https://github.com/eohne/performanceanalytics.jl
functions for performance and risk analysis in Julia
https://github.com/eohne/performanceanalytics.jl
Last synced: 9 months ago
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functions for performance and risk analysis in Julia
- Host: GitHub
- URL: https://github.com/eohne/performanceanalytics.jl
- Owner: eohne
- License: mit
- Created: 2024-08-07T08:11:33.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-10-16T09:52:18.000Z (about 1 year ago)
- Last Synced: 2025-02-07T17:48:49.635Z (11 months ago)
- Language: Julia
- Homepage:
- Size: 149 KB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# PerformanceAnalytics.jl
[](https://github.com/eohne/PerformanceAnalytics.jl/actions/workflows/CI.yml?query=branch%3Amain) [![][docs-latest-img]][docs-latest-url]
Given the lack of packages that deal with financial analysis in Julia I have decided to translate the functionality of the PerformanceAnalytics package in R to Julia.
Since I took a long break from this and never published it a new package has emerged. This package is registered and implements many but not all the functions available here: [PortfolioAnalytics.jl](https://github.com/doganmehmet/PortfolioAnalytics.jl)
# List of all functions implemented:
## Table of Contents
- [Asset Price and Return Functions](#asset-price-and-return-functions)
- [Statistical Measures](#statistical-measures)
- [Risk Measures](#risk-measures)
- [Performance Measures](#performance-measures)
- [Factor Analysis Functions](#factor-analysis-functions)
- [Risk Decomposition](#risk-decomposition)
- [Utility Functions](#utility-functions)
## Asset Price and Return Functions
- ```julia
AssetPrice(ticker::String, from::TimeType, to::TimeType, freq::String; exchange_local_time=true)
```
- Downloads prices using YFinance.jl
- ```julia
AssetReturn(x::AssetPrice)
```
- ```julia
AssetReturn(ticker::String, from::TimeType, to::TimeType, freq::String)
```
- Downloads prices using YFinance.jl and converts them to returns
### Return Calculation Functions
- ```julia
log_diff(x)
```
- ```julia
pct_change(x)
```
- ```julia
simple_diff(x)
```
## Statistical Measures
### Basic Measures
- ```julia
mean_arith(x)
```
- ```julia
mean_geo(returns::Vector{Float64})
```
- ```julia
mean_geo_log(returns::Vector{Float64})
```
- ```julia
stdvp(x)
```
- ```julia
stdvs(x)
```
- ```julia
varp(x)
```
- ```julia
vars(x)
```
### Advanced Measures
- ```julia
skew(R, method="simple")
```
- Methods: Population, Sample, Fisher
- ```julia
kurt(R, method="simple")
```
- Methods: population, excess, sample, sampleexcess, fisher
- ```julia
covariance(Ra::AbstractArray{<:Number}, Rb::AbstractArray{<:Number}; corrected::Bool=false)
```
- Methods:
- Normal (corrected=false, default)
- Corrected (corrected=true)
- ```julia
coskew(Ra::AbstractArray{<:Number}, Rb::AbstractArray{<:Number})
```
- ```julia
cokurt(Ra::AbstractArray{<:Number}, Rb::AbstractArray{<:Number})
```
- ```julia
hurstindex(R::AbstractArray{<:Number})
```
## Risk Measures
### Drawdown Measures
- ```julia
drawdown(x::AbstractArray{<:Number})
```
- ```julia
drawdownpeak(x::AbstractArray{<:Number})
```
- ```julia
drawdown_table(x::AbstractArray{<:Number}, dates::AbstractArray{<:TimeType})
```
- ```julia
avgdrawdown(x::AbstractArray{<:Number})
```
- ```julia
maxdrawdown(x::AbstractArray{<:Number})
```
### Downside Risk Measures
- ```julia
downsidedeviation(R::AbstractArray{<:Number}, MAR::Number = 0)
```
- ```julia
downsidepotential(R::AbstractArray{<:Number}, MAR::Number=0)
```
- ```julia
semideviation(R::AbstractArray{<:Number})
```
- ```julia
semivariance(R::AbstractArray{<:Number})
```
### Value at Risk and Expected Shortfall
- ```julia
valueatrisk(R::AbstractArray{<:Number}, p::Number, method::AbstractString="gaussian")
```
- Methods: gaussian, historical, cornish_fisher
- ```julia
expectedshortfall(R::AbstractArray{<:Number}, p::Number, method::AbstractString="gaussian")
```
- Methods: gaussian, historical, cornish_fisher
### Other Risk Measures
- ```julia
painindex(R::AbstractArray{<:Number})
```
## Performance Measures
### Risk-Adjusted Return Measures
- ```julia
sharperatio(R::Number, σ::Number, Rf::Number=0.; scale=1)
```
- ```julia
sharperatio(R::AbstractArray{<:Number}, Rf::Number=0.; scale)
```
- ```julia
adjustedsharpe(R::AbstractArray{<:Number}, Rf::Number=0.; method::AbstractString="population")
```
- ```julia
adjustedsharpe(R::Number, σ::Number, S::Number, K::Number, Rf::Number=0.)
