Original title: “Data analysis on the BTC chain, has this cycle peaked?”

Original source: Mint Ventures

Moderator: Alex Mint Ventures Research Partner

Guest: Colin, freelance trader, online data researcher

Recording time: 2025.2.15

Hello everyone, welcome to WEB3 Mint To Be sponsored by Mint Ventures. Here, we continue to ask questions and think deeply to clarify facts, explore reality, and find consensus in the WEB3 world. It will help everyone clarify the logic behind hot spots, provide insight into the event itself, and introduce diverse thinking perspectives.

Statement: What we discuss in this podcast does not represent the views of the institutions where you work, 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 events before, and we have also exchanged some periodic narratives, such as when we talked about meme before. But today we are going to discuss on-chain data analysis, especially BTC’s on-chain data analysis. We will take a closer look at its working principle and key indicators 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 ease of understanding.

Some of the data indicators and concepts mentioned in this podcast:

Glassnode: A commonly used online data analysis platform that requires payment.

Realized Price: Calculated based on the weighted price when Bitcoin was last moved on-chain, reflecting Bitcoin’s on-chain historical cost and 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 benefit. Used to measure the ratio of unrealized profits of all currency holders to total market value in the Bitcoin market.

Coinwww.gushiio.com True Markewww.gushiio.coman Price: An on-chain average price indicator based on the Coinwww.gushiio.com Economics system, which aims to more accurately evaluate the long-term value of BTC by introducing the “time weight” of Bitcoin. Compared with the current market price of BTC and the realized market price, True Markewww.gushiio.coman Price under the Coinwww.gushiio.com system also comprehensively considers the impact of time and is suitable for the price of BTC under the large cycle.

Shiller ECY: A valuation indicator 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. It is improved from the Shiller Price-to-Income Ratio Indicator (CAPE) and mainly considers the impact of the interest rate environment.

Opportunities to learn online data analysis

Alex：The guest we invited today is Colin, a freelance trader and online data researcher. Let’s ask Colin to say hello to our audience first.

Colin：Hello everyone, first of all, thank Alex for the invitation. I was a little surprised when I first received this invitation, because I was an unknown small retail investor with no special title, so I was silently making my own transaction. My name is Colin, and I run an account on Twitter called Mr. Berger. I usually share some teaching on chain data, analysis of current market conditions, and some trading concepts. I have about three positions for myself: the first is an event-driven trader, and I usually think about event-driven trading strategies; the second is an analyst of on-chain data, which is also my main part of what I usually share on Twitter; The third one is more conservative. I call myself an index investor. I will choose to allocate some of my funds in the U.S. stock market. The Beta invested by this part of my funds will reduce the overall volatility of my asset curve, while maintaining a certain level of defense in the overall position. This is probably my position for myself.

Alex：Thanks to Colin for introducing himself. I invited Colin to participate in the show because I saw his on-chain data analysis on Bitcoin on Twitter, which was very enlightening. This is a topic that we have rarely talked about before, and it is also a missing part of my own section. I read the series of articles he wrote and felt that the logic was clear and the words were meaningful, so I invited him. I want to remind everyone that today, whether it is my views or the guests, are highly subjective in the program, and the information and opinions may change in the future. Different people may have different interpretations of the same data and indicators. The content of this issue is not used as any investment advice. This program will mention some data analysis platforms, which are only used as sharing and examples for personal use, and not as commercial recommendations. This program has not received commercial sponsorship from any platform. Let’s get to the point and talk about on-chain data analysis of cryptographic assets. I mentioned that Colin is a trader just now, so when did you first come into contact with and learn online data analysis of crypto assets?

Colin：I think this question should be answered in two parts. First of all, I think that no matter who I am, as long as I want to enter or have entered the financial market, including myself, the main goal should be to make money and use profits to improve their quality of life. So my philosophy has always been very consistent, that is, I will learn whatever can help my profits. In this way, I can increase the expectations of my overall trading system. Simply put, I will learn what makes money.

