[Returnanalytics-commits] r3511 - pkg/PerformanceAnalytics/R

[noreply at r-forge.r-project.org]

Author: kylebalkissoon
Date: 2014-08-20 01:29:55 +0200 (Wed, 20 Aug 2014)
New Revision: 3511

Modified:
   pkg/PerformanceAnalytics/R/table.ProbOutperformance.R
Log:
Line endings switched to unix

Modified: pkg/PerformanceAnalytics/R/table.ProbOutperformance.R
===================================================================
--- pkg/PerformanceAnalytics/R/table.ProbOutperformance.R	2014-08-19 23:29:27 UTC (rev 3510)
+++ pkg/PerformanceAnalytics/R/table.ProbOutperformance.R	2014-08-19 23:29:55 UTC (rev 3511)
@@ -1,52 +1,52 @@
-#' Calculates Count of trailing periods where a fund outperformed its benchmark and calculates the proportion of those periods, this is commonly used in marketing as the probability of outperformance on a N year basis
-#' 
-#'  
-#' @param R an xts, timeSeries or zoo object of asset returns
-#' @param Rb an xts, timeSeries or zoo object of the benchmark returns
-#' @param period_lengths a vector of periods the user wants to evaluate this over i.e. c(1,3,6,9,12,18,36)
-#' @author Kyle Balkissoon
-#' @keywords Performance Reporting Fund vs Benchmark
-#' 
-#' @export table_ProbOutperformance
-
-table.ProbOutPerformance = function(R,Rb,period_lengths=c(1,3,6,9,12,18,36)){
-  if(nrow(R)!=nrow(Rb)){
-    stop("R and Rb must be the same length")
-  }
-  
-  
-  ###Create Trailing frequency analysis
-  R_periods = xts(data.frame(matrix(ncol=length(period_lengths),nrow=nrow(R))),order.by=index(R))
-  colnames(R_periods) = paste0("period_",period_lengths)
-  Rb_periods = R_periods
-  for(i in 1:nrow(R_periods)){
-    for(p_len in period_lengths){
-      #if there aren't enough occurences yet don't calculate anything
-      if(p_len>i){}else{
-      tdf = first(R,i)
-      tdf_b = first(Rb,i)
-    eval(parse(text=paste0("R_periods[",i,",]$period_",p_len," = Return.cumulative(last(tdf,",p_len,"))")))
-    eval(parse(text=paste0("Rb_periods[",i,",]$period_",p_len," = Return.cumulative(last(tdf_b,",p_len,"))")))
-    }}}
-    
-    
-  ##Calculate periods ahead 
-  #Differences
-  diff_mat = R_periods-Rb_periods
-  
-  ##Result
-  result = data.frame(period_lengths)
-  result[,2] = NA
-  result[,3]=NA
-    for(p_len in 1:length(period_lengths)){
-      result[p_len,2] = eval(parse(text=paste0("sum(ifelse(as.numeric(diff_mat$period_",period_lengths[p_len],")>0,1,0),na.rm=T)")))
-      result[p_len,3] = eval(parse(text=paste0("sum(ifelse(as.numeric(diff_mat$period_",period_lengths[p_len],")<0,1,0),na.rm=T)")))   
-          }
-  result[,4] = result[,2]+result[,3]
-  result[,5] = result[,2]/result[,4]
-  result[,6] = result[,3]/result[,4]
-  
-  colnames(result) = c("period_lengths",colnames(R),colnames(Rb),"total periods",paste0("prob_",colnames(R),"_outperformance"),paste0("prob_",colnames(Rb),"_outperformance"))
-  return(result)
-  
+#' Calculates Count of trailing periods where a fund outperformed its benchmark and calculates the proportion of those periods, this is commonly used in marketing as the probability of outperformance on a N year basis
+#' 
+#'  
+#' @param R an xts, timeSeries or zoo object of asset returns
+#' @param Rb an xts, timeSeries or zoo object of the benchmark returns
+#' @param period_lengths a vector of periods the user wants to evaluate this over i.e. c(1,3,6,9,12,18,36)
+#' @author Kyle Balkissoon
+#' @keywords Performance Reporting Fund vs Benchmark
+#' 
+#' @export table_ProbOutperformance
+
+table.ProbOutPerformance = function(R,Rb,period_lengths=c(1,3,6,9,12,18,36)){
+  if(nrow(R)!=nrow(Rb)){
+    stop("R and Rb must be the same length")
+  }
+  
+  
+  ###Create Trailing frequency analysis
+  R_periods = xts(data.frame(matrix(ncol=length(period_lengths),nrow=nrow(R))),order.by=index(R))
+  colnames(R_periods) = paste0("period_",period_lengths)
+  Rb_periods = R_periods
+  for(i in 1:nrow(R_periods)){
+    for(p_len in period_lengths){
+      #if there aren't enough occurences yet don't calculate anything
+      if(p_len>i){}else{
+      tdf = first(R,i)
+      tdf_b = first(Rb,i)
+    eval(parse(text=paste0("R_periods[",i,",]$period_",p_len," = Return.cumulative(last(tdf,",p_len,"))")))
+    eval(parse(text=paste0("Rb_periods[",i,",]$period_",p_len," = Return.cumulative(last(tdf_b,",p_len,"))")))
+    }}}
+    
+    
+  ##Calculate periods ahead 
+  #Differences
+  diff_mat = R_periods-Rb_periods
+  
+  ##Result
+  result = data.frame(period_lengths)
+  result[,2] = NA
+  result[,3]=NA
+    for(p_len in 1:length(period_lengths)){
+      result[p_len,2] = eval(parse(text=paste0("sum(ifelse(as.numeric(diff_mat$period_",period_lengths[p_len],")>0,1,0),na.rm=T)")))
+      result[p_len,3] = eval(parse(text=paste0("sum(ifelse(as.numeric(diff_mat$period_",period_lengths[p_len],")<0,1,0),na.rm=T)")))   
+          }
+  result[,4] = result[,2]+result[,3]
+  result[,5] = result[,2]/result[,4]
+  result[,6] = result[,3]/result[,4]
+  
+  colnames(result) = c("period_lengths",colnames(R),colnames(Rb),"total periods",paste0("prob_",colnames(R),"_outperformance"),paste0("prob_",colnames(Rb),"_outperformance"))
+  return(result)
+  
 }
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