Mean-Variance Analyzer

Learn about modern portfolio theory - interactively! View live site

Tutorial

Welcome to Mean-Variance Analyzer! MVA is an educational tool meant to help people new to financial engineering start their journey learning about the history of portfolio optimization. To get started, click either the "Get started!" link on the home page (below) or the "Analyzer" link in the navigation menu.

This should take you to the input form (below). Enter all the assets of interest into the corresponding input fields. You can start typing a ticker or company name and, if it is in the preloaded dataset of over 100 popular assets, it should appear in the dropdown datalist and be clickable (you are encouraged to read more about how the data was collected - note that the developer is not liable for the accuracy or freshness of any data or information given on the site as per the Terms of Service). You must enter at least two unique tickers from the dataset and you can press the "+ Add Asset" button to add up to 15 assets. Once you have chosen all your assets, you have the option to set the maximum allocation that can be given to any individual asset in the portfolio (the default is 100%). This number must be larger than 100% / (#assets - 1) and less than or equal to 100%. Then you have the option to enter a custom benchmark - the default value is the long term average 3-month Treasury bill rate at the time of this site's development, but you can enter any custom risk-free rate between -50% and 50% as your benchmark.

If there are no errors in the input fields, a scatter plot will appear (example below) giving a visual representation of the approximated maximum Sharpe ratio (or tangency) portfolio, individual assets, efficient frontier, Markowitz bullet and capital market line (explained on the Background page). You can hover over or click on points on the plot to see the portfolios that produced each point on the efficient frontier, the allocations that produced the tangency portfolio, and the information for each individual asset. Below that will be a pie chart visualizing the allocations that produced the tangency portfolio along with its corresponding information.

Now you're ready to have some fun experimenting. Thank you for visiting this site and reading the tutorial. I hope you enjoy it and learn something new!

Version

1.0.0

Author

Paul Fischer

• Email: paulfischerdev@gmail.com
• Twitter: @PaulFis43236408
• GitHub: pfischer1687
• Website: https://paulfischer.dev/

Dependencies

• chart.js@3.9.1
• formik@2.2.9
• gatsby@5.4.2
• gatsby-plugin-image@3.4.0
• gatsby-plugin-manifest@5.4.0
• gatsby-plugin-offline@6.4.0
• gatsby-plugin-sharp@5.4.0
• gatsby-source-filesystem@5.4.0
• gatsby-transformer-sharp@5.4.0
• react@18.2.0
• react-chartjs-2@4.3.1
• react-dom@18.2.0
• react-katex@3.0.1
• react-spinners@0.13.8
• yup@0.32.11

Dev Dependencies

• babel-jest@29.4.1
• babel-preset-gatsby@3.5.0
• identity-obj-proxy@3.0.0
• jest@29.4.1

Keywords

• Mean-Variance Analysis
• Modern Portfolio Theory (MPT)
• Efficient Frontier
• Ex Post Sharpe Ratio
• Capital Asset Pricing Model (CAPM)
• Monte Carlo Simulation
• Markowitz Bullet
• Capital Market Line (CML)
• Capital Allocation Line (CAL)
• Tangency Portfolio
• Financial Engineering
• Quantitative Finance

License

Unlicensed - © 2023 All rights reserved

Please see the Terms of Service for more details.

Scripts

• develop/start: gatsby develop
• build: gatsby build
• serve: gatsby serve
• clean: gatsby clean
• test: jest

Repository

git: https://github.com/pfischer1687/mean-variance-analyzer

Bugs

https://github.com/pfischer1687/mean-variance-analyzer/issues