The second one, at the beginning, I came into contact with the data on the chain by mistake. About six or seven years ago, I didn’t understand it at all. Take a look at this and that. While exploring various fields, I saw very interesting research theories and wanted to learn them. At that time, I accidentally saw that Bitcoin had a so-called on-chain data analysis field, so I began to study and research. In the later stages of learning, I will combine the knowledge I have learned in other fields, mainly the quantitative transaction development part, combine it with on-chain data, then develop some trading models, and finally integrate these models into my own trading system.

Alex：So how many years have it been since you officially started working on online data analysis?

Colin：I don’t think this is easy to define. In fact, I have never really learned it systematically. Because from the past to the present, I have encountered a problem myself, that is, I have actually not seen any systematic teaching at all. When I first saw this field, it might have been several years ago. I discovered it at that time, but I didn’t study it in depth. I just read two or three articles and learned about it. After a while, I came back to see some more in-depth content. At that time, I was focusing on other things, so I came back here and saw that it was quite interesting, so I continued to study it. There is no time for systematic learning, just to gather together.

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

Colin：This boundary is difficult to define, but I think a cycle close to two rounds of Bitcoin… it can’t be counted as two rounds, depending on whether you start with a bull market or a bear market. I started coming into contact around 2020 and 2019, but there was no practical application at that time because I didn’t dare to. At that time, I was not very familiar with this thing, but I had already begun to learn it.

The value and principle of on-chain data analysis

Alex：Understood. We will talk about many specific concepts about on-chain data analysis next, including some indices. What are the general on-chain data observation platforms you use daily?

Colin：I mainly use one website now, which is Glassnode. Let’s just say briefly, it requires payment. There are two paid levels. One is the professional version, which is more expensive. I remember it costs more than 800 US dollars a month. I kind of forgot about the second one. It costs about thirty to forty units a month. It also has a free version, but the free version can actually see very little information. Of course, there are many others besides Glassnode. I finally chose it because this website was the most suitable for my taste when screening and researching at first.

Alex：I understand. After reading a lot of information about Colin, I also registered for Glassnode myself and became their paid member. Indeed, I feel that their data is very rich, and the timeliness is also relatively good. So let’s talk about the second question. I just mentioned that you are a trader, and 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：Okay. 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 together because they are actually quite simple. Our traditional financial markets, whether they trade stocks, futures, bond options, even real estate, or some raw materials, one of the most fundamental differences between Bitcoin and them is that it uses blockchain technology. The most important and most often cited value of this technology is its transparency. All these bitcoin transfer information is open and transparent, so you can see directly on the chain, say, 300 bitcoins moving from one address to another, which can be found on the blockchain browser. Although I have no way to know who is behind this string of addresses, this is not important, because no single individual can actually affect the entire Bitcoin price trend and its trend.

So normally, when we study on-chain data, we look at the overall market, its trends, and the consensus and behavior of the group. 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 the addresses to see whether they have taken profits or stopped losses, what their profits are, what their losses are, what price they prefer to buy a large amount of Bitcoin or what price they don’t like to buy Bitcoin, these data are actually visible. This is what I think is the greatest value of data analysis on the Bitcoin chain compared to other financial markets, because other markets cannot do this.

Alex：This is indeed important. Like when we do crypto investing, we also need to analyze the fundamentals as we look at stocks or other products. As you just said, the data on the chain is transparent and can be observed by everyone. If other professional investors look at the data on the chain and you don’t, it means that you have one less important weapon in investing than others.

Difficulties in on-chain data analysis

Alex：When you do online data analysis in actual combat, what do you think might be the main difficulties and challenges?

Colin：I think this question is a very good question, and I plan to answer it in two parts. First of all, the first part is easier to solve. There will be a difficult point in learning, which is basic knowledge. For most people, including me at the time, because I mentioned earlier, it was difficult to find a truly systematic teaching. Of course, I didn’t go offline to ask if there were such paid courses, but if there were, I wouldn’t dare to buy them, because I’ve made my own transactions, and I’m actually not sure how to pay to buy some courses. I have not been exposed to any systematic teaching courses, so in fact, all the content has 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 understand the calculation method and principle behind every indicator I have seen. This is actually a very time-consuming process, because if you only see a certain indicator, it will give you a calculation formula. My idea is to figure out what is going on behind the calculation formula and why it needs to be designed like this.

