Markowitz Portfolio Optimization
Monte Carlo simulation for optimal asset allocation using Modern Portfolio Theory.
Interactive Visualization
Efficient Frontier (Monte Carlo Simulation)
*Orange points indicate high Sharpe Ratio (>2.0). The line represents the optimal risk/return boundary.
The Concept (In Plain English)
Imagine you have $10,000 to invest. You could put it all in one stock (high risk, maybe high reward) or spread it out (lower risk, average reward). But what is the *perfect* mix? This project uses a mathematical formula to simulate thousands of possible portfolios to find the 'Efficient Frontier'—the sweet spot where you get the maximum possible return for the lowest possible risk. It's the math behind how hedge funds balance their bets.
The Build (Technical Deep Dive)
This project implements Modern Portfolio Theory (MPT) using a Monte Carlo simulation in Python. It generates 5,000 random portfolios based on historical stock data (AAPL, MSFT, GOOGL, AMZN). For each portfolio, it calculates the expected return, volatility (standard deviation), and Sharpe Ratio. The 'Efficient Frontier' is visualized by plotting volatility vs. return, highlighting the portfolio with the maximum Sharpe ratio. The interactive chart allows users to explore the risk/return trade-off of different asset allocations.