BTC On-chain Data Analysis, Has This Cycle Peaked?

Original Title: "BTC On-chain Data Analysis, Has This Cycle Peaked?"

Original Source: Mint Ventures

Host: Alex Mint Ventures Research Partner

Guest: Colin Independent Trader On-chain Data Researcher

Recording Date: 2025.2.15

Hello, everyone, and welcome to WEB3 Mint To Be initiated by Mint Ventures. Here, we continue to inquire and think deeply, clarifying facts, uncovering realities, and seeking consensus in the WEB3 world. We aim to clarify the logic behind hot topics, provide insights beyond the events themselves, and introduce diverse perspectives.

Disclaimer: The content discussed in this podcast does not represent the views of the guests' respective institutions, and the mentioned projects do not constitute any investment advice.

Alex: This episode is a bit special because previously we discussed many topics related to specific tracks or projects and also exchanged some cyclical narratives, such as the meme we talked about before. But today, we are going to discuss on-chain data analysis, especially BTC on-chain data analysis. We will closely examine its principles, key metrics, and learn its methodology. In today's show, we will mention many concepts about indicators, and we will list these concepts at the beginning of the text version to help everyone understand.

Some data metrics and concepts mentioned in this podcast:

Glassnode: A commonly used on-chain data analysis platform that requires a subscription.

Realized Price: Calculated based on the price at the last on-chain movement of Bitcoin, reflecting Bitcoin's on-chain historical cost, suitable for evaluating the overall profit/loss status of the market.

URPD: Realized Price Distribution. Used to observe the price distribution of BTC chips.

RUP (Relative Unrealized Profit): Relative unrealized profit. Used to measure the ratio of unrealized profits of all BTC holders to the total market value in the Bitcoin market.

Cointime True Market Mean Price: An on-chain average price indicator based on the Cointime Economics system, aiming to more accurately assess the long-term value of BTC by introducing Bitcoin's "time weight." Compared to BTC's current market price and Realized Price, the True Market Mean Price under the Cointime system also takes into account the influence of time, suitable for the long cycle price of BTC.

Shiller ECY: A valuation metric proposed by Nobel Prize-winning economist Robert Shiller to assess the stock market's long-term return potential and measure the attractiveness of stocks relative to other assets. It is derived from the Shiller Price-to-Earnings Ratio (CAPE) and primarily considers the impact of the interest rate environment.

The Opportunity to Learn On-Chain Data Analysis

Alex: Today we have a special guest with us, Colin, a freelance trader and on-chain data researcher. Let's have Colin say hello to our audience first.

Colin: Hello everyone, and thank you, Alex, for the invitation. When I received this invitation, I was somewhat surprised because I am just a small unknown retail trader with no special titles, quietly doing my own trading. My name is Colin, and I manage a Twitter account called Mr. Berg, where I mainly share on-chain data analysis tutorials, market analysis based on the current situation, and some trading concepts. I roughly position myself in three ways: first, as an event-driven trader, contemplating event-driven trading strategies; second, as an on-chain data analyst, which is the main content I share on Twitter; third, more conservatively, I call myself an index investor. I allocate some funds to the broad U.S. market, using this portion to invest in the Beta to reduce the overall volatility of my asset curve while maintaining a certain defensive position. That's roughly how I position myself.

Alex: Thank you, Colin, for the introduction. I invited Colin to the show because I saw his enlightening on-chain data analysis of Bitcoin on Twitter. This is a topic we haven't discussed much before and an area where I feel lacking. After reading his series of articles, I found the logic clear and substantive, so I invited him. I want to remind everyone that today, both my views and those of the guest are highly subjective, and the information and opinions may change in the future. Different people may have different interpretations of the same data and metrics. This episode does not serve as investment advice. The program will mention some data analysis platforms only as personal use sharing and examples, not as endorsements. This program has not received any sponsorship from any platform. Let's get into it and discuss on-chain data analysis of crypto assets. Colin, you mentioned being a trader earlier, so when did you start engaging with and learning on-chain data analysis of crypto assets?

Colin: I think I can answer this question by breaking it down into two parts. First, I believe that anyone around me, including myself, who wants to enter or has already entered the financial market's main goal should be to make money and improve their quality of life with profits. So my philosophy has always been consistent—whatever can help me make a profit, I will learn it. By doing so, I enhance the expected value of my overall trading system. In simple terms, I learn anything that can make money. The second part is that my initial exposure to on-chain data was purely coincidental. About six or seven years ago, I was completely clueless, just looking around here and there. While exploring various fields, I came across interesting research theories I wanted to learn more about. At that time, I stumbled upon the so-called on-chain data analysis field related to Bitcoin. I started to study and research it. As I progressed, I would combine the knowledge I gained from other areas, mainly in quantitative trading development, integrate it with on-chain data, develop some trading models, and finally incorporate these models into my trading system.

Alex: So, how long have you been formally engaged in on-chain data analysis, with a relatively systematic study and research?

Colin: I think that's hard to define. Actually, I have never truly studied this systematically. Because from the past to now, I have encountered a problem myself, which is that I haven't really seen any systematic teaching. When I first saw this field, it was probably several years ago, I found it at that time, but I didn't delve into it, just read two or three articles to understand this thing. After a while, I came back and saw some more in-depth content, at that time I was focusing on researching other things, then came back here, found it quite interesting, so I continued to study. There hasn't been a systematic learning period, it's been a patchwork.

