如何在R中向量化功能

Question

我需要一些帮助对以下代码进行矢量化处理，因为我相信它将变得更加高效。 但是我不知道该如何开始...我创建了一个遍历z的循环。 z具有3列和112847行，这可能是花费较长时间的原因。 3列包含在MACD()函数中使用的数字...

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)

}

我想知道如何对MACD()函数进行矢量化处理，以加快处理过程，因为stretches的长度大约为。 112847.我的计算机花了很多时间才能完成循环。

Answer 1

首先，针对具体情况进行优化-删除nFast > nSlow的情况，因为从技术上讲这没有意义。

其次-您正在创建对象并一遍又一遍地复制它们。 这非常昂贵。

第三-您可以通过在一个循环中创建信号矩阵并以矢量化方式执行其余操作来更好地编写此代码。

我会编码您正在做的事情。

如果您mapply ，请阅读mapply ， do.call ， merge和sapply帮助页面。

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：这是您应该进行的第一个优化的地方。 删除i> k的情况

z <- z[z[,1]<z[,2], ]

案件数量从112847到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)

结果如下。 i ， j ， k哪个组合属于哪一列是无关紧要的。

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