  This article proposes an artificial data generating algorithm that is simple
and easy to customize. The fundamental concept is to perform random permutation
of Monte Carlo generated random numbers which conform to the unconditional
probability distribution of the original real time series. Similar to
constraint surrogate methods, random permutations are only accepted if a given
objective function is minimized. The objective function is selected in order to
describe the most important features of the stochastic process. The algorithm
is demonstrated by producing simulated log-returns of the S\&P 500 stock index.
