Using R to analyse MAN AHL Trend

December 30, 2015
By

(This article was first published on Copula, and kindly contributed to R-bloggers)

Let’s use the great PerformanceAnalytics package to get some insights on the risk profile of the MAN AHL Trend Fund. It’s a program with a long track record – I believe in the late 80′. The UCITS Fund NAV Data can be downloaded from the fund webpage as xls file- starting 2009.

First let’s import the data into R. I’m using a small function, to import .csv which returns an .xts object named ahl.

#Monthly NAV MAN AHL
loadahl<-function(){
  a=read.table(“ahl_trend.csv”,sep = “,”,dec = “,”)
  a$date = paste(substr(a$V1,1,2),substr(a$V1,4,5),substr(a$V1,7,10),sep=”-“)
  ahl=a$date
  ahl=cbind(ahl,substr(a$V2,1,5))
  a=as.POSIXct(ahl[,1],format=”%d-%m-%Y”)
  ahl=as.xts(as.numeric(ahl[,2]),order.by=a)
  rm(a)
  return(ahl)
}

next we would like to have the monthly returns

monthlyReturn(x, subset=NULL, type='arithmetic',
leading=TRUE, ...)
 
 

 which we store in retahl.

 retahl=monthlyReturn(ahl,type=”log”)

Next, I usually plot the chart.Drawdown to get a visual idea, if the product is designed for my risk appetite.

chart.Drawdown(retahl)

table.AnnualizedReturns(retahl)
 
                          monthly.returns
Annualized Return 0.0212
Annualized Std Dev 0.1246
Annualized Sharpe (Rf=0%) 0.1702
 
table.DownsideRisk(retahl)
monthly.returns
Semi Deviation 0.0254
Gain Deviation 0.0222
Loss Deviation 0.0222
Downside Deviation (MAR=10%) 0.0289
Downside Deviation (Rf=0%) 0.0241
Downside Deviation (0%) 0.0241
Maximum Drawdown 0.2478
Historical VaR (95%) -0.0521
Historical ES (95%) -0.0748
Modified VaR (95%) -0.0573
Modified ES (95%) -0.0730
 
 

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