我有一个R投资组合构造代码，它使用来自雅虎的每日调整后的收盘价数据。我对NA值有一些问题，但代码已经工作了一段时间。直到本周末（例如，2015年2月28日）。
现在，当我使用tseries函数get.hist.quote（）时，雅虎数据源似乎完全被破坏了。破碎我的意思是它不会正确返回VTV和其他一些ETF的数据。我不知道Yahoo时间序列源是否已关闭或是什么。
有一篇文章（https://stackoverflow.com/a/3507948/2341077）建议将get.hist.quote（）中的URL从chart.yahoo.com更改为ichart.yahoo.com可以解决问题。但这对我没有任何改变。我还确保安装了最新版本的tseries。
有没有其他人在雅虎的收盘时间序列中遇到问题？我一直想知道是否应该更改我的代码以使用quantmod函数getSymbols，显然，它可以使用Google财务作为数据源。
编写下面的代码是为了读取数百个ETF符号并返回包含ETF时间序列数据的矩阵。尝试按日期对齐数据。
即使雅虎似乎在提供数据，仍然缺少值，这就是fillHoles（）函数要解决的问题。
<pre>
#
# Fill "NA" holes in the time series.
#
fillHoles = function(ts.zoo) {
v_approx = na.approx(ts.zoo, maxgap=4, na.rm=FALSE)
v_fill = na.fill(v_approx, fill="extend")
return( v_fill)
}
<i>
#
# The yahoo market data has problems (at least when it's fetched with get.hist.quote()) when the compression
# argument is used to fetch weekly adjusted close data.
#
# Two time series are shown below, for VXF and MINT. The weekly boundaries appear on different dates.
#
# VXF
# 2007-04-04 48.55
# 2007-04-09 48.98
# 2007-04-16 49.52 <==
# 2007-04-23 49.70
# 2007-04-30 50.03
# 2007-05-07 50.04 <==
#
# MINT
# 2007-04-04 8.03
# 2007-04-09 8.03
# 2007-04-17 7.88 <==
# 2007-04-23 8.11
# 2007-04-30 8.92
# 2007-05-08 9.14 <==
#
# If the two time series are merged via a cbind NA values
# end up being inserted where the time series don't line up:'
#
# VXF MINT
# 2007-04-04 48.55 8.03
# 2007-04-09 48.98 8.03
# 2007-04-16 49.52 NA
# 2007-04-23 49.70 8.11
# 2007-04-30 50.03 8.92
# 2007-05-07 50.04 NA
#
# To avoid this problem of data alignment, the function fetches daily adjusted close that can then be converted
# into weekly adjusted close.
#
# Given a vector of symbols, this function will fetch the daily adjusted close price data from
# Yahoo. The data is aligned since not all time series will have exactly the same start and end
# dates (although with daily data, as noted above, this should be less of an issue)
#
</i>
getDailyCloseData = function(symbols, startDate, endDate )
{
closeData.z = c()
firstTime = TRUE
minDate = c()
maxDate = c()
fetchedSyms = c()
startDate.ch = as.character( findMarketDate(as.Date(startDate)))
endDate.ch = as.character( findMarketDate(as.Date(endDate)))
for (i in 1:length(symbols)) {
sym = symbols[i]
print(sym)
symClose.z = NULL
timeOut = 1
tsEndDate.ch = endDate.ch
while ((timeOut < 7) && is.null(symClose.z)) {
try(
(symClose.z = get.hist.quote(instrument=sym, start=startDate.ch, end=tsEndDate.ch, quote="AdjClose",
provider="yahoo", compression="d", retclass="zoo", quiet=T)),
silent = TRUE)
tsEndDate.ch = as.character( findMarketDate( (as.Date(tsEndDate.ch) - 1)))
timeOut = timeOut + 1
}
if (! is.null(symClose.z)) {
fetchedSyms = c(fetchedSyms, sym)
dateIx = index(symClose.z)
if (firstTime) {
closeData.z = symClose.z
firstTime = FALSE
minDate = min(dateIx)
maxDate = max(dateIx)
} else {
minDate = max(minDate, min(dateIx))
maxDate = min(maxDate, max(dateIx))
matIx = index(closeData.z)
repeat {
startIx = which(matIx == minDate)
if (length(startIx) > 0 && startIx > 0) {
break()
} else {
minDate = minDate + 1
}
} # repeat
repeat {
endIx = which(matIx == maxDate)
if (length(endIx) > 0 && endIx > 0) {
break()
} else {
maxDate = maxDate - 1
}
}
matIxAdj = matIx[startIx:endIx]
closeData.z = cbind(closeData.z[matIxAdj,], symClose.z[matIxAdj])
}
} # if (! is.null(symClose.z))
} # for
if (length(closeData.z) > 0) {
dateIx = index(closeData.z)
# fill any NA "holes" created by daily date alignment
closeData.mat = apply(closeData.z, 2, FUN=fillHoles)
rownames(closeData.mat) = as.character(dateIx)
colnames(closeData.mat) = fetchedSyms
}
return( closeData.mat )
} # getDailyCloseData
</pre>
答案 0 :(得分：0)