Alex: I see. So, from your learning of on-chain data to applying it to your actual investment practice, how long has that been going on?

Colin: This boundary is quite hard to define, but I think it's close to about two Bitcoin cycles... well, maybe not two cycles, it depends on whether you define it from a bull market or a bear market. It's probably been around since 2020, 2019, but back then there was no practical application because I was hesitant, I was not very familiar with this thing at the time, but I had already started learning.

The Value and Principle of On-Chain Data Analysis

Alex: Got it. Next, we will discuss many specific concepts about on-chain data analysis, including some indices. What are the on-chain data analytics platforms you generally use in your daily routine?

Colin: Right now, I mainly use a website, which is Glassnode. Let me briefly explain, it is a paid service. There are two paid levels, one is the professional version which is relatively expensive, I remember it's over $800 a month. The second one I kind of forget, it's around thirty to forty-something US dollars a month. It also has a free version, but the information you can see in the free version is actually very limited. Of course, besides Glassnode, there are many others, but I chose it in the end because initially when screening and researching, this website suited me the most.

Alex: Understood. After reading a lot of Colin's information, I also registered for Glassnode and became a paid member. Indeed, I feel their data is very rich, and the timeliness is also quite good. So, let's move on to the second question. Just mentioned that you are a trader, and you value its assistance in practical investment. So, what is the core value of on-chain data analysis in your investment, and what is the principle behind it? Please introduce it to us.

Colin: Alright. Let's start with the first point, which is the value and principle of on-chain data analysis. I plan to combine these two topics because they are actually quite simple. In our traditional financial markets, whether trading stocks, futures, options, bonds, real estate, or some commodities, Bitcoin has a fundamental difference from them, which is that it uses blockchain technology. The most important and often-mentioned value of this technology is its transparency. All information about Bitcoin transfers is publicly available, so you can directly see on the blockchain when, for example, 300 bitcoins move from one address to another, which can be found on a blockchain explorer. Although I cannot know who is behind a specific address, this is not important because in fact, no single entity can influence the overall price movement and trend of Bitcoin. So typically, when we analyze on-chain data, we look at the overall market, its trends, and observe the consensus and behavior of the community. Even though I don't know who is behind this or that address, I can analyze the flow of these chips by aggregating all addresses, see if they have already taken profit or stopped loss, how their profitability is, how their losses are, where they tend to buy a large amount of Bitcoin, or where they don't like to buy Bitcoin at which price point. All this data is actually visible. This is what I consider the greatest value of Bitcoin on-chain data analysis compared to other financial markets because other markets cannot do this.

Alex: Indeed, this point is very important. When we do crypto investments, just like when we look at stocks or other products, we also need to analyze the fundamentals. As you just mentioned, on-chain data is transparent, and everyone can observe it. If other professional investors are looking at on-chain data and you are not, then you are essentially missing out on a very important weapon in your investment.

Challenges of On-Chain Data Analysis

Alex: When you are hands-on doing on-chain data analysis, what do you think are the main challenges and difficulties?

Colin: I think this is a very good question, and I plan to answer it in two parts. Firstly, the first part, which is relatively easily solved, poses a somewhat challenging point in learning, that is, basic knowledge. For most people, including myself at the time, as I mentioned earlier, it was difficult to find a truly systematic teaching. Of course, I didn't ask offline if there were paid courses like this, but if there were, I probably wouldn't dare to buy it because since I started trading until now, I'm not really inclined to pay for courses. I haven't encountered any systematic teaching courses, so all the content needs to be explored and discovered by myself. There are many types of on-chain data. In the process of research, my approach is to understand the calculation methods and principles behind every indicator I have seen. This is actually a very time-consuming process because when you see a certain indicator, and it gives you a calculation formula, my idea is to figure out what the calculation formula is really about, why it is designed that way. After understanding these indicators, the next thing to do is filtering. If someone has experience in quantitative strategy development or has studied indicators, one will know one thing, which is that the correlation of many indicators is very high. Too much correlation can cause a problem; you are very likely to generate noise in judgment or over-interpret. For example, suppose I have a system for identifying tops today, this top identification system may have 10 signals from Signal 1 to Signal 10, and if Signals 1 to 4 have too high a correlation, it will cause a problem. For example, if the price of Bitcoin undergoes a certain behavior or change today, it may directly trigger Signals 1 to 4 simultaneously, which is quite troublesome. Because if their correlation is too high, this is an inevitable phenomenon. If today, out of the 10 signals, 4 are activated, and you say this is very dangerous, but actually, it's not very reasonable because they were supposed to activate anyway. If you don't segment them based on correlation, this phenomenon is very likely to occur. After researching the principles behind each indicator and data, by looking at the calculation formulas, I can tell if they have a high correlation, and I segment them based on correlation. For example, if these 5 have very high correlations, then I will slightly segment and filter them, and finally select one or two.

