Obtaining an error when running exact code from a blog - r

Question

I am following a tutorial here. A few days ago I was able to run this code without error and run it on my own data set (it was always a little hit and miss with obtaining this error) - however now I try to run the code and I always obtain the same error.
Error in solve.QP(Dmat, dvec, Amat, bvec = b0, meq = 2) :
constraints are inconsistent, no solution!
I get that the solver cannot solve the equations but I am a little confused as to why it worked previously and now it does not... The author of the article has this code working...
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)
})
SessionInfo:
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

Answer 1

tseries::portfolio.optim disallows short selling by default, see argument short. If short = FALSE asset weights may not go below 0. And as the weights must sum up to 1, also no individual asset weight could be above 1. There's no leverage.
(Possibly, in an earlier version of tseries default could have been short = TRUE. This would explain why it previously worked for you.)
Your target return (pm) cannot exceed the highest return of any of the input assets.
Solution 1: Allow short selling, but remember that that's a different efficient frontier. (For reference, see any lecture or book discussing Markowitz optimization. There's a mathematical solution to the problem without short-selling restriction.)
op <- portfolio.optim(as.matrix(df), er, shorts = T)
Solution 2: Limit the target returns between the worst and the best asset's return.
er_vals <- seq(from = min(colMeans(df)), to = max(colMeans(df)), length.out = 1000)
Here's a plot of the obtained efficient frontiers.
Here's the full script that gives both solutions.
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")