An interactive financial analysis web report built with Python and Dash, covering the top 10 most liquid stocks in the S&P 500 index over a 5-year period. Built as part of a BI Developer portfolio to demonstrate quantitative analysis and data visualization skills.
Free tier — may take ~50s to wake up on first load.
The report is divided into 10 interactive sections:
| # | Section | What it shows |
|---|---|---|
| 01 | Price History | Normalized closing prices across all tickers |
| 02 | Returns Distribution | Daily return histograms with normal distribution fit |
| 03 | Correlation Matrix | Pearson correlation heatmap between assets |
| 04 | Rolling Volatility | 30-day annualized rolling standard deviation |
| 05 | Risk / Return | Annualized return vs. volatility scatter plot |
| 06 | Risk Metrics | Sharpe Ratio, Max Drawdown, VaR 95%, CVaR 95% |
| 07 | Technical Analysis | Interactive Bollinger Bands with RSI |
| 08 | Efficient Frontier | Monte Carlo simulation with optimal portfolios |
| 09 | Clustering | KMeans clustering of stocks by risk/return profile |
| 10 | Conclusions | Key takeaways from the full analysis |
- Dash / Plotly — interactive web charts and layout
- Pandas / NumPy — data pipeline and feature engineering
- Scikit-learn — KMeans clustering
- SciPy — normal distribution fitting
- Gunicorn — production WSGI server
S&P 500 Stock Data — Kaggle
https://www.kaggle.com/datasets/camnugent/sandp500
Historical daily OHLCV data for S&P 500 companies from 2013 to 2018.
The report automatically selects the top 10 stocks by average daily trading volume.
The dataset CSV is not included in this repository. Download it from Kaggle and place
all_stocks_5yr.csvin the project root before running.
# 1. Clone the repository
git clone https://github.com/thigmen/sp500-financial-report.git
cd sp500-financial-report
# 2. Install dependencies
pip install -r requirements.txt
# 3. Download the dataset from Kaggle and place it in the project root
# File: all_stocks_5yr.csv
# 4. Run the app
python report.pyThen open http://127.0.0.1:8050 in your browser.
- Financial metrics: Sharpe Ratio, Max Drawdown, Value at Risk (VaR), Conditional VaR (CVaR)
- Portfolio optimization: Monte Carlo Efficient Frontier (10,000 simulations)
- Technical analysis: Bollinger Bands, RSI (Relative Strength Index)
- Machine learning: KMeans clustering for asset grouping
- Statistics: Normal distribution fitting, rolling correlation/volatility
- Data visualization: 11 interactive Plotly charts with dark theme
sp500-financial-report/
├── report.py # Main Dash application
├── analysis.py # Standalone analysis script
├── assets/
│ └── report.css # Custom CSS (smooth scroll, sticky nav, print)
├── requirements.txt
└── Procfile # Gunicorn entry point for deployment