I was trying to avoid the code because I wanted to simplify it but here we
go:
mat2<- matrix(nrow = 30, ncol = 7)
colnames(mat2) <-c( "A", "B", "C", "D", "E", "F", "G")
mat1<-mainMat[1,]
I get mainMat[1,] from the following function:
ComputeSignalReturns <- function(vec1, prices){
removingNA <- as.matrix(removeNA(cbind(prices,vec1)))
prices<-as.matrix(removingNA[,1])
vec1<- as.matrix(removingNA[,2])
nbDays <- length(vec1)
returnsOneDay <- abs(vec1[1:(nbDays - 1)]) *
((as.ts(lag(prices,1))/as.ts(prices)) ^vec1[1:nbDays - 1)] - 1)
returnsOneDay<-as.matrix(cbind(vec1[1:(nbDays -
1)],returnsOneDay)[which(vec1[1:(nbDays - 1)]! =0),2])
returnsOneDayAnnualized <- as.matrix(apply(returnsOneDay,1,
Return.annualized,scale=252,geometric=FALSE))
returnsTwoDays <- abs(vec1[1:(nbDays - 2)]) *
((as.ts(lag(prices,2))/as.ts(prices))^vec1[1:(nbDays - 2)] - 1)
returnsTwoDays<-as.matrix(cbind(vec1[1:(nbDays -
2)],returnsTwoDays)[which(vec1[1:(nbDays - 2)]!=0),2])
returnsTwoDaysAnnualized <- as.matrix(apply(returnsTwoDays,1,
Return.annualized,scale=252/2,geometric=FALSE))
returnsThreeDays <- abs(vec1[1:(nbDays - 3)]) *
((as.ts(lag(prices,3))/as.ts(prices))^vec1[1:(nbDays - 3)] - 1)
returnsThreeDays<-as.matrix(cbind(vec1[1:(nbDays -
3)],returnsThreeDays)[which(vec1[1:(nbDays - 3)]!=0),2])
returnsThreeDaysAnnualized <- as.matrix(apply(returnsThreeDays,1,
Return.annualized,scale=252/3,geometric=FALSE))
returnsFiveDays <- abs(vec1[1:(nbDays - 5)]) *
((as.ts(lag(prices,5))/as.ts(prices))^vec1[1:(nbDays - 5)] - 1)
returnsFiveDays<-as.matrix(cbind(vec1[1:(nbDays -
5)],returnsFiveDays)[which(vec1[1:(nbDays - 5)]!=0),2])
returnsFiveDaysAnnualized <- as.matrix(apply(returnsFiveDays,1,
Return.annualized,scale=252/5,geometric=FALSE))
returns <- list(returnsOneDay, returnsTwoDays, returnsThreeDays,
returnsFiveDays)
returnsAnnualized <- list( returnsOneDayAnnualized,
returnsTwoDaysAnnualized, returnsThreeDaysAnnualized,
returnsFiveDaysAnnualized)
avgReturn <- data.matrix(data.frame(lapply(returns, mean)))
cumReturn <- data.matrix(data.frame(lapply(returns, sum)))
volReturn<- data.matrix(data.frame(lapply(returns, sd)))
sharpeRatio <-
as.matrix((as.numeric(data.matrix(data.frame(lapply(returnsAnnualized,
mean))) - rep(0.0025,4)) / as.numeric( matrix(lapply(returnsAnnualized,
sd)))))
nbSignals <- data.matrix(data.frame(lapply(returns, length)))
nbPositives<-list(returnsOneDay[which(returnsOneDay>0)],returnsTwoDays[which(returnsTwoDays>0)],returnsThreeDays[which(returnsThreeDays>0)],returnsFiveDays[which(returnsFiveDays>0)])
nbPositives<- t(data.matrix(data.frame(lapply(nbPositives,length))))
posRatio<- matrix( as.numeric(nbPositives) / as.numeric(nbSignals))
summary<- matrix(cbind(avgReturn, cumReturn, volReturn,
sharpeRatio,nbSignals,nbPositives,posRatio),nrow = 4, ncol = 7,
dimnames=list(c("one day", "two days", "three days", "five days"),c("A",
"B", "C", "D", "E", "F", "G")))
return( summary)
}
I hope it won't be confusing...
-----
Anna Lippel
--
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