One of the big characteristics that often separates great traders from mediocre ones is the willingness and ability to sit on the sidelines when it's necessary.
A brief adage we often mention is the distinction between seeing a setup and looking for one. Be patient, prepare your perfect swing, and the setups will come to you.
There's lots of chatter about the Ethereum $ETH merge, and rightfully so.
It's a significant development for the entire space and is paving an ideological divide in the community from proponents of proof-of-work (PoW) to that of proof-of-stake (PoS).
But when it comes to our job as technicians -- that is, following money flow -- we like to sweep the narrative aside and see what's really happening.
Broadly speaking, risk assets have caught a well-received bid over the last few weeks. This recent period has proven to be an incredibly risk-on tape.
When it comes to crypto, the big narrative driving capital markets seems to be the upcoming Ethereum $ETH merge. This update will see the network move away from proof-of-work (PoW) to a proof-of-stake (PoS) framework.
Of course, as technicians, we naturally follow money flow as opposed to getting stuck in the weeds of narratives. It's quite clear to see traders are bidding Ethereum leading into the merge in what looks like a "buy the rumor, sells the news" event.
In recent weeks, we've found ourselves revisiting the following question: Is Ethereum outperforming Bitcoin a bullish characteristic for the asset class?
We addressed this question a year ago. The conclusion we reached back then was that, while ETH outperforming BTC is not a necessary condition for a bull market, it’s always an encouraging sign when we see it.
In light of the recent disparity in performance between ETH and BTC, we thought we'd re-examine this topic.
After some work and many days of cleaning data, we're happy to introduce some great new metrics we're working up to supplement our cryptocurrency research.
Before we dive in, we want to set a brief framework.
It’s important we lay down a foundation before analyzing this data; there’s little point dedicating the man hours to the research if we don’t know why and how to apply it.