我正在尝试在循环中合并动物园类,在每次迭代时将动物园系列累积为新的“列”。我在运行循环之前初始化一个空zoo()
。在我的代码完成后,当我在其上调用str()
时,我得到了“没有观察到的动物园系列”。然而,当我尝试初始化一个空的动物园,然后将它与自身和另一个实例与数据合并时,它可以正常工作。下面讨论的动物园在第二个循环中是'monthlyReturns
'。顺便说一句我知道使用申请家庭我会得到更好的服务。那将是下一个。我做错了什么?
library(tseries)
library(zoo)
symbs = c('XLF', 'XLE', 'XLU', 'SPY')
importData = vector('list', length(symbs))
cumInvestmentReturns = zoo()
monthlyReturns = zoo()
#Get monthly pricing data.
for (sIdx in 1:length(symbs)){
#Import the data for each symbol into the list.
importData[sIdx] = get.hist.quote(instrument= symbs[sIdx], start="2000-01-01", end="2013-07-15",
quote="AdjClose", provider="yahoo", origin="1970-01-01", compression="m", retclass="zoo")
names(importData[sIdx]) = symbs[sIdx]
}
#Loop over length of lookback months (1-12) to check for performance of best etf in past period.
for (numbOfMonths in 1:12){
#Calculate performances on each symbol, using the lookback length of variable numbOfMonths.
monthlyPctChgs = lapply(importData, function(x) diff(x, lag =numbOfMonths) / lag(x, k=-numbOfMonths))
names(monthlyPctChgs) = symbs
#combine all ticker time series into one time series.
tsPctChgs = merge(monthlyPctChgs[[1]], monthlyPctChgs[[2]], monthlyPctChgs[[3]], monthlyPctChgs[[4]],
monthlyPctChgs[[5]], monthlyPctChgs[[6]], monthlyPctChgs[[7]], monthlyPctChgs[[8]],
monthlyPctChgs[[9]], monthlyPctChgs[[10]], monthlyPctChgs[[11]], monthlyPctChgs[[12]])
names(tsPctChgs) = symbs
curBestLagPerfs <- rollapplyr(tsPctChgs, 2, function(x) x[2,which.max(x[1,])], by.column=FALSE)
monthlyReturns = merge(monthlyReturns, curBestLagPerfs)
#finalSet = finalSet[2:length(finalSet$SPY),] #Remove first value, since there is an na.
lookbackReturns = cumprod(1+curBestLag) * 10000
cumInvestmentReturns = merge(cumInvestmentReturns, lookbackReturns)
#names(investmentsPaired) = c('SPY', 'ETFRotation')
}
答案 0 :(得分:1)
如果你想避免这种错误并在R中实现高效的代码,你必须学习如何使用像lapply,apply和其他* apply这样的函数。有一篇很棒的帖子here
以下是我对代码的简化,未经过全面测试,但它可以帮助您深入了解如何改进代码并纠正错误。
require(tseries)
require(zoo)
symbs <- c('XLF', 'XLE', 'XLU', 'SPY')
importData <- lapply(symbs, function(symb)
get.hist.quote(instrument= symb,
start = "2000-01-01",
end = "2013-07-15",
quote="AdjClose", provider = "yahoo",
origin="1970-01-01", compression = "m",
retclass="zoo"))
names(importData) <- symbs
monthlyPctChgs <- lapply(1:12, function(y)
lapply(importData,
function(x) diff(x, lag = y) / lag(x, lag = - y)))
tsPctChgs <- lapply(monthlyPctChgs, do.call, what = merge)
curBestLagPerfs <- lapply(tsPctChgs, function(y)
rollapplyr(y, 2,
function(x) x[2,which.max(x[1,])],
by.column=FALSE))
curBestLagPerfs <- do.call(merge, curBestLagPerfs)
names(curBestLagPerfs) <- month.abb
str(curBestLagPerfs)
## ‘zoo’ series from 2000-03-01 to 2013-06-03
## Data: num [1:160, 1:12] 0.09156 0.00933 -0.00229 -0.06095 -0.01496 ...
## - attr(*, "dimnames")=List of 2
## ..$ : NULL
## ..$ : chr [1:12] "Jan" "Feb" "Mar" "Apr" ...
## Index: Date[1:160], format: "2000-03-01" "2000-04-03" "2000-05-01" ...
希望它会有所帮助