The first part is actually relatively easy to solve and is not considered the main challenge. The second part is the real challenge, which is about on-chain data. How do you prove your point to others or even to yourself? Here, I might need to give a somewhat crude example, but it is easy to understand. I have mentioned before in a tweet that in the field of quantitative finance, it is often said that trading cannot be too overfit. I previously gave an example like this: suppose there is a very strange trading strategy where the entry condition is that if my dog barks twice at home and it is raining outside, then I will enter a long position. After backtesting this strategy 1000 times, I found that it has a 95% success rate, far outperforming the market. Would anyone dare to use this strategy? It is quite strange that simply because my dog barked and it was raining outside, I can enter a trade, and the success rate is so high. This actually has a term called survivorship bias. If you cannot provide any logical support for it, even if the sample size is sufficient, this strategy cannot be used. Some may argue that since it has been backtested 1000 times with a 95% success rate, the backtest results support the viability of this strategy. However, as I mentioned earlier, the so-called survivorship bias means that if you flip a coin 10 times in a row, the probability of getting heads all 10 times is actually 1/1024. In other words, on average, out of every 1024 people attempting this, only 1 person will succeed in flipping heads 10 times in a row. This person is considered a survivor, while the other 1023 people attempting the same thing have failed, and we never see them. We always only see the successful cases. Returning to Alex's question earlier, where is the main challenge? Because we mainly focus on high-level consensus and trends, looking back at the history of Bitcoin, the most obvious three cycle tops are in 2013, 2017, and 2021, which only gives us four samples, which is absolutely not enough. Since the sample size is insufficient, if we try to overfit the data by looking at where certain indicators were in 2013, where they were in 2017, and then assume the same will happen this year, it is unreasonable. Because the sample size is completely inadequate, if at this point we do not apply logic to our research, our theory is very likely to be flawed. One of the main issues is that with such a small amount of historical data, I have to use deductive reasoning rather than simply inductive reasoning to study it. After conducting my research and reaching a conclusion based on deductive reasoning, I need time to prove whether my viewpoint is right or wrong. If it is correct, then it suggests that my previous deductive reasoning process may have been reasonable. If it is wrong, then I need to continue refining my deductive logic. However, if today I rely solely on inductive reasoning, which is actually what most retail investors prefer to do, thinking that because past trends look similar to current trends, a surge or a crash should follow, this is unreasonable. Going back to the very first sentence I mentioned, I believe the biggest challenge is proving to others or to myself that my conclusions are correct. Therefore, I must constantly revise my logic and assumptions and identify any flaws. Because Bitcoin is still relatively young, on-chain data analysis will always face the issue of insufficient sample size. In such cases, you have no choice but to rely purely on deductive reasoning in your research, using logic to deduce and then waiting for time to confirm your judgment. This is currently the biggest challenge I am facing.

Key On-Chain Metrics to Focus On

Alex: I see. I feel quite inspired after listening to that. Earlier, when I asked you that question, it was also related to some confusion I had when I started looking at various metrics on Glassnode. There are so many metrics, so which one should I use as a reference for my trades? Because many metrics have different calculation logics. I tend to select the logic behind those metrics myself, which is quite similar to what you just mentioned. Firstly, I need to look at the calculation logic behind the metric, and I need to feel that this logic is sound, rather than backtesting and saying that this metric is very accurate, so I will use this accurate metric to predict the future. As you mentioned, the reference in deductive reasoning needs to be more significant in order for us to adopt it as our primary metric. So, based on your recent insights, in your daily analysis of Bitcoin, which on-chain metrics have you been consistently focusing on or do you consider to be more important?

Colin: Actually, I mentioned this question earlier. I will try to do the selection based on relevance. I look at a lot of on-chain data metrics in my daily routine, so today I will split them into three levels from different perspectives, namely, trying to split them from low-relevance parts.

The first one I will focus on in the long term and pay particular attention to is the URPD metric. It is a chart presented as a row of bar graphs, with the horizontal axis representing Bitcoin's price and the vertical axis representing the quantity of Bitcoin. Suppose today we see a very high and large bar at the 90,000 position; then, we will know that a significant amount of Bitcoin is being accumulated at this position, meaning the price at which they bought, and the bar graph will show how much Bitcoin they bought at this price. So, based on this, we can easily see that if there is a large accumulation above 100,000, then we can know that many people are buying above 100,000. This URPD chart has two main observation points. The first one is the simplest chip structure. For example, if I see that the current market situation is around 87,000, there has already been a large amount of accumulation above 87,000, according to data from last week, it should be around 4.4 million. Then we know that there is a large amount of turnover in this range, meaning that someone bought here. Since someone buys, a certain consensus is likely to form. In this range of large accumulation, it is easy to create an attractive effect on the price, meaning the price is likely to oscillate in this range, and if it falls, it is easy to recover after a period and rise again. If it rises, the chips below have all turned into floating profits, so they are easy to sell and engage in short-term trading, then sell the price back. Thus, it is easy to oscillate in this range. This is the first observation point. The second observation point is that we can observe the Bitcoin distribution process through URPD. The so-called distribution is when those early bear market chips bought Bitcoin at a low price and then sold their cheap chips at a higher price, and I define this process as distribution. For example, if today there are 300,000 more chips at the 100,000 price point, with a cost of 20,000 for the chips, assuming it is 20,000, and exactly 300,000 fewer, then we can see that those with a cost of 20,000 sold 300,000 chips today, and their average selling price is around 100,000. We can see if those low-cost chips are experiencing some drastic changes. Of course, now the price is around 100,000, over 90,000, so any drastic changes would definitely be decreases, not increases, because the current price range is over 90,000, not over 20,000. So, the changes there will only be decreases, not increases. Based on this, we can observe the rate of distribution, roughly that's the idea. This is the first metric I will focus on in the long term.

