Web3 Beginner's Essential Guide

What is Realized Cap?

Realized Cap, also known as realized market value, refers to “multiplying each unit of BTC by the price at the last transfer, then summing.”

In simple terms: each transfer can be viewed as a “transaction,” so multiplying the number of BTC involved by the previous transfer price is equivalent to the cost spent on that transaction. Summing these costs gives us the Realized Cap.

Realized Cap Chart

What is Realized Price?

Realized Price (RP), also called realized value, is the number obtained by dividing “Realized Cap by the current circulating supply of BTC.”

We can interpret it as: “Total market cost to buy BTC / how many BTC have been bought.”

In other words, this is the “average cost of BTC across the entire market.” By the way, when the price is below RP, it’s usually a good buying opportunity!

Realized Price Chart

(Price less than Realized Price)

What is MVRV?

MVRV stands for Market Value to Realized Value, representing the market’s profit and loss status.

MV refers to the current BTC market cap, which is “market price x circulating supply”; RV is the Realized Cap.

At the same time, if we divide both numerator and denominator by the circulating supply, MVRV can also be expressed as “market price / RP.” For example: if MVRV = 2, it indicates an average market profit of 100%.

What is LTH?

LTH = Long Term Holder, defined by Glassnode as “BTC held for more than 155 days.”

As for why 155 days, Glassnode provides a detailed explanation on their official website; due to the complexity, we’ll omit it here. Interested readers can explore on their own.

Introduction to LTH-RP

LTH-RP is the Realized Price of long-term holders, i.e., their average holding cost. The calculation is LTH-Realized Cap divided by the circulating supply.

As shown in the chart below, light green is the Realized Price of the entire market, dark green is the Realized Price of LTHs. Long-term holders’ holding costs are usually lower than the market’s average cost.

Comparison of Realized Price and LTH-RP

Introduction to LTH-MVRV

Represents the profit status of long-term holders, similar in calculation to MVRV. The formula for LTH-MVRV is “current market value / LTH-Realized Cap,” which can also be written as “current market price / LTH-Realized Price.”

As shown below, the changes in LTH-MVRV are usually more pronounced than MVRV because long-term holders tend to be more profitable (meaning they make more money).

Comparison of MVRV and LTH-MVRV, orange line is LTH-MVRV, yellow line is MVRV

Application of LTH-MVRV for Bottom Fishing

When LTH-MVRV < 1 (or the market price is below LTH-RP), it indicates that even long-term holders are on average losing money, which is often a good bottom-fishing signal.

As shown below, I marked the times when LTH-MVRV < 1, which correspond almost to cyclical major bottoms. When designing bottom-fishing strategies, consider including this indicator—TLDR

• This article introduces the on-chain indicator Realized Profit
• Realized Profit shows the daily profit-taking volume in the market
• Massive Realized Profit is usually caused by low-cost chips holders
• Tops are often accompanied by huge Realized Profit

Introduction to Realized Profit & Realized Loss

Realized Profit, translated as “已实现利润,” is based on the last transfer price of each BTC and the previous transfer price, calculating how many BTC are profitably sold each day. Summing these profits gives the daily Realized Profit.

Of course, if the last transfer price is lower than the previous transfer price, it’s counted as Realized Loss.

Chart of Realized Profit & Realized Loss

Huge Realized Profit is usually caused by low-cost chips holders

As shown below: because high-cost chips holders don’t have much profit margin, when they sell, the amount of Realized Profit generated is limited.

Therefore, when we see a massive Realized Profit, it usually indicates that low-cost chips are selling BTC.

Illustration of Realized Profit Calculation

Tops are often accompanied by huge Realized Profit

When many low-cost chips holders sell their BTC, we see a concentrated surge in realized profits on the chart.

At this point, since the remaining market participants are mostly high-cost buyers, the market price is close to their cost basis. A slight change in sentiment can trigger panic selling, causing a chain reaction of price drops and forming a top.

What is URPD?

URPD stands for UTXO Realized Price Distribution.

Because BTC has a unique UTXO blockchain structure,

we can track data on-chain that is unavailable in traditional financial markets.

The URPD chart is derived from this principle,

showing the amount of chips at each price level,

equivalent to “the buy-in cost of each $BTC .”

URPD, Chip Turnover, Accumulation, and Distribution

Once you understand URPD,

you can observe the turnover of chips at different price levels based on daily URPD changes.

For example:

The first chart shows URPD on May 1,

the second on October 1.

After five months of wide-range oscillation,

it’s clear to see the distribution of low-cost chips being dispersed upward.

