Can anybody tell me the precise method of calculating Sortino ratio?

I believe the general formula is:

(AROR-RFROR)/DR;

where:
AROR = Annualized Rate of Return
RFROR = Risk Free Rate of Return
DR = Downside Risk

Downside risk is generally defined as the standard deviation of negative returns.
 
Quote from Pro_Trader720:

I believe the general formula is:

(AROR-RFROR)/DR;

where:
AROR = Annualized Rate of Return
RFROR = Risk Free Rate of Return
DR = Downside Risk

Downside risk is generally defined as the standard deviation of negative returns.

So you sift out all the negative returns and calculate the standard deviation of this subset of returns and you call that downside risk?

Okay then it's a simple change from Sharpe ratio.
 
Quote from Stoxtrader:

You could use the PerformanceAnalytics library in R. Source code is available:

http://braverock.com/brian/R/PerformanceAnalytics/html/SortinoRatio.html

http://cran.r-project.org/web/packa...erformanceAnalyticsPresentation-UseR-2007.pdf

Note, IMHO it only matters going forward. If in backtesting a Sharpe ratio or Sortino ratio is high/low, that doesn't mean it will continue to be so. It's kindof nice to have some measure of risk, but risk is tricky to predict.

Thanks. So you are a R trader?

I really like R however I really really hate R's debugging functionality.

It's just not suitable for writing a little large program in bug-free manner.

And also I like those fancy stats packages however their quality assurance is my concern...
 
Quote from Pro_Trader720:

I believe the general formula is:

(AROR-RFROR)/DR;

where:
AROR = Annualized Rate of Return
RFROR = Risk Free Rate of Return
DR = Downside Risk

Downside risk is generally defined as the standard deviation of negative returns.

The formulas for Sortino ratio of you two guys are different? Reconciliation?
 
Quote from mizhael:

Thanks. So you are a R trader?

I really like R however I really really hate R's debugging functionality.

It's just not suitable for writing a little large program in bug-free manner.

And also I like those fancy stats packages however their quality assurance is my concern...


- Am I an R trader? I use the R GUI for statistical analysis, not for actual trading, although there are packages to allow trading with R.

- Debugging might be an issue. Quality should not be, because it's open source. With a closed-source system you really don't know if the implementation of the algorithm(s) is(are) correct or not. With open source, you can correct any errors you see.

Either way, with closed source or not, you shouldn't automatically assume the quality is good or not. You also shouldn't automatically assume the quality of your own code is good. Test. Verify. Question your assumptions, your data, everything.
 
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