Tool for Quantitative Portfolio Analytics
I’m happy to announce PortfolioAnalytics.jl, which aims to provide users with functionality for performing quantitative portfolio analytics.
Introduction · PortfolioAnalytics.jl (doganmehmet.github.io)
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.
Contributions are most welcome
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.