README.md

Building Stock Index App with Streamlit

^{(Image Source: https://www.investopedia.com/terms/i/index.asp)}

You can open the app by clicking on the Streamlit icon

The accompanied Jupyter notebook can be best rendered using NBViewer

Main goals of project:

This project focuses on Stock indices, which are measures of the performance of a group of stocks that represent a particular market or sector. A stock index is calculated based on the performance of a selected group of stocks, and it provides a snapshot of the overall performance of the market or sector that the index represents. In this project, my main goal is to:

• Visualize closing prices and volumes of major stock indices around the world, where the list of indices is scraped from Yahoo Finance
• Construct the Efficient Frontier curve through random sampling and simulating performances of portfolios, each of which consists of different indices. Calculate Value at Risk (VaR) and show information of high-performance portfolios
• Generate price predictions for stock indices based on historical closing prices. The neural network with Multi-layer Perceptron regressor (MLP Regressor) from the Sklearn library was used to construct the prediction model
• Create a Streamlit app that contains both the visualization, the efficient frontier simulation and the price prediction model

In reality, this app could be helpful for risk-averse investment funds (typically pension funds or mutual funds) who often have a long-term investment approach and want to make risk-adjusted returns.

Screenshots of app UI: