On May 6th, 2017, Bitcoin hit an all-time high in transactions processed on the network in a single day: it moved 375,000 transactions which accounted for a nominal output of about $2.5b. Average fees on the Bitcoin network had climbed over a dollar for the first time a couple days prior.
Coinmetrics was created to publish hard-to-acquire data about major public blockchains, and to promote some ratios we thought were instructive. Since the founding of this website, the field of cryptoasset valuation has matured and grown significantly. The cryptoassets in question also continue to grow and change, meriting thoughtfulness about various analytical tools. While users are more empowered than ever, uncertainty remains about a) whether ratio analysis is appropriate, b) how to interpret major ratios, and c) the shortcomings of such analyses. In this piece, we’ll discuss ratio analysis and discuss its shortcomings and some common mistakes. As always, we urge skepticism and restraint in the interpretation of our data.
Being certain is a lovely thing. Despite what many would allege about the poor finality of proof of work, the relative certainty it provides is part of the appeal. Once that inbound transaction is buried six confirmations deep, it’s almost certainly yours. Of course, even more certainty is achievable with an in-person cash transaction. But you can’t send those over the internet.
If you head over to coinmetrics.io/charts, you’ll see a new addition: time-series correlation charts. There are other places to find cryptoasset correlations: see cointrading.ninja and sifrdata, as well as the individual charts on onchainfx, but we wanted to build a tool to visualize multiple correlations of major cryptoassets on the same graph.