Sibyl Stock

🚧 Work in Progress 🚧

This project is in pre-alpha stage and under active development. Features may change frequently, and bugs are expected.

Sibyl-stock is a powerful Streamlit app that provides in-depth stock analysis and investment recommendations using machine learning and advanced algorithms. Whether you're a seasoned investor or just starting out, Sibyl-stock helps you make informed decisions by offering a comprehensive view of a company's performance and potential.

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🚀 Features

• Company Overview:
  Get detailed information about any stock, including company profile, key metrics, and financials.

• Analytics & Visualizations:
  Explore various analytics such as price trends, volume, moving averages, and more. Gain insights with interactive charts and visualizations.

• Investment Insights:
  Leverage machine learning models and advanced algorithms to determine if a stock is a good investment.

• Stock Selector:
  Easily search and select from a wide range of stocks to analyze.

• Customizable Analysis:
  Tailor your analysis with custom parameters for deeper insights.

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🛠️ Technologies Used

• Frontend:
  Built using Streamlit, providing an intuitive and user-friendly interface.

• Backend:
  Powered by Python with libraries such as:

  • yfinance for stock data retrieval.
  • pandas and numpy for data manipulation.
  • tensorflow, scikit-learn and other ML libraries for predictive models.

• Visualization:
  Interactive and dynamic plots using Plotly and matplotlib.

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🔧 Setup and Installation

Clone the repository:

git clone https://github.com/your-username/sibyl-stock.git
cd sibyl-stock

1. Using python locally

Install dependencies: Ensure you have Python 3.10 or above installed. Then, run:

pip install -r requirements.txt
streamlit run index.py  # launch the server

Open your browser and navigate to http://localhost:8501.

2. Using Docker

Build the Docker image:

docker build -t sibyl_stock .
docker run -p 8501:8501 sibyl_stock

Again access the app through your browser and navigate to http://localhost:8501.

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📊 Machine Learning Models

The app includes various ML models to predict stock performance, such as:

Regression models for price prediction.

Classification models for buy/sell/hold recommendations.

Sentiment analysis based on news and social media.

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🧪 Future Improvements

Incorporate advanced sentiment analysis using large language models (LLMs). Provide portfolio optimization features.

🤝 Contributing

We welcome contributions!

Fork the repository. Create a feature branch: git checkout -b feature-name. Commit your changes: git commit -m 'Add some feature'. Push to the branch: git push origin feature-name. Open a pull request.

📜 License

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

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🌟 Acknowledgements

Streamlit for the interactive web app framework. yfinance for financial data APIs. tensorflow and scikit-learn for the ML capabilities.