URPD, Chip Turnover, Accumulation, and Distribution (2)

Historically, at market tops, there is often a phase of high-profit chip distribution nearing completion;

at bottoms, large amounts of chips are accumulated within narrow ranges.

Therefore, in analysis, URPD can be combined with other data (like realized profit, MVRV, etc.) for a more comprehensive understanding of market conditions.

Support, Resistance, and Consensus

When a single price range begins to accumulate a large amount of chips,

it indicates a developing supply-demand consensus in that range.

If the price then quickly rises away from this range,

the large turnover of chips in this zone can provide future support;

Conversely, if the price breaks below this range,

the chips in this zone become trapped, turning into resistance levels for future rallies.

What is PSIP?

PSIP, or Percent Supply in Profit, is defined as “the proportion of circulating supply that is in profit.”

Calculation:

Compare each $BTC ’s last transfer price with the current price to distinguish profitable from unprofitable chips.

When the current price is higher than a $BTC ’s last transfer price, that $BTC is considered profitable.

What if most chips are in loss…?

An important application of PSIP is bottom-fishing. When most chips are in loss, it’s often a good time to buy.

The logic is straightforward:

• All else equal, the more profit-taking chips there are, the greater the selling pressure, and vice versa.

As shown in the chart, historical points where $BTC PSIP < 50% are marked, indicating very precise bottom-fishing opportunities.

What if most chips are in profit?

As shown, the maximum profit chip proportion is 100%, so high PSIP alone makes it hard to determine a top.

Here’s an interesting logical insight: “Observe the correlation change between PSIP and price.”

1. Introduction to Cointime Price

Cointime Price originated from a study on 2023/08/23 by Ark Invest and Glassnode, called “Cointime Economics.”

Cointime Price calculation is relatively complex; I will try to explain the principle simply.

Cointime Price = a pricing model designed for $BTC ’s unique UTXO structure

Traditional pricing methods involve validation processes for block creation and transfers. Cointime Price differs by using a “time-weighted” approach.

(As shown in Chart 2, the green line is Cointime Price)

Key concepts in Cointime Price calculation:

· Coin Blocks Created (CBC):

CBC = total circulating BTC at block N creation.

· Coin Blocks Destroyed (CBD):

When BTC is transferred, it’s considered destroyed; calculated as: transferred BTC amount × holding time (number of blocks), resulting in CBD (time-weighted BTC).

· Coin Blocks Stored (CBS):

CBS = CBC - CBD, understood as “the total time-weighted unspent BTC.”

In the formula, the numerator Cointime Value Destroyed is CBD multiplied by the BTC price at transfer, representing the “BTC U-value at transaction.”

· Main features of Cointime Price:

· Time-weighted design: when long-term holders transfer large amounts of BTC (distribute), Cointime Price accelerates.

· Buyer perspective: the numerator in the formula represents “total time-weighted expenditure” in the market; dividing by CBS yields the time-weighted average cost of chips.

· Excludes lost chips: since CBD accounts for transfer behavior, untransferred BTC are not included, so it’s unaffected by ancient lost BTC.

2. Comparison with LTH-RP

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LTH-RP vs. Cointime Price

· LTH-RP calculation:

• Glassnode defines LTH as BTC held > 155 days
• LTH-RP = average cost of these LTH BTC
• Limitation: only applies to long-term holders, with a relatively coarse definition.

· Cointime Price calculation:

• Considers the holding time of each BTC at transfer
• More precise and sensitive than LTH-RP

Performance of Cointime Price vs. LTH-RP in market trends

As shown in Chart 3, before each major market rally, Cointime Price always reacts earlier than LTH-RP, better reflecting chip distribution behavior in real time.

Therefore, in personal analysis, I prefer using Cointime Price for market judgment. In my weekly market reports, I include Cointime Price in the top detection models.

3. Application 1: Bottom-fishing

Cointime Price = a time-weighted fair valuation of BTC, so when the market price falls below Cointime Price, it indicates the market is undervalued, often a good bottom-fishing opportunity.

· Historical validation

As shown in Chart 4, I marked points where BTC price is below Cointime Price; these moments tend to be good entry points.

Brief review of Cointime Price

Originates from Cointime Economics, using a “time-weighted” approach to assess BTC’s fair value.

Compared to just LTH (long-term holders) and STH (short-term holders), Cointime Price is more flexible and sensitive, and effectively excludes the impact of lost BTC from ancient times.

This article has introduced Cointime Price and its bottom-fishing application. If you understand the concept, let’s move on to today’s main topic: Top-escape strategies.

