Ten thousand words conversation: In-depth discussion of on-chain data, is this cycle really over?

Host: Alex Mint Ventures Research Partner

Guest: Colin Free Trader On-chain Data Researcher

Recording time: 2025.2.15

Hello everyone, welcome to WEB3 Mint To Be initiated by Mint Ventures. Here, we continue to ask questions and think deeply, clarify facts, explore reality, and find consensus in the WEB3 world. We will clarify the logic behind hot topics, provide insights into the incident itself, and introduce diverse thinking angles.

Statement: The content we discussed in this podcast does not represent the views of the institution where the guests are located, and the projects mentioned do not constitute any investment advice.

Alex: This episode is a bit special because we have discussed a lot of topics about specific tracks or projects before, and have also communicated some periodic narratives, such as we have talked about meme before. But today we are going to discuss on-chain data analysis, especially on-chain data analysis of BTC. We will examine its principle of action and key indicators at a close distance and learn its methodology. In today's program, we will mention many concepts about indicators, and list these concepts at the beginning of the text version for easy understanding.

Some data metrics and concepts mentioned in this podcast:

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

Realized Price: Based on the price weighted calculation of Bitcoin's last on-chain move, it reflects the on-chain historical cost of Bitcoin and is suitable for evaluating the overall profit/loss status of the market.

URPD: Price distribution has been achieved. Used to observe the price distribution of BTC chips.

RUP (Relative unrealized profit): Relative unrealized profit. Used to measure the ratio of unrealized profits to total market value of all coin holders 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 evaluate the long-term value of BTC by introducing Bitcoin’s “time weight”, compared with the current market price of BTC, and has been achieved. Realized Price, True Market Mean Price under the Cointime system also takes into account the impact of time and is suitable for prices under the large BTC cycle.

Shiller ECY: Valuation indicators proposed by Nobel Prize winner Robert Schiller to assess the long-term return potential of the stock market and measure the attractiveness of stocks relative to other assets, Schiller's Principal Equity Index (CAPE) The improvement mainly considers the impact of the interest rate environment.

Opportunities for learning data analysis on the chain

Alex: Today we invited guests are freelance trader and on-chain data researcher Colin. Please ask Colin to say hello to our audience first.

Colin: Hello everyone, first of all, thank you Alex for the invitation. I was a little surprised when I received this invitation, because I am an unknown small retail investor and have no special title, so I am silently making my own trading. My name is Colin. I run an account on Twitter called Mr. Berg . I usually share some teaching on chain data, analysis of current market conditions, and sharing some trading concepts. I have about three positions for myself: the first is an event-driven trader, I usually think about event-driven trading strategies; the second is an analyst of on-chain data, which is also what I usually do in Content shared on Twitter; the third one is more conservative. I call myself an index investor. I will choose to allocate some of the funds to the US stock market. Through this part of the funds, the beta of the investment will reduce the overall fluctuation of my asset curve while maintaining the overall Defensiveness in the area. The above is probably my positioning.

Alex: Thanks to Colin for introducing himself. I invited Colin to the show because I saw his on-chain data analysis on Bitcoin on Twitter, which was very inspiring. This is a topic we talked about less before, and it is also a part of my own sector that is lacking. I read the series of articles he wrote and felt that the logic was clear and meaningful, so I went to invite him. I would like to remind everyone that today, both my and the guests’ opinions are highly subjective in the program, and the information and opinions may change in the future. Different people may have the same data and indicators for the same data and indicators. Different interpretations. This issue is not intended as any investment advice. This program will mention some data analysis platforms, which are only used as sharing and examples for personal use, and are not commercial recommendations. This program has not received commercial sponsorship from any platform. Let’s get to the topic and talk about on-chain data analysis of crypto assets. I just mentioned that Colin is a trader. Under what circumstances did you start to contact and learn on-chain data analysis of crypto assets?

Colin: I think this question should be answered in two parts. First of all, I think no matter who you are around you, as long as you want to enter or have entered the financial market, including myself, your main goal should be to make money and use profits to improve your quality of life. So my philosophy has always been consistent, that is, I will learn whatever things can help my profits. In this way, I will learn what I can make money. The second part is that at the beginning, I would come across the data on the chain simply by accident. I didn’t understand it at all six or seven years ago. Let’s take a look at this and that one. When I was exploring various fields, I wanted to learn interesting research theories. At that time, I accidentally saw that Bitcoin has a so-called on-chain data analysis field, so I started to learn and research. After learning later, I will combine the knowledge I have learned in other fields, mainly the quantitative transaction development part, combine it with on-chain data, and then develop some transaction models, and finally integrate these models into my own transactions. In the system.

Alex: So how many years have you officially started to get involved in on-chain data analysis so far?

Colin: I think this is difficult to define. In fact, I have never really learned it systematically. Because from the past to now, I have encountered a problem, that is, I have not seen any systematic teaching at all. When I first saw this field, it may have been several years ago. I made a discovery at that time, but without in-depth research, I only read two or three articles and learned about this. After a while, I came back to see some more in-depth content. At that time, I was focusing on other things and came back here. Seeing this is quite interesting, I continued to study it. There is no time for systematic learning, just like this.

Alex: Understand, how long has it been since you learned the data on the chain to applying it to your actual investment practice?

Colin: This boundary is difficult to define, but I think it is close to the two-round Bitcoin cycle...it can't be considered two rounds, it depends on whether you start with a bull market or a bear market. I started to get in touch with it around 2020 and 2019, but there was no practical application at that time because I didn’t dare. I was not very familiar with this thing at that time, but I had already started learning it.

