[Uwgarp-commits] r86 - in pkg/GARPFRM: R sandbox
noreply at r-forge.r-project.org
noreply at r-forge.r-project.org
Sun Feb 16 16:16:05 CET 2014
Author: rossbennett34
Date: 2014-02-16 16:16:05 +0100 (Sun, 16 Feb 2014)
New Revision: 86
Modified:
pkg/GARPFRM/R/monte_carlo.R
pkg/GARPFRM/sandbox/test_performance_measure.r
Log:
short comments to performance measures sandbox script
Modified: pkg/GARPFRM/R/monte_carlo.R
===================================================================
--- pkg/GARPFRM/R/monte_carlo.R 2014-02-16 00:08:13 UTC (rev 85)
+++ pkg/GARPFRM/R/monte_carlo.R 2014-02-16 15:16:05 UTC (rev 86)
@@ -17,7 +17,7 @@
S <- vector("numeric", steps)
S[1] <- starting_value
for(i in 2:length(S)){
- S[i] <- S[i-1] * exp((mu - (sigma^2 / 2)) * dt + sigma * rnorm(1) * sqrt(dt))
+ S[i] <- S[i-1] * exp((mu - 0.5 * sigma^2) * dt + sigma * rnorm(1) * sqrt(dt))
}
return(S)
}
@@ -36,14 +36,14 @@
#' is the price of the asset.
#' @return matrix of Monte Carlo simulated price paths
monteCarlo <- function(mu, sigma, N=100, Time=1, steps=52, starting_value=100, log=TRUE){
- mc_mat <- matrix(0, N, steps)
+ mc_mat <- matrix(0, steps, N)
if(log){
for(i in 1:N){
- mc_mat[i,] <- generateLogMC(mu, sigma, Time, steps, starting_value)
+ mc_mat[,i] <- generateLogMC(mu, sigma, Time, steps, starting_value)
}
} else {
for(i in 1:N){
- mc_mat[i,] <- generateMC(mu, sigma, Time, steps, starting_value)
+ mc_mat[,i] <- generateMC(mu, sigma, Time, steps, starting_value)
}
}
class(mc_mat) <- "monte_carlo"
@@ -51,16 +51,16 @@
}
plot.monte_carlo <-function(x, y, ..., main="Monte Carlo Simulation", xlab="Time Index", ylab="Price"){
- plot(x[1,], type="n", ylim=range(x), main=main, xlab=xlab, ylab=ylab)
- for(i in 1:nrow(x)){
- lines(x[i,])
+ plot(x[,1], type="n", ylim=range(x), main=main, xlab=xlab, ylab=ylab)
+ for(i in 1:ncol(x)){
+ lines(x[,i])
}
}
#' Ending Prices of Monte Carlo Simulation
#'
endingPrices <- function(mc){
- mc[, ncol(mc)]
+ mc[nrow(mc),]
}
plotEndingPrices <- function(mc){
Modified: pkg/GARPFRM/sandbox/test_performance_measure.r
===================================================================
--- pkg/GARPFRM/sandbox/test_performance_measure.r 2014-02-16 00:08:13 UTC (rev 85)
+++ pkg/GARPFRM/sandbox/test_performance_measure.r 2014-02-16 15:16:05 UTC (rev 86)
@@ -1,259 +1,271 @@
-# 'Load the GARPFRM package and the CAPM dataset.