Application 2: Top-escape Model Design: Cointime Price Deviation

Cointime Price Deviation is a model I designed during on-chain data research, used in weekly top-escape analysis reports.

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Next, I will explain the model’s design principles and how to use it to identify market tops. All content is original research; the process is complex, so your support is appreciated.

1. Quantifying the deviation between current price and Cointime Price

Why measure deviation?

• Cointime Price strongly reflects the true holding cost of BTC chips, especially long-term holders.
• Since long-term holders influence Cointime Price more, when BTC’s current price is significantly above Cointime Price, their profit-taking motivation increases, possibly triggering distribution.

· Calculation: Deviation Rate = (Current Price - Cointime Price) / Current Price

· Observe the deviation (distribution ratio)

As shown, we get a distribution ratio curve (purple line). When the ratio is high, it often corresponds to a market top.

How to define “high”? Next, we use statistical methods.

2. Defining extreme values of Cointime Price Deviation

Historical data shows that the peaks of Deviation are not fixed; in each bull cycle, the Deviation peak slightly declines. So, using a fixed value to define “high” is not rigorous.

To address this, I adopt the statistical concept of “standard deviation”:

· Calculate the mean and standard deviation of historical Deviation data.

· Define “mean + n standard deviations” as “high level (top signal),” called Threshold.

· Smooth the Deviation data with a moving average to reduce noise.

· When the smoothed Deviation exceeds the Threshold, trigger a top signal.

· Why use standard deviation?

· Deviation tends to revert to the mean (see chart).

· Standard deviation measures volatility; when BTC price volatility decreases, the Threshold adjusts dynamically, making it more flexible.

The above process yields a chart like this.

· Additional notes:

• The parameter n in “mean + n standard deviations” is adjustable: larger n means fewer signals, more conservative.
• The moving average smoothing filters short-term market fluctuations, increasing reliability.

3. Example of top-escape signal

As shown, when the purple line (distribution ratio) exceeds the orange line (Threshold), BTC price is often at a cyclical top.

Conclusion

This is the second article in the Cointime Price series, continuing from previous concepts, sharing how I use Cointime Price to design top-escape models.

· Core summary:

• Cointime Price Deviation quantifies the deviation of BTC’s current price from Cointime Price, inferring long-term holders’ distribution motive, used to identify market tops.
• Uses “standard deviation” to dynamically define top signals, making the model adaptive.
• The model has been applied in weekly reports and effectively captures high-level signals.

Application 2: Daily Distribution Rate of Cointime Price

1. The pattern of Cointime Price

Before continuing, let’s look at the Cointime Price chart:

Careful readers will notice a clear pattern: “Rapid rise — plateau — rapid rise — plateau…”

From the first article, we know:

Cointime Price only rises rapidly when long-term holders are distributing heavily, as it essentially reflects the “market’s chip time-weighted average cost.” During distribution phases, remaining holders accept distribution, raising their holding costs, which manifests as a rapid increase in Cointime Price.

Using this feature, I designed an indicator called “Cointime Price Daily Distribution Rate.”

2. Cointime Price Daily Distribution Rate

To measure the rate of change, I use a simple formula: (Today’s CP - Yesterday’s CP) / Today’s CP, then smooth the result with a moving average (*note: CP is shorthand for Cointime Price).

Applying this formula in Glassnode yields the following chart:

We see that each major bull run is accompanied by a high distribution rate of Cointime Price. Apart from a high distribution rate near the bottom in 2019, whenever a high rate appears, it usually signals accelerated distribution by long-term holders. The 2019 case does not lead to false signals because price action alone indicates it’s not a top.

3. Historical top daily distribution rates & current market stage

Generally, at market cycle tops, #BTC does not just have one “distribution” event. Indicators like UPDR, Realized Profit, etc., also show this, because distribution is a process, not a one-off event.

What is RUPL?

RUPL, or Relative Unrealized Profit & Loss, is a “relative unrealized profit/loss” indicator. It can be broken into two parts: RUP and RUL.

For RUP calculation:

1. Compare current price with each $BTC ’s last transfer price, classifying chips with “current price > last transfer price” as profitable.

2. Multiply each profitable chip’s profit by its quantity to get Unrealized Profit.

3. Normalize this data by market cap at that time.

In other words, Unrealized Profit is the total unrealized profit in the market; RUP standardizes it by market cap for cross-period comparison. RUL is calculated similarly; details are omitted here.

