I’m happy to announce PortfolioAnalytics.jl, which aims to provide users with functionality for performing quantitative portfolio analytics.
The package is under heavy development, and new functionalities will be added as part of ongoing releases.
The following functions are available in the stable version:
- Return( )
- PortfolioReturn( )
- SharpeRatio( )
- VaR( )
- PortfolioOptimize( )
- MeanReturns( )
- StdDev( )
- Moments( )
- ExpectedShortfall( )
This package generally requires return (rather than price) data. Almost all functions will work with any periodicity, from annual, monthly, daily, to even minutes and seconds, either regular or irregular.
- The best place to get started with PortfolioAnalytics is the Tutorials section of the documentation, where you can find the demonstration of functions’ use.
- Go to the Installation guide to learn how you can install PortfolioAnalytics.
- Read the Functions section to see functions’ parameters and default arguments.
The package is inspired by PerformanceAnalytics and PortfolioAnalytics packages in R and pyfolio in Python.
I sincerely thank @odow for patiently answering my questions on JuMP, @kellertuer for helping me figure out the issues with documentation, @chiraganand and @pdeffebach for TSFrames package, and answering my questions.
I greatly value contributions of any kind. Contributions could include but are not limited to documentation improvements, bug reports, new or improved code, scientific and technical code reviews, community help/building, education, and outreach.
Please report any issues via the GitHub issue tracker. All kinds of issues are welcome and encouraged; this includes bug reports, documentation typos, feature requests, etc.