In this article, we analyze a previously suggested portfolio based on BSE SENSEX companies and provide recommendations for a new portfolio for the upcoming week. The article includes a detailed analysis of the performance of the previous portfolios and provides new portfolio recommendations based on Sharpe Ratio and Machine Learning optimization.
This article is significant for individuals interested in creating an optimized portfolio of stocks using Python. It provides a comprehensive guide on how to gather historical data on stocks, calculate the mean historical return and covariance matrix, and use the Efficient Frontier, Sharpe ratio, and Discrete Allocation methods to create an optimized portfolio. By leveraging the “yfinance,” “expected_returns,” “risk_models,” and “pypfopt” libraries, users can automate the process of portfolio optimization and allocation. However, it’s important to note that stock investments always carry a level of risk, and users should conduct proper research on the stocks and markets in question. Overall, this code is a great starting point for individuals looking to create an optimized portfolio and streamline their investment process.
Discover how to use machine learning algorithms to optimize your investment portfolio using a mean-variance approach. Learn how to download stock price data from Yahoo Finance, perform mean-variance optimization with a linear regression model, and choose the best model using cross-validation. Achieve better performance and reduce risk in your investment portfolio with machine learning.
