需要更快的滚动应用函数以及开始停止索引

下面是一段代码。它给出滚动 15 分钟（历史）窗口的交易价格水平的百分位。如果长度为 500 或 1000，它运行得很快，但正如您所看到的，有 45K 个观测值，对于整个数据来说，它的运行速度非常慢。我可以应用任何 plyr 功能吗？欢迎任何其他建议。

贸易数据如下所示：

> str(trade)
'data.frame':   45571 obs. of  5 variables:
 $ time    : chr  "2013-10-20 22:00:00.489" "2013-10-20 22:00:00.807" "2013-10-20 22:00:00.811" "2013-10-20 22:00:00.811" ...
 $ prc     : num  121 121 121 121 121 ...
 $ siz     : int  1 4 1 2 3 3 2 2 3 4 ...
 $ aggress : chr  "B" "B" "B" "B" ...
 $ time.pos: POSIXlt, format: "2013-10-20 22:00:00.489" "2013-10-20 22:00:00.807" "2013-10-20 22:00:00.811" "2013-10-20 22:00:00.811" ...

这就是新列 trade$time.pos 之后数据的样子

trade$time.pos <- strptime(trade$time, format="%Y-%m-%d %H:%M:%OS") 

> head(trade)
                     time      prc siz aggress                time.pos
1 2013-10-20 22:00:00.489 121.3672   1       B 2013-10-20 22:00:00.489
2 2013-10-20 22:00:00.807 121.3750   4       B 2013-10-20 22:00:00.807
3 2013-10-20 22:00:00.811 121.3750   1       B 2013-10-20 22:00:00.811
4 2013-10-20 22:00:00.811 121.3750   2       B 2013-10-20 22:00:00.811
5 2013-10-20 22:00:00.811 121.3750   3       B 2013-10-20 22:00:00.811
6 2013-10-20 22:00:00.811 121.3750   3       B 2013-10-20 22:00:00.811

#t_15_index function returns the indices of the trades that were executed in last 15 minutes from the current trade(t-15 to t).
t_15_index <- function(data_vector,index) {
  which(data_vector[index] - data_vector[1:index]<=15*60)
}

get_percentile <- function(data) {
  len_d <- dim(trade)[1]  

  price_percentile = vector(length=len_d)  

  for(i in 1: len_d) {   

    t_15 = t_15_index(trade$time.pos,i)
    #ecdf(rep(..)) gets the empirical distribution of the the trade size on a particular trade-price level
    price_dist = ecdf(rep(trade$prc[t_15],trade$siz[t_15]))
    #percentile of the current price level depending on current (t-15 to t) subset of data
    price_percentile[i] = price_dist(trade$prc[i])
  }
  trade$price_percentile = price_percentile
  trade
}


res_trade = get_percentile(trade)

可能有一种方法可以加速滚动应用程序，但由于窗口大小的变化，我认为标准工具（例如rollapply）不起作用，尽管也许一些更熟悉它们的人会有想法。同时，您可以优化百分位计算。而不是使用ecdf它创建了一个具有所有相关开销的函数，您可以直接计算一个合适的近似值：

> vec <- rnorm(10000, 0, 3)
> val <- 5
> max(which(sort(vec) < val)) / length(vec)
[1] 0.9543
> ecdf(vec)(val)
[1] 0.9543
> microbenchmark(max(which(sort(vec) < val)) / length(vec))
Unit: milliseconds
expr      min       lq   median       uq      max neval
max(which(sort(vec) < val))/length(vec) 1.093434 1.105231 1.116364 1.141204 1.449141   100
> microbenchmark(ecdf(vec)(val))
Unit: milliseconds
expr      min       lq   median       uq      max neval
ecdf(vec)(val) 2.552946 2.808041 3.043579 3.439269 4.208202   100

大约提高 2.5 倍。对于较小的样本，改进更大。