After I figure out these indicators, I have to do the second thing called screening. If you have experience in quantitative strategy development or have studied indicators, you will actually know one thing, that is, the correlation of many indicators is very high. Too high a correlation can cause a problem, that is, you can easily produce noise in your interpretation, or you will over-interpret. For example, suppose I have an escaped system today. This escaped system may have 10 signals from No. 1 to No. 10. 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 a certain behavior or change today, it may directly cause the 1st to 4th to turn on at the same time, which is actually very troublesome. Because if they are too correlated, this is an inevitable phenomenon. If 4 out of 10 lights are on today, you would say it’s dangerous, but in fact it doesn’t make sense because they will be on. If you don’t cut them based on correlation, this phenomenon is very easy to happen. After I studied the principle of each indicator and data, I actually knew whether their correlation was high or not directly by looking at the calculation formula. I cut it according to the correlation. For example, if these five are highly correlated, I will cut and filter them a little and finally select one or two.

This first part is actually easy to solve, 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 opinion is correct to the people around you or to yourself? I may want to give a more vulgar example here, but it is easy to understand. I have written on Twitter before that in fact, the quantitative field will tell you that transactions are not very easy to use. Well, I have given an example before. Suppose there is a very strange trading strategy today. Its entry criterion is, suppose my dog at home barks twice and it is raining outside, then I will enter and go long. As a result, I went back testing based on this strategy and tested it 1000 times. I found that the winning rate was 95%, and I still beat the market far. Does anyone dare to use this strategy? In fact, it’s quite strange. If the dog barks for no reason, and then it’s raining outside, you can do more, and then the winning rate is still so high. This actually has a term called survivor bias. If you can’t give it any logical support today, even if the sample size is enough, this strategy cannot be used. Some people will refute that it was tested back 1000 times and the winning rate was 95%. The backtest results support that this strategy can be used.

Well, I mentioned the so-called survivor bias just now. Simply put, if I toss a coin 10 times, the probability of being positive all 10 times is actually 1 in 1024. In other words, on average, when every 1024 people do this thing, one person will succeed. When throwing positive four times in a row, this situation is actually the so-called survivor. The other 1023 people fail when doing this thing. We actually don’t see it. What we see will always be successful cases. Going back to Alex’s question just now, what are the so-called main difficulties? Because what we mainly look at is large-level consensus and trends. Looking back at the history of Bitcoin, the most obvious peaks of the three cycles are the two peaks in 2013, 2017 and 2021. In this way, only 4 samples are not enough. Since the sample size is not enough, it is unreasonable for us to go and see where a certain indicator has been in 2013 and where a certain indicator has been in 2017, so we have to go there this year. Because the sample size is no longer enough, if we don’t give it logic to conduct research at this time, your theory will be very easy to miss.

One of the main problems is that in the face of such a small sample number in history, I must use a deductive method rather than simply an inductive method to study it. After I finished my research, I came to a conclusion based on the deduction method that I needed time to prove whether my opinion was right or wrong. If it is correct, it means that my previous deduction process may be reasonable. If it is wrong, then I need to continue to correct the previous deductive logic. However, if we only rely solely on induction today, in fact, most retail investors like to do this thing most. They feel that the previous trend is very similar to the current trend, so there should be a sharp rise or a sharp fall in the future. This is actually unreasonable. Going back to the first sentence at the beginning, I think the biggest problem is that I have to prove to others or to myself that my inference is correct, so I have to revise my logic and assumptions all the time, and then Check whether there are any flaws. Because Bitcoin is too young, you will always face the problem of insufficient sample size in on-chain data analysis. At this time, you actually have to use a purely deductive method in your research, and also use a logical way to infer it, and then wait for time to prove your judgment. This is the biggest difficulty I have encountered at present.

