Bitcoin Quantile Model Explained in 60s 📊🔥 #Chart #Indicador #Metric
https://www.youtube.com/watch?v=Ack33vpTuXA
https://www.youtube.com/watch?v=Ack33vpTuXA
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Bitcoin Quantile Model Explained in 60s 📊🔥
Quick insights with @Sina_21st on the Bitcoin Quantile Model-----Schedule a personalized demo with 21st Capital and let us show you how to secure your Bitcoi...
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For people who are watching traditional Bitcoin cycle top #metric's and wondering... "Why haven't any of them triggered if the cycle top is in?"
Diminishing pressure is likely the answer.
As returns diminish, so do metric values.
In 2021, data like #MVRV, RHODL, and many others failed to reach their #cycle top zones. They had to be reworked with lower zones to potentially work in the next cycle.
Several popular log growth curve models and rainbow curves completely failed to achieve their cycle top levels, and during the bear market price broke through them to the downside and never returned inside the model.
Just because a model or data piece has been good in the past doesn't guarantee that going forward. Models that have price expectations that are too optimistic will break.
This is why it's important to use data from as many sources as possible to come to a good conclusion.
>> Rota Hodler
For people who are watching traditional Bitcoin cycle top #metric's and wondering... "Why haven't any of them triggered if the cycle top is in?"
Diminishing pressure is likely the answer.
As returns diminish, so do metric values.
In 2021, data like #MVRV, RHODL, and many others failed to reach their #cycle top zones. They had to be reworked with lower zones to potentially work in the next cycle.
Several popular log growth curve models and rainbow curves completely failed to achieve their cycle top levels, and during the bear market price broke through them to the downside and never returned inside the model.
Just because a model or data piece has been good in the past doesn't guarantee that going forward. Models that have price expectations that are too optimistic will break.
This is why it's important to use data from as many sources as possible to come to a good conclusion.
>> Rota Hodler