在R包fPortfolio中使用目标风险或目标收益

如何解决在R包fPortfolio中使用目标风险或目标收益

我使用R软件包fPortfolio对滚动投资组合（自适应资产分配）进行投资组合优化。因此，我使用了回测功能。

我的目标是为一组资产构建投资组合，以实现预定义的目标收益（和最小的风险）或预定义的目标风险和最大的收益。

即使允许卖空（如5年前另一篇文章中所述）似乎也不起作用。此外，我不想在我的方法中允许卖空。

我无法弄清楚为什么更改目标收益或目标风险的值根本不会影响解决方案。 我哪里出问题了？

require(quantmod)
require(fPortfolio)
require(PortfolioAnalytics)

tickers= c("SPY","TLT","GLD","VEIEX","QQQ","SHY")
getSymbols(tickers)

data.raw = as.timeSeries(na.omit(cbind(Ad(SPY),Ad(TLT),Ad(GLD),Ad(VEIEX),Ad(QQQ),Ad(SHY))))
data.arith = na.omit(Return.calculate(data.raw,method="simple"))

colnames(data.arith) = c("SPY","SHY")

cvarSpec <- portfolioSpec(
  model = list(
    type = "CVAR",optimize = "maxReturn",estimator = "covEstimator",tailRisk = list(),params = list(alpha = 0.05,a = 1)),portfolio = list(
    weights = NULL,targetReturn = NULL,targetRisk = 0.08,riskFreeRate = 0,nFrontierPoints = 50,status = 0),optim = list(
    solver = "solveRglpk.CVAR",objective = NULL,params = list(),control = list(),trace = FALSE))

backtest = portfolioBacktest()
setWindowsHorizon(backtest) = "12m"

assets <- SPY ~ SPY + TLT + GLD + VEIEX + QQQ + SHY

portConstraints ="LongOnly"

myPortfolio = portfolioBacktesting(
  formula = assets,data = data.arith,spec = cvarSpec,constraints = portConstraints,backtest = backtest,trace = TRUE)  

setSmootherLambda(myPortfolio$backtest) <- "1m"
myPortfolioSmooth <- portfolioSmoothing(myPortfolio)
backtestPlot(myPortfolioSmooth,cex = 0.6,font = 1,family = "mono")