Stock price prediction
predicting stock prices using machine learning
Description
A web app empowering users to seamlessly deduct insights and analyse predicted stock prices
Scope
Our web application offers advanced stock price prediction by integrating several simulation models: Monte Carlo, Black-Scholes, and Vasicek. The Monte Carlo simulation provides a probabilistic approach to forecast future stock prices by generating numerous potential outcomes based on historical data and volatility. The Black-Scholes model is employed for option pricing, utilizing a mathematical framework to determine the fair value of options, thus aiding in investment decisions. The Vasicek model, on the other hand, is used to predict interest rate movements, providing insights into the impact of interest rate changes on stock prices. Together, these simulations empower users with comprehensive tools to make informed investment strategies and risk assessments.
Role
Establishing robust communication channels between the frontend and the Python backend, ensuring smooth data flow and processing. For this we used Flask framework to build RESTful APIs that the frontend can interact with. Also, ensuring the application is easy to navigate, with clear instructions to guide users through the process of inputting data and interpreting results.
Persona
UI interfaces
Technologies & tools
- React
- Axios
- Tailwind
- Python
- Flask
- YFinance