计算R中1年时间窗口内的累计产品_R_Xts_Rollapply

计算R中1年时间窗口内的累计产品

计算R中1年时间窗口内的累计产品,r,xts,rollapply,R,Xts,Rollapply,我有一个问题，要求我计算一系列1期收益的滚动乘积。滚动窗口的长度是可变的。目的是获得1期回报的滚动产品，该产品尽可能涵盖12个月的窗口我已经能够通过for循环和if语句使用蛮力生成一个有效的解决方案，但是我想知道是否有一个优雅的解决方案。我花了很多时间尝试使用rollappy和其他类似的函数，但一直没有找到解决方案下面的数据说明了这个问题 date rt_1_period rt_12_mth_window 1 04-04-13 NA NA 2

我有一个问题，要求我计算一系列1期收益的滚动乘积。滚动窗口的长度是可变的。目的是获得1期回报的滚动产品，该产品尽可能涵盖12个月的窗口

我已经能够通过

for

循环和

if

语句使用蛮力生成一个有效的解决方案，但是我想知道是否有一个优雅的解决方案。我花了很多时间尝试使用

rollappy

和其他类似的函数，但一直没有找到解决方案

下面的数据说明了这个问题

    date        rt_1_period rt_12_mth_window
1   04-04-13    NA          NA
2   10-04-13    0.729096362 NA
3   24-05-13    1.002535647 NA
4   30-05-13    0.993675716 NA
5   21-07-13    1.002662843 NA
6   03-08-13    1.009516582 NA
7   01-09-13    0.963099395 NA
8   20-10-13    1.012470278 NA
9   25-10-13    1.01308502  NA
10  03-11-13    1.005440704 NA
11  01-01-14    1.024208021 NA
12  11-01-14    0.996613924 NA
13  17-02-14    1.009811368 NA
14  24-02-14    1.008139557 NA
15  30-03-14    1.002794709 NA
16  30-04-14    0.998745849 1.042345473
17  02-05-14    1.002324076 1.044767963
18  27-06-14    0.997741026 1.046389027
19  24-08-14    1.015767546 1.050072129
20  05-09-14    1.014405005 1.106010894
21  02-11-14    1.013830296 1.09319212
22  09-11-14    1.013127219 1.101549487
23  16-11-14    1.012614177 1.115444628
24  18-01-15    0.986893629 1.078458006
25  24-01-15    1.028120919 1.108785236
26  10-04-15    0.912452762 0.991025615
27  09-08-15    1.004676152 0.981376513
28  07-01-16    1.004236123 0.934086003
29  01-04-16    1.02341302  0.94215696

在该示例中，第29行的12个月回报率计算为第26行到第29行的1个期间回报率的乘积，因为02-04-15（01-04-16的365天）包含在第25行和第26行之间。另一方面，第15行的12个月回报率为NA，因为30-03-13（从30-03-14开始的365天）超出了我可以观察到的1期回报的时间窗口

如果有人能提出解决这个问题的办法，我会很高兴的

为了清楚起见，如果所提供的数据没有多大意义，那是因为这是我为演示目的创建的大型数据库的精简版本。

您可以使用

xts

和

lubridate

来简化日期操作

数据：

require(xts)
require(lubridate)


DF = read.csv(text="
date,rt_1_period,rt_12_mth_window
04-04-13,           ,
10-04-13,0.729096362,
24-05-13,1.002535647,
30-05-13,0.993675716,
21-07-13,1.002662843,
03-08-13,1.009516582,
01-09-13,0.963099395,
20-10-13,1.012470278,
25-10-13,1.01308502 ,
03-11-13,1.005440704,
01-01-14,1.024208021,
11-01-14,0.996613924,
17-02-14,1.009811368,
24-02-14,1.008139557,
30-03-14,1.002794709,
30-04-14,0.998745849,1.042345473
02-05-14,1.002324076,1.044767963
27-06-14,0.997741026,1.046389027
24-08-14,1.015767546,1.050072129
05-09-14,1.014405005,1.106010894
02-11-14,1.013830296,1.09319212
09-11-14,1.013127219,1.101549487
16-11-14,1.012614177,1.115444628
18-01-15,0.986893629,1.078458006
24-01-15,1.028120919,1.108785236
10-04-15,0.912452762,0.991025615
09-08-15,1.004676152,0.981376513
07-01-16,1.004236123,0.934086003
01-04-16,1.02341302 ,0.94215696",header=TRUE,stringsAsFactors=FALSE,na.strings="")