-suppressMessages(library(GARPFRM))
-options(digits=3)
-data(crsp.short)
-stock_rets.df <- largecap.ts
-Measures = rep(NULL,4)
-
-# Definitioned/Constant variables
-relation = c("Less Than","Equal To","Greater Than")
-
-# Some of run time parameters
-# Should be amongst the input for shiny
-# Minimum Acceptable Return for Sortino Ratio or Downside Deviation
-# defaults to 0
-MAR_set = 0
-
-# Denominator for Sortino Ratio or Downside Deviation
-# one of "full" or "subset", indicating whether to use the length of the full series
-# or the length of the subset of the series below the MAR as the denominator,
-# defaults to "full"
-denom = "full"
-
-# Portfolio name used in output
-PortName = "Portfolio"
-
-# Market name used in output
-MrktName ="Market"
-
-#Market Name in data set
-mrktDataName = "market"
-
-# Risk free rate name used in output
-RFName="Risk_Free_Rate"
-
-# Risk Free Name in data set
-rfDataName = "t90"
-
-Measures = rbind(Measures, c("Series Name",PortName,MrktName,RFName))
-colnames(Measures) = c("Measure","Portfolio","Market","RiskFree")
-
-# List column headers predominantly tickers/name of securities
-
-# Summarize the first and last data values corresponding to the first 5 dates for the first 5 returns.
-
-colnames(stock_rets.df)
-head(stock_rets.df)
-tail(stock_rets.df)
-
-
-# Number time periods for year for data set
-freQ = 12
-
-# Estimate a zooreg object: regularly spaced zoo object.
-#
-stock.z = zooreg(stock_rets.df[,-1], start=c(1993, 1), end=c(2013,11),
- frequency=freQ)
-
-index(stock.z) = as.yearmon(index(stock.z))
-
-# Summarize Start, End, and Number of Rows
-start(stock.z)
-end(stock.z)
-nRow = nrow(stock.z)
-nRow
-
-# pointer to column with the market returns
-ptrMkt = which(colnames(stock.z)==mrktDataName,arr.ind=TRUE)
-ptrMkt
-# pointer to column of Risk Free Ratw
-ptrRF = which(colnames(stock.z)==rfDataName,arr.ind=TRUE)
-ptrRF
-
-# as.data.frame to check if an object is a data frame, or coerce it if possible.
-returns.mat = as.matrix(coredata(stock.z))
-
-# Number of time series in data set
-nSec = ncol(returns.mat)
-
-# Random create a portfolio from the non-market non RF series
-# Get a normalized set of weights
-# Could set up the application to create a random portfolio from a subset or
-# Input specific securities and weights
-coefs = runif(nSec-2)
-alphas = coefs/sum(coefs)
-sum(alphas)
-
-
-portfolio = returns.mat[,1:(nSec-2)]%*%as.vector(alphas)
-colnames(portfolio) ="PortRets"
-
-salient = matrix(data=c("Mean",mean(portfolio),mean(returns.mat[,ptrMkt]),mean(returns.mat[,ptrRF]),
- "Stdev",sd(portfolio),sd(returns.mat[,ptrMkt]),sd(returns.mat[,ptrRF]),
- "AnnuallyFrequency",rep(freQ,3),
- "Number of Samples",rep(nRow,3)),nrow=4,ncol=4,byrow=TRUE)
-
-Measures = rbind(Measures, salient)
-
-
-# Start to compute performance measures using performance analytic and as a check
-# compute from underlying formula
-
-# Some of comparison match, but some are inexplicable different.
