最后，我设法解决了它.Dirk 和 Darren，非常感谢您的评论 - PerformanceAnalytics 包中的maxdrawdown"函数并不是我所需要的，但这让我关注 PerformanceAnalytics 并通过该站点和 Internet 进行了一些搜索.来自同一个包的 findDrawdowns 函数接近我的需要，但无论如何都不是我想要的(它需要更新最后一个高点以开始计算新的回撤，而我什至需要取局部最大值和最小值考虑在内).经过进一步的反复试验，我编写了自己的代码，无需 FOR 循环即可解决我的任务.:) 这是代码.作为奖励 - 它返回带有资产不断增长/下降的条数的向量.如果有人可以建议如何改进它，我会很高兴.

库(rusquant)图书馆(quantmod)图书馆(系列)na.zero <- 函数(x) {tmp 

I'm learning R (and its application to trading tasks via quantmod lib) and looking through the community pretty regularly to get a lot of new knowledge and tricks from here. My impression about R in general and quantmod lib in particular - it's awesome.

At this point I need help of seasoned R users. I'm using timeseries downloaded via getSymbols and I need to calculate cumulative growth/drawdown from local minimum/maximum respectively.

I can solve my task using FOR cycles as well as I can do necessary modelling in MS Excel, but I want to figure out more simple solution that does not require FOR cycles and that is more "native" in R.

Example. Input data:

20121121    79810
20121122    79100
20121123    80045
20121126    81020
20121127    80200
20121128    81350
20121129    81010
20121130    80550
20121203    80780
20121204    81700
20121205    83705
20121206    83350
20121207    83800
20121210    85385

Result:

            CLOSE   Cumulative gr/dd
20121121    79810   N/A
20121122    79100   0.58%
20121123    80045   1.55%
20121126    81020   2.37%
20121127    80200   -0.10%
20121128    81350   0.06%
20121129    81010   -0.76%
20121130    80550   -0.82%
20121203    80780   0.73%
20121204    81700   3.78%
20121205    83705   5.19%
20121206    83350   -1.50%
20121207    83800   1.67%
20121210    85385   2.22%

Finally, I've managed to solve it. Dirk and Darren, many thanks for your comments - the "maxdrawdown" function from PerformanceAnalytics package was not exactly what I needed, but this made me paying attention to PerformanceAnalytics and make some search through this site and the Internet. The findDrawdowns function from the same package that was close to my need, but anyway was not exacly what I was looking for (it needs the last high to be updated to start calculating new drawdown, while I need even local maxima and minima to be taken into account). Making further trials-and-errors, I made my own code that solves my task without FOR cycles. :) Here is the code. As a bonus - it returns vector with number of bars of constant growing/falling of the asset. I'll be happy if anyone can advise how to improve it.

library(rusquant)
library(quantmod)
library(tseries)

na.zero <- function(x) {
  tmp <- x
  tmp[is.na(tmp)] <- 0

  return(tmp)
}

my.cumulative.grdd <- function(asset) {
  # creating list for temporary data
  tmp <- list()
  # 
  #   tmp$asset.lag <- na.locf(lag(asset), fromLast=TRUE)

  # calculating ROC for the asset + getting ROC shifted by 1 element to the left and to the right
  # to compare ROC[i] and ROC[i+1] and ROC[i-1]
  tmp$asset.roc <- na.zero(ROC(asset))
  tmp$asset.roc.lag <- na.zero(lag(tmp$asset.roc))
  tmp$asset.roc.lag1 <- na.locf(lag(tmp$asset.roc, k=-1))

  # calculating indices of consequent growth/drawdown waves start and end
  tmp$indexfrom <- sapply(index(tmp$asset.roc[sign(tmp$asset.roc) * sign(tmp$asset.roc.lag) <= 0]), function(i) which(index(tmp$asset.roc) == i), simplify=TRUE)
  tmp$indexto <- c(sapply(index(tmp$asset.roc[sign(tmp$asset.roc) * sign(tmp$asset.roc.lag1) <= 0]), function(i) which(index(tmp$asset.roc.lag1) == i), simplify=TRUE), length(index(tmp$asset.roc)))

  # this is necessary to work around ROC[1] = 1
  tmp$indexfrom <- tmp$indexfrom[-2]
  tmp$indexto <- tmp$indexto[-1]

  # calculating dates of waves start/end based on indices
  tmp$datesfrom <- (sapply(tmp$indexfrom, FUN=function(x) format(index(asset)[x])))
  tmp$datesto <- (sapply(tmp$indexto, FUN=function(x) format(index(asset)[x])))
  tmp$dates <- apply(cbind(tmp$indexfrom, tmp$indexto), 2, FUN=function(x) format(index(asset)[x]))

  # merging dates for selection (i.e. "2012-01-02::2012-01-05") and calculation of cumulative product
  tmp$txtdates <- paste(tmp$datesfrom, tmp$datesto, sep="::")
  # extracting consequent growth/drawdowns
  tmp$drawdowns.sequences <- lapply(tmp$txtdates, function(i) tmp$asset.roc[i])
  # calculating cumulative products for extracted sub-series
  tmp$drawdowns.sequences.cumprods <- lapply(tmp$drawdowns.sequences, function(x) cumprod(1+x)-1)

  # generating final result
  result <- list()
  result$len <- tmp$indexto - tmp$indexfrom + 1
  result$cumgrdd <- xts(unlist(tmp$drawdowns.sequences.cumprods), index(tmp$asset.roc))

  return(result)
}

# let's test
getSymbols("SPY", from="2012-01-01")
spy.cl <- Cl(SPY)
spy.grdd <- my.cumulative.grdd(spy.cl)
spy.grdd