#Convert to xts time series for ease in date manipulation

DF_xts = xts(DF[,-1],order.by = as.Date(DF[,1],format="%d-%m-%y"))
head(DF_xts)
#   
#              rt_1_period rt_12_mth_window
#2013-04-04          NA               NA
#2013-04-10 0.729096362               NA
#2013-05-24 1.002535647               NA
#2013-05-30 0.993675716               NA
#2013-07-21 1.002662843               NA
#2013-08-03 1.009516582               NA




#set lag period as 1 year
lagPeriod = 1

#Merge with original time series for final dataset

new_DF = merge.xts(DF_xts,rt_12_mth_window_Calc)

#Calculate difference in original and calculated 12 month returns
new_DF$delta = new_DF$rt_12_mth_window_Calc - new_DF$rt_12_mth_window
new_DF

#           rt_1_period rt_12_mth_window rt_12_mth_window_Calc           delta
#2013-04-04          NA               NA                    NA              NA
#2013-04-10 0.729096362               NA                    NA              NA
#2013-05-24 1.002535647               NA                    NA              NA
#2013-05-30 0.993675716               NA                    NA              NA
#2013-07-21 1.002662843               NA                    NA              NA
#2013-08-03 1.009516582               NA                    NA              NA
#2013-09-01 0.963099395               NA                    NA              NA
#2013-10-20 1.012470278               NA                    NA              NA
#2013-10-25 1.013085020               NA                    NA              NA
#2013-11-03 1.005440704               NA                    NA              NA
#2014-01-01 1.024208021               NA                    NA              NA
#2014-01-11 0.996613924               NA                    NA              NA
#2014-02-17 1.009811368               NA                    NA              NA
#2014-02-24 1.008139557               NA                    NA              NA
#2014-03-30 1.002794709               NA                    NA              NA
#2014-04-30 0.998745849      1.042345473           1.042345470 -2.64001643e-09
#2014-05-02 1.002324076      1.044767963           1.044767960 -2.54864396e-09
#2014-06-27 0.997741026      1.046389027           1.046389025 -1.97754613e-09
#2014-08-24 1.015767546      1.050072129           1.050072127 -1.66086833e-09
#2014-09-05 1.014405005      1.106010894           1.106010893 -1.34046041e-09
#2014-11-02 1.013830296      1.093192120           1.093192120 -6.47777387e-11
#2014-11-09 1.013127219      1.101549487           1.101549488  5.99306826e-10
#2014-11-16 1.012614177      1.115444628           1.115444628 -1.89856353e-10
#2015-01-18 0.986893629      1.078458006           1.078458005 -1.15637744e-09
#2015-01-24 1.028120919      1.108785236           1.108785235 -9.57268265e-10
#2015-04-10 0.912452762      0.991025615           0.991025613 -1.54581248e-09
#2015-08-09 1.004676152      0.981376513           0.996850412  1.54738992e-02
#2016-01-07 1.004236123      0.934086003           0.934086002 -9.15302278e-10
#2016-04-01 1.023413020      0.942156960           0.942156960 -1.82048598e-10

累计1200万件产品：

require(xts)
require(lubridate)


