The MVO algorithm treats all variables as interrelated, assuming a complete cluster. In other words, traditional asset allocation does not recognize the complexity immerse in the data. This work presents some novel, robust and flexible methods with visual interpretations to construct risk-adjusted portfolios. Clustering methods showed a better trade-off between return and risk than MVO algorithm. The empirical results indicate that hierarchical algorithms have a better performance when building diversified portfolios measured by the Omega ratio. One of the most important results is the stable behavior of clustering-based portfolios addressing a special issue in financial markets, the volatility.