SciPy optimization with grouped bounds

Question

I am trying to perform a portfolio optimization that returns the weights which maximize my utility function. I can do this portion just fine including the constraint that we

Answer 1

After much time this seems to be the only solution that fits...

def solve_weights(Rt, b_ = None):

    W = np.ones(len(Rt.columns)) / len(Rt.columns)
    if  b_ is None:
        b_ = [(0.01, 1.0) for i in Rt.columns]
        c_ = ({'type':'eq', 'fun': lambda W: sum(W) - 1})
    else:
        covar = Rt.cov()
        c_ = ({'type':'eq', 'fun': lambda W: sum(W) - 1},
              {'type':'eq', 'fun': lambda W: sqrt(np.dot(W, np.dot(covar, W)) * 252) - risk_tgt})

    optimized = opt.minimize(fitness, W, args = [Rt], method='SLSQP', constraints=c_, bounds=b_)  

    if not optimized.success: 
        raise ValueError(optimized.message)

   return optimized.x  # Return optimized weights

class_cont = Rt.ix[0].copy()
class_cont.ix[:] = np.around(np.hstack(Rt.groupby(axis=1, level=0).apply(solve_weights).values),3)
scalars = class_cont.groupby(level=0).sum()
scalars.ix[:] = np.around(solve_weights((class_cont * port_rets).groupby(level=0, axis=1).sum(), list(model['tactical'].values)),3)

class_cont.groupby(level=0).transform(lambda x: x * scalars[x.name])