DF = read.csv(text="
date,rt_1_period,rt_12_mth_window
04-04-13,           ,
10-04-13,0.729096362,
24-05-13,1.002535647,
30-05-13,0.993675716,
21-07-13,1.002662843,
03-08-13,1.009516582,
01-09-13,0.963099395,
20-10-13,1.012470278,
25-10-13,1.01308502 ,
03-11-13,1.005440704,
01-01-14,1.024208021,
11-01-14,0.996613924,
17-02-14,1.009811368,
24-02-14,1.008139557,
30-03-14,1.002794709,
30-04-14,0.998745849,1.042345473
02-05-14,1.002324076,1.044767963
27-06-14,0.997741026,1.046389027
24-08-14,1.015767546,1.050072129
05-09-14,1.014405005,1.106010894
02-11-14,1.013830296,1.09319212
09-11-14,1.013127219,1.101549487
16-11-14,1.012614177,1.115444628
18-01-15,0.986893629,1.078458006
24-01-15,1.028120919,1.108785236
10-04-15,0.912452762,0.991025615
09-08-15,1.004676152,0.981376513
07-01-16,1.004236123,0.934086003
01-04-16,1.02341302 ,0.94215696",header=TRUE,stringsAsFactors=FALSE,na.strings="")


#Convert to xts time series for ease in date manipulation

DF_xts = xts(DF[,-1],order.by = as.Date(DF[,1],format="%d-%m-%y"))
head(DF_xts)
#   
#              rt_1_period rt_12_mth_window
#2013-04-04          NA               NA
#2013-04-10 0.729096362               NA
#2013-05-24 1.002535647               NA
#2013-05-30 0.993675716               NA
#2013-07-21 1.002662843               NA
#2013-08-03 1.009516582               NA




#set lag period as 1 year
lagPeriod = 1

#Merge with original time series for final dataset

new_DF = merge.xts(DF_xts,rt_12_mth_window_Calc)

#Calculate difference in original and calculated 12 month returns
new_DF$delta = new_DF$rt_12_mth_window_Calc - new_DF$rt_12_mth_window
new_DF

#           rt_1_period rt_12_mth_window rt_12_mth_window_Calc           delta
#2013-04-04          NA               NA                    NA              NA
#2013-04-10 0.729096362               NA                    NA              NA
#2013-05-24 1.002535647               NA                    NA              NA
#2013-05-30 0.993675716               NA                    NA              NA
#2013-07-21 1.002662843               NA                    NA              NA
#2013-08-03 1.009516582               NA                    NA              NA
#2013-09-01 0.963099395               NA                    NA              NA
#2013-10-20 1.012470278               NA                    NA              NA
#2013-10-25 1.013085020               NA                    NA              NA
#2013-11-03 1.005440704               NA                    NA              NA
#2014-01-01 1.024208021               NA                    NA              NA
#2014-01-11 0.996613924               NA                    NA              NA
#2014-02-17 1.009811368               NA                    NA              NA
#2014-02-24 1.008139557               NA                    NA              NA
#2014-03-30 1.002794709               NA                    NA              NA
#2014-04-30 0.998745849      1.042345473           1.042345470 -2.64001643e-09
#2014-05-02 1.002324076      1.044767963           1.044767960 -2.54864396e-09
#2014-06-27 0.997741026      1.046389027           1.046389025 -1.97754613e-09
#2014-08-24 1.015767546      1.050072129           1.050072127 -1.66086833e-09
#2014-09-05 1.014405005      1.106010894           1.106010893 -1.34046041e-09
#2014-11-02 1.013830296      1.093192120           1.093192120 -6.47777387e-11
#2014-11-09 1.013127219      1.101549487           1.101549488  5.99306826e-10
#2014-11-16 1.012614177      1.115444628           1.115444628 -1.89856353e-10
#2015-01-18 0.986893629      1.078458006           1.078458005 -1.15637744e-09
#2015-01-24 1.028120919      1.108785236           1.108785235 -9.57268265e-10
#2015-04-10 0.912452762      0.991025615           0.991025613 -1.54581248e-09
#2015-08-09 1.004676152      0.981376513           0.996850412  1.54738992e-02
#2016-01-07 1.004236123      0.934086003           0.934086002 -9.15302278e-10
#2016-04-01 1.023413020      0.942156960           0.942156960 -1.82048598e-10

