def objective(x):
    Q = np.asmatrix(DF.cov())       # Covariance matrix
    x = np.asmatrix(x)
    return x.transpose() * Q * x

minimize(objective, x0, method='Nelder-Mead',options={'xtol': 1e-6, 'disp': True})

def der_objective(x):
    Q = np.asmatrix(DF.cov())       # Covariance matrix
    x = np.asmatrix(x)
    return Q * x