I have a model that generates alphas and sigma for a set of stocks. Have coded a long-only optimisation using CVXOPT by passing the function sol=solvers.qp(Q, p, G, h, A, b)
Now I would like to add two further optimisation problem to the script I already have so that I can also have results for a Long Short and Market Neural (sum weights = 0) portfolio. In order to do that I would like to use/import CVXPY without adding too many lines of code given I have already loaded up alphas sigma and weight bounds.
Below you can find the data I am loading to currently optimise a long only portfolio using CVXOPT. I will appreciate if anyone would be so kind to provide me with some help on how to set CVXPY to return an optimal Long Short and Market Neutral using those data. I could also share the whole code
### Parameters setup
Alpha = np.array(-np.transpose(opt.matrix(np.loadtxt('C:\Alpha.txt'))))
Var_Cov = np.loadtxt('C:\VAR_COV.txt')
n = len (Var_Cov)
r_min = 0.03
maxW = np.loadtxt('C:\maxW.txt')
minW = np.loadtxt('C:\minW.txt')
### Solve
solution = optimize_portfolio(n, Alpha, Var_Cov, r_min)