对于每个日期，构造一个窗口[prevYearDate，date]，该窗口中的返回值，计算累积产品并选择最后一个产品

rt_12_mth_window_Calc = do.call(rbind,lapply(as.Date(index(DF_xts)),function(x) {

prevYearDate = x-years(lagPeriod)

rt_12_mth_window_Calc = last(cumprod(DF_xts[paste0(prevYearDate,"/",x),"rt_1_period"]))
colnames(rt_12_mth_window_Calc) = "rt_12_mth_window_Calc"

return(rt_12_mth_window_Calc)

}))

最终数据集：

require(xts)
require(lubridate)


DF = read.csv(text="
date,rt_1_period,rt_12_mth_window
04-04-13,           ,
10-04-13,0.729096362,
24-05-13,1.002535647,
30-05-13,0.993675716,
21-07-13,1.002662843,
03-08-13,1.009516582,
01-09-13,0.963099395,
20-10-13,1.012470278,
25-10-13,1.01308502 ,
03-11-13,1.005440704,
01-01-14,1.024208021,
11-01-14,0.996613924,
17-02-14,1.009811368,
24-02-14,1.008139557,
30-03-14,1.002794709,
30-04-14,0.998745849,1.042345473
02-05-14,1.002324076,1.044767963
27-06-14,0.997741026,1.046389027
24-08-14,1.015767546,1.050072129
05-09-14,1.014405005,1.106010894
02-11-14,1.013830296,1.09319212
09-11-14,1.013127219,1.101549487
16-11-14,1.012614177,1.115444628
18-01-15,0.986893629,1.078458006
24-01-15,1.028120919,1.108785236
10-04-15,0.912452762,0.991025615
09-08-15,1.004676152,0.981376513
07-01-16,1.004236123,0.934086003
01-04-16,1.02341302 ,0.94215696",header=TRUE,stringsAsFactors=FALSE,na.strings="")


#Convert to xts time series for ease in date manipulation

DF_xts = xts(DF[,-1],order.by = as.Date(DF[,1],format="%d-%m-%y"))
head(DF_xts)
#   
#              rt_1_period rt_12_mth_window
#2013-04-04          NA               NA
#2013-04-10 0.729096362               NA
#2013-05-24 1.002535647               NA
#2013-05-30 0.993675716               NA
#2013-07-21 1.002662843               NA
#2013-08-03 1.009516582               NA




#set lag period as 1 year
lagPeriod = 1

#Merge with original time series for final dataset

new_DF = merge.xts(DF_xts,rt_12_mth_window_Calc)

#Calculate difference in original and calculated 12 month returns
new_DF$delta = new_DF$rt_12_mth_window_Calc - new_DF$rt_12_mth_window
new_DF

#           rt_1_period rt_12_mth_window rt_12_mth_window_Calc           delta
#2013-04-04          NA               NA                    NA              NA
#2013-04-10 0.729096362               NA                    NA              NA
#2013-05-24 1.002535647               NA                    NA              NA
#2013-05-30 0.993675716               NA                    NA              NA
#2013-07-21 1.002662843               NA                    NA              NA
#2013-08-03 1.009516582               NA                    NA              NA
#2013-09-01 0.963099395               NA                    NA              NA
#2013-10-20 1.012470278               NA                    NA              NA
#2013-10-25 1.013085020               NA                    NA              NA
#2013-11-03 1.005440704               NA                    NA              NA
#2014-01-01 1.024208021               NA                    NA              NA
#2014-01-11 0.996613924               NA                    NA              NA
#2014-02-17 1.009811368               NA                    NA              NA
#2014-02-24 1.008139557               NA                    NA              NA
#2014-03-30 1.002794709               NA                    NA              NA
#2014-04-30 0.998745849      1.042345473           1.042345470 -2.64001643e-09
#2014-05-02 1.002324076      1.044767963           1.044767960 -2.54864396e-09
#2014-06-27 0.997741026      1.046389027           1.046389025 -1.97754613e-09
#2014-08-24 1.015767546      1.050072129           1.050072127 -1.66086833e-09
#2014-09-05 1.014405005      1.106010894           1.106010893 -1.34046041e-09
#2014-11-02 1.013830296      1.093192120           1.093192120 -6.47777387e-11
#2014-11-09 1.013127219      1.101549487           1.101549488  5.99306826e-10
#2014-11-16 1.012614177      1.115444628           1.115444628 -1.89856353e-10
#2015-01-18 0.986893629      1.078458006           1.078458005 -1.15637744e-09
#2015-01-24 1.028120919      1.108785236           1.108785235 -9.57268265e-10
#2015-04-10 0.912452762      0.991025615           0.991025613 -1.54581248e-09
#2015-08-09 1.004676152      0.981376513           0.996850412  1.54738992e-02
#2016-01-07 1.004236123      0.934086003           0.934086002 -9.15302278e-10
#2016-04-01 1.023413020      0.942156960           0.942156960 -1.82048598e-10

