```
opt = Opt(:LD_SLSQP,4)
opt.lower_bounds = [0,0,0,0]
opt.ftol_abs = 1e-20
opt.min_objective = risk_budget_objective
equality_constraint!(opt,weights -> total_weight_constraint(x))
(optf,optx,ret) = optimize(opt,[0.25,0.25,0.25,0.25])
```

Sorrry for asking a silly question…

There must be something wrong with the arguments passed to the function but I couldn’t figure it out

The returned value of optx and numevals are the initial value and 1, relatively.

Thank you for your patience and kindness to read this.

The following is for reference, if needed:

```
using CSV,DataFrames,StatsBase,Statistics,LinearAlgebra,NLopt
df = DataFrame(CSV.File("D:\\Julia\\data.csv";select=["000852.SH","H11008.CSI","AU9999.SGE","CBA00301.CS"]))
dropmissing(df)
X = Matrix(df)
dfcov = cov(X)
function risk_budget_objective(weights::Vector,cov)
cov = dfcov
sigma = sqrt.(weights'*(cov'*weights))
MRC = (cov'*weights)./sigma
TRC = weights .* MRC
delta_TRC = [sum((i .- TRC).^2) for i in TRC]
return sum(delta_TRC)
end
function total_weight_constraint(x::Vector)
return sum(x)-1.0
end
opt = Opt(:LD_SLSQP,4)
opt.lower_bounds = [0,0,0,0]
opt.ftol_abs = 1e-20
opt.min_objective = risk_budget_objective
equality_constraint!(opt,weights -> total_weight_constraint(x))
(optf,optx,ret) = optimize(opt,[0.25,0.25,0.25,0.25])
```