Exponential growth of the complexity in the financial market requires more and more the use of computational quantitative approach in any products or services that investment banks offer, not only in the portfolio management. However, despite the huge improvement of the computational power up to date, the investment banks’ trading desks do not fully integrate the optimization process, still using primitive methods such as the manual calculation. The main reason is that the classical optimization process, originally proposed by Harry Markowitz in 1952 is very sensitive to the estimation error of inputs making the model limited to the real world application. Moreover, the classical model does not consider the stochastic characteristics of the real world. This work has two main proposes. The first, build and implement the robust quantitative investment framework and the second, modify and adapt a more robust model to the real world investment bank’s trading desk. We use the Black-Litterman model and the robust optimization process to accomplish the objective that may help to maximize the economic gain of the institution.