The value and principle of on-chain data analysis

Alex: I understand. Next, we will talk about a lot of specific concepts about on-chain data analysis, including some indices. What are the on-chain data observation platforms you use in your daily life?

Colin: I mainly use one website now, which is Glassnode. Let me briefly talk about it, it requires a fee. There are two paid levels, one is the professional version that is more expensive, I remember it costs more than 800 US dollars a month. The second one I a little forgotten, about thirty to forty U a month. It also has a free version, but the free version can actually see very little information. Of course, there are many other things besides Glassnode. I chose it in the end because at first, this website was the most suitable for my appetite.

Alex: I understand, after reading a lot of Colin's information, I also signed up for Glassnode and became their paid member. Indeed, I feel that their data is very rich, and their immediacy is also relatively good. So let’s talk about the second question. I just mentioned that you are a trader. What you value is its help in actual investment. So what is the core value of on-chain data analysis in your investment? What is the principle behind it? Please introduce it to us.

Colin: OK. First of all, let’s talk about the first one, which is the value and principle of on-chain data analysis. I plan to combine these two, because they are actually quite simple. Our traditional financial market, whether it is trading stocks, futures, bond choices, or even real estate, or some raw materials, has the most fundamental difference between Bitcoin and them, that is, it uses blockchain technology. The most important and most commonly mentioned value of this technology is its transparency. All of these bitcoin transfer information is open and transparent, so you can directly see on the chain, for example, 300 bitcoins transferred from one address to another, which can be found on the blockchain browser. Although I can't know who is behind this string of addresses, this is not important, because there is actually no single entity that can affect the price trend and its trend of the entire Bitcoin. So normally, when we study on-chain data, we look at the overall market, its trends, group consensus and behavior. Even if I don’t know who is behind this address or that address, I can analyze the flow of these chips by summarizing all addresses to see if they have already made a profit or stopped loss, how are their profits and losses. What is the situation? They are more inclined to buy a large amount of Bitcoin at which price or they don’t like to buy Bitcoin at which price. These data can actually be seen. This is the greatest value of Bitcoin on-chain data analysis compared to other financial markets, because other markets cannot do this.

Alex: It's really important. Just like when we do crypto investment, we also need to analyze fundamentals just like when we look at stocks or other products. As you just said, the data on the chain is transparent and everyone can observe it. If other professional investors look at the data on the chain and don’t read it, it is equivalent to having a very important weapon that you have less than others in your investment.

Difficulties in on-chain data analysis

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

Colin: I think this question is very good, and I plan to answer it in two parts. First of all, the first part is easier to solve, and there will be a relatively difficult point in learning, which is the basic knowledge. For most people, including me at that time, it is difficult to find a truly systematic teaching because I mentioned it before. Of course, I didn’t ask offline whether there are paid courses of this type, but if there is, I shouldn’t be very brave to buy them, because I’ve been doing my own transactions until now, and I don’t know how to pay to buy some courses. I have never been exposed to any systematic teaching courses, so in fact, all the content needs to be explored and explored by myself. There are many types of data on the chain. During the research process, my own philosophy is to clarify the calculation method and principles behind every indicator I have seen. This is actually a very time-consuming process, because when you see a certain indicator, it will give you a calculation formula. My idea is to think about what is behind this calculation formula and why does it design it like this . After I figured out these indicators, I will do the second thing next called screening. If someone has experience in quantitative strategy development or has studied indicator-related information, he will actually know one thing, that is, the correlation between many indicators is very high. Too high correlation can cause a problem, that is, you are prone to noise in your interpretation, or you may over-interpret it. For example, suppose I have a system that escapes to the top today. This system that escapes to the top may have 10 signals from No. 1 to No. 1. Suppose that if the correlation between No. 1 and No. 4 is too high, it will cause a problem. . For example, if the price of Bitcoin has undergone a certain behavior or change today, it may directly turn on the lights from No. 1 to No. 4 at the same time, which is actually very troublesome. Because if they are too relevant, this is an inevitable phenomenon. If 4 out of 10 lights are on today, you say that this is very dangerous, but in fact it is not reasonable because it will be light up. This phenomenon is very easy to happen if you do not cut them based on correlation. After I studied the principles of each indicator and data, I actually just looked at the calculation formula to know whether their correlation is high. I cut it based on the correlation. For example, if these 5 are highly correlated, I will cut them a little and filter them, and finally select one or two.

