Combining MVRV's From Different Time Frames to Measure Valuation
We have covered our MVRV metric quite extensively throughout the history of Santiment, with some great writeups and studies. Take our article about ETH concerns due to MVRV inflation and other bearish metrics. Yes, Ethereum's price did go on to scale all the way to $485 before dropping back in a hurry. But our 30-day MVRV metric did eventually predict that FOMO was going to hurt a lot of people, as we can see has historically been the case:
Generally anything close to +20% for the 7-day and 30-day MVRV is a major danger zone, according to the clip taken from our late July article on Ethereum above. Other long-term MVRV's can have a little more slack for extremely high overly inflated numbers. When average traders have returns above this mark in a short time frame, this has historically indicated that a correction would be imminent. After all, crypto trading is still a zero sum game in spite of the communal feeling Crypto Twitter may have you feeling and being on the same team on your way to your collective lambos.
So why was Ethereum able to continue its run-up to nearly $500 before finally getting the expected rug pulled out from traders' feet in late August? Well, for starters, not all MVRV time ranges were in the danger zone in late July. 30-day MVRV is often regarded as a nice middle-ground time range that markets often fluctuate with a long enough period of time for trader to react in advance, but short enough where fluctuations in the metric can be taken advantage of multiple times a week by traders reacting to the average profiting and hemorrhaging happening for an asset. 6-month and 12-month MVRV's were in their respective danger zones as well when we expressed concern in late July, as indicated by this screenshot. But notice how the 1-day average trader was actually at -1% returns, and the 7-day average trader was up a very modest +6%?
These very short-term time ranges indicated there could still be some room to run if FOMO really wanted to take over. And that's exactly what happened... and three days later, on July 31st, all five of these time ranges were in their respective danger zones in unison. The ETH price move from $315 on the 28th to $355 on the 31st was enough to push all MVRV's into their respective danger zones, with 1-day MVRV now at +6% instead of -1%. And sure enough, the price went through about 12 straight days of ranging from here before trader returns from various time ranges came back to Earth. As FUD kicked in and short-term MVRV's again went back into the negatives, this allowed ETH to propel even higher.
The moral of this story is that MVRV's tend to be most effective as an indicator when several different time ranges all align in greedily high or fearfully low return ranges. In this situation above, all trader average returns were close to as high as they historically get. You can see that this again happened on August 12th before price hit a local top a couple days later on August 14th:
And again on August 31st, all five of these MVRV's were uniformly in the green before the final local top (and 2020 high of $485) for Ethereum was hit:
This pattern of identifying when multiple MVRV's are all aligning in the positive range (or negative range) for average traders over different time periods, is not an easy one to identify. This is why we now have a Sansheets model to update our Sanbase PRO traders for several of the most popular assets at once. And a stacked chart like this one can show just how in sync the major MVRV timeframes are:
This image shows that the foundation assets like Bitcoin and Ethereum are showing all positive average returns for MVRV's from four major timeframes. Generally, this is when markets are really indicating that they're overheated.
This model is now available for anyone with Sanbase PRO status to find patterns that show a particularly uniform trend. We will be adding more assets in the near future, along with more refined and clear ways to read this model and see where the best buy and sell opportunities to profitably go against crowd FOMO and FUD mistakes in real-time.