The first thing that we note is the nature, as a binary system. Given the different use case of each crypto asset, each tends to have a number of specialized services around its orbit. However, many other are shared (with a higher density at the BTC side, top left). If we start mapping the relationships between services as well, the problem will quickly become intractable. The common sources in both sets bring the variables of interest from 1000 to 196, and we can further prioritize sources by traffic contribution. But ultimately we use genetic programming as a way to detect invariance, and capture general relationships by using prices are the response variables. This treatment of the data reduces the variable set to 22, with time series distributions shown in Figure 2.