The second concept I'd like to introduce is an indicator called RUP, which stands for Relative Unrealized Profit. This indicator serves a specific purpose — to help us measure the overall market's profit status, specifically describing the market's profit situation concerning the current Bitcoin price. It aims to determine whether market participants are largely in profit, slightly in profit, or significantly in profit relative to the current price. The principle behind this indicator is quite simple. Through the transparent mechanism of the blockchain, we can track the majority of chips' entry prices. By comparing these entry prices with the current price, for example, if someone bought at $50,000 and the current price is $100,000, we can calculate how much profit they have made. For instance, if someone bought 10 bitcoins at $50,000 and the price is now $100,000, they have made $50,000 per bitcoin, totaling a $500,000 profit. By summing up all these unrealized gains and losses and normalizing this number based on the current market value, we can obtain a number between 0 and 1. This range between 0 and 1 is quite informative. For example, if today's RUP is high, such as 0.7, 0.68, 0.75, it indicates that the overall market's profit status is high, potentially prompting more people to take profits. Therefore, when RUP is too high, it is usually considered a relative warning signal.

The third dimension I'd like to discuss is a market's fair valuation model. There are actually many different Bitcoin valuation models in the market, with each model using a different method to assess the fair value of Bitcoin. The so-called fair value is essentially how much a Bitcoin is worth. After reviewing many models, I find the Cointime Price model to be the most robust. I have not seen a Chinese translation for this term elsewhere. In simple terms, we often hear about a name called Cathie Wood, also known as "Wood Sister," from her ARK Invest, and the on-chain data website, Glassnode, which I mentioned earlier. This concept is discussed in a document co-produced by both parties. The key feature of this model is that it introduces the concept of time-weighting to calculate Bitcoin's fair value. The calculated number has two main use cases. Firstly, it can be used for bottom fishing. For example, during a bear market when the price keeps falling and eventually drops below the valuation provided by Cointime Price, which is essentially the true value of Bitcoin. If today's price falls below this level, it means you are buying at a very favorable position. Based on historical backtesting and its logic, whenever the price falls below the Cointime Price, it has often been a great buying opportunity. The second application is top dodging. By monitoring the current price relative to Cointime Price, you can assess how far they deviate. If the deviation from Cointime Price is significant, you can evaluate whether this deviation indicates that the market may be near a top. These three dimensions — chip structure, profit status, and fair valuation model — are the three indicators I wanted to share.

How to Address Data Discrepancy

Alex: Alright, you explained it very clearly just now. Many users may have a question: the three key metrics you mentioned represent different aspects and align with what you said about their relatively low correlation, so they can be combined as a reference metric. In the scenario where such metrics diverge in practical application, for example, if one metric indicates a distribution phase while the other two suggest we are not yet at the top based on the current cycle, how would you address the situation of data discrepancy?

Colin: I think this is not only encountered in on-chain data analysis but also in other areas such as technical analysis or macro analysis. In the on-chain field, my approach is simple - I assign different weights to different aspects. The aspect I value the most is actually the chip distribution structure, which represents the progress of distribution. Because in terms of profit status, it also assists me in observing which low-cost chips in the market, for example, those who bought Bitcoin at 15000 or 16000 during a bear market, have been distributed. There is a peculiar phenomenon that in each cycle of Bitcoin over the years, there are usually two very obvious large-scale distributions. For example, looking ahead to 2024, the most prominent case was last year, March to April, where you could see a significant distribution phase based on the profit status. However, if I only see a large-scale distribution today, my next question would be: have they finished distributing? All judgment criteria stem from this question. If there is a large-scale distribution but it is not yet complete, then I can reassure myself that the bull market has not ended. Like last year in March to April, when Bitcoin surged to over 70,000, I was quite excited because the bull market had finally arrived, reaching a new all-time high. However, it then started to fluctuate for over half a year. At that time, analyzing this data did not provide a conclusive bottoming-out scenario; at best, it was just the first phase of distribution. Many indicators, like the midterm analysis and chip structure analysis I did before, based on the average cost of short-term holders, indicated a different scenario from the actual end of the bull market. So, at that time, I was quite at ease. When you talk about data discrepancy and if it indicates a distribution, should I sell at the peak? Actually, there is no need because the main issue is the question I mentioned earlier: has the distribution finished? Using this question as the criteria for filtering each indicator and as the benchmark for judgment, you can easily draw this conclusion. Even if distribution has occurred, and it is significant, as long as you assess whether it has ended or not, you can effectively address the so-called data discrepancy issue.

Alex: So let's now outline a scenario. For example, let's look at URPD. This metric has already experienced two distribution events, similar to what you just mentioned. One in March and April of last year, and another peak distribution at the end of December to January. Let's say it has experienced this distribution event, but perhaps the other two valuation metrics didn't spike as high. In this situation, as you mentioned, you would assign different weights to them. Would you then adjust a portion of the position based on the weightage, or would you consider all three metrics uniformly, making one or two key decisions when necessary?