Key on-chain indicators

Alex：I understand. I think it’s still very enlightening. The question I asked you just now was also some confusion when I started looking at various indicators on Glassnode. It has so many indicators, which indicator should I use as my trading reference? Because many indicators have various calculation logics. The logic of my subsequent tendency to select those indicators is quite similar to the logic you just mentioned. That is, first of all, I want to look at the arithmetic logic behind this indicator, and I want to think that this logic makes sense, rather than backtesting and pulling it out and saying that this indicator seems to be very accurate, and then use this accurate indicator to predict the future in the future. As you said, the reference in the deduction method needs to be greater before it can be used as an indicator we mainly adopt. So after what you have just talked about, in your current daily analysis of Bitcoin, what chain indicators have you been paying attention to for a long time or think are important?

Colin：I actually mentioned this question before, and I will try my best to filter it based on relevance. I usually look at a lot of data indicators on the chain, so today I will break them into three levels from different dimensions, that is, from the parts with low relevance as much as possible.

The first indicator that I will pay attention to for a long time and focus on must be URPD. It is a chart, presented as a row of bar charts, with the horizontal axis showing the price of bitcoins and the vertical axis showing the number of bitcoins. Suppose we see a very tall and large column at the 90,000 position today, then we will know that a very large number of bitcoins are opening positions at this position, which is the cost of buying them. The bar graph will show how many bitcoins they bought at this price. So in fact, based on this incident, we can see at a glance that assuming a large amount of accumulation of more than 100,000 yuan is very large, then we can know that many people buy more than 100,000 yuan.

This URPD chart has two main points of observation. The first is the simplest chip structure. Suppose I see that the current market situation is around 87,000 today, and a very large number of chips have accumulated above 87,000. According to last week’s data, it should be 4.4 million. Then we know that there are a very large number of changes in this range. Or someone is buying here. So since someone buys, it is very likely that a certain consensus will be formed. In this large accumulation range, it is easy to have an attractive effect on prices, which means that prices are likely to fluctuate all the time in this range, and it will easily be repaired after a period of time if they fall, and then rise back. If they go up and all the chips below have become floating profits, then it will be easy for them to sell, do short-term transactions, and then sell the price back. So it is actually easy for it to oscillate in this range. This is the first focus of observation.

The second observation focus is that we can observe the Bitcoin distribution process through URPD. The so-called distribution means that in the early bear market, they bought Bitcoin chips at a low price, and then sold the cheap chips they had. Then I define this process as distribution. Assuming that there are 300,000 more chips at the price of 100,000 today, and the chips costing 20,000, assuming that it is 20,000, which also happens to be reduced by 300,000 chips. Then we can actually see that people costing 20,000 sold 300,000 chips today, and their average selling price is about 100,000. We can see whether there are some drastic changes in low-cost chips. Of course, the current price is 100,000 to more than 90,000, so if they change violently, it will definitely be a decrease, not an increase, because the current price range is more than 90,000 and will not reach more than 20,000, so there will only decrease, not increase. So we can observe the distribution rate based on this matter, which is roughly what it means. This is the first indicator I will pay attention to for the long term.

The second indicator I want to introduce is called RUP, and its Chinese is called relative unprofitable status. This indicator actually serves one purpose. It helps us measure the profitability of the overall market, that is, the profitability of the entire market corresponding to the current price of Bitcoin. For example, how much do you earn, or not much, or a lot, is probably this concept. The principle of this indicator is actually very simple, because through the so-called blockchain transparency mechanism, we can track the purchase price of most chips. We can use these chips to compare the price we bought with the current price. Assuming he bought it at 50,000 yuan and the current price is 100,000 yuan, we will know that this bitcoin is currently making money, so we will calculate how much it makes. For example, 10 bitcoins were bought for 50,000 yuan, and now it is 100,000 yuan. One of them earned 50,000 yuan, and 10 of them earned 500,000 yuan. We add up all these floating profits and losses, and then standardize this figure based on the current market value, so we can get a number between 0 and 1. It is easy to observe between 0 and 1. Assuming that today’s RUP is high, such as 0.7, 0.68, and 0.75, then we know that the overall profitability of the market is now very high, which may make more people want to take profits. So RUP is too high and is usually regarded by us as a relative warning.

