jcrichard / pyrb Goto Github PK

Hi, thanks for sharing and for doing reproducible research, highly appreciated.
Not an issue really, but was wondering how to achieve negative weights in the optimal solution if feasible at all? Noticed in your notebook example below and thought that weights were constrained to be less than -0.3 and -0.05, but x is all positives. So, can you explain how constraints work? I did try bounds=[-1,1] but still would never get negative weights.

C = np.array([[0,0,0,0,-1.0,-1.0,-1.0,-1.0],[1,-1,0,0,1,-1,0,0]])
d = [-0.3,-0.05]

CRB = ConstrainedRiskBudgeting(cov,C=C,d=d)
CRB.solve()
print(CRB)

solution x: [24.5238 28.6911 9.5175 7.2674 6.9707 7.8033 9.2305 5.9956]

When I upgrade the version of NumPy from 1.19.4 to 1.20.2, The program can not run.

Hi,

I am very interested in testing risk parity. When installing this package, I realize that I need quadprog and it requires MS Visual C++. I am wondering if there's any workaround? It would be nice if I don't need to install it. Thanks.

• Jaehyuk

error: Microsoft Visual C++ 14.0 or greater is required. Get it with "Microsoft C++ Build Tools": https://visualstudio.microsoft.com/visual-cpp-build-tools/****