Colin: My approach is the former. Because in reality, no one can pinpoint exactly whether we are truly at the top. If someone could do that, they would be extraordinary, and I would definitely want to meet them. The top, in my interpretation, is a gradual process. Even though it may seem quick when looking at the daily chart, when you are in the moment, let's say at $69,000 during the last cycle's peak, you wouldn't feel like it's the top. We can only make an assessment based on data to determine that the conditions for a top may be present. So with that premise, I would take a staged approach. For example, when I believe the conditions for a top are gradually forming, if I see a warning signal during that period, such as the RUP divergence I shared on Twitter before, I would correspondingly reduce my position. Of course, the extent of this reduction should be pre-determined. I wouldn't just reduce randomly without a planned approach. I would have a rough plan, like dividing my position into four parts, and when a particular type of warning signal appears, I would reduce one part. Another signal, another reduction. At the same time, I would plan that the last portion of funds must exit no matter what. For example, if it's confirmed that the bear market has ended but other warnings haven't appeared yet, we need to establish an extreme exit strategy for the final escape.

Alex: Understood. So it's still based on different warning signals triggering us to progressively exit or reduce our position.

Colin: Exactly.

Evaluation of BTC's Position in the Current Cycle and Its Basis

Alex: Understood. I have also been closely following your Twitter account recently. You also practice your trading based on these indicators we just discussed, including the underlying principles behind these indicators. Now, looking at Bitcoin, it has been oscillating in the range of $91,000 to $109,000 for almost three months. Currently, there is a significant divergence in the market regarding this price range. Unlike in December and January, where many believed the bull market was far from over and that it would surge to $150,000, $200,000, or even $300,000, there are now varied opinions in the market. Some think that BTC's top in this cycle is around $100,000, while others believe that this cycle's BTC has not peaked yet and there will be a major uptrend in 2025. So, based on your current comprehensive assessment, what is your view? At what point is BTC in this large cycle? And what are the data sources supporting your judgment?

Colin: Before answering this question, I might need to issue a disclaimer. I am actually very bearish on 2025. I believe that BTC is currently in a condition conducive to forming a top. I know many people, including some participants around me, whose returns were not good during the so-called special bull market process in 2024 because the market's performance in 2024 was quite different from every previous cycle. The most obvious point is the absence of an altcoin season. This fact has hurt many people, including some of my non-professional trader friends who also entered this market and suffered losses in altcoins. Why did this happen? Let's briefly review 2024. There was one altcoin season at the beginning of the year, and the second occurred in November last year when Trump was elected President of the United States. These two altcoin seasons were significantly different from our previous cycles in that their sustainability was not good. Even during the rally in November and December of last year, altcoins did not all rise simultaneously; there was a very clear sector rotation instead. At that time, there was the DeFi sector, and after it surged, it rotated back to old coins like XRP and Litecoin. This sector rotation was very apparent. From this, we can see that this bull market cycle in 2024, if we consider it a bull market, has a significant gap compared to previous cycles. Another theory is that there must always be a so-called altcoin season before the bull market ends. In my opinion, you cannot say that the bull market can only end when an altcoin season occurs; there is clearly no strong correlation. We can't rely on this to determine whether the bull market is over. As mentioned earlier, on-chain data analysis has an inherent shortcoming — the sample size is always insufficient. Simply using historical data to infer today's market conditions is not very effective. If you want to use historical data, the peaks in 2013, 2017, and 2021 should have occurred around the end of the year, based on the timing.

Personally, I believe that the conditions for forming a top are already in place. The reasons are very complex, and I used many indicators and data to make this judgment. Let me briefly explain a few key points. First, as we just mentioned, is the chip structure, also known as the URPD chart. We can see one thing: in 2022 and 2023, those chips bought at low cost have accumulated a large amount of BTC at the bottom. So far, many chips have been distributed. Simply put, they have sold off; they are no longer playing. Some listeners may wonder, why do I care if they sold? There is a concept that may need to be explained here. Almost every bull market end is because those low-cost chips have finished distributing, and then the bull market ends. There is a somewhat counterintuitive point here — it's not because they hammered the market that the bull market ends; it's precisely because as the price kept rising, they kept selling. When they finished selling and the price stopped rising, the bull market is about to end. This is not just me guessing; there is some logic to it. Suppose that today, every BTC chip participating in the market is a high-cost chip, bought for example at 90,000 or above, and those bought at 50,000, 20,000, 30,000 have all exited. At this point, as long as there is no clear or strong uptrend in the market, even if it just has a so-called wide oscillation, like the oscillation between 70,000 and 50,000 last year, or the oscillation between around 90,000 and 109,000 now, it will put immense pressure on these high-cost chips. High holding pressure will lead to an issue. For instance, the price is currently around 95,000 to 96,000. Suppose it drops to 89,000; it's actually less than 10%, but the pressure on these chips is significant. Many of them may even be short-term traders. Once the pressure is high, they may choose to sell, causing a further price drop. This further drop would make other high-cost chips unable to withstand the pressure, so they also sell, leading to a chain reaction. This is what I see in the URPD chart — many low-cost chips have already been distributed.

