converting back to raw/original scale from time series tranformations and standard deviation

If I have a time series of Xt observations. I convert them to returns by:

Rt = Ln(Pt) - Ln(Pt-1).


I then calculate the 20 period moving average and subtract from the Return to find the de-trended return DRt:

DRt = Rt- MA(Rt)


Lastly I calculate the 20 period Moving Average of the de-trended return and calculate the standard deviation SD using:

SDt = SQRT(SUM(MA(DRt)-DRt)^2/20)


So basically I am doing a differencing and de-trending on time series, and then finding the standard deviation.

My question is how do I convert back to the original scale the value MA(DRt) + SDt? I cant seem to get the transformations back correctly. I have done this:

SDt + MA(DRt) + MA(Rt) = Rt,


so

exp(Rt) = Yt - Xt,


where Yt is the value of SDt +MA(DRt) in terms of the original time series.

therefore,

Yt = exp(Rt) + Xt


or in other words

Yt = exp(SDt + MA(DRt) + MA(Rt)) + Xt


Pretty sure this is not correct!

Also is there a better math function toolkit I can use to write out more clearly what I am doing on stackexchange?

Thanks for any help.