R: Efficiently subsetting dataframe based on time

Question

I have a large (150,000x7) dataframe that I intend to use for back-testing and real-time analysis of a financial market. The data represents the condition of an investment vehicle at 5 minute intervals (although holes do exist). It looks like this (but much longer):

        pTime     Time  Price       M1       M2        M3        M4
1  1212108300 20:45:00 1.5518 12.21849 -0.37125   4.50549 -31.00559
2  1212108900 20:55:00 1.5516 11.75350 -0.81792  -1.53846 -32.12291
3  1212109200 21:00:00 1.5512 10.75070 -1.47438  -8.24176 -34.35754
4  1212109500 21:05:00 1.5514 10.23529 -1.06044  -8.46154 -33.24022
5  1212109800 21:10:00 1.5514  9.74790 -1.02759 -10.21978 -33.24022
6  1212110100 21:15:00 1.5513  9.31092 -1.17076 -11.97802 -33.79888
7  1212110400 21:20:00 1.5512  8.84034 -1.28428 -13.62637 -34.35754
8  1212110700 21:25:00 1.5509  8.07843 -1.63715 -18.24176 -36.03352
9  1212111000 21:30:00 1.5509  7.39496 -1.49198 -20.65934 -36.03352
10 1212111300 21:35:00 1.5512  7.65266 -1.03717 -18.57143 -34.35754

The data is pre-loaded into R, but during my back-test I need to subset it by two criteria:

The first criteria is a sliding window to avoid peeking into the future. The window must be such that, each new 5 minute interval on the back-test shifts the whole window into the future by 5 minutes. This part I can do like this:

require(zoo)
zooser <- zoo(x=tser$Close, order.by=as.POSIXct(tser$pTime, origin="1970-01-01"))
window(zooser, start=A, end=B)    

The second criteria is another sliding window, but one that slides through time of day and contains only those entries that are within N minutes of the input time on any given day.

Example: If the window's size is 2 hours, and the input time is 12:00PM then the window must contain all rows with Time between 10:00AM and 2:00PM

This is the part that I am having trouble figuring out.

Edit: My data has holes in it, two consecutive rows could be MORE than 5 minutes apart. The data looks like this (very zoomed in)

As the window moves through these gaps the number of points inside the windows should vary.

The following is my MySQL code that does what I want to do in R (same table structure):

SET @qTime = Time(FROM_UNIXTIME(SAMP_endTime));

SET @inc = -1;
INSERT INTO MetIndListBuys (pTime,ArrayPos,M1,M2,M3,M4)
SELECT pTime,@inc:=@inc+1,M1,M2,M3,M4
 FROM mergebuys USE INDEX (`y`) WHERE  pTime BETWEEN SAMP_startTime AND SAMP_endTime 
AND TIME_TO_SEC(TIMEDIFF(Time,@qTime))/3600 BETWEEN 0-HourSpan AND HourSpan
;  

Answer 1

1) If DF is the data frame shown in the question then create a zoo object from it as you have done and split it into days giving zs. Then lapply your function f to each successive set of w points in each component (i.e. in each day). For example, if you want to apply your function to 2 hours of data at a time and your data is regularly spaced 5 minute data then w = 24 (since there are 24 five minute periods in two hours). In such a case f would be passed 24 rows of data as a matrix each time its called. Also align has been set to "right" below but it can alternately be set to align="center" and the condition giving ix can be changed to double sided, etc. For more on rollapply see: ?rollapply

library(zoo)
z <- zoo(DF[-2], as.POSIXct(DF[,1], origin = "1970-01-01"))
w <- 3 # replace this with 24 to handle two hours at a time with five min data
f <- function(x) {
            tt <- x[, 1]
            ix <- tt[w] - tt <= w * 5 * 60 # RHS converts w to seconds
            x <- x[ix, -1]
            sum(x) # replace sum with your function
    }
out <- rollapply(z, w, f, by.column = FALSE, align = "right")

Using the data frame in the question we get this:

> out
$`2008-05-30`
2008-05-30 02:00:00 2008-05-30 02:05:00 2008-05-30 02:10:00 2008-05-30 02:15:00 
          -66.04703           -83.92148           -95.93558          -100.24924 
2008-05-30 02:20:00 2008-05-30 02:25:00 2008-05-30 02:30:00 2008-05-30 02:35:00 
         -108.15038          -121.24519          -134.39873          -140.28436 

By the way, be sure to read this post .

2) This could alternately be done as the following where w and f are as above:

n <- nrow(DF)
m <- as.matrix(DF[-2])
sapply(w:n, function(i) { m <- m[seq(length = w, to = i), ]; f(m) })

Replace the sapply with lapply if needed. Also this may seem shorter than the first solution but its not much different once you add the code to define f and w (which appear in the first but not the second).

If there are no holes during the day and only holes between days then these solutions could be simplified.

Answer 2

Say that you have your target time t0 on the same scale as pTime: seconds since epoch. Then t0 - pTime = (difference in the number of days since epoch between the two) + (difference in remaining seconds). Taking t0 - pTime %% (num. seconds per day) will leave us with the difference in seconds in clock arithmetic (wrapped around if the difference is negative). This suggests the following function:

SecondsPerDay <- 24 * 60 * 60
within <- function(d, t0Sec, wMin) {
  diff <- (d$pTime - t0Sec) %% SecondsPerDay
  wSec <- 60 * wMin
  return(d[diff < wSec | diff > (SecondsPerDay - wSec), ])
}