The second indicator I just mentioned is called RUP, which is used to measure the market's profit status. If you are interested in this indicator, you can look it up. It is quite interesting because if you overlay its line with the price line, their correlation is very, very high, almost moving together. This actually makes sense because as the price increases, the unrealized profit ratio (RUP) will also increase. The shapes of the two lines will be almost identical. So, as the price rises, RUP will rise, and as the price falls, RUP will fall – it's that simple. However, once a divergence occurs in RUP, it indicates that the market situation has changed. What is a divergence? For example, if Bitcoin rises to $90,000, then retraces and rises again to $100,000, creating a higher high, but at $100,000, RUP is not as high as it was at $90,000; instead, it decreases. This means RUP decreased while the price increased. Why does this happen? The only logical explanation for this phenomenon, as we discussed earlier, is that RUP is calculated using unrealized profits, and most of the unrealized profits in the market are actually contributed by those holding low-cost chips. For example, if you bought a Bitcoin at $16,000 today and it's now at $96,000, the unrealized profit for just that one Bitcoin is $80,000. But if you bought a Bitcoin at $86,000 today and it's now at $96,000, the profit for that one coin is only $10,000. So, in terms of proportion, the majority of the contribution comes from those holding low-cost chips. Therefore, when the price increases but RUP decreases, it means that a certain proportion, or even a significant number, of low-cost chips have already been sold off earlier, causing those who invested at a higher price to realize some of their unrealized profits, which do not reflect in RUP, resulting in a lower RUP and a divergence. This can help me confirm in interpreting RUP that indeed there are low-cost chips exiting the market.

As for the third aspect, there is still much to discuss regarding on-chain data, but I personally want to share a unique viewpoint related to the U.S. stock market. If someone has researched the stock market, they would know that there is a concept of valuation, such as the P/E ratio or Price-to-Earnings ratio. There are many different variations of valuation methods, and the indicator I reference is called Shiller ECY. This indicator is from Professor Shiller at Yale University. He measures the dividend yield of stock assets relative to bond assets. This indicator was mentioned in a paper he published in 2020 after the pandemic. He believed that his previous model or data, called Shiller PE, was not suitable after the pandemic due to global market structural changes. Therefore, he invented a new indicator called Shiller ECY to assess the market and found that this indicator's predictive power is indeed better. In simple terms, this indicator currently shows that the U.S. stock market's valuation is a bit too high. It is important to clarify that a high valuation does not necessarily mean a decline; after being high, valuations can continue to rise further. However, it measures a spectrum, meaning it is getting closer to a dangerous zone. The current position is one that I personally consider quite risky. The valuation of the stock market is currently mainly driven by the hottest topic, which is AI. Some time ago, there was a DeepSeek that came out of nowhere and caught everyone off guard, causing a sudden downward revision in the valuation of the U.S. stock market. However, at this point, my own view is somewhat pessimistic in the medium term. Because even though DeepSeek is a chip decrease in the long run and certainly a big boost for the AI industry, in the short term, I don't think this valuation effect will end so quickly. So, I believe there is still room for downward valuation. If the U.S. stock market is not doing well, then Bitcoin, as the little brother, naturally won't look good either. However, these are just my personal biases, my personal biases, for your reference.

Alex: Alright, just now Colin explained in great detail, so let's summarize his points briefly. He believes that the current price range has met many conditions indicative of past valuation or price tops, including some scenarios of chip distribution, unrealized profit-to-value ratios, and a reference to Professor Shiller's ECY indicator from the traditional financial market. He thinks that currently, there are many signs of a top.

Getting Started with On-Chain Data Analysis

Alex: Today, we have already discussed many principles of on-chain data analysis, including how to observe some common data and how to apply these insights in practice. Many of our listeners may not have delved deeply into this concept or system before. So, imagine a beginner comes to you and says, "Colin, I found your talk today very inspiring, and I also want to start learning this knowledge from scratch to guide my BTC investments. What learning advice would you give to help me start this journey?"

