R '；滞后'；在不规则时间序列中

我有data.frame，它显示了股票的当前买入价和卖出价以及我当时的信号

time            bid_price   ask_price   signal
10:10:01.000500 50.02       50.05       50.03
10:10:01.000855 50.02       50.03       50.05
10:10:01.000856 50.02       50.03       50.06

在10:10:01.000856，当我在50:06有信号时，我不能使用它。我只能在50微秒前使用信号

所以我需要这个结果数据框架

50微秒前的10:10:01.000856，时间是10:01:01.000806，因此该时间的可用信号是50.03

time            bid_price   ask_price   signal  signal_50microseconds_ago
10:10:01.000500 50.02       50.05       50.03   NA
10:10:01.000855 50.02       50.04       50.05   50.03
10:10:01.000856 50.02       50.04       50.06   50.03

是否有生成结果data.frame的R/python解决方案？ 例如，假设我们首先将data.frame加载到

xts

对象中，那么我们可能会

xts_obj$signal_50microseconds_ago <- get_time_lag_wish_this_function_exists(xts_obj$signal,lag=0.000050) 

---

这是我将采取的方法，以使这些值与之前的最新观察结果保持一致。它仅使用

xts

merge函数和

na.locf（）

向前填充合并的时间值：

d <- read.table(stringsAsFactors=F, header=T, text="
time            bid_price   ask_price   signal
10:10:01.000500 50.02       50.05       50.03
10:10:01.000855 50.02       50.03       50.05
10:10:01.000856 50.02       50.03       50.06
")

t <- as.POSIXct(paste0("2015-05-28 ", d$time))
#format(t, "%Y-%m-%d %H:%M:%OS9")

library(xts)
d_xts <- xts(d[,-1], order.by=t)

##  Lag the signal by 50 microseconds:
signal_lag <- xts(d[,"signal"], order.by=t+0.000050)

merge_xts <- merge(d_xts, signal_lag)

##  Carry last lagged value forward:
merge_xts$signal_lag <- na.locf(merge_xts$signal_lag)

##  Finally subset back to only original rows:
merge_xts <- merge_xts[ !is.na(merge_xts$signal) ]

d <- read.table(stringsAsFactors=F, header=T, text="
time            bid_price   ask_price   signal
10:10:01.000500 50.02       50.05       50.03
10:10:01.000855 50.02       50.03       50.05
10:10:01.000856 50.02       50.03       50.06
")

t <- as.POSIXct(paste0("2015-05-28 ", d$time))
#format(t, "%Y-%m-%d %H:%M:%OS9")

library(xts)
d_xts <- xts(d[,-1], order.by=t)

##  Lag the signal by 50 microseconds:
signal_lag <- xts(d[,"signal"], order.by=t+0.000050)

merge_xts <- merge(d_xts, signal_lag)

##  Carry last lagged value forward:
merge_xts$signal_lag <- na.locf(merge_xts$signal_lag)

##  Finally subset back to only original rows:
merge_xts <- merge_xts[ !is.na(merge_xts$signal) ]

> merge_xts
                    bid_price ask_price
2015-05-28 10:10:01     50.02     50.05
2015-05-28 10:10:01     50.02     50.03
2015-05-28 10:10:01     50.02     50.03
                    signal signal_lag
2015-05-28 10:10:01  50.03         NA
2015-05-28 10:10:01  50.05      50.03
2015-05-28 10:10:01  50.06      50.03