Hello, I tried to run this code and it seems to produce no errors but at the end i dont get the plot for some reason.
Please help me out ive been staring at this for hours
library(PortfolioAnalytics)
library(quantmod)
library(PerformanceAnalytics)
library(zoo)
library(plotly)
library(foreach)
library(DEoptim)
library(iterators)
library(fGarch)
library(Rglpk)
library(quadprog)
library(ROI)
library(ROI.plugin.glpk)
library(ROI.plugin.quadprog)
library(ROI.plugin.symphony)
library(pso)
library(GenSA)
library(corpcor)
library(testthat)
library(nloptr)
library(MASS)
library(robustbase)Get data
getSymbols(c(“MSFT”, “SBUX”, “IBM”, “AAPL”, “^GSPC”, “AMZN”))
Assign to dataframe
Get adjusted prices
prices.data ← merge.zoo(MSFT[,6], SBUX[,6], IBM[,6], AAPL[,6], GSPC[,6], AMZN[,6])
Calculate returns
returns.data ← CalculateReturns(prices.data)
returns.data ← na.omit(returns.data)Set names
colnames(returns.data) ← c(“MSFT”, “SBUX”, “IBM”, “AAPL”, “^GSPC”, “AMZN”)
Save mean return vector and sample covariance matrix
meanReturns ← colMeans(returns.data)
covMat ← cov(returns.data)Start with the names of the assets
port ← portfolio.spec(assets = c(“MSFT”, “SBUX”, “IBM”, “AAPL”, “^GSPC”, “AMZN”))
Box
port ← add.constraint(port, type = “box”, min = 0.05, max = 0.8)
Leverage
port ← add.constraint(portfolio = port, type = “full_investment”)
Generate random portfolios
rportfolios ← random_portfolios(port, permutations = 5000, rp_method = “sample”)
Get minimum variance portfolio
minvar.port ← add.objective(port, type = “Risk”, name = “var”)
Optimize
minvar.opt ← optimize.portfolio(returns.data, minvar.port, optimize_method = “random”,
rp = rportfolios)Generate maximum return portfolio
maxret.port ← add.objective(port, type = “Return”, name = “mean”)
Optimize
maxret.opt ← optimize.portfolio(returns.data, maxret.port, optimize_method = “random”,
rp = rportfolios)Generate vector of returns
minret ← 0.06/100
maxret ← maxret.opt$weights %*% meanReturnsvec ← seq(minret, maxret, length.out = 100)
eff.frontier ← data.frame(Risk = rep(NA, length(vec)),
Return = rep(NA, length(vec)),
SharpeRatio = rep(NA, length(vec)))frontier.weights ← mat.or.vec(nr = length(vec), nc = ncol(returns.data))
colnames(frontier.weights) ← colnames(returns.data)for(i in 1:length(vec)){
eff.port ← add.constraint(port, type = “Return”, name = “mean”, return_target = vec[i])
eff.port ← add.objective(eff.port, type = “Risk”, name = “var”)eff.port ← add.objective(eff.port, type = “weight_concentration”, name = “HHI”,
conc_aversion = 0.001)
eff.port ← optimize.portfolio(returns.data, eff.port, optimize_method = “ROI”)
eff.frontier$Risk[i] ← sqrt(t(eff.port$weights) %% covMat %% eff.port$weights)
eff.frontier$Return[i] ← eff.port$weights %*% meanReturns
eff.frontier$Sharperatio[i] ← eff.port$Return[i] / eff.port$Risk[i]
frontier.weights[i,] = eff.port$weights
print(paste(round(i/length(vec) * 100, 0), “% done…”))
}feasible. sd ← apply(rportfolios, 1, function(x){
return(sqrt(matrix(x, nrow = 1) %% covMat %% matrix(x, ncol = 1)))
})feasible.means ← apply(rportfolios, 1, function(x){
return(x %*% meanReturns)
})feasible. sr ← feasible.means / feasible. sd
p ← plot_ly(x = feasible. sd, y = feasible.means, color = feasible. sr,
mode = “markers”, type = “scattergl”, showlegend = F,marker = list(size = 3, opacity = 0.5, colorbar = list(title = "Sharpe Ratio"))) %>%add_trace(data = eff.frontier, x = ‘Risk’, y = ‘Return’, mode = “markers”,
type = “scattergl”, showlegend = F,
marker = list(color = “#F7C873”, size = 5)) %>%layout(title = “Random Portfolios with Plotly”,
yaxis = list(title = “Mean Returns”, tickformat = “.2%”),
xaxis = list(title = “Standard Deviation”, tickformat = “.2%”),
plot_bgcolor = “#434343”,
paper_bgcolor = “#F8F8F8”,
annotations = list(
list(x = 0.4, y = 0.75,
ax = -30, ay = -30,
text = “Efficient frontier”,
font = list(color = “#F6E7C1”, size = 15),
arrowcolor = “white”)
))