In this paper, we are dealing with financial high frequency data; any time an order reaches the market, any time a cancellation or transaction occurs, a new record is made, ending up with a huge amount of data. Hence the time interval between two events is not fixed, forbidding the use of standard statistical tools. In the recent literature, several authors proposed time deformation techniques to deal with this problem. The practical importance of time deformation is to give a preprocessing technique to obtain a regularly spaced grid of data. The main contribution of this paper is to survey most of the time deformations proposed in the literature in a general setting and to compare them from both a statistical and financial point of view. We provide a new trading strategy in which the time to invest is endogeneous. Moreover, we highlight the fact that changing time scale can improve the daily gain following such a strategy.