我尝试使用SharpeRatio有一个目标函数来优化我的投资组合，但我得到以下错误：
objective name SharpeRatio generated an error or warning: Error in t(w) %*% M3 : requires numeric/complex matrix/vector arguments
我搜索过，似乎问题与重量有关，但我找不到解决它的方法。
下一个代码复制错误：
library(PortfolioAnalytics)
data(edhec)
asset_names <- colnames(edhec)
port_spec <- portfolio.spec(asset_names)
port_spec <- add.constraint(portfolio = port_spec, type = "weight_sum", min_sum = 0.99, max_sum = 1.01)
port_spec <- add.constraint(portfolio = port_spec, type = "long_only")
port_spec <- add.objective(portfolio = port_spec, type = "return", name = "SharpeRatio", FUN = "StdDev")
opt_DE <- optimize.portfolio(R = edhec, portfolio = port_spec, optimize_method = "DEoptim", search_size=5000, trace = TRUE, traceDE = 0)
已请求， 会话信息()
R version 3.4.3 (2017-11-30)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows >= 8 x64 (build 9200)
Matrix products: default
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] FactoMineR_1.39 nFactors_2.3.3 lattice_0.20-35
[4] boot_1.3-20 psych_1.7.8 MASS_7.3-47
[7] PortfolioAnalytics_1.0.3636 PerformanceAnalytics_1.4.3541 foreach_1.4.4
[10] xts_0.10-1 zoo_1.8-0
loaded via a namespace (and not attached):
[1] cluster_2.0.6 leaps_3.0 mnormt_1.5-5 scatterplot3d_0.3-40
[5] quadprog_1.5-5 ROI_0.3-0 TTR_0.23-2 tools_3.4.3
[9] quantmod_0.4-12 parallel_3.4.3 grid_3.4.3 nlme_3.1-131
[13] registry_0.5 iterators_1.0.9 yaml_2.1.16 GenSA_1.1.7
[17] codetools_0.2-15 curl_3.1 slam_0.1-42 ROI.plugin.quadprog_0.2-5
[21] compiler_3.4.3 flashClust_1.01-2 DEoptim_2.2-4 foreign_0.8-69
我建议查看PortfolioAnalytics Demo文件。特别是其中之一
演示最大夏普比率：
https://github.com/R-Finance/PortfolioAnalytics/blob/master/demo/demo_max_Sharpe.R
将特别有用地参考。阅读一些代码和注释后，您将看到一些内容。首先，您指定了冲突的参数，例如type = "return", name = "SharpeRatio", FUN = "StdDev".
"return"是一种约束类型，"StdDev"是"risk"约束的名称，"SharpeRatio"是您要求解的。
如果使用"ROI"方法进行优化，则需要指定要在优化"maxSR=TRUE"中最大化夏普比率，如果要使用"DEOptim"优化方法，则需要放宽杠杆约束。
每个示例可以在下面找到。它们直接取自上面引用的演示文件。
library(PortfolioAnalytics)
# Examples of solving optimization problems to maximize mean return per unit StdDev
data(edhec)
R <- edhec[, 1:8]
funds <- colnames(R)
# Construct initial portfolio
init.portf <- portfolio.spec(assets=funds)
init.portf <- add.constraint(portfolio=init.portf, type="full_investment")
init.portf <- add.constraint(portfolio=init.portf, type="long_only")
init.portf <- add.objective(portfolio=init.portf, type="return", name="mean")
init.portf <- add.objective(portfolio=init.portf, type="risk", name="StdDev")
init.portf
# The default action if "mean" and "StdDev" are specified as objectives with
# optimize_method="ROI" is to maximize quadratic utility. If we want to maximize
# Sharpe Ratio, we need to pass in maxSR=TRUE to optimize.portfolio.
maxSR.lo.ROI <- optimize.portfolio(R=R, portfolio=init.portf,
optimize_method="ROI",
maxSR=TRUE, trace=TRUE)
maxSR.lo.ROI
# Although the maximum Sharpe Ratio objective can be solved quickly and accurately
# with optimize_method="ROI", it is also possible to solve this optimization
# problem using other solvers such as random portfolios or DEoptim. These
# solvers have the added flexibility of using different methods to calculate
# the Sharpe Ratio (e.g. we could specify annualized measures of risk and return).
# For random portfolios and DEoptim, the leverage constraints should be
# relaxed slightly.
init.portf$constraints[[1]]$min_sum=0.99
init.portf$constraints[[1]]$max_sum=1.01
# Use DEoptim
maxSR.lo.DE <- optimize.portfolio(R=R, portfolio=init.portf,
optimize_method="DEoptim",
search_size=2000,
trace=TRUE)
希望这有所帮助;通常我发现 R 中许多更复杂的包都会有演示文件来帮助您入门。