This first part is actually easy to solve, and is not the main difficulty. The second part is the real challenge, which is the part about the data on the chain. How do you prove that your point of view is correct to the people around you or to yourself? I might want to give a more vulgar example here, but it is easy to understand. I have written on my tweet before, and in fact, the quantitative field will tell you that trading is not very good at finding a sword. I have given an example before, suppose there is a very strange trading strategy today, and its entry standard is, suppose the dog I have barked twice and then it was raining outside, then I will enter Do long in the field. As a result, I went backtested based on this strategy. After 1,000 backtests, I found that the winning rate was 95%, and I still beat the market far away. So, does anyone dare to use this strategy? Actually, it’s quite strange. The dog barked for no reason, and it was raining outside, so you could go long, and the winning rate was still so high. There is actually a noun called survivor bias. If you can't give it any logical support today, even if the number of samples is enough, this strategy cannot be used. Some people will retort that it backtested 1,000 times, with a winning rate of 95%. The backtest result is that it can be used to support this strategy. Then I just mentioned the so-called survivor bias. Simply put, suppose I lost a coin 10 times in a row, the probability of 10 times being positive is actually 1/1024. In other words, on average, every 1024 people are in When doing this, one person will succeed, throw 4 times in a row. This situation is actually the so-called survivor. The other 1023 people fail when doing this. We actually won’t see it. What we see will always be successful cases. Going back to the Alex problem just now, what is the so-called main difficulty? Because we are mainly looking at large-level consensus and trends. Looking back at the history of Bitcoin, the top three most obvious tops are the two tops in 2013, 2017 and 2021. In this way, only 4 more will be added to OnePlus. The number of samples is absolutely not enough. Since the number of samples is not enough, if we go to Kezhou and find out where a certain indicator has been in 2013 and where a certain indicator has been in 2017, so where it will be in this year, it is unreasonable. Because the number of samples is completely insufficient, if we do not give it logic to do research at this time, your theory will be very easy to make mistakes. One of the most important problems is that in the face of such a small number of samples in history, I must use deductive methods rather than simply inductive methods to study. After I finished researching it, I came to a conclusion based on the deductive method, and time needed to prove whether my view was right or wrong. If it is correct, it means that my previous deduction inference process may be reasonable. If it is wrong, then I still need to continue to correct the previous deductive logic. But if today is just based on induction, most retail investors like to do this thing the most, and they think that the trend in the past looks very similar to the current trend, so they should have to rise or fall in the future, which is actually unreasonable. Going back to the first sentence I said at the beginning, I think the biggest problem is that I have to prove to others or prove to myself that my inference is correct, so I have to correct my logic and assumptions all the time, and then go Check if there are any defects. Because Bitcoin is too young, on-chain data analysis will always face the problem of insufficient number of samples. At this time, you have to use a simple deduction method in your research, and you also use a logical way to infer it. Wait for time to prove your judgment. This is the biggest difficulty I have encountered at the moment.

On-chain indicators that are focused on

Alex: I understand, I think it's still very inspiring after listening to it. The question I asked you just now was also some of the confusion when I started looking at the various indicators on Glassnode. It has so many indicators, which indicator should I use as my trading reference? Because many indicators have various calculation logic. I tend to choose the logic of those indicators in the future, which is quite similar to the logic you just mentioned. First of all, I need to look at the operation logic behind this indicator, and I need to think that this logic makes sense, rather than backtesting and deducting it, it seems that this indicator is very accurate, and in the future, I use this accurate indicator to predict the future. The reference in the deduction method you mentioned needs to be greater in order to be used as an indicator we mainly adopt. So after the experience you just talked about, in your daily analysis of Bitcoin, which on-chain indicators are you paying attention to for a long time or do you think it is more important?

Colin: Actually, I have mentioned this question before, and I will try to screen based on the relevance. I usually look at a lot of data indicators on the chain, so today I will introduce them from different dimensions, that is, try to split them into three levels from the low correlation part.

The first thing I will pay attention to for a long time and focus on is the URPD indicator. It is a chart, presented in a row of bar charts, the horizontal axis is the price of Bitcoin, and the vertical axis is the quantity of Bitcoin. Suppose we see a very high and large cylinder at 90,000 today, then we will know that there is a very large number of Bitcoins that are building positions at this position, which is the cost of their purchase. The bar chart will show how many bitcoins they buy at this price. So based on this incident, we can see at a glance that assuming that the accumulation of more than 100,000 is very large, then we can know that many people buy more than 100,000. This URPD chart has two main points of observation. The first is the simplest chip structure. Suppose today I see that the current market situation is around 87,000, and a lot of chips have been accumulated above 87,000. According to the data from last week, it should be 4.4 million. Then we know that there are a lot of turnovers in this range. , or someone bought it here. Since someone buys it, it is very likely to form a certain consensus. In this large-scale accumulation range, it is easy to form an attractive effect on the price, that is, the price is likely to fluctuate all the time in this range, and it is easy to repair after a period of time and then rise back. If the chips below have all become floating profits, they will actually be easy to sell, sell, make short-term trading, and then sell the price back. So it is actually easy to oscillate in this range. This is the first focus of observation. The second observation point is that we can observe the process of Bitcoin distribution through URPD. The so-called distribution is when buying Bitcoin chips at a low price in the early bear market, and then they sell the cheap chips on their hands up. Then I define this process as distribution. Suppose there are 300,000 more chips at the price of 100,000 today, and the cost is 20,000. Suppose it is 20,000, which is exactly 300,000 less. Then we can actually see that people with a cost of 20,000 today. After 300,000, their average selling price was about 100,000. We can see whether the low-cost chips usually have some drastic changes. Of course, the current price is more than 100,000 or 90,000, so they have undergone drastic changes that will definitely decrease, and will not increase, because the current price range is more than 90,000, and it will not reach more than 20,000, so it will only decrease there. Will increase. So we can observe the distribution rate based on this matter, which is probably what it means. This is the first indicator I will pay attention to for a long time.

