Observing cycles through Bitcoin network profitability (3 new indicators)

Introducing 3 newly developed indicators to track the points of maximum selling and loss exhaustion in Bitcoin.

Bitcoin network profitability can be one of the best tools to find the moments of maximum selling force exhaustion, since these periods correspond to the moments of reversal and bottom finding.

Below we see the monthly return of Bitcoin, note that the periods of biggest declines are close to reversal.

Why does this occur?

We can understand capitulations in finance as events that generate strong selling pressure, this makes less experienced investors or other network participants who have a cross-cost basis (Current price below its average price), end up selling at a loss.

This sell-off at a loss purges the low-conviction ones, and the high-conviction ones who don't panic sell probably won't sell anymore. This means that everyone who wanted to sell out of fear, or because they had no other choice, has already sold and now the market can recover.

But how do we track these moments on-chain?

There are some on-chain metrics that calculate the amount of coins at a loss, both already realized (sold) and not realized. These metrics calculate the base cost of network currencies (Realized Price) and compare to the current market price.

With that in mind, I developed 3 on-chain indicators through these metrics that can help to understand in which periods these capitulations occurred in the history of Bitcoin.

Realized Loss BTC Terms Z-Score by @caueconomy

The first metric is calculated using the realized loss of the network, that is, the coins that were moved below their acquisition cost, therefore, sold at a loss. Above we see each peak in the indicator corresponding with the bottoms of previous cycles as well as in the current cycle.

However, to visualize in terms of BTC and not in dollars, it was necessary to divide the losses incurred in USD by the price in USD, which generates the amount in BTC realized.

Still to bring a better view compared to previous cycles, it was divided by the amount of coins in circulation. Thus, we have a comparison of the amount of capitulated coins compared to the amount of coins in circulation at the time of observation. For a better graphical view, the indicator was normalized using z-score, a statistical formula that normalizes data according to its standard deviation.

Realized PnL Oscillator by @caueconomy

The second indicator is a way of observing when the amount of coins sold at a loss exceeds those sold at a profit, remembering what was said at the beginning, the moment when most of the network is in loss are the moments of capitulation.

The indicator was created by dividing the sold coins into profit/loss and adding a 30-day exponential average to improve the visualization.

We clearly see periods of maximum depletion of loss sales reaching the top (Green line) on the chart. The last 3 “generational” cycle funds or by the theory of 4-year cycles in Bitcoin.

Here we already have a curiosity: we have not yet reached the point of maximum exhaustion in the amount of selling at a loss previously supported by the network.

Supply PnL Oscillator by @caueconomy

The last and perhaps the most accurate indicator of the 3 introduced here is an oscillator built by dividing the amount of currencies in loss and in profit not yet moved.

For better visualization, a simple arithmetic mean of 7 days was added. The result can be seen above, where the indicator demonstrates the periods after or during the final capitulations where we find the best entry points based on long-term risk/return.

Values ​​below the green line indicate maximum points for the amount of damage not yet realized, supported by the network. Whenever the indicator was below, it indicated long-term entries based on the capitulation process followed by reversal.

Final conclusions of the current market

What we can take away by putting the 3 indicators together is that we are in a process of capitulation typical of bear market funds, which signal great long-term buying opportunities.

On the other hand, as we have seen, 2 indicators have not yet signaled the “optimal point” or the maximum exhaustion of the selling force in the network, which may indicate that network participants can still withstand a higher level of loss when compared to cycles previous.

This does not make the allocation unfeasible, since many other indicators signal potential bottom, but it is good to keep in mind that there is still room for more short-term loss taking and that a signal in these 2 indicators could demonstrate the point of better use in a more aggressive.