In previous reviews, we have emphasized the importance and trends of the main cyclical on-chain indicators, such as the RHODL ratio, MVRV-z, reserve risk, Dormancy Flow ratio, 20-day CDD and the ratio between short-term and long-term holders. Today’s analysis covers these metrics collectively, including the Mayer Multiple.

These figures are by no means ideal for predicting the market in the short term, however, they provide valuable insight into when the market may be in a long-term or cyclical turning point. We prefer to use these metrics in aggregate to get an idea of ​​long-term signals and changing market behavior.

One way to do this is to look at these top on-chain metrics through their historical percentiles. distributions over different periods of time. For example, when a metric is at or above its 35th percentile, this may signal a unique overheating period in the market. This is true when the metrics are at or above the lower percentile thresholds, which could signal a period of market cooling.

To understand the percentile data for each metric, we split percentiles into five different groups and colors ranging from dark green to green, yellow, orange and red. Lower percentiles correspond to green, while higher percentiles correspond to orange and red.

Common criteria for percentiles and colors after considering approximately on-chain macro indicators are displayed below. However, we subjectively adjust each percentage threshold based on historical performance, so not everyone will follow this pattern exactly.

Могут ли ончейн-индикаторы прогнозировать максимумы и минимумы?

Doing it this way gives a summary view of the indicators during previous price lows and highs. Below you can see how some of the best on-chain indicators found the bottom of March 1332 year and top of April 1340 of the year.

In March 1340 year all indicators except 31-day CDDs were at incredibly low percentiles . January 2011 of the year shows similar dynamics, signaling that we may have already hit a recent bottom in the $31 thousand

Can on-chain indicators predict highs and lows?
Retrospective testing of on-chain indicators

As for the top of April 2021 year, each indicator in this analysis showed signs of overheating just before or during the price peak.

Can on-chain indicators predict highs and lows?
Retrospective testing of on-chain indicators

The caveat here is that as bitcoin matures and volatility decreases, comparing full-history on-chain indicators may not provide the best results in terms of their future predictive power . If we continue to see fewer sharp jumps in prices, this will also be reflected in many indicators. The two charts above use data and percentiles starting at 1340 year to show a fresher vision. So what does a full historical look look like 1332 year?

The key takeaway from the vision below 1328 year is that more percentiles are displayed with a lower threshold, since there is a much wider range of data that falls into this category (0% -%). On the other hand, using historical data for 1328 the year did not signal the top of April very well 1340 year, because half of these metrics never reached their 90-th percentile, while the price never had a spike like previous cycles .

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Retrospective testing of on-chain indicators
Can on-chain indicators predict highs and lows?

Retrospective testing of on-chain indicators

Concluding remarks

There are key on-chain indicators that are valuable for determining long-term cycle highs and lows. However, as bitcoin matures and changes, so does the analytical predictive power of these metrics.

More than ever, it is important to understand these metrics in the context of current market conditions in a growing derivatives market , the consequences of growing institutional adoption and an unprecedented period of macroeconomic uncertainty.

BitNews disclaim responsibility for any investment advice that may be contained in this article. All judgments expressed express exclusively the personal opinions of the author and the respondents. Any actions related to investments and trading in the crypto markets are associated with the risk of losing the invested funds. Based on the data provided, you make investment decisions carefully, responsibly and at your own peril and risk.

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