configuration for the project.
• template.py: Contains template code to create the basic project structure.
• research/: Directory for research-related files and data.
• src/: Source code directory containing modules and packages.

⚠️ Disclaimer

This application is powered by AI technologies including language models and vector search. While the bot is designed to provide helpful insights, it may occasionally produce inaccurate, outdated, or incomplete information.

Users are strongly advised to do their own research and consult with a qualified financial advisor before making any investment decisions. The creators of this project are not responsible for any financial losses or actions taken based on the information provided by this tool.

Use this application at your own risk.

📄 License

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

🙌 Acknowledgements

• Streamlit for the interactive web interface.
• Gemini AI for AI-powered stock analysis.
• FAISS for efficient similarity search.

Feel free to contribute to this project by submitting issues or pull requests. For any questions or suggestions, please contact Vish501.