The second one I want to introduce an indicator called RUP, which is called relatively unprofitable in Chinese. This indicator is actually one purpose, helping us measure the overall market's profitability, which is the profitability of the entire market for the current Bitcoin price. For example, how much you make, or how much you make, or how much you make, probably this is the concept. The principle of this indicator is actually very simple, because through the so-called blockchain transparent mechanism, we can track the prices of most chips to buy. We can compare the price of buying these chips with the current price. Suppose he buys at 50,000 and the current price is 100,000, we will know that this Bitcoin is currently making money, so we will calculate how much it makes. For example, 10 Bitcoins are bought at 50,000, and now it is 100,000. One of them makes 50,000, and 10 of them makes 500,000. We add all these floating profits and losses, and then standardize this number based on the current market value, and then we can get a number between 0 and 1. Then 0 and 1 are easy to observe. Assuming that the RUP is very high today, such as 0.7, 0.68, and 0.75, then we know that the overall profitability of the market is very high now, which may make more people want to make a profit. So the RUP is too high and is usually regarded as a relative warning by us.

The third dimension I want to talk about is a fair valuation model of a market. There are actually many different Bitcoin valuation models on the market, and each model will actually use different methods to evaluate the fair value of this Bitcoin. The so-called fair value is actually how much a Bitcoin is worth. After reading so many models, I think the Cointime Price model is the most testable. Actually, I have never seen the Chinese translation of this noun elsewhere. Simply put, we often hear a name called Cathie Wood, her ARK Invest, and the on-chain data website, which is the Glassnode I mentioned just now. This concept is a document produced by the two of them together. What is mentioned in it. The biggest feature of this model is that it introduces the concept of time-weighting and then calculates the fair value of Bitcoin. There are two main uses of the calculated numbers. The first one is very simple, which is to buy at the bottom. Suppose that today, it fell and fell during a bear market, and finally fell below the valuation given by Cointime Price. I just said that this number is actually how much a Bitcoin should be worth. If you fall below this position today, it is equivalent to buying at a very cost-effective position. According to historical backtesting, it can actually be seen based on its logic that whenever the price falls below Cointime Price, it is actually a very good bottom-buying position. The second application is Escape Top. We can monitor the current price and how far it is from the Cointime Price. If it deviates too much from Coin Time Price, we can evaluate whether this deviation is too large, does it mean that the market may be close to the top. The above three dimensions are the chip structure, profit status and fair valuation model, which are the three indicators and directions I want to share.

How to view data fights

Alex: OK, I've already made it very clear. Many users may ask a question. The three attention indicators you listed just now may represent different faces, and they also conform to what you said just now. The correlation between them is not that high, so they can be put together as a reference. index. So assuming that such indicators have diverged in actual application, for example, if the indicators feel that they are currently in a distribution situation, indicators 2 and 3 may show that they are currently at the top, and it seems not that high in terms of cycles. In this case, how would you deal with data fights?

Colin: I think this is not just in the field of on-chain data analysis, but also in other fields such as technical analysis or macro fields, which may encounter so-called fights. On the chain, my personal approach is very simple, and I will give different weights to different levels. What I value most is actually the chip structure, that is, the distribution progress. Because in terms of profit status, it also helps me observe the low-cost chips in the market. During the bear market, such as the Bitcoin chips bought at 15,000 or 16,000, have they paid off? There is a very special phenomenon that in every cycle of Bitcoin in previous years, there will actually be two very obvious large-scale distributions. For example, in 2024, the most obvious case was from March to April last year. In fact, from the perspective of profitability, you can definitely see that there were large-scale distributions at that time. But if I only see large-scale distribution today, then my next question will be to think about it. Have they finished distributing? All the criteria for judgment start from this question. If they have a large-scale distribution but have not finished distribution yet, I can tell myself with peace of mind that the bull market is not over yet. For example, from March to April last year, Bitcoin reached more than 70,000. I was actually quite excited because the bull market finally came and hit a new high. As a result, it began to fluctuate all the way for about half a year. At that time, I could not reach the bottom when I observed these data. At most, it was the first distribution. Then a lot of data are the same. I have posted some medium-term analysis and chip structure analysis before. At that time, based on the average cost of short-term holders, his situation is different from the end of a real bull market. So I was actually very at ease at that time. Then you said data fight, and now he said it was distributed, so am I going to escape? Actually, it doesn’t need to be, because the main problem is the one I just mentioned: Is the distribution over? Using this question to screen every indicator and make a benchmark for judgment, you can actually easily draw this conclusion, that is, even if the distribution appears and it is still a large scale, I just need to judge whether it is over. Using this as a criterion can effectively deal with the so-called data fight problem.

Alex: Let's now plan a scenario, for example, we are looking at URPD. Its indicator assumes that there have been two distributions, which is more like what you just said, once in March and April last year, and then there are also from December to January at the end of the year. A peak of distribution. Suppose it has this distribution situation, but the other two valuation indicators may not be that high. When this situation occurs, you just said that it will give it different weights, so you will use the proportion of weights. Should we reduce part of the position, or would we think about the three indicators in a unified way, and then we would not adjust the position based on the weight, but make one or two important decisions at critical moments?

