分析師可以使用許多模型和指標來評估周期中任何給定點的市場風險。在本文中，我們將特別將“風險”視為比特幣現貨價格大幅下跌的風險。

因此，“高風險?”被定義為市場可能陷入投機泡沫的點。相比之下，“低風險?”環境被認為是大部分投機性過剩已被清除的環境，并且市場更有可能處于底部形成模式內。

價格泡沫

作為第一個構建塊，我們監控價格與兩個長期均值回歸基線的偏差：

• MVRV 模型

  ?：該模型衡量現貨價格與市場整體成本基礎（已實現價格）之間的比率。

• Mayer Multiple

  ：利用 200D-SMA 作為技術周期中線，衡量相對于該基線的溢價或折價。

在下圖中，我們結合MVRV

和

模型定義了以下風險類別

。

目前現貨價格為 42,900 美元，實現價格和 200 天移動平均線交易價格分別為 22,800 美元和 34,100 美元。這使市場處于

環境中。

衡量供應盈利能力

利潤中的供應百分比 (PSIP) 指標衡量成本基礎低于當前現貨價格的代幣比例。隨著投資者看到獲利的動機不斷增強，該指標可以幫助識別拋售壓力增加的潛在風險。

當該指標交易高于上限時，從歷史上看，它與市場進入牛市的“欣快階段”保持一致。在最近圍繞現貨 ETF 推出的市場反彈期間，該指標達到了

?，隨后價格收縮至 38,000 美元。

衡量恐懼和貪婪

量化市場上日益增長的

情緒相關風險的另一個強大工具是

指標。該指標檢查凈利潤或虧損總額的美元價值占市值的百分比。

因此，在使用利潤中的供應百分比

估算出盈利的幣數后

，我們可以使用 NUPL 來衡量投資者盈利的大小。

Following the October 2023 rally, NUPL entered the High Risk ? range, reaching a value of 0.47. Despite the significant jump in coin volume held in profit, the magnitude of USD profit did not reach the Very High Risk ? state. This suggests a large proportion of coins were accumulated at a cost basis around the ~$30k consolidation range throughout H2-2023.

Realized Profit and Loss

The next step is to assess how market participants are adjusting their spending patterns, with the Realized Profit/Loss Ratio (RPLR) being an excellent compass to achieve this goal.

This indicator tracks the ratio between profit taking, and loss taking events occurring on-chain. We use a 14D-MA of this ratio to smooth out the daily noise and more clearly identify macro shifts in investor behavior.

This indicator recently flagged a Very High Risk ? regime as prices hit the recent $48.4k peak. The Realized Profit/Loss Ratio is currently at 4.1, residing in the High Risk ? condition.

Activity Risk Analysis

Switching gears slightly, we will now assess risk through the lens of demand, measured using a suite of adoption metrics related to network activity.

Demand For Blockspace

Given the capped blockspace in the Bitcoin network, a powerful method to gauge demand is via examination of the fee market. Usually, consistent growth in demand leads to a sustained rise in fees, as competition for inclusion in the next block increases.

The Miners Fee Revenue Binary Indicator (MFR-BI) shows the proportion of days over the last 30 days where the fee market has seen increasing pressure day-on-day.

During the sell-off drop to $38k, this indicator triggered a Very Low Risk ? signal. As spot prices bounced back to $43k, this metric returned back to the Low Risk ? zone (~46%).

Speculation Momentum

As the last component of the activity risk analysis suite, we revisit the Exchange Volume Momentum metric, which compares the monthly and yearly average of volume transferred from/into all exchanges. This tool acts as a proxy for the market's appetite for speculation.

This risk indicator measures the magnitude and direction of change in the faster monthly moving average’s (30D-MA) relative to the slower yearly moving average (365-MA).

The monthly average of Exchange Inflow Volumes has been trending strongly higher since October, putting this indicator in the Very High Risk ? regime. This suggests that the market is currently in a relatively speculative state.

Short-Term and Long-Term Risk Analysis

The risk analysis above considers a relatively macro and global perspective. In this next section, we will assess patterns at a more granular level, considering the behavior of short-term and long-term holder cohorts.

New Investors in Profit

Revisiting the conclusions made in prior reports (WoC 38, 2023 and WoC 50, 2023), Short-Term Holders tend to have an outsized influence in shaping near-term price action, such as local tops and bottoms.

