我有以下数据：
dput(head(trade.wide,10))
structure(c(54.7, 54.5, 54.5, 54.6, 54.65, 54.6, 54.65, 54.65,
54.65, 54.7), .indexCLASS = c("POSIXct", "POSIXt"), .indexTZ = "", tclass = c("POSIXct",
"POSIXt"), tzone = "", class = c("xts", "zoo"), index = structure(c(1459482300,
1459482302, 1459482305, 1459482306, 1459482307, 1459482308, 1459482312,
1459482314, 1459482315, 1459482317), tzone = "", tclass = c("POSIXct",
"POSIXt")), .Dim = c(10L, 1L), .Dimnames = list(NULL, "PRICE"))
dput(tail(trade.wide,10))
structure(c(84.15, 84.1, 84.1, 84.05, 84.1, 84.05, 84, 84.1,
84.1, 84.2), .indexCLASS = c("POSIXct", "POSIXt"), .indexTZ = "", tclass = c("POSIXct",
"POSIXt"), tzone = "", class = c("xts", "zoo"), index = structure(c(1472637583,
1472637584, 1472637585, 1472637586, 1472637588, 1472637595, 1472637596,
1472637597, 1472637598, 1472637600), tzone = "", tclass = c("POSIXct",
"POSIXt")), .Dim = c(10L, 1L), .Dimnames = list(NULL, "PRICE"))
我试图从开始（9:15:00）估计超过30分钟的高低。
df.OHLC<-to.period(trade.wide,period = "minutes", k=30, indexAt="startof")
这就是我得到的：
head(df.OHLC,10)
trade.wide.Open trade.wide.High trade.wide.Low trade.wide.Close
2016-04-01 09:15:00 54.70 54.85 54.05 54.65
2016-04-01 09:30:07 54.65 56.50 54.65 56.05
2016-04-01 10:00:02 56.15 56.15 55.75 55.85
2016-04-01 10:30:03 55.80 56.20 55.70 56.10
2016-04-01 11:00:12 56.10 56.35 55.75 55.75
2016-04-01 11:30:12 55.75 55.80 55.40 55.50
2016-04-01 12:00:20 55.50 55.70 55.45 55.60
2016-04-01 12:30:24 55.55 55.75 55.25 55.50
2016-04-01 13:00:10 55.50 56.40 55.35 55.90
2016-04-01 13:30:17 55.85 57.35 55.75 57.20
但是，它有时间戳 09：15：00,09：30：07,10：00：02，... 我需要的是 09：15：00,09：45：00,10：15：00，...
我也尝试了period.max()功能，但它也有类似的问题。
df.OHLC1<- do.call(rbind, lapply(split(trade.wide, "days"),function(x) period.max(x,endpoints(x,on= "minutes",k=30))))
head(df.OHLC1,10)
[,1]
2016-04-01 09:29:59 54.85
2016-04-01 09:59:56 56.50
2016-04-01 10:29:53 56.15
2016-04-01 10:59:59 56.20
2016-04-01 11:29:54 56.35
2016-04-01 11:59:52 55.80
2016-04-01 12:29:59 55.70
2016-04-01 12:59:54 55.75
2016-04-01 13:29:45 56.40
2016-04-01 13:59:59 57.35
tail(df.OHLC1)
[,1]
2016-08-31 13:29:59 86.55
2016-08-31 13:59:56 86.30
2016-08-31 14:29:59 85.85
2016-08-31 14:59:59 85.15
2016-08-31 15:29:58 84.90
2016-08-31 15:30:00 84.20
我想知道为什么这些功能会不成比例地划分时间？请帮我解决这个问题。感谢
答案 0 :(得分：1)
使用您的样本数据，我首先得到：
trade.wide <- readRDS("sample")
df.OHLC <- to.period(trade.wide, period = "minutes", k = 30, indexAt = "startof")
head(df.OHLC, n = 4)
## trade.wide.Open trade.wide.High trade.wide.Low trade.wide.Close
## 2016-04-01 05:45:00 54.70 54.85 54.05 54.65
## 2016-04-01 06:00:07 54.65 56.50 54.65 56.05
## 2016-04-01 06:30:02 56.15 56.15 55.75 55.85
## 2016-04-01 07:00:03 55.80 56.20 55.70 56.10
给出的时间戳实际上是trade.wide中出现的区间中的第一个时间戳。您可以使用align.time()：
aligned <- align.time(df.OHLC, n = 30*60)
head(aligned, n = 4)
## trade.wide.Open trade.wide.High trade.wide.Low trade.wide.Close
## 2016-04-01 06:00:00 54.70 54.85 54.05 54.65
## 2016-04-01 06:30:00 54.65 56.50 54.65 56.05
## 2016-04-01 07:00:00 56.15 56.15 55.75 55.85
## 2016-04-01 07:30:00 55.80 56.20 55.70 56.1
在aligned中，行按时间间隔的时间标记，因为align.time向上舍入到30分钟的下一个30倍。如果您想用间隔的开头标记它们，则需要从时间戳中减去30分钟，如下所示：
index(aligned) <- index(aligned) - 30*60
head(aligned, n = 4)
## trade.wide.Open trade.wide.High trade.wide.Low trade.wide.Close
## 2016-04-01 05:30:00 54.70 54.85 54.05 54.65
## 2016-04-01 06:00:00 54.65 56.50 54.65 56.05
## 2016-04-01 06:30:00 56.15 56.15 55.75 55.85
## 2016-04-01 07:00:00 55.80 56.20 55.70 56.10
（见How can I alter a time series (XTS or ZOO) in R?）
选择的时间间隔为00:00，00:30，01:00等。如果您需要00:15，00:45，01:15等时间间隔等等，您可以从时间戳中减去15分钟，然后按照与上述类似的程序开始：
index(trade.wide) <- index(trade.wide) - 15*60
df.OHLC <- to.period(trade.wide, period = "minutes", k = 30, indexAt = "startof")
aligned <- align.time(df.OHLC, n = 30*60)
index(aligned) <- index(aligned) - 15*60
head(aligned, n = 4)
## trade.wide.Open trade.wide.High trade.wide.Low trade.wide.Close
## 2016-04-01 05:45:00 54.70 55.65 54.05 55.65
## 2016-04-01 06:15:00 55.60 56.50 55.30 55.90
## 2016-04-01 06:45:00 55.85 56.20 55.70 55.85
## 2016-04-01 07:15:00 55.85 56.35 55.70 55.90