-# Really should investigate the latter, I am sure the users will do so
-
-# Compute beta
-beta = cov(portfolio[,1],returns.mat[,ptrMkt])/var(returns.mat[,ptrMkt])
-cat('\n Beta =',beta,', between the portfolio represented by ',PortName,' and the market represented by ',MrktName,' \n')
-
-Measures = rbind(Measures,c("Beta",beta,"",""))
-
-# Compute Treynor
-
- # Performance Analytics (PA) computation of Treynor for portfolio and market
-tr = TreynorRatio(portfolio[,1,drop=FALSE], returns.mat[,ptrMkt,drop=FALSE],returns.mat[,ptrRF,drop=FALSE],freQ )
-trMarket = TreynorRatio(returns.mat[,ptrMkt,drop=FALSE], returns.mat[,ptrMkt,drop=FALSE],returns.mat[,ptrRF,drop=FALSE],freQ )
-
- # From formula (Hand) computation of Treynor for portfolio and market
-trHand = freQ*(mean(portfolio[,1])-mean(returns.mat[,ptrRF]))/beta
-# beta of Treynor for market is 1
-trHandM = freQ*(mean(returns.mat[,ptrMkt])-mean(returns.mat[,ptrRF]))
-
- # Output results from Treynor Ratio computation
-cat('\n PA computed for ',PortName,' Treynor Ratio = ',tr,'\n')
-cat('\n Hand computed for ',PortName,' Treynor Ratio = ',trHand,'\n')
-cat('\n PA computed for ',MrktName,' Treynor Ratio = ',trMarket,'\n')
-cat('\n Hand computed for ',MrktName,' Treynor Ratio = ',trHandM,'\n')
-
-Measures = rbind(Measures,c("Treynor Ratio",tr,trMarket,""))
-
- # Compare Treynor Ratio for market returns against Treynor Ratio for portfolio returns
-cat('\n Treynor Ratio of ',MrktName,' is ',relation[sign(trMarket - tr) + 2],' Treynor Ratio of ',PortName,'\n' )
-TR_Mrkt2Port = relation[sign(trMarket - tr) + 2]
-
-# Compute Sharpe
-
- # PA computation of Sharpe
-shr = SharpeRatio(portfolio[,1,drop=FALSE], returns.mat[,ptrRF,drop=FALSE], p = 0.95,
-FUN = "StdDev")
-
- # Compute excess portfolio returns (portfolio returns net of RF returns)
-xCess = portfolio[,1] - returns.mat[,ptrRF]
- # Hand computation of Sharpe
-shrHand = mean(xCess)/sd(xCess)
-
- # Output results from Sharpe Ratio computation
-cat('\n PA computed ',PortName,' Sharpe Ratio = ',shr,'\n')
-cat('\n Hand computed ',PortName,' Sharpe Ratio = ',shrHand,'\n')
-
-Measures = rbind(Measures,c("Sharpe Ratio",shr,"",""))
-
-# Compute Jensen's Alpha
-
- # Mean of risk free rate over sampling time series
-meanRF = mean(returns.mat[,ptrRF])
-
- # PA computation of Jensen's Alpha
-ja = CAPM.jensenAlpha(portfolio[,1,drop=FALSE], returns.mat[,ptrMkt,drop=FALSE],meanRF)
-
-# Hand computation of Jensen's Alpha using regression apprach
-
-
- # Precompute excess returns
-xCessP1 = portfolio[,1]-meanRF
-xCessM1 = returns.mat[,ptrMkt]-meanRF
-
- # Regression computation and salient results
-lm_ja1.fit = lm(xCessP1 ~ xCessM1)
-values1 = summary(lm_ja1.fit)
-values1
-analT1 = values1[[4]]
-
- # Annualize Jensen's Alpha
-jaHand = (1+(mean(portfolio[,1]) - meanRF - beta*(mean(returns.mat[,ptrMkt])-meanRF)))^12 -1
-
- # Use Delta Method to approximate standard error of analyzed Jensen's Alpha
-deltaCoef = (freQ*(1+analT1[1,1])^(freQ-1))^2
-
- # Output salient results for Jensen's Alpha
-cat('\n PA computed ',PortName,' Jensen\'s Alpha = ',ja,'\n')
