从局部最小值/最大值计算累积增长/下降 [英] Calculate cumulatve growth/drawdown from local min/max

问题描述

我正在学习 R(及其通过 quantmod lib 在交易任务中的应用)，并定期浏览社区以从这里获得很多新知识和技巧.我对 R 的总体印象，尤其是对 quantmod lib 的印象——太棒了.

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.

此时我需要经验丰富的 R 用户的帮助.我正在使用通过 getSymbols 下载的时间序列，我需要分别从局部最小值/最大值计算累积增长/下降.

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.

我可以使用 FOR 循环来解决我的任务，也可以在 MS Excel 中进行必要的建模，但我想找出更简单的解决方案，不需要 FOR 循环，并且在 R 中更原生".

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.

示例.输入数据:

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

结果:

            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%

推荐答案

终于，我设法解决了.Dirk 和 Darren，非常感谢您的评论——PerformanceAnalytics 包中的maxdrawdown"功能并不是我所需要的，但这让我开始关注 PerformanceAnalytics，并通过该站点和 Internet 进行了一些搜索.findDrawdowns 函数来自与我的需求接近的同一包，但无论如何并不是我想要的(它需要更新最后一个高点才能开始计算新的回撤，而我什至需要取局部最大值和最小值考虑).通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.

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

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