'lagging' in irregular time series

Question

I have data.frame, that shows current bid and ask prices of a stock and my signal at that time.

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

at 10:10:01.000856, while i have signal at 50.06, I can't use it. I can only use signal 50 microseconds ago

So I need this outcome data.frame

at 10:10:01.000856, 50 microseconds ago, the time is 10:01:01.000806, so the usable signal that time is 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

Is there a R / python solution that generates the outcome data.frame? For example, say we first load the data.frame into xts object then we might have

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

Note: I don't think I can simply usexts.lag 1 because I would end up moving 50.05 down, not 50.03

time            bid_price   ask_price   signal  signal_from_lag1
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   50.05

Answer 1

This is the approach I would take to align the values with the most recent previous observation. It only uses the xts merge function and the na.locf() to fill merged by time values forward:

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) ]

The resulting merge_xts object:

> 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