-cat('\n Regression computed ',PortName,' Jensen\'s Alpha, Not Annualized= ',analT1[1,1],'\n')
-cat('\n Hand computed Annualized ',PortName,' Jensen Alpha = ',jaHand,'\n')
-cat('\n Delta Method Coefficient Value = ', deltaCoef,'\n')
-
- # t stat and pvalue under H0: alpha = 0 against HA: alpha != 0
-tStat = jaHand/(analT1[1,2]*deltaCoef^0.5)
-pValue = 2*(1-pt(tStat,nRow-2))
-cat('\n H0: alpha = 0, HA: alpha != 0 p-value: ',pValue,'\n')
-
-Measures = rbind(Measures,c("Jensen's Alpha",ja,"",""))
-Measures = rbind(Measures,c("Jensen's Alpha P Value",pValue,"",""))
-
-# Compute Tracking Error
-
- # PA computation of Tracking Error
-te = TrackingError(portfolio[,1,drop=FALSE], returns.mat[,ptrMkt,drop=FALSE], scale = freQ)
-
- # Precompute required values to computr Tracking Error by Hand
-RR = portfolio[,1]-returns.mat[,ptrMkt]
-ARR = mean(RR)
-
- # Hand computation of Tracking Error
-teHand = sqrt(sum((RR-ARR)^2)/(nRow-1))*sqrt(freQ)
-
- # Output results from Tracking Error computation
-cat('\n PA computed ',PortName,' Tracking Error = ',te,'\n')
-cat('\n Hand computed ', PortName,' Tracking Error = ',teHand,'\n')
-
-Measures = rbind(Measures,c("Tracking Error",te,"",""))
-
-# Compute Information Ratio
-
- # PA computation of Information Ratio
-ir = InformationRatio(portfolio[,1,drop=FALSE], returns.mat[,ptrMkt,drop=FALSE], scale = freQ)
-
- # Hand computation of Information Ratio
-irHand = (ARR/(sqrt(sum((RR-ARR)^2)/(nRow-1))))*sqrt(freQ)
-
- # Output results from Information Ratio computation
-cat('\n PA computed ',PortName,' Information Ratio = ',ir,'\n')
-cat('\n Hand computed ',PortName,' Information Ratio = ',irHand,'\n')
-
-Measures = rbind(Measures,c("Information Ratio",ir,"",""))
-
-# Compute Sortino Ratio (including Downside Deviation)
-
- # PA computation of Sortino Ratio
-sr = SortinoRatio(portfolio[,1,drop=FALSE], MAR = MAR_set, weights = NULL)
-
- # Set value of denominator for Downside Deviation from the parameter denom set earlier
-denomVal = nRow
-if (denom != "full")
- {
- denomVal = sum(ifelse((portfolio[,1] - MAR_set)<0,1,0))
- }
-cat('\n Denominator parameter is set to: ',denom,' which results in a denominator value of ',denomVal,'\n')
-
- # PA computation of Downside Deviation
-dwn = DownsideDeviation(portfolio[,1], MAR = MAR_set, method = "full")
-
- # Hand computation of Downside Deviation
-dwnHand = sqrt(sum((min((portfolio[,1,drop=FALSE] - MAR_set),0))^2)/(denomVal))
-
- # Output computation of Downside Deviation
-cat('\n PA computed ',PortName,' Downside Deviation = ',dwn,'\n')
-cat('\n Hand computed ',PortName,' Downside Deviation = ',dwnHand,'\n')
-
- # Hand computation of Sortino Ratio
-srHand = sqrt(freQ)*ARR/dwn
-
- # Output computation of Sortino Ratio
-cat('\n PA computed ',PortName,' Sortino Ratio = ',sr,'\n')
-cat('\n Hand computed ',PortName,' Sortino Ratio = ',srHand,'\n')
-
-Measures = rbind(Measures,c("Downside Deviation",dwn,"",""))
-Measures = rbind(Measures,c("Denominator DD",denomVal,"",""))
-Measures = rbind(Measures,c("Sortino Ratio",sr,"",""))
-Measures.df= as.data.frame(Measures,stringsAsFactors=FALSE)
-
-print(Measures.df)
+# 'Load the GARPFRM package and the CAPM dataset.