Colin: Alright, I've actually received dozens of direct messages so far asking similar questions. My personal advice has always been the same. Firstly, my main strengths lie in two areas: on-chain data and technical analysis. When most people ask me, they usually have a chart with some patterns drawn or an indicator like MACD or RSI, and they want to know if these can be correlated with on-chain data views. I always start with one recommendation: I strongly discourage beginners from starting with technical analysis. The main reason is simple; there are too many schools of thought, and some views within these schools are not scientifically sound. They are purely based on induction without logical reasoning, making it easy to fall into what I call the "barking dog and rainfall" example. It could very well be survivorship bias, but most newcomers lack the ability to distinguish whether it is a usable concept or merely survivorship bias. Personally, I believe that on-chain data is a field very suitable for beginners, as I will explain shortly. I believe it is suitable for beginners for a simple reason: Firstly, most retail traders or part-time traders, such as high school students, college students, or office workers, have their primary careers. If you cannot spend a lot of time actively trading, then on-chain data trading suits you well. As mentioned earlier, on-chain data observation is on a large scale, starting at least from the daily timeframe. Since you are observing at a daily timeframe, it means that your trades based on on-chain signals, such as buying or selling, are infrequent. You do not need to make 5 or 10 trades a day; you may only make four or five trades a year at most. Therefore, I believe that in terms of observation, it aligns very well with the lifestyle of students or office workers. You don't need to spend too much time; you may just need to allocate half an hour to an hour each day to observe these alerts, checking for any significant changes in the data. The second part is about how to learn. As I mentioned earlier, I have not come across any free, systematic teachings in my learning process so far. There are many teachings, but none are systematic. They might give you an article, explain one or two indicators in great detail. While these articles are excellent, the problem is that you still lack a structured curriculum from 0 to 1. Learning under such circumstances can be painful as you may think, "This indicator looks great, should I learn it, should I research it further?" The next indicator also seems impressive, then which one should I start with? My approach is basic; I am direct because I was clueless about what was good or bad initially, so I learned all of them. I delved into the principles of each, understanding the calculation basis, why the author designed a particular formula, what the author intended to observe with this formula, and whether this formula truly helps them see what they wanted to see. This process takes a lot of time. Having gone through all these indicators, you need to filter them. However, for beginners, this process requires a lot of patience. You must carefully consider each one. Because trading is inherently challenging, and from what I have observed, whether in simplified or traditional Chinese, there are relatively few resources available in the Chinese community. Therefore, my advice is, if you want to study a specific indicator, if you can find the original author's article, that's the best, try to avoid looking at others' work; the original author definitely has the deepest understanding of that indicator. If you genuinely cannot find it, at least read through their formula. As I mentioned earlier, Glassnode's website has a section called Weekly Onchain; they share the current market conditions weekly in a report format based on various dynamic indicators, not fixed ones. This gives you the opportunity to study various indicators; you can grab each one and delve into it, creating a substantial learning material repository. I have some educational material on my Twitter, although it's not systematic. If you are interested, you can take a look.

Alex: Quite systematic indeed. I have been following your updates, and it seems you have already written over ten articles, basically covering one indicator concept in each issue, right? Everyone can go take a look. Another question, you mentioned earlier that your first identity is as a trader. Today, we spent a lot of time discussing how on-chain data helps with trading. However, in actual trading, apart from analyzing on-chain data indicators, do you also consider other factors? For example, macro factors, such as some fundamental events of Bitcoin, maybe progress in a state or national treasury's Bitcoin reserve. Besides on-chain data analysis, what other indicators do you use as references for your trades, and approximately what weight do they each carry in your overall trading decision-making?

Colin: Alright, I think this question is very profound. First, in terms of my system, the on-chain data part is actually considered a separate system for my position allocation. I have a relatively large long-tail spot allocation, and even at the bottom of a bear market, I might slightly leverage it, for example, around 1.5 times or 1.3 times. This is a system, and the main trading decision basis of this system lies in on-chain data. On-chain data provides me with a general framework. It lets me know whether we are in the early, middle, or late stage of the market, in a bull or bear market, providing a directional guidance benefit. As for other parts, as I mentioned earlier, another strength of mine is the technical analysis part. This part cannot be explained in too much detail because it is too complex. Many schools of thought and some foundational assumptions need to be clarified first. The technical analysis part is used for short to medium-term trades in my own trading system. Technical analysis mainly helps refine the final entry point. Assuming I have already confirmed that I want to pursue a certain opportunity today, where exactly should I enter this trade, I will use technical analysis to refine my entry point. Just as an example, this is not financial advice, assuming that entering Ethereum between 2000 and 2600 is viable, and I believe it will definitely rise thereafter. If I were all-knowing, knowing it will rise, then of course, I'd simply buy. But because I am not all-knowing, I will try to use technical analysis to find a more satisfactory entry point within this range. As for the specific number, I have to evaluate it each time, so there is no way to obtain an exact figure, but I have a set of benchmark measurements. Moving on to the macro level, I am more concerned about the global market's supply chain and the Federal Reserve's decisions because the US still has a significant influence on the financial market. Expectations of their interest rate hikes or cuts will have a very serious impact on the risk market. For example, if recent CPI data is not favorable, the risk market will make a corresponding pricing adjustment because the market always prices in advance; they trade expectations. They cannot wait for the actual rate cut to rise or wait for the actual rate hike to fall; there will always be an early expectation. Futures traders or options traders will make an overall judgment on the market and price accordingly. So, this is also something I pay more attention to, but my macro perspective is not as in-depth as my technical analysis or on-chain data; this area is considered relatively weaker for me. Lastly, there is the news aspect or fundamental aspect that Alex just mentioned, the so-called strategic reserve news. This part actually goes back to what I said at the beginning, which is something I am pretty keen on doing myself—designing event-driven trading strategies. This is about executing high-certainty trades based on specific events. Let me give you an example, around late May last year, Bloomberg had a senior ETF analyst named Eric, and the market paid a lot of attention to his articles. Suddenly, around 3 am GMT+8, he posted an article saying the probability of an Ethereum ETF approval had been raised to 75%. At that time, the market as a whole did not anticipate the Ethereum ETF approval. When this news came out, Ethereum surged by 20% within 24 hours, with the increase directly surpassing Solana, which was quite impressive. After such news, the first thing that crossed my mind was to start preparing to enter into an event-driven trade. That is, be ready to go long on Solana while shorting ETH. The rationale behind this was quite simple because the whole world knew an ETF approval would be a huge positive, so Ethereum immediately rallied; it was straightforward. The real thing to ponder was who would be next? In the prevailing market conditions at the time, Litecoin and Dogecoin did not have as much support or buzz as Solana. So, at that time, my first target was Solana, and about a week later, I began laying out a long-short strategy for Solana against ETH. Simply put, I used contracts to go long on Solana and short ETH, capitalizing on the uptrend in the exchange rate between the two. I believed the next expected hype was Solana since Ethereum's approval was already a fact. Assuming Ethereum did get approved, Solana was bound to receive a related uplift. Some people might say, can your idea stand the test? I dare not say 100%, but one of the most obvious examples was in January 2024 when Bitcoin ETF was approved. On that day, if I remember correctly, which was within 24 hours, the ETH-to-BTC exchange rate surged by about 30 percentage points. Many people were puzzled: Bitcoin ETF was approved, so what about Ethereum? The next hype was Ethereum. So, this is what is called one of the event-driven trades. Back to Alex's question, I believe concentrating on news or fundamental aspects is too difficult to quantify. Therefore, I personally prefer to design event-driven strategies to exploit potential market inefficiencies in pricing.

