This repo explores two foundational strategies in portfolio optimization — Markowitz Efficient Frontier and Risk Parity — using Python. It compares their allocations, returns, and performance metrics on real financial data, helping investors understand risk-return tradeoffs in modern portfolio theory.
A collection of Python implementations for portfolio optimization techniques – from classic Markowitz Efficient Frontier to Risk Parity and Black-Litterman.
- ✅ Markowitz Portfolio Optimization (Max Sharpe, Min Volatility, Max Return)
- ✅ Risk Parity Portfolio
- Python
- NumPy, Pandas, Matplotlib
yfinancefor pulling real-time stock data
- Compare different optimization strategies
- Visualize portfolio weights using pie charts
- Plot cumulative returns for each strategy
- Easily adaptable for custom stock selections