You can view the HTML version of this notebook:

• Project Overview: index.html – Interactive landing page with project details
• Full Analysis: notebook.html – Complete notebook with all code and results

A practical exploration of portfolio optimization under long-only constraints.
Combines Monte Carlo simulation, Markowitz theory, and numerical optimization (SLSQP).
Built as a personal tool to understand the efficient frontier for real-world investing.

• Monte Carlo simulation of random portfolios
• Analytical Markowitz mean-variance optimization
• Numerical optimization using SLSQP with:
  • Minimum and maximum weight constraints
  • Sector constraints (optional)
  • Sharpe ratio maximization

• Efficient frontiers under varying constraints
• Capital Market Line (CML) and Sharpe-optimal portfolios
• Pie charts of asset allocations
• Side-by-side comparisons of how constraints affect shape and allocation

• Based on historical mean returns and covariances
• Assumes normally distributed returns
• No transaction costs or turnover limits yet

• Add more advanced models (e.g., Black-Litterman model)
• Include transaction costs / turnover constraints

This project began as a personal tool to help me understand how portfolio optimization works in practice, not just in theory. I wanted to learn how to construct efficient, long-only portfolios using Python, based on historical data. Since I don’t short assets in my own investing, I focused on long-only constraints throughout. I’ve gradually added features like minimum/maximum weight constraints and visualizations to help understand the tradeoffs involved. The notebook is designed to be a clear, practical reference that can be extended in the future as I explore more advanced models.

pip install -r requirements.txt