Alex: Understood, thank you for your logical and systematic explanation, Colin. He made it very clear the thought process behind each operational strategy, including what kind of scenario might be applicable. It is evident that he has a very rich toolbox and knows when to use what tool in which scenario, rather than making a vague decision based on intuition.

A Day in the Life of an On-chain Data Researcher

Alex: So, moving on to the last question, as a trader and an on-chain data analyst, what does a typical day look like for you? Besides focusing on on-chain data, what other information might you look at or what tools might you use?

Colin: Well, this question is quite interesting because, in reality, my typical day is quite dull and boring. My routine is not very normal, but I try to stay awake around the time of the U.S. stock market opening because that is usually when the Crypto market has the best liquidity. If my energy allows it, I will look for some short-term trading opportunities during this time period. This habit has actually been cultivated for several years. During the day, if I am really tired, I will take a nap to catch up on sleep because missing out on opportunities during the day is less likely, while missing out at night is more probable, and monitoring the market at night is more valuable. As you can see, most weekends or weekdays during the daytime in Asia, the market is mostly boring, just moving sideways with low trading volume and poor liquidity, so that's why I try to stay awake at night. After I wake up, in addition to observing any changes in on-chain data as Alex mentioned, I will observe and record some additional data I want to see. Besides candlestick charts, I will regularly review all the trading pairs I usually pay attention to and manually record the net inflows and outflows of U.S. Bitcoin and Ethereum ETFs, as well as market volatility and the Fear Greed Index, which is another quantified indicator of market sentiment. I also look at the open interest in the futures market. If there is an extreme price surge or drop during the day, I may also look at the liquidation volume. I record all these data points as I am quite sensitive to them. The remaining data involves checking for any additional events. Once an event occurs, I want to see if there have been any changes in the data. Normally, I focus on the aforementioned data points: futures market open interest, market volatility, Fear Greed Index, ETF net inflows and outflows, and some others. Another data point I like to look at is the price difference of Coinbase contracts compared to mainstream exchanges such as Binance or OKEx, to see if there is a premium or discount. I personally believe this is a quantifiable sentiment indicator targeted at U.S. fund sentiment. For example, if there is a clear premium on Coinbase, it means their buy orders may be stronger. This was very evident when Trump was elected. I observe these numbers for any abnormalities every day and maintain this sensitivity. Once I notice something, I start to think whether it is random or if there are some trading opportunities hidden within. Besides recording this data, I also spend time monitoring the market. As I mentioned earlier, technical analysis is one of the few strengths I boast about. I will spend a few hours monitoring the market, observing if my daily planned and adjusted trading strategies are meeting my expectations. If they are close or have already met them, I will focus all my attention on the market and the data I want to see. I have two screens, and on the other screen, I keep Twitter open, managing my account, Mr. Berg, on Twitter. The part of my day outside of trading is actually quite boring. Occasionally, I go out for a run, but not very frequently, just to keep myself moving and not be sedentary the whole day. The rest of the time is mainly spent with my family. So, my day is quite monotonous, with no particularly noticeable points because trading is my job. Thus, my routine is not significantly different from that of a typical office worker or student. It mainly involves working, then off-duty activities, meals, sleep, and that's about it.

Alex: I see. Just now, Colin talked about his day's work, the amount of information involved, and the intellectual effort required are quite significant. However, he may have systematized and modularized it, so his brain doesn't need a special startup every day to carry out a series of important tasks, including data tracking and so on. He has habits for what to do in each time period, with a very clear schedule, enabling himself to quickly get into a state. We can also observe that Colin himself is very curious about trading, investment, and the business world; he gains not only money but also a lot of fun from them. I feel that such a state is an essential talent for a good trader and investor. Thank you, Colin, for coming to the show today and sharing with us so much about on-chain data analysis, investment, and trading, a series of reflections, and a systematic explanation. I hope in future episodes, we can invite Colin again to share more knowledge in other areas. Thank you, Colin.

Colin: Alex, you are too kind. Just sharing my personal views, thank you.

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