Identifying Seller Exhaustion Across Multiple Timeframes

From UkuriaOC, CryptoVizArt, Glassnode

Executive Summary:

Establishing a Seller Exhaustion Framework

When evaluating a Bitcoin bull market over a macro timeframe, price action can be broadly characterised by elevated volatility to the upside, with corrections and consolidations along the way. All investors are aware that financial markets do not move upwards only over time, and which creates the sort of cat-and-mouse game between supply and demand which creates local and global correction events.

We will start by assessing the Realized Loss for both Long and Short-Term Holders. We can observe that losses taken by LTHs 🔵 almost exclusively occur around macro cycle downturns. Conversely, losses are locked in by recent buyers throughout all phases of the market, and are typically the dominant source of loss during bull market.

Live Chart

In this report, we will take advantage of the fact that the Short-Term Holder cohort is the dominant group locking in losses, and use this to identify periods of seller exhaustion. The goal is to identify inflection points during corrections and consolidations within a dominant bull trend.

Here, we define Seller Exhaustion as a point of oversold conditions, where a majority of the sellers who are likely to sell, and done so. Since every seller is matched with a buyer, we can simultaneously gauge periods when this sell-side is absorbed by an influx of buy-side demand, which helps establish a local bottom.

Inflection points in market trends tend to reverberate upwards from smaller timeframes (minutes to hours) through to larger timeframes (days to months). In order to capture this effect, we will utilize our newly released age breakdown metrics to isolate two distinct sets of investors from within the STH cohort:

In order to model points of extreme financial pressure and reaction, we call upon three profit/loss metrics which help us establish the severity of the pressure investors are experiencing.

💡View all charts in this edition in  The Week On-chain Dashboard .

Daily Traders

Initially, we start with an assessment of the Daily Trader group, and utilize the 24hr age breakdown.

This is inherently the fastest moving cohort, and the most sensitive to the spot price, reacting near instantaneously to any and all fluctuations in price action. Thus, this cohort will produce a large amount of seller exhaustion signals, but with a larger proportion of noise due to the erratic nature of the selected timeframe.

Live Workbench

First, we evaluate the MVRV Z-Score (with a 90 data point look-back period) for the Day Trader cohort. We can see this metric consistently flips from high to low values as a result of the intraday volatility of price, which then impacts the unrealized profit/loss held by this cohort.

Since we are looking for seller exhaustion signals during a correction, we isolate points when the MVRV Z-Score trades more than -1σ below the mean. These are points of heightened unrealized loss, and points of statistically significant pressure placed upon Day Traders.

Live Workbench

Next, we shall incorporate the SOPR metric to assess whether the unrealized financial pressure was acted upon, and whether those losses were crystallized, or not. Again, we isolate for Z-Score values more than -1σ below the mean, which identifies periods of micro-capitulation, and large sell-side pressure.

Live Workbench

Finally, we can bolster the above observations by assessing the USD denominated realized loss locked in by the Day Trader cohort. Here, we are evaluating the absolute magnitude of the sell-side pressure. Keeping consistency, and applying a similar Z-Score framework to 24hr realized losses, we are able to confirm periods of micro-capitulation, indicated when values are 2σ above the mean.

Live Workbench

Weekly-Monthly Traders

Moving onto the Weekly-Monthly traders, we first observe that the oscillation within the signal has slowed noticeably. This is the expected result as we expand up to longer term age bands, which smooths out the average cost basis. This cohort is less price sensitive than the Day Trader group, however they are also more likely to experience volatility as the spot price fluctuates around their cost basis during their holding time.

Live Workbench

Investigating the 1d-1m MVRV Z-Scores, we can see that this variant is less responsive to price action compared to the 24hr variant. As a result, this MVRV variant is slower to move, generates fewer signals overall, but also has less noise overall.

The chart below highlights when the MVRV Z-Score trades below the mean (negative Z-Score values), as an indicator that significant unrealized losses have transferred up to the larger 1d-1m age band.

Live Workbench

Again, we use the SOPR Ratio Z-Score to confirm whether these investors are under sufficient pressure to capitulate and lock in those losses. Similar to the MVRV Ratio Z-Score, we isolate points when the Z-Score trades below the mean. This provides a degree of confluence that realized losses are starting to reverberate from smaller to larger timeframes.

Live Workbench

Lastly, we can establish one final layer of confluence using the with the Realized Loss Z-Score. In this instance, we distinguish for Z-Score values trading +2σ above the mean to confirm a significant USD denominated loss has been locked in by the Weekly-Monthly swing trader cohort.

Live Workbench

Summary and Conclusions

On-chain data provides analysts and investors with a remarkable degree of transparency into the positioning, incentives, and actions of various market participants. We can use these tools to build models and tools for assessing how investor behavior changes in response to price action.

Utilizing our newly released breakdown metrics, we are able to isolate individual subsets of investors using the heuristic of age bands, as a proxy for their holding time. We then use confluence across three onchain metrics describing the profitability of these investor groups to identify points of micro-capitulation, which tend to accompany local market lows.

This framework helps us think about what incentives and actions we would expect to see when identifying points of seller exhaustion.