po3.py

import cvxpy as cp

# compute covariance matrix (df being the dataframe of historical returns)
Sigma = np.cov(df.transpose())
# number of assets
n = Sigma.shape[0]
# average returns
mu = df.mean().values
# asset SDs
asset_vols = np.sqrt(Sigma.diagonal())
# variable to optimize over - portfolio weights
w = cp.Variable(n)
# objectives to optimize
# portfolio return
ret = mu.T @ w 
# volatility
vol = cp.quad_form(w, Sigma)

prob = cp.Problem(cp.Minimize(vol),  # minimize volatility
                  [cp.sum(w) == 1,   # sum of weights = 1
                   w >= 0]           # weights > 0 (long-only)
                 )
prob.solve()
wts = [float('%0.4f' % v) for v in w.value]
minvol = vol.value