+suppressMessages(library(GARPFRM))
+options(digits=3)
+data(crsp.short)
+stock_rets.df <- largecap.ts
+Measures = rep(NULL,4)
+
+## keep it simple and use this thoughout
+## no need to transform to zoo, matrix, or df objects
+# Equal weight portfolio
+R.portfolio <- Return.portfolio(largecap.ts[, 1:5])
+
+# Market returns
+R.market <- largecap.ts[, "market"]
+
+# risk free rate
+rf <- largecap.ts[, "t90"]
+##
+
+# Definitioned/Constant variables
+relation = c("Less Than","Equal To","Greater Than")
+
+# Some of run time parameters
+# Should be amongst the input for shiny
+# Minimum Acceptable Return for Sortino Ratio or Downside Deviation
+# defaults to 0
+MAR_set = 0
+
+# Denominator for Sortino Ratio or Downside Deviation
+# one of "full" or "subset", indicating whether to use the length of the full series
+# or the length of the subset of the series below the MAR as the denominator,
+# defaults to "full"
+denom = "full"
+
+# Portfolio name used in output
+PortName = "Portfolio"
+
+# Market name used in output
+MrktName ="Market"
+
+#Market Name in data set
+mrktDataName = "market"
+
+# Risk free rate name used in output
+RFName="Risk_Free_Rate"
+
+# Risk Free Name in data set
+rfDataName = "t90"
+
+Measures = rbind(Measures, c("Series Name",PortName,MrktName,RFName))
+colnames(Measures) = c("Measure","Portfolio","Market","RiskFree")
+
+# List column headers predominantly tickers/name of securities
+
+# Summarize the first and last data values corresponding to the first 5 dates for the first 5 returns.
+
+colnames(stock_rets.df)
+head(stock_rets.df)
+tail(stock_rets.df)
+
+
+# Number time periods for year for data set
+freQ = 12
+
+# Estimate a zooreg object: regularly spaced zoo object.
+#
+stock.z = zooreg(stock_rets.df[,-1], start=c(1993, 1), end=c(2013,11),
+ frequency=freQ)
+
+index(stock.z) = as.yearmon(index(stock.z))
+
+# Summarize Start, End, and Number of Rows
+start(stock.z)
+end(stock.z)
+nRow = nrow(stock.z)
+nRow
+
+# pointer to column with the market returns
+ptrMkt = which(colnames(stock.z)==mrktDataName,arr.ind=TRUE)
+ptrMkt
+# pointer to column of Risk Free Ratw
+ptrRF = which(colnames(stock.z)==rfDataName,arr.ind=TRUE)
+ptrRF
+
+# as.data.frame to check if an object is a data frame, or coerce it if possible.
+returns.mat = as.matrix(coredata(stock.z))
+
+# Number of time series in data set
+nSec = ncol(returns.mat)
+
+# Random create a portfolio from the non-market non RF series
+# Get a normalized set of weights
+# Could set up the application to create a random portfolio from a subset or
+# Input specific securities and weights
+coefs = runif(nSec-2)
+alphas = coefs/sum(coefs)
+sum(alphas)
+
+
+portfolio = returns.mat[,1:(nSec-2)]%*%as.vector(alphas)
+colnames(portfolio) ="PortRets"
+
+salient = matrix(data=c("Mean",mean(portfolio),mean(returns.mat[,ptrMkt]),mean(returns.mat[,ptrRF]),
+ "Stdev",sd(portfolio),sd(returns.mat[,ptrMkt]),sd(returns.mat[,ptrRF]),
+ "AnnuallyFrequency",rep(freQ,3),
+ "Number of Samples",rep(nRow,3)),nrow=4,ncol=4,byrow=TRUE)
+
+Measures = rbind(Measures, salient)
+
+
+# Start to compute performance measures using performance analytic and as a check
+# compute from underlying formula
+
+# Some of comparison match, but some are inexplicable different.
