I'll go ahead and post a practical answer for you, and we can both see if someone can post a theoretical explanation.
The shape you see comes from the offset in your data. If you were measuring voltage, I'd say you have an AC signal on a DC offset.
I don't know what you are measuring, but it spends most of its time at around 100. It has short intervals where it changes drastically, and makes small changes almost constantly.
You have two ways to get rid of the triangle.
Take the average of all data points and subtract that average from all the data points.
Use a high pass filter to remove the offset.
If this were an audio signal, you could pick a lower cutoff frequency and be happy - there's some point where the audio frequencies are too low to be interesting.
Since you don't mention the source of the data (could be the sum of loans and borrows of elephants in an elephant lending library for all I know,) I'd go with the first option.
If it is some physical quantity, then you may want to consider the high pass filter, after all.
If the process generating the signal has some natural low frequency, then you could use that as your cutoff.
Or, you set the cutoff by the longest time period you want to consider.
If your data set represents 1 second, then autocorrelation for more than 1second is pointless - you could set the cutoff for one hertz. At any rate, you would use 1/(time period) as the cutoff frequency.
If you have all the data in blocks at hand, you can use filtfilt to do your filtering. That preserves the phase and the absolute timing of your signal.