In this video, we dive deep into the world of Modern Portfolio Theory by demonstrating how to find the efficient frontier using Monte Carlo simulations with the help of excel vba and Python . We'll walk you through the entire process of building thousands of portfolios with randomly assigned weights, calculating their performance, and identifying the set of optimal portfolios that offer the highest expected returns for a given level of risk. You'll learn how to download stock prices, calculate simple returns, and use these to determine average annual returns and the covariance matrix. We'll also show you how to compute portfolio metrics, including expected returns, volatility, and the Sharpe ratio. Finally, we'll visualize the efficient frontier and highlight the maximum Sharpe ratio and minimum volatility portfolios. This video is perfect for anyone interested in portfolio optimization, risk management, and enhancing their investment strategies using Python and financial data analysis.
0:00 Introduction: Overview of finding the efficient frontier using Monte Carlo simulations.
Import Libraries: Import yfinance, numpy, pandas, and matplotlib.
Set Parameters: Define the number of portfolios, trading days, and assets.
Download Stock Prices: Use yfinance to get historical data.
Calculate Returns and Covariance: Compute annualized average returns and covariance matrix.
Simulate Portfolio Weights: Generate random weights that sum to one.
Calculate Portfolio Metrics: Compute expected returns, volatility, and Sharpe ratio.
Create DataFrame: Organize the data for analysis.
Find Efficient Frontier: Identify points with the highest returns for given levels of risk.
Plot Results: Visualize the efficient frontier, individual assets, and simulated portfolios.
Highlight Key Portfolios: Identify and plot the maximum Sharpe ratio and minimum volatility portfolios.
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