Colin: My own approach is the former, because in fact no one can know whether it is the real top now. No one can escape from that highest position. If there is, it would be too powerful. I will definitely want to know one time. Top, my personal interpretation is a slow process. Although you feel it very fast when you look at the daily chart, in fact, if you are in the present moment, for example, you are at 69,000, at that time, the top of the previous cycle. , you won't feel that it's the top now. We can only make one judgment based on the data, and it is possible to meet the conditions for top formation now. So based on this premise, I actually have a segmented position. For example, when I think the top conditions have gradually begun to mature, once I see a certain indicator during this period, I give me a warning, for example, I have shared a divergence of a RUP on Twitter before, I will do it Corresponding positions reduction. Of course, the range of this position reduction must be formulated in advance from the beginning. It is impossible to say that it is diverging now. If you don’t know how much you can reduce it, just reduce it a little bit. This won’t be like this. I will first draw up a rough idea, for example, I divide my position into 4 parts, and then once something of a warning appears, I will first reduce one of them, and I will reduce another when the second warning comes out. . At the same time, I will plan and no matter what the last fund will definitely come out. For example, the bear market has been confirmed to end, but other warnings have not yet appeared. We need to formulate an extreme and final escape strategy to screen.

Alex: I understand, we should gradually leave the market and reduce positions according to different alarm signals.

Colin: Yes.

Judgment and basis for the position of BTC in this cycle

Alex: I understand. I have been following your Twitter account recently. You will practice your trading practice based on the indicators you just mentioned, including the concepts behind these indicators. Now when we look at Bitcoin, it has been fluctuating between 91,000 and 109,000 for almost three months. There are quite a big difference in the market about this price range. Unlike in December or January, everyone thinks that this bull market is far from over and it will reach 150,000, 200,000 or even 300,000, and many are very positive. viewpoint. The market is currently very different. Some people believe that the top of BTC in this round is around 100,000, but some people believe that BTC in this cycle has not yet reached its peak, and there will still be a main upward wave in 2025. So based on your current comprehensive judgment, what do you think? Where is BTC in our big cycle? Then what sources of data are supported by your judgment?

Colin: Before answering this question, you may need to get a vaccination first. I am actually very bearish in 2025. I think BTC is currently in a condition of being formed at the top. Actually, I know that many people, including some participants around me, have not had good returns during the so-called special bull market in 2024, because the overall market running method in 2024 is compared with every previous cycle. They are different, the most obvious point is that there is no copycat season. This hurts many people, including some of my friends from non-professional traders, who have also come in to participate in this market, but in fact they have suffered a lot of losses in altcoins. Why is this happening? Let’s take a closer look at the 2024 period. There was a copycat market at the beginning of the year, and the second time was in November last year, when Trump was elected President of the United States. The two counterfeit markets actually have a big and obvious point compared to our previous cycles, that is, their sustainability is actually not very good. Even in the wave of markets in November and December last year, altcoins did not rise in an all-round way, it was a very obvious sector rotation. At that time, there was a Defi sector. After the rise, it changed to old coins, such as XRP, and Litecoin, etc. The rotation of that sector was very obvious. From this incident, we can see that if everyone thinks it is a bull market in 2024, there is actually a big gap between this cycle and the past. There is another theory that there will be a so-called copycat season before the bull market ends. In fact, I think you can’t say that the copycat season will only end if the bull market ends. This is obviously not a strong correlation. We can't use this as a judgment on whether the bull market ends. As mentioned earlier, there is a shortcoming in the data analysis on the chain, that is, the number of samples will never be enough. We simply use the historical situation to analogize today's market, which is actually a way to carve a boat and seek a sword, which is not very good. If you want to carve a boat and seek a sword, the top of 2013, 2017, and 21 years should appear around the end of the year, depending on the time.

I personally think that the so-called top formation conditions are actually met now. The reason is very complicated, and I use a lot of indicators and data to make judgments. Let me briefly talk about a few more core ones. The first one is the chip structure we just mentioned, which is the URPD chart. We can see one thing: in 2022, there are some low-cost chips accumulated in 2023. At that time, they bought a lot of BTC at a low level, and so far, a lot of chips have been distributed. . To put it bluntly, they have sold it and they will not play. Some listeners may have a problem, that is, what does it matter to me if they sell it? I have a concept that when every bull market ends, almost every time it is due to the distribution of low-cost chips, and then the bull market ends. There is a relatively unintuitive point in this place. It is not because they smashed the market and the bull market ended. It is because the price has been rising. They sell all the way. After the sale, they sell it, and then the price stops and the bull market is about to end. This is not just that I slap my head and say it must be like this, there is a logic behind it. Suppose that every BTC chip that participates in the market today is a high-cost chip, such as buying more than 90,000 yuan, and then buying chips of 50,000 yuan, 20,000 yuan, 30,000 yuan have all run away. At this time, as long as the price does not show a very obvious or strong main upward trend, even if you simply make a so-called wide range of fluctuations, such as the fluctuation range between 70,000 and 50,000 last year, or now it is roughly the same as the current fluctuation range. The fluctuation between 90,000 and 109,000 will put a lot of pressure on holding these high-cost chips. A large pressure on holding positions will lead to a problem. The price is about 95,000 or 96,000 now. Assuming it falls to 89,000 today, it is actually less than 10%, but the pressure on these chips is very high. Many of them are even short-term traders. Once the pressure is on If it is big, you may choose to sell, which will lead to a further decline in the price. If it falls, other high-cost chips will not be able to withstand the pressure. If they sell again, it will cause a chain reaction. This is what I think I see from the URPD chart, which is that many low-cost chips have been distributed.

