We study a class of fractional distributionally robust optimization problems that maximize ambiguous fractional functions representing reward-risk ratios. They have wide applicability in finance for the construction of portfolios based on risk-adjusted measures. We derive a new fully parameterized closed form to compute a bound on the size of the Wasserstein ambiguity ball and design a data-driven reformulation and solution framework. We specify new ambiguous portfolio optimization models for the Sharpe and Omega ratios. The computational study shows the applicability and scalability of the framework to solve quickly large, industry-relevant size problems, which cannot be solved in one day with state-of-the-art MINLP solvers. The cross-validation study attests to the out-of-sample performance of the proposed models.