+# Really should investigate the latter, I am sure the users will do so
+
+# Compute beta
+beta = cov(portfolio[,1],returns.mat[,ptrMkt])/var(returns.mat[,ptrMkt])
+cat('\n Beta =',beta,', between the portfolio represented by ',PortName,' and the market represented by ',MrktName,' \n')
+
+Measures = rbind(Measures,c("Beta",beta,"",""))
+
+# Compute Treynor
+
+ # Performance Analytics (PA) computation of Treynor for portfolio and market
+tr = TreynorRatio(portfolio[,1,drop=FALSE], returns.mat[,ptrMkt,drop=FALSE],returns.mat[,ptrRF,drop=FALSE],freQ )
+trMarket = TreynorRatio(returns.mat[,ptrMkt,drop=FALSE], returns.mat[,ptrMkt,drop=FALSE],returns.mat[,ptrRF,drop=FALSE],freQ )
+
+ # From formula (Hand) computation of Treynor for portfolio and market
+trHand = freQ*(mean(portfolio[,1])-mean(returns.mat[,ptrRF]))/beta
+# beta of Treynor for market is 1
+trHandM = freQ*(mean(returns.mat[,ptrMkt])-mean(returns.mat[,ptrRF]))
+
+ # Output results from Treynor Ratio computation
+cat('\n PA computed for ',PortName,' Treynor Ratio = ',tr,'\n')
+cat('\n Hand computed for ',PortName,' Treynor Ratio = ',trHand,'\n')
+cat('\n PA computed for ',MrktName,' Treynor Ratio = ',trMarket,'\n')
+cat('\n Hand computed for ',MrktName,' Treynor Ratio = ',trHandM,'\n')
+
+Measures = rbind(Measures,c("Treynor Ratio",tr,trMarket,""))
+
+ # Compare Treynor Ratio for market returns against Treynor Ratio for portfolio returns
+cat('\n Treynor Ratio of ',MrktName,' is ',relation[sign(trMarket - tr) + 2],' Treynor Ratio of ',PortName,'\n' )
+TR_Mrkt2Port = relation[sign(trMarket - tr) + 2]
+
+# Compute Sharpe
+
+ # PA computation of Sharpe
+shr = SharpeRatio(portfolio[,1,drop=FALSE], returns.mat[,ptrRF,drop=FALSE], p = 0.95,
+FUN = "StdDev")
+
+ # Compute excess portfolio returns (portfolio returns net of RF returns)
+xCess = portfolio[,1] - returns.mat[,ptrRF]
+ # Hand computation of Sharpe
+shrHand = mean(xCess)/sd(xCess)
+
+ # Output results from Sharpe Ratio computation
+cat('\n PA computed ',PortName,' Sharpe Ratio = ',shr,'\n')
+cat('\n Hand computed ',PortName,' Sharpe Ratio = ',shrHand,'\n')
+
+Measures = rbind(Measures,c("Sharpe Ratio",shr,"",""))
+
+# Compute Jensen's Alpha
+
+ # Mean of risk free rate over sampling time series
+meanRF = mean(returns.mat[,ptrRF])
+
+ # PA computation of Jensen's Alpha
+ja = CAPM.jensenAlpha(portfolio[,1,drop=FALSE], returns.mat[,ptrMkt,drop=FALSE],meanRF)
+
+# Hand computation of Jensen's Alpha using regression apprach
+
+
+ # Precompute excess returns
+xCessP1 = portfolio[,1]-meanRF
+xCessM1 = returns.mat[,ptrMkt]-meanRF
+
+ # Regression computation and salient results
+lm_ja1.fit = lm(xCessP1 ~ xCessM1)
+values1 = summary(lm_ja1.fit)
+values1
+analT1 = values1[[4]]
+
+ # Annualize Jensen's Alpha
+jaHand = (1+(mean(portfolio[,1]) - meanRF - beta*(mean(returns.mat[,ptrMkt])-meanRF)))^12 -1
+
+ # Use Delta Method to approximate standard error of analyzed Jensen's Alpha
+deltaCoef = (freQ*(1+analT1[1,1])^(freQ-1))^2
+
+ # Output salient results for Jensen's Alpha
+cat('\n PA computed ',PortName,' Jensen\'s Alpha = ',ja,'\n')