计算值和原始值非常接近所有观测值，除了

2015-08-09，数值偏差为1.55%，您能确认这段时间的计算结果吗？

这里有一个只依赖于xts的解决方案，可能更直接一些

library(xts)
x <- as.xts(read.zoo(text="date,rt_1_period,rt_12_mth_window
04-04-13,           ,
10-04-13,0.729096362,
24-05-13,1.002535647,
30-05-13,0.993675716,
21-07-13,1.002662843,
03-08-13,1.009516582,
01-09-13,0.963099395,
20-10-13,1.012470278,
25-10-13,1.013085020,
03-11-13,1.005440704,
01-01-14,1.024208021,
11-01-14,0.996613924,
17-02-14,1.009811368,
24-02-14,1.008139557,
30-03-14,1.002794709,
30-04-14,0.998745849,1.042345473
02-05-14,1.002324076,1.044767963
27-06-14,0.997741026,1.046389027
24-08-14,1.015767546,1.050072129
05-09-14,1.014405005,1.106010894
02-11-14,1.013830296,1.09319212
09-11-14,1.013127219,1.101549487
16-11-14,1.012614177,1.115444628
18-01-15,0.986893629,1.078458006
24-01-15,1.028120919,1.108785236
10-04-15,0.912452762,0.991025615
09-08-15,1.004676152,0.981376513
07-01-16,1.004236123,0.934086003
01-04-16,1.023413020,0.94215696", header=TRUE, sep=",", format="%d-%m-%y"))
ix <- index(x)       # index values
ixlag <- ix-365      # 1-year lag index values
x$rt_12 <- NA_real_  # initialize result column
for(i in which(ixlag > ix[1])) {
   # 1-year subset
   xyear <- window(x, start=ixlag[i], end=ix[i])
   # calculate product and update result column
   x[i,"rt_12"] <- prod(xyear[,"rt_1_period"])
}

库（xts）
非常感谢你的回答！非常感谢！你在2015-08-09上说得对。我使用的数据是从模拟Excel电子表格复制粘贴的，您的版本是正确的。我已经开始使用您的代码，并且似乎工作得很好。谢谢刚刚尝试在真实数据集上运行代码，该数据集是我发布的内容的扩展版本，并在apply（coredata（x），2，函数（y）cumprod（y））中得到以下错误错误：dim（x）必须具有正长度
您知道什么可能是源代码吗？提前感谢您是否可以更新您的问题，并包括您正在进行的函数调用以及dput（head（data））
当数据
是您的输入数据时。只有这样，我们才能复制错误。我尝试了你发布的代码片段，只做了一次更改，它运行良好，将rt_1_mth
替换为rt_1_period
，查看原子向量的链接并应用你是正确的！为这个愚蠢的错误道歉，这是我们的初衷。将删除对问题的编辑，因为它与问题无关。再次感谢！感谢您的替代解决方案！