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r - 运行博客中的确切代码时出现错误

问题描述

我在这里学习教程。几天前，我能够毫无错误地运行此代码并在我自己的数据集上运行它（获得此错误总是有点命中注定） - 但是现在我尝试运行代码并且我总是得到相同的错误。

solve.QP(Dmat, dvec, Amat, bvec = b0, meq = 2)
中的错误：约束不一致，无解！

我知道solver无法解方程，但我有点困惑为什么它以前可以工作，现在却不行……文章的作者有这段代码可以工作……

library(tseries)
library(data.table)
link <- "https://raw.githubusercontent.com/DavZim/Efficient_Frontier/master/data/mult_assets.csv"

df <- data.table(read.csv(link))

df_table <- melt(df)[, .(er = mean(value),
                         sd = sd(value)), by = variable]

er_vals <- seq(from = min(df_table$er), to = max(df_table$er), length.out = 1000)

# find an optimal portfolio for each possible possible expected return 
# (note that the values are explicitly set between the minimum and maximum of the expected returns per asset)
sd_vals <- sapply(er_vals, function(er) {
  op <- portfolio.optim(as.matrix(df), er)
  return(op$ps)
})

会话信息：

R version 3.5.3 (2019-03-11)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows >= 8 x64 (build 9200)

Matrix products: default

locale:
[1] LC_COLLATE=Spanish_Spain.1252  LC_CTYPE=Spanish_Spain.1252    LC_MONETARY=Spanish_Spain.1252
[4] LC_NUMERIC=C                   LC_TIME=Spanish_Spain.1252    

attached base packages:
[1] parallel  stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
[1] lpSolve_5.6.13.1  data.table_1.12.0 tseries_0.10-46   rugarch_1.4-0    

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.0                  MASS_7.3-51.1               mclust_5.4.2               
 [4] lattice_0.20-38             quadprog_1.5-5              Rsolnp_1.16                
 [7] TTR_0.23-4                  tools_3.5.3                 xts_0.11-2                 
[10] SkewHyperbolic_0.4-0        GeneralizedHyperbolic_0.8-4 quantmod_0.4-13.1          
[13] spd_2.0-1                   grid_3.5.3                  KernSmooth_2.23-15         
[16] yaml_2.2.0                  numDeriv_2016.8-1           Matrix_1.2-15              
[19] nloptr_1.2.1                DistributionUtils_0.6-0     ks_1.11.3                  
[22] curl_3.3                    compiler_3.5.3              expm_0.999-3               
[25] truncnorm_1.0-8             mvtnorm_1.0-8               zoo_1.8-4

标签： r

解决方案

tseries::portfolio.optim默认情况下不允许卖空，请参阅参数short。如果short = FALSE资产权重不能低于 0。并且由于权重总和必须为 1，那么单个资产的权重也不能高于 1。没有杠杆。

（可能，在早期版本的 tseries 中，默认值可能是short = TRUE. 这可以解释为什么它以前对你有用。）

您的目标回报 ( pm) 不能超过任何输入资产的最高回报。

解决方案 1：允许卖空，但请记住，这是一个不同的有效边界。（作为参考，请参阅任何讨论马科维茨优化的讲座或书籍。这个问题有一个数学解决方案，没有卖空限制。）

op <- portfolio.optim(as.matrix(df), er, shorts = T)

解决方案 2：将目标回报限制在最差和最佳资产回报之间。

er_vals <- seq(from = min(colMeans(df)), to = max(colMeans(df)), length.out = 1000)

这是获得的有效边界的图。

在此处输入图像描述

这是提供两种解决方案的完整脚本。

library(tseries)
library(data.table)
link <- "https://raw.githubusercontent.com/DavZim/Efficient_Frontier/master/data/mult_assets.csv"

df <- data.table(read.csv(link))

df_table <- melt(df)[, .(er = mean(value),
                         sd = sd(value)), by = variable]

# er_vals <- seq(from = min(df_table$er), to = max(df_table$er), length.out = 1000)
er_vals1 <- seq(from = 0, to = 0.15, length.out = 1000)
er_vals2 <- seq(from = min(colMeans(df)), to = max(colMeans(df)), length.out = 1000)

# find an optimal portfolio for each possible possible expected return 
# (note that the values are explicitly set between the minimum and maximum of the expected returns per asset)
sd_vals1 <- sapply(er_vals1, function(er) {
  op <- portfolio.optim(as.matrix(df), er, short = T)
  return(op$ps)
})

sd_vals2 <- sapply(er_vals2, function(er) {
  op <- portfolio.optim(as.matrix(df), er, short = F)
  return(op$ps)
})

plot(x = sd_vals1, y = er_vals1, type = "l", col = "red", 
     xlab = "sd", ylab = "er", 
     main = "red: allowing short-selling;\nblue: disallowing short-selling")
lines(x = sd_vals2, y = er_vals2, type = "l", col = "blue")

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