def p_portfolio(I,sigma,r,alpha,beta):
    """p_portfolio -- modified markowitz model for portfolio optimization.
    Parameters:
        - I: set of items
        - sigma[i]: standard deviation of item i
        - r[i]: revenue of item i
        - alpha: acceptance threshold
        - beta: desired confidence level
    Returns a model, ready to be solved.
    """

    model = Model("p_portfolio")

    x = {}
    for i in I:
        x[i] = model.addVar(vtype="C", name="x(%s)"%i)  # quantity of i to buy
    rho = model.addVar(vtype="C", name="rho")
    rhoaux = model.addVar(vtype="C", name="rhoaux")

    model.addCons(rho == quicksum(r[i]*x[i] for i in I))
    model.addCons(quicksum(x[i] for i in I) == 1)

    model.addCons(rhoaux == (alpha - rho)*(1/phi_inv(beta))) #todo
    model.addCons(quicksum(sigma[i]**2 * x[i] * x[i] for i in I) <=  rhoaux * rhoaux)

    model.setObjective(rho, "maximize")

    model.data = x
    return model
p_portfolio -- modified markowitz model for portfolio optimization.
    Parameters:
        - I: set of items
        - sigma[i]: standard deviation of item i
        - r[i]: revenue of item i
        - alpha: acceptance threshold
        - beta: desired confidence level
    Returns a model, ready to be solved.