This project is a Streamlit app for visualizing and analyzing stock data from the S&P 500 index. It allows users to select a company, choose different data types to visualize, and compare stock performance against the S&P 500 Index (^GSPC) as a benchmark.
- Interactive Visualizations: Plot various financial metrics such as OHLC prices, stock returns, and rolling alpha and beta.
- Customizable Time Range: Select custom start and end dates for data analysis.
- Comparison with Benchmark: Optionally compare the selected stock's performance against the S&P 500 index.
- Data Analysis: Includes calculation of stock returns, log returns, and cumulative returns.
- Advanced Metrics: Plot rolling alpha and beta values with customizable lookback windows.
StreamlitPandasPlotlyScikit-learnNumPyyfinance
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Clone the repository:
git clone https://github.com/Yosri-Ben-Halima/SP500-Investment-Data-Visuals-App cd SP500-Investment-Data-Visuals-App -
Install the required packages:
pip install -r requirements.txt
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Run the Streamlit app:
streamlit run app.py
- Select a Company: Choose a company from the dropdown menu in the sidebar.
- Date Range: Set the start and end dates for data analysis. Optionally, use the current date as the end date.
- Data Options: Select the data you want to plot (e.g., Candlestick, OHLC, Stock Returns) using the multiselect box.
- Benchmark Comparison: Check the box to compare the selected stock's performance with the S&P 500 index.
- Rolling Alpha and Beta: Choose a lookback window for calculating rolling alpha and beta values.
Feel free to submit issues or pull requests to enhance the project.
This project is licensed under the MIT License - see the LICENSE file for details.
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