R投资组合分析图表.EfficientFrontier功能 [英] R portfolio analytics chart.EfficientFrontier function

问题描述

我正在尝试使用R中的Portfolioanalytics包中的chart.EfficientFrontier函数来绘制我创建的有效边界对象的图表，但该对象一直失败.基本上，我正在尝试找到一个边界，该边界将年化标准偏差最小化.最终，一旦我开始工作，我还希望最大化年化回报.

I am trying to use the chart.EfficientFrontier function in the portfolioanalytics package in R to chart an efficient frontier object that I have created but it keeps failing. Basically I am trying to find a frontier that will minimize annaulized standard deviation. Eventually once I get this working I would also like to maximize annualized return.

首先，我使用此代码创建了一个年度标准差函数

Firstly I created an annualized standard deviation function using this code

pasd <- function(R, weights){
  as.numeric(StdDev(R=R, weights=weights)*sqrt(12)) # hardcoded for monthly data
  # as.numeric(StdDev(R=R, weights=weights)*sqrt(4)) # hardcoded for quarterly data
}

我导入了一个具有月收益的csv文件，我的投资组合对象如下所示:

I imported a csv file with monthly returns and my portfolio object looks like this:

> prt
**************************************************
PortfolioAnalytics Portfolio Specification 
**************************************************

Call:
portfolio.spec(assets = colnames(returns))

Number of assets: 3 
Asset Names
[1] "Global REITs"      "Au REITs"          "Au Util and Infra"

Constraints
Enabled constraint types
        - leverage 
        - long_only 

Objectives:
Enabled objective names
        - mean 
        - pasd 

现在，我使用此行成功创建了一个有效的边界对象:

Now I successfully create an efficient frontier object using this line:

prt.ef <- create.EfficientFrontier(R = returns, portfolio = prt, type = "DEoptim", match.col = "pasd")

但是当我尝试绘制它时，我收到以下错误消息.

But when I try to plot it I am getting the following error messages.

> chart.EfficientFrontier(prt.ef, match.col="pasd")
Error in StdDev(R = R, weights = weights) : 
  argument "weights" is missing, with no default
In addition: There were 26 warnings (use warnings() to see them)
Error in StdDev(R = R, weights = weights) : 
  argument "weights" is missing, with no default
Error in StdDev(R = R, weights = weights) : 
  argument "weights" is missing, with no default
Error in xlim[2] * 1.15 : non-numeric argument to binary operator

有人知道为什么会这样吗?当我使用summary(prt.ef)时，我可以看到权重，但是为什么chart.EfficientFrontier函数会失败?

Anyone know why this is the case? When I use summary(prt.ef) I can see the weights, but why is the chart.EfficientFrontier function failing?

推荐答案

正如@WaltS所建议的那样，您需要在实现函数上保持一致，以对均值和风险回报进行年度化处理.

As @WaltS suggested, you need to be consistent in implementing functions to annualize mean and risk returns.

但是实际上要获得年度统计数据，您有两个选择，您没有使用任何选择:

But actually to get annualized statistics you have two options, you are not using any:

1)使用月度数据进行优化，并使用规范中的原始风险回报功能.对于绘图，您可以分析

1) Make the optimization with monthly data, with the original risk return functions in the specification. For plotting you can anualize making

Port.Anua.Returns=prt.ef$frontier[,1]*12 
Port.Anua.StDev=prt.ef$frontier[,2]*12^.5

月度或年度化投资组合的权重将相同.

The weights will be the same for monthly or annualized portfolios.

prt.ef$frontier[,-(1:3)]

2)将您的每月回报转换成年化回报乘以12.然后按照常规过程进行优化，所有风险和回报都已经在prt.ef$frontier中进行了年化.

2) Transform your monthly returns in annualized returns multiplying by 12. Then do the optimization with the usual procedure, all risk and return will be already annualized in prt.ef$frontier.

与EF中的锯齿线有关.使用您的投资组合规范，我还能够重新创建相同的行为.对于下面的绘图，我使用了edhec数据，即您在物镜中使用原始mean和StdDev的规范:

Related to the jagged line in EF. Using your portfolio specification I was also able to recreate the same behavior. For the following plot I used edhec data, your specification with original mean and StdDev in the objectives:

data(edhec)
returns <- edhec[,1:3]

该行为必须受到所使用的规范或优化算法的影响.我对软件包quadprog中的solve.QP做了相同的优化.这就是结果.

That behavior must be influenced by the specification or the optimization algorithm you are using. I did the same optimization with solve.QP from package quadprog. This is the result.

更新

代码在这里:

require(quadprog) 
#min_x(-d^T x + 1/2 b^T D x) r.t A.x>=b
MV_QP<-function(nx, tarRet, Sig=NULL,long_only=FALSE){
  if (is.null(Sig)) Sig=cov(nx)
  dvec=rep(0,ncol(Sig))
  meq=2
  Amat=rbind(rep(1,ncol(Sig)),
             apply(nx,2,mean) )
  bvec=c(1,tarRet )
  if (long_only) {
    meq=1
    Amat=Amat[-1,]
    Amat=rbind(Amat,
               diag(1,ncol(Sig)),
               rep(1,ncol(Sig)),
               rep(-1,ncol(Sig)))
    bvec=bvec[-1]
    bvec=c(bvec,
               rep(0,ncol(Sig)),.98,-1.02)
  }
  sol  <- solve.QP(Dmat=Sig, dvec, t(Amat), bvec, meq=meq)$solution 
}

steps=50
x=returns
 µ.b <- apply(X = x, 2, FUN = mean) 
long_only=TRUE
range.bl <- seq(from = min(µ.b), to = max(µ.b)*ifelse(long_only,1,1.6), length.out = steps) 
risk.bl <- t(sapply(range.bl, function(targetReturn) { 
  w <- MV_QP(x, targetReturn,long_only=long_only) 
  c(sd(x %*% w),w)  }))

weigthsl=round(risk.bl[,-1],4)
colnames(weigthsl)=colnames(x)
weigthsl
risk.bl=risk.bl[,1]
rets.bl= weigthsl%*%µ.b
fan=12
plot(x = risk.bl*fan^.5, y = rets.bl*fan,col=2,pch=21,
     xlab = "Annualized Risk ", 
     ylab = "Annualized Return", main = "long only EF with solve.QP")

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