The Stock Sentiment Analysis Application is a containerized, end-to-end solution for predicting stock prices based on sentiment analysis. This application fetches stock prices and sentiment data, processes it using Bayesian regression modeling, and provides a web interface for users to visualize predictions across different time horizons.
- Automated Data Fetching: A C++ service fetches updated stock prices and sentiment data from Polygon.io and initializes the database.
- Predictive Modeling: Bayesian regression modeling implemented in Python using PyMC3 to predict stock prices based on sentiment over three different time horizons.
- REST API: An Express.js backend provides a REST API for interacting with the data and models.
- Interactive Frontend: A React frontend allows users to visualize stock sentiment data and prediction results.
- Containerization: The entire application (C++ service, Python scripts, Express.js backend, and React frontend) is containerized using Docker for consistent deployment.
- Deployment: The application is deployed on AWS Lightsail for scalability and reliability.
- C++: For database initialization, sentiment and price data fetching, and running Python scripts, among other miscellaneous tasks.
- Python & PyMC3: For data analysis and Bayesian regression modeling.
- Express.js: Backend API for managing requests and interfacing with the data processing services.
- React: Frontend for data visualization and user interaction.
- PostgreSQL: Relational database to store stock data and sentiment analysis results.
- Docker: Containerization of the entire application stack for easy deployment.
- AWS Lightsail: Cloud deployment for scalability and reliability.