How to vectorize a function in R - r

Question

I need some help vectorizing the following code because I believe that it will become more efficient. However i do not know how to begin... I created a loop that goes through z. z has 3 columns and 112847 rows, which might be a reason it takes a long time. The 3 columns contain numbers that are used in the MACD() function...
library(quantmod)
library(TTR)
# get stock data
getSymbols('LUNA')
#Choose the Adjusted Close of a Symbol
stock <- Ad(LUNA)
#Create matrix for returns only
y <- stock
#Create a "MATRIX" by choosing the Adjusted Close
Nudata3 <- stock
#Sharpe Ratio Matrix
SR1<- matrix(NA, nrow=1)
# I want to create a table with all possible combinations from the ranges below
i = c(2:50)
k = c(4:50)
j = c(2:50)
# stores possible combinations into z
z <- expand.grid(i,k,j)
colnames(z)<- c("one","two","three")
n = 1
stretches <- length(z[,1])
while (n < stretches){
# I am trying to go through all the values in "z"
Nuw <- MACD((stock), nFast=z[n,1], nSlow=z[n,2], nSig=z[n,3], maType="EMA")
colnames(Nuw) <- c("MACD","Signal") #change the col names to create signals
x <- na.omit(merge((stock), Nuw))
x$sig <- NA
# Create trading signals
sig1 <- Lag(ifelse((x$MACD <= x$Signal),-1, 0)) # short when MACD < SIGNAL
sig2 <- Lag(ifelse((x$MACD >= x$Signal),1, 0)) # long when MACD > SIGNAL
x$sig <- sig1 + sig2
#calculate Returns
ret <- na.omit(ROC(Ad(x))*x$sig)
colnames(ret)<- c(paste(z[n,1],z[n,2],z[n,3],sep=","))
x <- merge(ret,x)
y <- merge(y,ret) #This creates a MATRIX with RETURNs ONLY
Nudata3 <- merge(Nudata3, x)
((mean(ret)/sd(ret)) * sqrt(252)) -> ANNUAL # Creates a Ratio
ANNUAL->Shrat # stores Ratio into ShRat
SR1 <- cbind(SR1,Shrat) # binds all ratios as it loops
n <- (n+1)
}
I would like to know how to vectorize the MACD() function, to speed up the process since the length of stretches is approx. 112847. It takes my computer quite some time to go through the loop itself.

Answer 1

First and foremost - case specific optimization - remove the cases where nFast > nSlow as it doesn't make sense technically.
Secondly - you are creating objects and copying them over and over again. This is very expensive.
Thirdly - you can code this better perhaps by creating a matrix of signals in one loop and doing rest of the operations in vectorized manner.
I would code what you are doing something like this.
Please read help pages of mapply, do.call, merge and sapply if you don't understand.
require(quantmod)
getSymbols("LUNA")
#Choose the Adjusted Close of a Symbol
stock <- Ad(LUNA)
# I want to create a table with all possible combinations from the ranges below
i = c(2:50)
k = c(4:50)
j = c(2:50)
# stores possible combinations into z
z <- expand.grid(i,k,j)
IMO : This is where your first optimization should be. Remove cases where i > k
z <- z[z[,1]<z[,2], ]
It reduces the number of cases from 112847 to 57575
#Calculate only once. No need to calculate this in every iteration.
stockret <- ROC(stock)
getStratRet <- function(nFast, nSlow, nSig, stock, stockret) {
x <- MACD((stock), nFast=nFast, nSlow=nSlow, nSig=nSig, maType="EMA")
x <- na.omit(x)
sig <- Lag(ifelse((x$macd <= x$signal),-1, 0)) + Lag(ifelse((x$macd >= x$signal),1, 0))
return(na.omit(stockret * sig))
}
RETURNSLIST <- do.call(merge, mapply(FUN = getStratRet, nFast = z[,1], nSlow = z[,2], nSig = z[,3], MoreArgs = list(stock = stock, stockret = stockret), SIMPLIFY = TRUE))
getAnnualSharpe <- function(ret) {
ret <- na.omit(ret)
return ((mean(ret)/sd(ret)) * sqrt(252))
}
SHARPELIST <- sapply(RETURNSLIST, FUN = getAnnualSharpe)
Results will be as below. Which column belongs to which combo of i, j, k is trivial.
head(RETURNSLIST[, 1:3])
## LUNA.Adjusted LUNA.Adjusted.1 LUNA.Adjusted.2
## 2007-01-10 0.012739026 -0.012739026 0
## 2007-01-11 -0.051959739 0.051959739 0
## 2007-01-12 -0.007968170 -0.007968170 0
## 2007-01-16 -0.007905180 -0.007905180 0
## 2007-01-17 -0.005235614 -0.005235614 0
## 2007-01-18 0.028315920 -0.028315920 0
SHARPELIST
## LUNA.Adjusted LUNA.Adjusted.1 LUNA.Adjusted.2 LUNA.Adjusted.3 LUNA.Adjusted.4 LUNA.Adjusted.5 LUNA.Adjusted.6
## 0.04939150 -0.07428392 NaN 0.02626382 -0.06789803 -0.22584987 -0.07305477
## LUNA.Adjusted.7 LUNA.Adjusted.8 LUNA.Adjusted.9
## -0.05831643 -0.08864845 -0.08221986
system.time(
+ RETURNSLIST <- do.call(merge, mapply(FUN = getStratRet, nFast = z[1:100,1], nSlow = z[1:100,2], nSig = z[1:100,3], MoreArgs = list(stock = stock, stockret = stockret), SIMPLIFY = TRUE)),
+ SHARPELIST <- sapply(RETURNSLIST, FUN = getAnnualSharpe)
+ )
user system elapsed
2.28 0.00 2.29