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Introduction To Stationary And Non-Stationary Processes

Check this article. It is interesting, according to this article we need to use two types of indicators that transform non stationary data into stationary data.

"Using non-stationary time series data in financial models produces unreliable and spurious results and leads to poor understanding and forecasting. The solution to the problem is to transform the time series data so that it becomes stationary. If the non-stationary process is a random walk with or without a drift, it is transformed to stationary process by differencing. On the other hand, if the time series data analyzed exhibits a deterministic trend, the spurious results can be avoided by detrending. Sometimes the non-stationary series may combine a stochastic and deterministic trend at the same time and to avoid obtaining misleading results both differencing and detrending should be applied, as differencing will remove the trend in the variance and detrending will remove the deterministic trend." 

Read more: http://www.investopedia.com/articles/trading/07/stationary.asp#ixzz2IjGOueCq

That means at least two types of oscillators need to be applied one that is using differencing and another that uses detrending. Hm... interesting.