The second one is also an indicator I just mentioned called RUP, which is used to measure market profitability. If you are interested in this indicator, you can check it out. It is very interesting. If you put its lines and price lines together, their correlation is very, very high, and they are almost done together. This is actually a very reasonable thing, because the higher the price, the higher the profitability of the position cost, and the shapes of the two lines will be almost the same. So the higher the price, the higher the RUP will follow; the lower the price, the lower the RUP will follow, which is very simple. However, once the RUP has a so-called divergence, it actually means that the market situation has changed. What is divergence? For example, Bitcoin rose to 90,000, and then pulled back to 100,000, setting a higher high, but when RUP was 100,000, it was not as high as 90,000, but instead went down. , this is the so-called RUP that has become lower but the price has become higher. That's strange why this situation occurs? There is only one logic that can reasonably explain this matter, which is that we just said that RUP is calculated using unrealized profits. The main large number of unrealized profits in the market are actually contributed by those low-cost chips. For example, if you buy a Bitcoin at 16,000 today, now you have 96,000 yuan. The floating profit of this Bitcoin alone is 80,000. But if you buy Bitcoin at 86,000 today, and now 96,000, this one is only 10,000, so the main contribution in proportion is made by those low-cost chips. So once you have a higher price but the RUP is lower, it means that there must be a certain amount of low-cost chips that have been sold in advance, resulting in the fact that when your subsequent price is higher, these low-cost chips have already left the market. So they turn some unrealized profits into realized profits, so they can't see them on RUP, which will lead to lower RUP and create a divergence. This can help me get a verification in interpreting RUP, that is, there are indeed low-cost chips leaving the market.

For the third aspect, there is actually a lot to talk about on-chain data, but I personally share another unique point of view, called the US stock market. If someone has research on the stock market, he will actually know that the stock market has a so-called concept of valuation, that is, the price-to-earnings ratio, or the price-to-earnings ratio. There are many different deformations in this method of valuation. The indicator I personally refer to is called Shiller ECY. This indicator comes from Professor Schiller of Yale University. He measures the yield rate of stock targets relative to bond targets. This indicator is his 2020 It was mentioned in a paper published after the outbreak of the epidemic in 2019. Because he believes that another model or data before him is called Shiller PE, Schiller PBE. He believed that after the epidemic, many situations of that model were actually different from before due to the changes in the structure of the global market, so he invented a new indicator called Shiller ECY to measure the market and then discovered the predictive effect of this indicator. It's really better. To put it simply, this indicator currently shows that the valuation of the US stock market is a little too high. One thing to clarify here is that a high valuation does not mean that it must fall. After a high valuation, it can be higher, and it can be higher. But it measures a concept similar to a spectrum, which is that it is now getting closer and closer to the danger zone. In fact, the close position is a relatively dangerous position I think. The valuation of the stock market is currently mainly contributed by the hottest topic, namely AI. Some time ago, there was a DeepSeek that caught off guard, causing a sudden downward revision in the valuation of the US stock market. But in fact, on this point, I am pessimistic in the medium and short term. Because DeepSeek is a bargaining chip decline in the long run, and of course it is absolutely beneficial to the AI ​​industry, in the short term, I think this valuation effect will not end so soon, so I think there is still room for downward revision of the valuation . If the US stock market is not good, then Bitcoin, as a younger brother, will naturally not look very good. But these are my personal biases, my personal bias, for your reference.

Alex: OK, Colin just explained it in detail, so let’s briefly sort out his views. He believes that the current price range has met many conditions for the past valuation or price peak, including the situation where he just mentioned some of the situations of chip distribution, the failure to achieve profit ratio, and he also quoted traditional financial markets Professor Schiller's ECY indicators are currently in line with many signs of peaking.

How to get started with on-chain data analysis

Alex: Today we have talked a lot about the analysis principles of on-chain data, including how to observe some commonly used data and how to practice these data. Many of our listeners may not have studied this concept or system in depth before. So suppose there is a beginner asking you for advice and saying Colin I think what you said today is very attractive to me, and I also want to learn this knowledge from the beginning. I will make some investments in BTC myself. What kind of learning suggestions will you give them to start this period of learning?

Colin: OK, actually I have received dozens of private messages to ask similar questions so far. My personal advice has always been the same. First of all, I have two main strengths. The first strength is on-chain data, and the second strength I think is the field of technical analysis. In fact, when most people come to ask me, they usually take a line diagram, draw some morphology or draw an indicator, MACD, RSI, they use these things to ask me if there is any way to link this thing. Make a cooperation with the data perspective. Actually, I must give you a suggestion first. I personally do not recommend that novices start learning from the field of technical analysis. The main reason is very simple, because there are too many genres, and the views in many genres cannot stand the test of science. Because they are simply induction, and there is no logic behind it, it is easy to go back to the example of the dog barking and heavy rain I just mentioned. In fact, it is entirely possible that it is a survivor deviation, but ordinary novices do not have the ability to distinguish this to the end. Is it really useful or is it actually a survivor bias. My personal suggestion is that on-chain data is a very suitable field for beginners, and I will mention the learning method later. I think the reason why he is suitable for novices is very simple, because the first one is that most of the retail investors around us, or our traders, are not full-time traders. Most of them may be high school students, college students or office workers. , they actually have their own careers. If you can't spend a lot of time doing the so-called market-focusing thing, the trading role of data on the chain is actually very suitable for you. Because we mentioned earlier, the observation level of data on the chain is very large, at least it starts at the daily level. Since you are observing the daily level, it means that you are doing operations because of the on-chain signal. For example, The frequency of buying or selling is actually very low. You don’t need to do 5 or 10 transactions a day. You may only do it four or five times a year at most, so I think this is actually very important in observation. It is in line with the daily routine of students or office workers. You don’t have to spend too much time. You may take half an hour to an hour a day to observe the set warnings, and you can observe whether there are any different changes in these data. The second part is how to learn. I mentioned earlier that during my own learning process, I have not seen any free and systematic teaching so far. There is a lot of teaching, but not systematic. He may give you an article that is written very long and introduces one or two indicators, which is very detailed. In fact, I think these articles are great, but the problem is that you still do not have a structure from 0 to 1, so you are learning It is actually quite painful to take the lead. This is the standard looks very powerful. So should I learn it and study it in depth. The next indicator also looks very powerful, so which one should I start learning? My own approach is to make steel by making local steel. I am more direct because I didn’t know which one is better and which one is bad at the beginning, so I learned it all. I took a look at the principle, and I went to see what the calculation principle is, why did the author design such a formula, what he wanted to see, this formula can really help him see what he wanted Something to see? This takes time. After reading all these indicators, you have to filter them. But for novices, this process requires patience and you must really watch it one by one. Because trading is not an easy thing. As far as I can see, whether it is Simplified or Traditional, the Chinese area can provide quite a few resources. So my suggestion here is that if you want to study a certain indicator and if you can find the original author's article, that is the best. Try not to look at other people's words. The original author himself is definitely the person who understands the most important indicator . If you really can't find it, at least you have to finish reading his formula. The Glassnode website mentioned just now has a column called Weekly onchain. They will send a weekly report to share the current market situation in a form similar to weekly reports and why they think they are now. This is the market situation. Then you can see various indicators from above. You can capture and study each indicator, and there will be a large library of learning materials. I have some teaching on Twitter, which is not systematic, and if you are interested, you can also take a look.

