R data.table: row-based conditions split/apply/combine

Question

I have the following data.table

initial.date <- as.POSIXct(\'2018-10-27 10:00:00\',tz=\'GMT\')
last.date <- as.POSIXct(\'2018-12-28 17:00:         


        

Answer 1

OP did not mention the size of the new dataset. But a Rcpp solution should speed things up.

As per previous comment:

mtd1 <- function() {
    ndf[, rn:=.I]
    iidx <- ndf[
        .(inst=InstrumentSymbol, prevMin=datetime-60L, nextMin=datetime+60L, idx=id, tp=TradePrice),

        .SD[id != idx, rn[which.min(abs(TradePrice - tp))]],

        by=.EACHI,

        on=.(InstrumentSymbol=inst, datetime>=prevMin, datetime<=nextMin)];

    ndf[, c("minpricewithin60", "index.minpricewithin60") := .SD[iidx$V1, .(TradePrice, id)]]
}

arg0naut's approach:

mtd2 <- function() {
    res2[, `:=` (min_60 = datetime - 60, plus_60 = datetime + 60, idx = .I)][
        res2,  on = .(InstrumentSymbol = InstrumentSymbol, datetime >= min_60, datetime <= plus_60), allow.cartesian = TRUE][
            idx != i.idx, .SD[which.min(abs(i.TradePrice - TradePrice))], by = id][
                , .(id, minpricewithin60 = i.TradePrice, index.minpricewithin60 = i.idx)][
                    res, on = .(id)][, `:=` (min_60 = NULL, plus_60 = NULL, idx = NULL)]

}

A possible Rcpp approach:

library(Rcpp)
cppFunction('
NumericVector nearestPrice(NumericVector id, NumericVector datetime, NumericVector price) {
    int i, j, n = id.size();
    NumericVector res(n);
    double prev, diff;

    for (i=0; i= datetime[i]-60 && j>=0) {
            diff = std::abs(price[i] - price[j]);

            if (diff < prev) {
                res[i] = id[j];
                prev = diff;
            }
            j--;
        }

        j = i+1;
        while (datetime[j] <= datetime[i]+60 && j<=n) {
            diff = std::abs(price[i] - price[j]);

            if (diff < prev) {
                res[i] = id[j];
                prev = diff;
            }
            j++;
        }
    }

    return(res);
}
')

mtd3 <- function() {
    setorder(ndf2, InstrumentSymbol, PriorityDateTime)
    iidx <- ndf2[, nearestPrice(.I, datetime, TradePrice), by=.(InstrumentSymbol)]
    ndf2[, c("minpricewithin60", "index.minpricewithin60") := .SD[iidx$V1, .(TradePrice, id)]]
}

timing code:

library(microbenchmark)
microbenchmark(mtd1(), mtd2(), mtd3(), times=3L)

timings:

Unit: milliseconds
   expr         min          lq        mean      median          uq         max neval
 mtd1() 49447.09713 49457.12408 49528.14395 49467.15103 49568.66737 49670.18371     3
 mtd2() 64189.67241 64343.67138 64656.40058 64497.67034 64889.76466 65281.85899     3
 mtd3()    17.33116    19.58716    22.36557    21.84316    24.88277    27.92238     3

data:

set.seed(0L)
initial.date <- as.POSIXct('2018-01-01 00:00:00', tz='GMT')
last.date <- initial.date + 30 * (180000/2)
PriorityDateTime <- seq.POSIXt(from=initial.date, to=last.date, by='30 sec')

library(data.table)
ndf <- data.table(PriorityDateTime=c(PriorityDateTime, PriorityDateTime),
    TradePrice=rnorm(length(PriorityDateTime)*2, 100, 20),
    InstrumentSymbol=rep(c('asset1','asset2'), each=length(PriorityDateTime)),
    datetime=c(PriorityDateTime, PriorityDateTime))
setorder(ndf, InstrumentSymbol, PriorityDateTime)[, id := .I]
res <- copy(ndf)
res2  <- copy(ndf)
ndf2 <- copy(ndf)