The third dimension I want to talk about is the fair valuation model of a market. There are actually many different bitcoin valuation models on the market, and each model actually uses a different method 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 one that stands the test most is the Coinwww.gushiio.com Price model. In fact, I have not seen a Chinese translation of this term anywhere else. To put it simply, we often hear of a name called Cathie Wood, her ARK Invest, and the online data website, which is the Glassnode I mentioned just now. This concept is a document jointly produced by the two parties. 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 for the calculated numbers. The first one is very simple to copy. Suppose today during the bear market, it fell and fell, and finally fell below the valuation given by Coinwww.gushiio.com Price. As I said earlier, this figure is actually how much a Bitcoin should be worth. If you fall below this position today, it is equivalent to buying a very cost-effective position. According to historical backtesting and its logic, we can actually see that whenever the price falls below Coinwww.gushiio.com Price, it is actually a very good bottom-hunting position. The second application is escape from top. We can monitor the distance between the current price and the price of Coinwww.gushiio.com Price. If it deviates too much from the Coin www.gushiio.com Price, we can evaluate if this deviation is too large, it means that the market may be near the top. The above three dimensions are chip structure, profitability status and fair valuation model, which are the three indicators and orientations that I want to share.

How to view the data fight

Alex：Okay, I made it very clear just now. Many users may ask a question. The three indicators of concern you just listed may represent different aspects, which is also in line with what you just said that the correlation between them is not so high, so they can be put together as a reference indicator. So suppose there are differences in the actual application of such indicators. For example, indicator 1 may feel that it is currently in a distribution situation, and indicators 2 and 3 may show that it is currently far from the top, which does not seem so high from a cyclical perspective. In this case, how would you handle a data fight?

Colin：I think this is not just in the field of on-chain data analysis, but in other fields such as technical analysis or macro fields, there may be so-called fighting situations. Well, in this part of the chain, my personal handling method is very simple, and I will give different weights to different levels. Among them, what I value most is actually the chip structure, that is, the progress of distribution. Because in fact, in terms of profitability, it also helps me observe whether the low-cost chips in the market have been distributed during the bear market, such as the Bitcoin chips purchased at 15000 and 16000. A very special phenomenon is that in every cycle of Bitcoin over the years, there are actually two very obvious large-scale distributions. For example, in 2024, the most obvious case was from March to April last year. In fact, in terms of profitability, you can definitely see large-scale distribution at that time.

But if I only saw large-scale distribution today, then my next question would have to be thinking about: Have they finished distributing? The criterion for all judgments is based on this issue. If they have a large-scale distribution but the distribution has not yet been completed, then I can safely tell myself that the bull market is not over yet. For example, when I was in March and April last year, Bitcoin surged to 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, when I observed these data, I couldn’t draw a bottom-up conclusion. At most, it would be the first distribution. Then there are many data, like I previously published some mid-term analysis and chip structure analysis. At that time, based on the average cost of short-term holders, his situation was different from the end of a real bull market. So I was actually very relieved at that time. Then you said there was a data fight, but now he said there was distribution. Then am I going to escape? Actually, there is no need, because the main question is still the one I mentioned just now: whether the distribution has ended. Using this question as the criterion for screening each indicator and the benchmark for judgment, it is actually easy to draw this conclusion, that is, even if the distribution occurs and is on a large scale, I only need to judge whether it is over. Using this as a guideline can effectively deal with the so-called data fight problem.

Alex：Then let’s draw up a scenario now. For example, when we look at URPD, this indicator assumes that there have been two distributions, which is more like the situation you just mentioned, once in March and April last year, and then there is also a distribution peak from December to January at the end of the year. Suppose that there is such a distribution situation, but maybe the other two valuation indicators are not so high. When this situation occurs, you just said that you would assign different weights to it, so will you subtract some positions based on the proportion of weights? Or will you think about the three indicators in a unified manner and not adjust positions based on weights, but make one or two important decisions at critical times?

