Now that we have our algorithm coded, let's look at practical applications for this method on real data. We will start by understanding how the algorithm performs, so that we can determine where we might use it.
So, what are the characteristics of this algorithm? Below is a list of strengths and weaknesses.
The advantages are as follows:
The algorithm is general, lending itself well to both stream based and Spark implementations
The theory is simple, yet effective
The implementation is fast and efficient
The result is visual and interpretable
The method is stackable and allows for multi scale studies; this is very simple when using Spark windows
The disadvantages are as follows:
A lagging indicator the algorithm finds trend reversals that occurred in the past, and cannot be used directly to predict a trend change as it happens
The lag accumulates for higher scales, meaning much more data (and thus time lag) is required to find...