Blockchain data can tell fascinating stories, but few are sifting through the transactions to identify the storylines. Recent blockchain forensic analysis on the fall of QuadrigaCX is a good example of this. A retroactive analysis of Quadriga's on-chain activities highlight just how dire their situation was – something that many now regret not researching earlier. The good news is, tools like Google's BigQuery and eth.events are making it increasingly easy to peer into blockchain data, and begin to tell these stories.
I've chosen to focus much of my ongoing analysis on a specific ERC-20 token, Chainlink. The entire Chainlink transaction data set (excluding internal transactions, and event data) weighs in at a very manageable 55MB.
Measuring Network Activity
There are a number of ways to visualize network activity, including a simple Daily Active Users approach – counting unique addresses transacting on-chain each day. I've been working on a few ways to visualize network activity in a more insightful way. I often start my research with a question:
Who has been most active on the network, and have they been reducing or increasing their position?
Even with a relatively small data set, grouping activity can be a helpful path to meaningful analysis. The Chainlink community created a way to group on-chain addresses using Marine Corps rankings based on how many LINK tokens are currently held. These ranks started as a meme to encourage accumulation, but I've found them to be very helpful during data analysis.
This interactive radar timeline chart shows daily net transaction value on the Chainlink network, broken down by group.
Addresses are grouped by Marine rank. The 18 Marine ranks are then divided into percentile groups in increments of 10, resulting in 180 total groups of addresses, based on their current LINK balance. The chart shows the daily net value of LINK transacted by each group in 2019. Addresses are only included if they have a LINK balance > 1 on the last day of the data set.