Dawn is expected. Data from multiple angles suggest that the market may have reached a local low.

With the new segmentation metrics we introduce in this article, we are now able to analyze in a more detailed way the specific moments when investors suffered severe unrealized losses and “throw up their hands” to the downside. In addition, we will introduce a new framework in this article to analyze and evaluate the exhaustion of selling potential of different investor groups over multiple time frames.

summary

• Long-term investors typically enjoy high profits during bull markets, so realized losses are primarily from the short-term holder group, whose investment activity can tell us a lot about market turning points during sell-off events.

• Given that market inflection points unfold from the inside out, we construct a framework using new metrics broken down by holding duration to describe seller exhaustion across investor groups within the day, week, and month.

• Within this framework, we leverage on-chain metrics to assess the unrealized and realized losses of different categories of investors to evaluate their investment decisions during the current market downturn.

Building a “Seller Exhaustion” Framework

When we evaluate the market conditions of the Bitcoin bull run in a macro time frame, it is not difficult to find that the price action at this time is generally characterized by volatile rises accompanied by corrections and consolidation. All investors know that financial markets do not just rise over time, and this volatile market creates a cat-and-mouse game between supply and demand, triggering local and global market adjustment events.

We first evaluate the realized losses of long-term and short-term holders. We can observe that the realized losses suffered by long-term holders occurred almost entirely during the macro recession cycle. However, in contrast, in the recent period, recent buyers have locked in losses in various periods of the market - indicating that such losses are almost entirely from short-term holders, and such losses from short-term holders are usually the main source of losses during bull markets.

In this report, we will use the fact that realized losses in the market come almost entirely from short-term holders to identify when sellers may have exhausted their investment potential. Our main goal is to identify market inflection points during corrections and consolidations in a bull-dominated market trend.

Here, we define seller exhaustion as the point at which an oversold event occurs, indicating that most of the people who intended to sell have completed their sales of the asset. Since there must be a buyer to match every seller in the trading pair, we can see what buyer demand absorbs the seller's selling, which helps us identify local bottoms in the market.

The impact of turning points in market trends tends to start from smaller time frames (minutes to hours) and then spread to larger time frames (days to months). To capture this spreading effect, we will use the newly released holding time segmentation indicator to separate two different groups of investors from the short-term holder group:

• Day traders (traders who hold positions within 24 hours)

• Weekly-Monthly Traders (Traders who hold their positions for 1 day to 1 month)

To model the times when investors experienced extreme economic stress, we will use three profit and loss metrics to help us understand the severity of the economic stress experienced by investors:

• MVRV ratio: Assess the unrealized profits or losses held by investors within the group.

• SOPR: Assess the extent of gains or losses of investors within a group.

• Realized Losses: Assess the extent of losses incurred by investors within the group in USD terms.

The Situation for Day Traders

First, let’s assess the group of day traders and analyze their investment activities using the corresponding 24-hour breakdown indicators.

Day traders are essentially the fastest-acting investor group, who are most sensitive to spot prices and can react almost instantly to any price fluctuations. Therefore, this group will generate a lot of signals of weakening selling power, but due to the large changes in the selected time period, this part of the data will generate more "noise" and interfere with the accuracy of the data we want.

We first evaluate the MVRV Z-score for the day trader group (we set a 90-point backtest to fully evaluate this metric). We can see that due to intraday price fluctuations, this metric has been declining from its previous high value, thus affecting the unrealized profits and losses of the day trader group.

Throughout the correction, we are always looking for seller exhaustion signals, and the MVRV Z-score is the key indicator we use - we highlight the current time points when the score is below the mean -1σ. These marked time points are the time when the unrealized losses of day traders increase, and are also the points where day traders are statistically under heavy investment pressure.

Next, we will combine the SOPR indicator to assess whether the day trader group has taken action to deal with unrealized financial pressures and whether those losses have turned into actual losses. Thirdly, we will analyze the Z-score points below the mean-1σ separately, because the emergence of these extreme values ​​represents a small-scale collapse of their confidence and a large selling pressure in the market at the same time.

Finally, we can further clarify the above observations by evaluating the dollar-denominated realized losses incurred by the day trader community to assess the extent of selling pressure. Here, we use the same Z-score framework to evaluate the realized losses of day traders over a 24-hour period as before - by finding specific points where the Z-score is above 2σ from the mean, we can also identify those periods that represent a significant setback in day trader confidence.

The situation of weekly-monthly traders

Now we turn our attention to the weekly-monthly traders. The first thing we observe in this group is a significant slowdown in the oscillations in market signals - this is what we would expect if we were to trade over a longer time horizon. This slowdown has undoubtedly smoothed the average cost basis of the weekly-monthly traders. We also see that this group is less sensitive to price changes and does not buy and sell as frequently as day traders, but they are also more likely to experience volatility. This is because the asset price fluctuates around their cost basis during their holding period.

By studying the MVRV Z-scores of weekly-monthly traders, we can see that compared with day traders, the MVRV Z-scores of weekly-monthly traders are not as sensitive to market price fluctuations as the former. Therefore, their MVRV indicator fluctuations are also more gradual than the former, and the overall market signals generated are less, but at the same time, the "noise" is also less severe.

The chart below highlights the cases where the MVRV Z-score for weekly-monthly traders is below average (i.e., negative), which we consider an important indicator because it suggests that a large portion of the burden of unrealized losses has shifted to weekly-monthly traders.

Similar to the previous analysis, we again use the Z-score corresponding to the SOPR ratio to confirm whether the weekly-monthly group of traders will experience a similar collapse of confidence when their financial pressure exceeds a certain threshold, thereby selling their losses and cashing out. Similar to the MVRV ratio Z-score, we extract these specific time points for separate analysis when they are below the average. We find that by observing and analyzing from this perspective, we also find a similar diffusion effect of realized losses spreading from shorter time periods to longer time periods.

At the end of our analysis, we can use the realized loss Z-score for further validation. In this analysis of this indicator, we define the Z-score values ​​above the mean + 2σ as outliers and use them to identify market locations where the weekly-monthly trading community has incurred significant losses (the losses incurred are also denominated in US dollars).

The Z-score framework is used to evaluate the realized loss metric. In this analysis of the metric, we define the Z-score values ​​above the mean + 2σ as outliers and use them to identify market locations where the weekly-monthly trading community has incurred significant losses (the losses incurred are also denominated in US dollars).

Summarize

The diverse on-chain data provides analysts and investors with a high degree of transparency into the different positioning, incentives, and actions of different market participants. We can use these tools and indicators to build models to assess how investor behavior is affected by market prices and how to change accordingly.

Using the new segmentation metric, we are able to further segment groups of short-term holders with different holding periods over time. We then use a combination of three on-chain metrics describing the profitability of these investor groups to identify specific points in time that indicate a possible collapse in their investment confidence or exhaustion of their investment potential, which are often accompanied by local market lows. This analytical framework helps us predict what motivations and behaviors investors typically have when identifying seller exhaustion points.