📈 Portfolio Optimizer

A full-stack Python application that leverages Modern Portfolio Theory (MPT) to identify the optimal asset allocation for a given set of stocks — maximizing the Sharpe Ratio while supporting custom risk constraints. The application features an interactive Streamlit web interface, a Monte Carlo Efficient Frontier visualization via Plotly, advanced parametric risk metrics, and a dedicated historical Stress Testing module.

---

✨ Features

• Automated Data Fetching — Downloads historical Adjusted Close prices for any set of tickers using yfinance, with automatic forward/backward fill for missing data. Implements native Streamlit caching to bypass rate limits and prevent redundant API calls.
• Portfolio Optimization — Uses scipy.optimize (SLSQP method) to find the maximum Sharpe Ratio portfolio under realistic constraints (no short-selling; configurable per-asset weight cap).
• Interactive Visualization — Monte Carlo simulation of 10,000 random portfolios plotted as a dynamic, interactive Plotly scatter chart, natively inheriting Streamlit's Light/Dark mode themes.
• Advanced Risk Metrics — Parametric (Gaussian) calculation of Value at Risk (VaR) and Conditional VaR (CVaR / Expected Shortfall) at a configurable confidence level.
• Historical Backtesting — Simulates the growth of a custom investment amount across the selected date range (extending back to Jan 2000), comparing the Optimized Portfolio, Equal-Weight Portfolio, and a Market Benchmark (e.g., SPY).
• Stress Testing Module — Simulates portfolio resilience across predefined historical market crashes (e.g., COVID-19 Crash, 2022 Tech Bear Market, 2008 Financial Crisis) and outputs comparative Maximum Drawdown (MDD) metrics.
• Production-Ready UI — Configurable sidebar, tabbed results view, session-state persistence, responsive KPI rows, and direct Streamlit Cloud deployment readiness.

---

🚀 Installation & Setup

1. Clone the Repository

git clone https://github.com/your-username/portfolio-optimizer.git
cd portfolio-optimizer

2. Create and Activate a Virtual Environment (Recommended)

# Windows
python -m venv venv
venv\Scripts\activate

# macOS / Linux
python -m venv venv
source venv/bin/activate

3. Install Dependencies

pip install -r requirements.txt

---

🖥️ Usage

Streamlit Web App (Recommended)

streamlit run streamlit_app.py

Open your browser and navigate to http://localhost:8501.

How to use the UI:

1. Enter your desired stock tickers in the sidebar multiselect box.
2. Set your desired Start Date and End Date for the historical data window.
3. Adjust optional parameters: Risk-Free Rate, VaR Confidence Level, Benchmark Ticker, Initial Capital, and the Maximum Weight per Asset constraint slider.
4. Click "Run Optimization".
5. Review results across three tabs:
   • 📊 Optimization Results — Optimal weights table, performance metrics, risk metrics, and the interactive Plotly Efficient Frontier chart.
   • 📅 Historical Backtest — Cumulative growth line chart and final portfolio value comparisons.
   • ⚠️ Stress Testing — Select historical crises to simulate and compare maximum drawdowns against the market.

CLI Alternative

python main.py

Outputs the optimal weights, expected return, volatility, Sharpe Ratio, VaR, and CVaR directly to the terminal.

---

📁 Project Structure

portfolio-optimizer/
│
├── streamlit_app.py        # Streamlit web application (main UI)
├── main.py                 # CLI execution script
├── requirements.txt        # Python dependencies
├── .gitignore
│
└── src/
    ├── __init__.py         # Package root
    ├── data_fetcher.py     # Cached yfinance Adj Close price retrieval
    ├── metrics_calc.py     # Annualized returns & covariance matrix
    ├── optimizer.py        # Max Sharpe Ratio optimizer (scipy SLSQP)
    ├── visualization.py    # Plotly Efficient Frontier & Stress charts
    ├── risk_metrics.py     # Parametric VaR & CVaR calculation
    └── backtester.py       # Backtesting and Crisis Scenario MDD calculations

---

🛠️ Technologies Used

Library	Purpose
Python 3.x	Core language
Streamlit	Interactive web application frontend & Session State control
Plotly	Interactive visualization and theming
yfinance	Historical market data retrieval
Pandas	Data manipulation and time-series handling
NumPy	Numerical computation and Monte Carlo simulation
SciPy	Constrained portfolio optimization (scipy.optimize)

---

📄 License

This project is open-source and available under the MIT License.