Alex: It's quite systematic. I've been following your updates. It seems that I have written more than ten articles. Basically, each issue talks about an indicator concept. You can also go and take a look. There is another question. I just mentioned that your identity is the first one to be a trader. Today we spent a lot of time talking about the help of on-chain data for trading. But in fact, when you are trading, in addition to the analysis of on-chain data indicators, do you refer to some other elements? For example, macros, some fundamental events of Bitcoin may be promoted, such as the state finances of the United States and even the national finances, and the reserves of Bitcoin. In addition to on-chain data analysis, other indicators as references for your trading, what will their respective weights in your entire trading decision?

Colin: OK, I think this question is very in-depth. First of all, in terms of my system, the on-chain data part can actually be considered an independent system for my position configuration. I will have a so-called spot configuration with a relatively large long tail, and I will even give it a little leverage effect at the bottom of the bear market, such as 1.5 times, 1.3 times. This is a system, and the main basis for transaction decisions in this system is on-chain data. The data on the chain will provide me with a framework for a general direction, and I will know whether it is the early, medium or late stage of the market, a bull market or a bear market, and it provides the benefits of guiding the general direction. For other parts, I mentioned earlier, another strength of my strength is the technical analysis part. In fact, there is no way to talk too much about this part because it is too complicated. Many schools and some premises and assumptions must be explained clearly first. If they are not explained clearly, it will easily mislead others. For technical analysis, I will use it to make short-term trading orders from mid-term trading orders. The main function of technical analysis in my own trading system is to refine the final entry point, which is to assume that I have confirmed today that I want to make a certain opportunity, where will I finally enter this trading opportunity? , I will find a way to use technical analysis to refine my entry point. Let me give you an example, this is not a financial suggestion. Assuming that Ethereum from 2000 to 2600 can enter, I think it will definitely rise in the future. Then assuming I am God, I know it will rise, so of course I will buy it. But because I am not God, I will find a way to get an entry point that I think is more satisfactory through technical analysis in this area. As for what this number is, I have to do an evaluation every time, so there is no way to get exactly one data, but I have a set of benchmarks for measurement. Next is the macro level. I am more concerned about the supply chain of the global market and the decisions of the US Federal Reserve, because the United States still has a relatively large influence in the financial market. Their expectations of interest rate hikes and cuts will cause risks to the risk market. Very serious impact. For example, if the CPI data is not released very well recently, the risk market will make a corresponding pricing, because the market is priced in advance. After trading expectations, it is impossible to wait until the interest rate is really reduced before it rises. It is impossible to wait for it. If the rate of interest rate falls after a real increase, there will be an early expectation. Those futures traders or option traders will make a price for the overall judgment of the market. So I will pay more attention to this part, but my macro level is not as deep as my technical analysis or on-chain data, which is my relative weakness. Finally, there is also news about the news or fundamentals mentioned by Alex just now, the so-called strategic reserves. This part actually goes back to what I said at the beginning that I like to do more, which is that I will design some event-driven trading strategies. This is to make some trading opportunities with high certainty for specific events. Let me give you an example. In about late May last year, Bloomberg had a senior ETF analyst named Eric. The market paid great attention to his post. He suddenly posted a post at 3 o'clock in the middle of the night in East Eighth time. It is said that the Ethereum ETF passes the chance to be adjusted to 75%. At that time, the entire market originally expected that Ethereum ETF would not pass. As soon as his news came out, Ethereum rose by 20% within 24 hours, and the increase in value directly exceeded Solana, which was very powerful. After news like this appeared, the first thing I thought of was to start looking for time to enter and do an event-driven trading, which is to be long for Solana while shorting ETH. This background is actually very simple, because the whole world knows that ETF is about to pass, which is a very big benefit, so Ethereum will immediately pull the market, which is very simple. Who is the next one? Judging from the market environment at that time, the support or voices of Litecoin and Dogecoin were not as high as those of Solana. At that time, the first thing I targeted was Solana, and then about a week later, I began to lay out trading opportunities for Solana's long short strategy for ETH. Simply put, it is to use a contract to long Solana, then short ETH, and to increase the price of the exchange rate between the two. I think the next expected hype is Solana, because Ethereum is already a sure fact. Assuming Ethereum really passes, Solana will inevitably receive a related rise. Some people may say, can your idea stand the test? I dare not say 100%, but there is the most obvious example. In January 2024, I don’t know how many people found out that the day when Bitcoin ETF passed, Ethereum soared, and the exchange rate also soared directly at that time. I didn’t remember If you are wrong, the exchange rate of ETH against BTC rose by about 30 percentage points within 24 hours. Many people have questions, if Bitcoin ETF is passed, what's the matter with Ethereum? The next hype is Ethereum. So this is what is called one of the event-driven transactions. Going back to the Alex question, I think it is too difficult to quantify the focus on news or fundamentals, so I personally prefer to design some event drive strategies to deal with these opportunities that may have inefficient pricing in the market.