+cat('\n Regression computed ',PortName,' Jensen\'s Alpha, Not Annualized= ',analT1[1,1],'\n')
+cat('\n Hand computed Annualized ',PortName,' Jensen Alpha = ',jaHand,'\n')
+cat('\n Delta Method Coefficient Value = ', deltaCoef,'\n')
+
+ # t stat and pvalue under H0: alpha = 0 against HA: alpha != 0
+tStat = jaHand/(analT1[1,2]*deltaCoef^0.5)
+pValue = 2*(1-pt(tStat,nRow-2))
+cat('\n H0: alpha = 0, HA: alpha != 0 p-value: ',pValue,'\n')
+
+Measures = rbind(Measures,c("Jensen's Alpha",ja,"",""))
+Measures = rbind(Measures,c("Jensen's Alpha P Value",pValue,"",""))
+
+# Compute Tracking Error
+
+ # PA computation of Tracking Error
+te = TrackingError(portfolio[,1,drop=FALSE], returns.mat[,ptrMkt,drop=FALSE], scale = freQ)
+
+ # Precompute required values to computr Tracking Error by Hand
+RR = portfolio[,1]-returns.mat[,ptrMkt]
+ARR = mean(RR)
+
+ # Hand computation of Tracking Error
+teHand = sqrt(sum((RR-ARR)^2)/(nRow-1))*sqrt(freQ)
+
+ # Output results from Tracking Error computation
+cat('\n PA computed ',PortName,' Tracking Error = ',te,'\n')
+cat('\n Hand computed ', PortName,' Tracking Error = ',teHand,'\n')
+
+Measures = rbind(Measures,c("Tracking Error",te,"",""))
+
+# Compute Information Ratio
+
+ # PA computation of Information Ratio
+ir = InformationRatio(portfolio[,1,drop=FALSE], returns.mat[,ptrMkt,drop=FALSE], scale = freQ)
+
+ # Hand computation of Information Ratio
+irHand = (ARR/(sqrt(sum((RR-ARR)^2)/(nRow-1))))*sqrt(freQ)
+
+ # Output results from Information Ratio computation
+cat('\n PA computed ',PortName,' Information Ratio = ',ir,'\n')
+cat('\n Hand computed ',PortName,' Information Ratio = ',irHand,'\n')
+
+Measures = rbind(Measures,c("Information Ratio",ir,"",""))
+
+# Compute Sortino Ratio (including Downside Deviation)
+
+ # PA computation of Sortino Ratio
+sr = SortinoRatio(portfolio[,1,drop=FALSE], MAR = MAR_set, weights = NULL)
+
+ # Set value of denominator for Downside Deviation from the parameter denom set earlier
+denomVal = nRow
+if (denom != "full")
+ {
+ denomVal = sum(ifelse((portfolio[,1] - MAR_set)<0,1,0))
+ }
+cat('\n Denominator parameter is set to: ',denom,' which results in a denominator value of ',denomVal,'\n')
+
+ # PA computation of Downside Deviation
+dwn = DownsideDeviation(portfolio[,1], MAR = MAR_set, method = "full")
+
+ # Hand computation of Downside Deviation
+dwnHand = sqrt(sum((min((portfolio[,1,drop=FALSE] - MAR_set),0))^2)/(denomVal))
+
+ # Output computation of Downside Deviation
+cat('\n PA computed ',PortName,' Downside Deviation = ',dwn,'\n')
+cat('\n Hand computed ',PortName,' Downside Deviation = ',dwnHand,'\n')
+
+ # Hand computation of Sortino Ratio
+srHand = sqrt(freQ)*ARR/dwn
+
+ # Output computation of Sortino Ratio
+cat('\n PA computed ',PortName,' Sortino Ratio = ',sr,'\n')
+cat('\n Hand computed ',PortName,' Sortino Ratio = ',srHand,'\n')
+
+Measures = rbind(Measures,c("Downside Deviation",dwn,"",""))
+Measures = rbind(Measures,c("Denominator DD",denomVal,"",""))
+Measures = rbind(Measures,c("Sortino Ratio",sr,"",""))
+Measures.df= as.data.frame(Measures,stringsAsFactors=FALSE)
+
+print(Measures.df)
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