Colin：My own approach is the former, because in fact, no one can know whether it is really at the top, and no one can escape to that highest position. If there is, it will be too powerful, and I will definitely want to get to know it. At the top, my personal interpretation of it is a slow process. Although you feel it quickly when you go to the daily chart, in fact, if people are in the present, for example, when you are at 69000, at the top of the previous cycle, you won’t feel that now is the top. We can only make a judgment based on the data and say that there may now be conditions for the formation of a top. So based on this premise, I will actually take a segmented stance. For example, when I think the top conditions are gradually beginning to mature, once I see a certain indicator giving me a warning during this period, such as a deviation from RUP I previously shared on Twitter, I will do the corresponding reduction. Of course, the range of this reduction must be determined in advance from the beginning. It is impossible to say that there is a deviation now. If you don’t know how much it will be reduced, you can just reduce it casually. This will not happen. I will first draw up a general idea, for example, if I divide my position into 4 shares, then once there is any type of warning sign, I will subtract one share first, and when the second warning sign comes out, I will subtract another share. At the same time, I will plan that the last fund must come out no matter what. For example, if the bear market has definitely ended, but no other warning signs have emerged yet, we need to formulate an extreme, last-ditch strategy to screen.

Alex：Understand, we should gradually leave and reduce our positions based on different warning triggers.

Colin：Yes.

Judgment and basis on the position of BTC in this cycle

Alex：Understood. I have also been following your Twitter account recently, and you will practice your trading practices based on the indicators just mentioned, including the concepts behind these indicators. Now when we look at Bitcoin, it has been fluctuating in the range of 91000 to 109000 for almost three months. At present, there are quite big differences in the market on this price range. It is not like December and January. Everyone feels that this bull market is far from over and will reach 150,000, 200,000 or even 300,000. Many are very positive views. At present, the market is very divided. Some people think that the peak of BTC this round will be around 100,000, but some people think that BTC in this cycle has not yet peaked and there will still be a major rising wave in 2025. So based on your current comprehensive judgment, what is your opinion? Where is BTC in our big cycle? Then what are the data sources that support your judgment?

Colin：Before answering this question, I may have to take a vaccination first. I am actually very bearish on 2025. I think BTC is currently in a condition for top-level formation. In fact, I know many people, including some participants around me, whose earnings were actually not good during the so-called special bull market in 2024, because the overall market running method in 2024 is different from every previous cycle. The most obvious point is that there is no copycat season. This has hurt many people, including some non-professional traders around me. They have also come in to participate in this market. In fact, they have suffered a lot of losses on altcoins. Why is this happening? Let’s look back a little in 2024. There was a copycat market at the beginning of the year, and the second time occurred in November last year, when Trump was elected President of the United States. In fact, compared with our previous cycles, there is a big and obvious point between these two shanzhai markets, that is, their sustainability is actually not very good. Even in the wave of market prices in November and December last year, the altcoins did not rise across the board at all. It was a very obvious sector rotation. At that time, there was the Defi sector, which was exchanged for old coins after the increase, such as XRP, and then Litecoin. 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, this cycle is actually very different from the past. There is also a theory that there will be a so-called copycat season before the end of the bull market. In fact, I personally think that you can’t say that the bull market will end until the copycat season occurs. This obviously has no strong correlation. We cannot use this as a judgment on whether the bull market is over. As mentioned earlier, on-chain data analysis has its own shortcoming, that is, the number of samples is never enough. If we simply use historical conditions to analogy today’s market, it 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 13, 17, and 21 years should appear around the end of the year, based on time.

