S&P 500 Stock Dashboard

Overview

The S&P 500 Stock Dashboard is an interactive web application designed to provide in-depth analytics and real-time data visualization for stocks within the S&P 500 index. Built with Streamlit, this dashboard offers a sector-based view to explore financial metrics, key ratios, quarterly financials, and historical stock performance.

Features

Real-Time Data Updates: Utilizes background threading to periodically update data without user interaction.
Sector-Based Filtering: Allows users to view stocks by specific sectors and analyze market trends within these sectors.
Interactive Time Series Visualization: Plots stock prices over time and explores different metrics like Open, High, Low, Close, and Volume.
Detailed Financial Metrics: Includes key financial ratios, quarterly statistics, and performance metrics.
Customizable Views: Enables users to select specific columns to display and configure pagination for handling large datasets efficiently.

Installation

To run the S&P 500 Stock Dashboard on your local machine, follow these steps:

Clone the repository:

git clone https://github.com/Habeeb-MD/stock-dashboard.git
cd stock-dashboard

Create a virtual environment:

sudo apt install python3-venv
python3 -m venv app_env
source app_env/bin/activate

Install the required Python packages:
```
pip install -r requirements.txt
```
Create and configure the secrets file:
```
mkdir .streamlit
cp secrets.toml .streamlit/secrets.toml
```
- Edit .streamlit/secrets.toml to set the configuration details like password, environment, and count of stocks for which data needs to be cached.
Run the Streamlit application:
```
streamlit run app/app_stock_dashboard.py
```
- Wait for the cache update to finish. You can check the status in the terminal.
- Navigate to http://localhost:8501 in your web browser to view the application.

Usage

Upon launching the dashboard, select a sector from the dropdown menu to view corresponding stocks. Use the pagination controls to navigate through different pages of stock data. Customize the data you wish to view using the column selector. Additional features include:

Fetching and plotting time series data for selected stocks.
Displaying financial ratios and quarterly statistics for deeper analysis.
Calculating and visualizing returns over specified time periods.

Technologies Used

Python: Primary programming language.
Streamlit: App framework used for building the dashboard.
Pandas: Data manipulation and analysis.
Plotly: Advanced interactive plotting library.

Acknowledgments

Special thanks to the Streamlit team for their amazing framework.
Data provided by Yahoo Finance API.