As shown in Chart 1, green line is RUP, red line is RUL. It’s clear that: price correlates positively with RUP and negatively with RUL. This makes sense because as prices rise, profitable chips and unrealized profits naturally increase.

However, further observation shows that RUL sometimes exceeds RUP (red line above green, e.g., yellow box), indicating the market is in an overall unrealized loss state. Do these periods have special significance? Continue reading.

Application of RUPL for Bottom-fishing

Building on that, there’s a saying: “Be greedy when others are fearful.” When most chips are in loss, it’s often a good time to buy.

In the chart above, I marked periods where RUL > RUP. It’s obvious these are near historical cycle bottoms.

This makes sense because:

“When the market is in overall loss, it means many investors holding low-price chips have mostly finished distributing; those trapped in losses tend to be reluctant to sell at low prices. The combination of these two emotions greatly reduces selling pressure, so a slight buy-in can reverse the trend and start an uptrend.”

This logic is similar to the LTH-RP bottom-fishing strategy I shared earlier. Interested readers can refer to this post: “On-Chain Data School (2): How much do Hodlers who always make money buy BTC at?”

Design logic of bottom-fishing model

Next, we focus on RUP alone, ignoring RUL. We notice that RUP tends to hover around a certain value at historical bottoms:

For example, I added a horizontal line at 0.4 on the chart, and RUP < 0.4 is clearly visible in that zone. (0.4 is a model parameter, adjustable, to be discussed later).

Since RUP shows a clear bottom zone, we can combine RUP < 0.4 with RUP < RUL to filter signals further, resulting in:

This is a common approach in model design, aiming to improve precision by combining signals.

The chart above shows the combination of (RUP < 0.4) + (RUP < RUL). Although the filtering effect isn’t very dramatic, it’s more rigorous than just RUP < RUL. If we lower 0.4 to, say, 0.38, the model becomes stricter, but parameter tuning must avoid overfitting, as overly fitting to historical data may cause future failure.

Additional note: Overfitting is like “carving a boat to fit the water,” overly tailoring the model to past data.

Summary

This is the first article in the RUPL series, mainly introducing the RUPL indicator and a bottom-fishing model based on it.

Application of RUPL for Top-escape

As mentioned in the conclusion of the previous article, today I will share a powerful top-escape application of RUPL. In this method, only RUP is used, RUL is temporarily ignored:

When RUP diverges from $BTC ’s price trend, it often signals a top.

1. Specifically, when $BTC ’s price makes a higher high, but RUP makes a lower high, divergence occurs.

2. The logic:

As discussed earlier, RUP’s calculation sums unrealized profits across the market. When many chips holders realize large unrealized profits, RUP should rise with price. But if a divergence occurs—price makes a higher high, yet RUP is lower—it suggests that “large holders are starting to sell chips and distribute.”

3. This logic is similar to Realized Profit and can serve as a cross-validation. For details, see: “On-Chain Data School (3): Are the Big Players Profiting from Bottoming?”

That’s the reasoning behind why RUP divergence can be a top signal. But caution is needed: for market stage judgment, the best approach is to combine multiple on-chain indicators to avoid “seeing the sky through a tube.”

Historical cycle top analysis

Once you understand RUP divergence, you might want to verify it with actual data. Here, I review the major historical tops:

2013 Bull Market Top

As shown, green RUP line and black BTC price line, at the top, show a “three-phase divergence”: as price makes two higher highs, RUP makes lower highs, perfectly fitting the divergence signal.

2017 Bull Market Top

Similarly, in 2017, divergence appeared at the top. RUP showed divergence at the peak, and again during the struggle and rebound, a second divergence appeared, providing a good escape signal.

2021 Bull Market Top

In 2021, a “double top” pattern appeared. I analyzed both peaks: the first was similar to 2013 with a “three-phase divergence”; the second also showed divergence at the top.

In these three historical cycle tops, RUP divergence appeared in all cases. While not guaranteeing future tops will always show divergence, the data so far suggests a consistent pattern. What about this cycle?

Forecast for 2025 potential top: current market stage analysis

Here is the chart:

It shows that in this cycle, a RUP divergence top signal has already appeared once. Combining with weekly escape reports, URPD, Cointime Price, Realized Profit, and other data, some signs of a top are visible.

Interestingly, in the previous three tops, except for the second top in 2021, the 2013, 2017, and 2021 first peaks all showed at least two divergence signals; currently, only one has appeared.

Based on historical patterns, if the $BTC price makes a new high soon, it’s highly likely to produce a three-phase divergence similar to 2013 or the first peak in 2021. That would be a key escape point to watch.