一些观察和问题。您正在使用get.history.quote返回动物园时间序列。您是否尝试过使用zoo包中的merge.zoo来合并来自不同资产的时间历史记录。这应该与日期保持一致，没有任何问题。其次，谷歌和雅虎以不同的方式纠正历史价格，因此两者的价格不同。雅虎提供开盘价，最高价，最低价和收盘价的历史价格，然后调整价格，调整价格以进行拆分，分红和分红。谷歌调整所有价格，但仅限于拆分，忽略股息和分配。您可以使用VXF查看2007年数据的这种差异。
我通过访问Yahoo没有问题 quantmod的getSymbols因此您可以使用它而不是切换到Google。最后，根据Pimco的说法，MINT的开始日期是11/16/2009，所以我不明白你们2007年的数据如何。
xts包是动物园的扩展，我发现它有一些有用的附加功能，例如to.weekly，它在下面使用。下面的代码是使用quantmod和xts包为您的ETF提供每日和每周价格的示例。请注意，MINT数据直到2009年11月17日才开始，与Pimco的开始日期一致。
library(quantmod)
library(xts)
getDailyCloseData = function(symbols, startDate, endDate ) {
close_daily <- getSymbols(symbols[1], src="yahoo", from=startDate, to=endDate, auto.assign=FALSE)[,6]
for(sym in symbols[-1]) {
close_daily <- merge(close_daily, getSymbols(sym, src="yahoo", from=startDate, to=endDate, auto.assign=FALSE)[,6])
}
colnames(close_daily) <- symbols
return(close_daily)
}
symbols <- c("VXF","MINT")
startDate <- "2007-03-15"
endDate <- Sys.Date()
close_daily <- getDailyCloseData(symbols, startDate, endDate)
close_weekly <- to.weekly(close_daily[,1], OHLC=FALSE)
for(sym in symbols[-1]) {
close_weekly <- merge(close_weekly, to.weekly(close_daily[,sym], OHLC=FALSE))
}
答案 1 :(得分：0)
我已切换到使用quantmod（）getSymbols函数。 Yahoo数据的问题不一致，因此很难知道这是否是一个完整的解决方案。但是代码比我上面发布的更清晰。
事实是，如果你投资真钱而不仅仅是做量化金融作业，你应该购买专业级数据。
#
# Find the nearest market date (moving backward in time)
#
findMarketDate = function( date )
{
while(! isBizday(x = as.timeDate(date), holidays=holidayNYSE(as.numeric(format(date, "%Y"))))) {
date = date - 1
}
return(date)
}
#
# Fill "NA" holes in the time series.
#
fillHoles = function(ts.zoo) {
v_approx = na.approx(ts.zoo, maxgap=4, na.rm=FALSE)
v_fill = na.fill(v_approx, fill="extend")
return( v_fill)
}
#
# Get daily equity market prices (e.g., stocks, ETFs). This code is designed to work
# with both Yahoo and Google. Yahoo is preferred because they have adjusted prices. An adjusted
# price is adjusted for splits and dividends. As a result, an ETF that doesn't move that much in price
# may still move in dividend adjusted price. Using these prices avoids omitting high divident assets.
#
getDailyPriceData = function(symbols, startDate, endDate, dataSource = "yahoo" )
{
closeData.z = c()
firstTime = TRUE
fetchedSyms = c()
startDate.d = findMarketDate(as.Date(startDate))
endDate.d = findMarketDate(as.Date(endDate))
for (i in 1:length(symbols)) {
sym = symbols[i]
print(sym)
close.m = NULL
timeOut = 1
while ((timeOut < 7) && is.null(close.m)) {
try(
(close.m = getSymbols(Symbols=sym,src=dataSource, auto.assign=getOption('loadSymbols.auto.assign', FALSE),
warnings=FALSE)),
silent = TRUE)
timeOut = timeOut + 1
} # while
if (! is.null(close.m)) {
dateIx = index(close.m)
startIx = which(startDate.d == dateIx)
endIx = which(endDate.d == dateIx)
if ((length(startIx) > 0 && startIx > 0) && (length(endIx) > 0 && endIx > 0)) {
fetchedSyms = c(fetchedSyms, sym)
closeAdj.m = close.m[startIx:endIx,]
price.z = NULL
if (dataSource == "yahoo") {
yahooAdjCol = paste(sym, "Adjusted", sep=".")
price.z = closeAdj.m[, yahooAdjCol]
} else {
highCol = paste(sym, "High", sep=".")
lowCol = highIx = paste(sym, "Low", sep=".")
price.z = (closeAdj.m[,highCol] + closeAdj.m[,lowCol])/2
}
if (firstTime) {
closeData.z = price.z
firstTime = FALSE
} else {
closeData.z = cbind(closeData.z, price.z)
}
} # if (! is.null(symClose.z))
} # if not null
} # for
closeData.m = c()
if (length(closeData.z) > 0) {
dateIx = index(closeData.z)
closeData.m = coredata(closeData.z)
numHoles = sum(is.na(closeData.m))
if (numHoles > 0) {
# fill any NA "holes" created by daily date alignment
closeData.m = apply(closeData.m, 2, FUN=fillHoles)
}
rownames(closeData.m) = as.character(dateIx)
colnames(closeData.m) = fetchedSyms
}
return( closeData.m )
} # getDailyPriceData