Therefore, we take a Cause and Effect approach for spotting high (or low) risk intervals. This is based on a two-step assessment:

• Unrealized profit (or loss) held by these new investors (the incentive to spend).
• Realized profit (and loss) locked in by new investors (the actual spending).

We start with the Short-Term Holder Supply Profit/Loss Ratio (STH-SPLR), which captures the balance between supply held in profit vs loss for new investors.

This indicator recently signalled a Very High Risk ? condition between mid-Oct-2023 and mid-Jan-2024 as ETF speculation peaked. This suggested that a super-majority of new investors were profitable, suggesting an increased probability of profit-taking. This has since cooled off towards the neutral Low Risk ? range.

Locking In Short-Term Gains

The next step is focusing on the actual spending by these Short-Term Holders, measured through the lens of their realized profit or loss. The chart below highlights examples of high profit-taking ? (or loss-taking ?) regimes since January 2016. As shown, these periods of high spending tend to coincide with both strong rallies and corrections.

We transform and normalize these metrics using a 90-day Z-Score function, which standardizes this USD-denominated activity of short-term holders. This technique helps to spot when short-term holder spending is outside statistical extremes, which can be translated into potential local top and bottom formations within the market.

Note that to improve the visual aspect of this risk indicator, we have inverted the realized loss z-score (multiplied by -1).

The recent correction to $38k following the ETF launches caused a notable reduction in market risk according to this metric. The STH-Realized Profit Z-Score is currently at -1.22, while the STH-Realized Profit Z-Score is at -0.24. This places the present market structure in the Low Risk ? regime.

Old Hands Locking In Profit

We introduced a framework similar to the Short-Term Holder Risk assessment above, except it focused on Long-Term Holders (LTHs) in a prior report (WoC-22-2023). The goal is to assess when the degree of unrealized profit held by the Long-term holders has advanced to statistically extreme levels, then trace whether this cohort ramps up their spending accordingly.

The first indicator gauges the unrealized profit component of LTHs using the Long-Term Holder MVRV Ratio. This measures the divergence between the market price, and the average LTH cost basis.

After the challenging recovery since the FTX collapse, this indicator has advanced to 2.06, entering the High Risk ? regime. As noted, these levels are typically seen during the early stages of bull markets, as long-term investors return to a relatively meaningful level of profitability.

Long-Term Holders Spending

In this risk analysis study's final step, we built a binary indicator to assess when LTH spending is increasing at a sustained rate. The Long-Term Holder Sending Binary Indicator (LTH-SBI) tracks periods when LTH spending is sufficient to decrease the total LTH Supply over a sustained 7-day period.

When LTH supply decreases, it indicates a reintroduction of long-dormant supply back into liquid circulation, working as an offset to new demand.

The ETF speculation rally towards $48.4k pushed this risk indicator from Low Risk ? into the High Risk ? range. The current value is 0.7, suggesting a degree of elevated spending by LTHs, as investors and ETF re-balancing (namely from GBTC) transfer coin ownership.

Conclusion

In this piece, we developed a procedure for assessing the risk of a drawdown within the Bitcoin market. These risk factors consider a wide set of data and investor behavior categories, helping to establish a framework for analysts and investors.

Whilst each indicator can be used individually, the combination often provides a more comprehensive picture of the state of the market. The chart below compiles these into a heatmap view of the various risk indicators over the last 5-years. From this, we can compare the indicators to noteworthy tops and bottoms, where significant confluence can be seen.

The levels and transformations are intended as an initial guide and should be iterated on by analysts and practitioners to optimize for specific points of interest.

Disclaimer: This report does not provide any investment advice. All data is provided for information and educational purposes only. No investment decision shall be based on the information provided here and you are solely responsible for your own investment decisions.

Exchange balances presented are derived from Glassnode’s comprehensive database of address labels, which are amassed through both officially published exchange information and proprietary clustering algorithms. While we strive to ensure the utmost accuracy in representing exchange balances, it is important to note that these figures might not always encapsulate the entirety of an exchange’s reserves, particularly when exchanges refrain from disclosing their official addresses. We urge users to exercise caution and discretion when utilizing these metrics. Glassnode shall not be held responsible for any discrepancies or potential inaccuracies. Please read our Transparency Notice when using exchange data.

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熱點：比特幣 特幣 牛市 研究