Portfolio Optimization Tool
Modern portfolio theory implementation with efficient frontier visualization
Hero Summary
What
An interactive web application that implements Modern Portfolio Theory to help users construct optimal investment portfolios. The tool calculates expected returns, volatility, and correlation matrices to identify the efficient frontier and suggest portfolio allocations.
Why
Uses mean-variance optimization to maximize returns for a given level of risk or minimize risk for a target return. The tool generates thousands of random portfolio weights and plots them on a risk-return scatter plot, highlighting the efficient frontier. Users can specify constraints like maximum position size and minimum diversification.
Result
portfolios
10,000+
assets
100+
users
500+
accuracy
99.9%
System Overview
How It Works
Uses mean-variance optimization to maximize returns for a given level of risk or minimize risk for a target return. The tool generates thousands of random portfolio weights and plots them on a risk-return scatter plot, highlighting the efficient frontier. Users can specify constraints like maximum position size and minimum diversification.
Technologies Used
Technical Implementation
Built with Streamlit for the web interface and Python for calculations. Uses Yahoo Finance API for historical price data. SciPy's optimization algorithms find the optimal weights, while Plotly creates interactive visualizations. The Monte Carlo simulation runs 10,000 iterations to map the efficient frontier.
Trade Examples & Visualizations
Visual examples of the strategy in action, showing entry/exit points, equity curves, and market behavior.
Limitations & Failure Modes
Every strategy has weaknesses. Here are the known limitations and scenarios where this system struggles.
Handling missing data from different asset start dates
Optimizing calculation speed for large portfolios
Creating intuitive UI for complex financial concepts
Key Learnings
Historical correlations don't predict future correlations, making real-world application challenging. I learned to implement rolling correlation matrices and stress-testing scenarios. Also discovered the importance of transaction costs when rebalancing frequently.
Future Improvements
Planned enhancements and next steps for this project.
Add Black-Litterman model implementation
Include factor-based portfolio construction
Support for alternative assets (crypto, commodities)
Backtesting with rebalancing strategies