Original title: 《Breakdown Metrics Applied: Identifying Local Bottoms in a Bull Market》
Original author: Ukaria OC, CryptoVizArt, Glassnode
Original translation: LadyFinger, BlockBeats
Editor's Note:
In the rapid evolution of the digital asset market, accurate data analysis has become the key for traders to grasp the pulse of the market. Glassnode's newly launched 28 segmented indicators also mark a major leap in the field of on-chain analysis. These indicators not only provide in-depth insights into the behavior of short-term holders, but also provide traders with powerful tools to identify market bottoms and optimize trading strategies.
In an important development in the field of on-chain analysis, Glassnode recently launched a set of 28 new indicators designed to provide a more nuanced perspective on the digital asset market. You can read our original announcement here to learn more.
These granular indicators are a game changer for traders. They provide highly detailed and intuitive insights that can be translated into potential buy and sell signals. One powerful use of these new indicators is to identify exhaustion levels of sellers of different age groups among short-term holders.
Applying our newly segmented indicators, traders can now more precisely determine the severity of unrealized losses and stop-loss timing, which often marks market bottoms and potential entry points during bull markets. This report explores this framework in detail and is summarized below, providing actionable insights for traders aiming to optimize market entry or DCA strategies.
In the context of on-chain analysis, long-term holders (LTH) and short-term holders (STH) represent groups with different behaviors and market impacts. Short-term holders are often new entrants or speculative traders who are more sensitive to price changes. During bull markets, these investors are often the ones who lose the most because they are more likely to be swayed by market fluctuations and sell. This phenomenon makes the analysis of short-term holders particularly valuable when used to identify market bottoms.
By segmenting the short-term holder group by age, we help traders more effectively identify periods of seller exhaustion. For example, we can observe how unrealized pressure and capitulation events spread within the short-term holder group, starting from the shortest timeframe (1 day) and spreading to longer timeframes (1 week to 1 month and even longer). This inside-out progression provides clearer signals of potential bottoms and market reversals.
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In other words, through the insights provided by more granular indicators, traders can detect the confluence of capitulation events across different short-term timeframes, greatly increasing the likelihood of identifying a short-term bottom.
To identify states of seller exhaustion, we use a range of key indicators that provide insight into unrealized and realized losses within the short-term holder community. The framework consists of the following indicators:
STH MVRV by Age:Market Value to Realized Value (MVRV) measures the unrealized profits or losses of an asset. When MVRV is significantly below average, it indicates a high level of unrealized losses among its holders. This helps detect early signs of seller pressure, as significant unrealized losses often precede actual selling activity by short-term holders.
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STH SOPR by Age: The Spent Output Profit Ratio (SOPR) provides insight into whether the unrealized financial stress observed in MVRV is being acted upon. A negative SOPR Z-score indicates that short-term holders are capitulating, selling their assets at a loss and exacerbating sell-side pressure.
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STH Realized Loss by Age: This metric assesses the magnitude of realized losses by short-term holders. High realized loss values confirm that the sell-side pressure identified by the MVRV and SOPR indicators has emerged, validating periods of seller exhaustion by quantifying the magnitude of realized losses.
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By using Z-Scores, we normalize these indicators, making it easier to compare and identify significant deviations from the mean. Z-Scores highlight periods of extreme behavior, making it easier to detect when unrealized and realized losses are unusually high. This normalization helps identify true periods of seller exhaustion, providing traders with clearer signals by filtering out noise and focusing on statistically significant events.
Using Glassnode’s new Segmentation Indicator to identify points of seller exhaustion during a bull market can provide many advantages to traders. Here are the main benefits:
Early Detection of Local Bottom: By identifying moments of severe unrealized losses and capitulation among short-term holders, traders can identify market bottoms before they become apparent, providing opportunities for early entry points.
Optimized Dollar Cost Averaging (DCA) Strategies: Understanding periods of seller exhaustion allows traders to more effectively implement DCA strategies, reducing average purchase costs by purchasing assets at lower prices at market bottoms.
Enhanced Market Timing: By observing how selling pressure propagates within the short-term holder community from the shortest timeframes to longer timeframes, traders can better time market entries and exits to maximize profits and minimize losses.
Strategic Flexibility: Segmenting short-term holders by age group allows traders to apply this framework to a variety of trading strategies, from day trading to swing trading to ensure they can adjust their approach based on specific market conditions. By leveraging the insights provided by our new segmented indicators, traders can gain a competitive advantage in the market and make more strategic and profitable decisions.
Currently, these powerful granular cohorts are only available on the Glassnode Enterprise plan. A full overview of the new metrics, dashboards, and documentation is available here.
If you are an institutional trader or investor and would like to leverage these metrics to gain a deeper edge when trading the Bitcoin and Ethereum markets, contact our institutional sales team today.
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