```
- ```julia
treynorratio(Ra::AbstractArray{<:Number}, Rb::AbstractArray{<:Number}, Rf::Number=0; modified::Bool=false, scale=1)
```
- ```julia
informationratio(Ra::AbstractArray{<:Number}, Rb::AbstractArray{<:Number}, scale::Number=1)
```
- ```julia
appraisalratio(Ra::AbstractArray{<:Number}, Rb::AbstractArray{<:Number}, Rf::Number=0, method::AbstractString = "appraisal", scale::Number=1)
```
- Methods:
- "appraisal" (default)
- "modified"
- "alternative"
### Drawdown-based Performance Measures
- ```julia
calmarratio(R::AbstractArray{<:Number}, scale::Float64=1.)
```
- ```julia
burkeratio(R::AbstractArray{<:Number}; Rf::Float64 = 0., modified::Bool = false, scale::Number=1)
```
- ```julia
burkeratio(Ra::AbstractArray{<:Number}, Drawdowns::AbstractArray{<:Number}; Rf::Number=0., scale=1)
```
### Relative Performance Measures
- ```julia
activepremium(Ra::Number, Rb::Number; scalea=1, scaleb=1)
```
- ```julia
jensensalpha(Y::AbstractArray{<:Number}, X::AbstractArray{<:Number}...; scale::Number=1)
```
- ```julia
trackingerror(Ra::AbstractArray{<:Number}, Rb::AbstractArray{<:Number}, scale::Number=1)
```
### Other Performance Measures
- ```julia
bernardoledoitratio(R::AbstractVector{<:Number})
```
- ```julia
kappa(R::AbstractArray{<:Number}, MAR::Number=0., k::Number=2)
```
- Notable cases:
- k=1: returns the Sharpe-Omega Ratio
- k=2: returns the Sortino Ratio
## Factor Analysis Functions
- ```julia
factor_alpha(Y::AbstractArray{<:Number}, X::AbstractArray{<:Number}...)
```
- ```julia
factor_loadings(Y::AbstractArray{<:Number}, X::AbstractArray{<:Number}...; intercept::Bool=true)
```
- ```julia
factor_regression(Y::AbstractArray{<:Number}, X::AbstractArray{<:Number}...; intercept::Bool=true)
```
- ```julia
factor_resid(Y::AbstractArray{<:Number}, X::AbstractArray{<:Number}...; intercept::Bool=true)
```
## Risk Decomposition
- ```julia
specificrisk(Ra::AbstractArray{<:Number}, Rb::AbstractArray{<:Number}, Rf::Number=0)
```
- ```julia
systematicrisk(Ra::AbstractArray{<:Number}, Rb::AbstractArray{<:Number}, Rf::Number=0)
```
- ```julia
totalrisk(Ra::AbstractArray{<:Number}, Rb::AbstractArray{<:Number}, Rf::Number=0)
```
## Utility Functions
- ```julia
roll_apply(data::AbstractVector{<:Number}...; fun::Function, window::Int, retain_length::Bool=false, fill_with = NaN, always_return::Bool=false)
```
- ```julia
to_annual_return(R::AssetReturn)
```
- ```julia
to_monthly_return(R::AssetReturn)
```
- ```julia
annualize(R::Number, scale::Float64=252)
```
A list of all functions and their definitions can be found in the documentation.
[docs-latest-img]: https://img.shields.io/badge/docs-latest-blue.svg
[docs-latest-url]: https://eohne.github.io/PerformanceAnalytics.jl/dev/