Alex: Understand, thanks to Colin for his very logical and organized explanation. He explained the way of thinking behind each operation strategy, including what scenarios may be applied, very clearly. It can be seen that he has a very rich toolbox and knows what kind of tools to use in what scenarios, rather than making a very vague decision based on his feelings.

Daily life of on-chain data researchers

Alex: So let’s come to the last question, as a trader and a data analyst on the chain, what is your typical day of work like? In addition to paying attention to the on-chain data, what information do you might look at or what tools do you use?

Colin: OK, this question is quite interesting, because my daily life is quite boring. My schedule is not normal, but I will try to keep myself awake at the opening of the US stock market. This is very simple, because when the US stock market opens, it is usually the best liquidity on the Crypto market. If my physical strength still allows, I will look for some short-term trading opportunities during this period. This is actually a habit that has been developed several years ago. If I am really tired during the day, I will sleep a little bit, because in fact, the chance of missing the market during the day is relatively low, the chance of missing the market at night is relatively high, and it is also valuable to watch the market. In fact, you can find that every weekend or weekday, during the day of Asian time, in most cases, the market fluctuates is boring. Even if it is horizontal, there is no trading volume and poor liquidity, so this is Why try to wake up in the middle of the night? After I get up, I will get up in the morning. In addition to observing what Alex said, I will definitely observe whether there are any changes in the data on the chain, I will observe and record some additional data I want to see. In addition to the K-line chart, I will surely scan all the targets of transactions I usually pay attention to, and those coins, I will also manually record the net inflows and outflows of US Bitcoin and Ethereum ETFs, and the market. I will look at the volatility, panic and greed index, because it is another quantitative indicator for measuring market sentiment. There is also the holding volume in the contract market. If there is an extreme surge or plummeting today, I may also look at the liquidation volume, which is liquidation. I will record all of these data, and I am quite sensitive to these data. The remaining data is to see if there are any additional events. Once this happens, I want to see if there are any changes in this data. Normally, what is fixed is what I just mentioned, the holding volume of the contract market, market volatility, panic and greed indicators, and the net inflow and outflow of ETFs, probably these. There is another data I like to look at, that is, Coinbase, compared with mainstream exchanges, such as Binance, such as OKX, do they have premiums or discounts in contract quotations on these exchanges? This is also an emotional indicator that I personally think can be quantified. Emotions are directed at the sentiment of US funds, which is the emotions of people in the United States. For example, if Coinbase has a very obvious premium, it means that their buying may be relatively strong. This happened very clearly when Trump was elected. If there are any abnormal movements in these numbers, I actually observe them every day. I want to maintain this sensitivity. Once I find out, I start thinking about whether this is unreasonable or there are actually some trading opportunities. In addition to the time I recorded these data above, I will also focus on the market at other times, because I mentioned earlier that technical analysis is one of the few things I can use as a small strength. I will spend a short period of time, such as a few hours of watching the market, and then observe whether the trading plan I plan and correct every day has reached my expected position. If I get close or have reached it, I will focus on the market and look at the data I want to see. Or whether there is any deviation in the transaction plan needs to be corrected. I have two screens, and the other one I just open Twitter and run my own Mr. Berger account on Twitter. The part other than the transaction is actually quite boring. I occasionally go out for a run, but the frequency is not very high. The purpose is to make me move. Don’t not exercise all day, and the rest of the time is to stay with my family. So my day was actually quite boring and there was nothing particularly eye-catching, because transactions were actually my job, so there was not much difference between me and ordinary office workers or students. They were mainly working and then getting off work , eat, sleep, probably that's it.

Alex: I understand, Colin just talked about his work every day, and the amount of information and his brain workload is quite large, but he may have fixed and modularized it, so the brain does not need to be started in a special way every day. You can do a series of important tasks, including data follow-up, etc. He is used to what he does at each period and has a very clear arrangement so that he can enter a state faster. We can also observe that Colin is very curious about trading, investment, and the business world. He gets more than money from it. I feel that he has a lot of fun. I think such a state is a very important talent for a good trader and a good investor. Thank you Colin for sharing so many thoughts and systematic explanations about on-chain data analysis, investment, and transactions today. I hope that in the future, we can invite Colin to tell us more. Other aspects of knowledge. Thanks Colin.

Colin: Alex is so kind, just sharing personal opinions, thank you.