I personally think that the so-called conditions for top formation are actually in place now. The reasons are very complicated, and I use a lot of indicators and data to make judgments. Let me briefly talk about a few more core ones. First of all, the first one is the chip structure we just mentioned, which is the chart of URPD. We can see one thing. In 2022, as well as the low-cost chips accumulated in 2023, they bought a large amount of BTC at low levels at that time. So far, a lot of chips have been distributed. To put it bluntly, they have sold it and stopped playing. Maybe some listeners will have a question, which is, what does it have to do with me if they sell it? There is a concept that I may want to explain to you. When every bull market ends, almost every time it ends because of the distribution of low-cost chips, and then the bull market ends. There is a relatively unintuitive point in this place. The bull market ended not because they smashed the market, but because the price kept rising. They sold all the way, and then the price stopped, and the bull market was about to end.

This is not just a matter of saying that this must be the case. There is a logic behind it. Suppose that every BTC chip participating in the market today is a high-cost chip. For example, those bought with more than 90,000 yuan, and then those bought with 50,000 yuan, 20,000 yuan, and 30,000 yuan have already escaped. At this time, as long as the price does not show an obvious or strong main rising wave, even if it is simply a so-called wide-range shock, for example, the shock range between 70,000 and 50,000 last year, or a shock within the range of about 90,000 to 109,000 now, it will put great pressure on these high-cost chips to hold positions. If the pressure on holding a position is high, a problem will arise. The price is now about 95000 or 96000. Suppose it drops to 89000 today, which is actually less than 10%. However, these chips are under great pressure. Many of them are even short-term traders. Once the pressure is high, they may choose to sell. Selling will lead to a further decline in the price. If it falls, other high-cost chips cannot withstand the pressure, and they will sell again. That would create a chain reaction situation. This is what I think I can see from the URPD chart, that is, many low-cost chips have been distributed.

The second indicator I just mentioned is called RUP, which is used to measure market profitability. If you are interested, you can check this indicator. It is very interesting, that is, if you put its line and the price line together, their correlation is very, very high, and they almost go together. This is actually a very reasonable thing, because the higher the price, the higher the cost and profitability of holding positions, and the shapes of the two lines will be almost identical. So the higher the price, the higher the RUP will be; the lower the price, the lower the RUP will be. This is very simple. But once there is a so-called divergence in RUP, it actually means that the market situation has changed. What is deviation? For example, Bitcoin rose to 90,000 yuan, and then adjusted back to 100,000 yuan, setting a higher high. However, when RUP was at 100,000 yuan, it was not as high as when it was at 90,000 yuan. Instead, it went down. This is the so-called situation where RUP has become lower but the price has become higher.

Then it’s very strange why this situation happened? The only logic that can reasonably explain this matter is that as we just said, RUP uses unrealized profits to calculate, and the main large amount of unrealized profits in the market are actually contributed by those low-cost chips. For example, if you buy a bitcoin at 16000 today, and now it is 96000, the floating profit of this bitcoin alone is 80,000. But if you buy Bitcoin at 86000 today, and now it is 96000, this one is only 10,000, so the main contribution in proportion is made by low-cost chips. So once your price is higher but RUP is lower, it means that some or even a large number of low-cost chips must have been sold first. When your price is higher in the future, these low-cost chips have already left the market, so they have turned part of the unrealized profits into realized profits, so they can’t see them in RUP, which will lead to RUP being lower, creating a deviation. This can help me get a verification when interpreting RUP, that is, there is really a low-cost chip to leave the market.

In terms of the third aspect, there is actually a lot to talk about online data, but I personally share another unique point of view, called the U.S. stock market. If anyone has studied the stock market, they will actually know that the stock market has a so-called valuation concept, that is, the price-to-earnings ratio, or the price-to-earnings ratio. There are many different variants of the valuation method. 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 underlying assets relative to bond underlying assets. This indicator was mentioned in a paper published after the outbreak of the epidemic in 2020. Because he thought that another model or data he had before was called Shiller PE, called Schiller’s price-to-profit ratio. He believed that after the epidemic, due to the changes in the structure of the global market, many situations in that model were actually different from before. So he invented a new indicator called Shiller ECY to measure this market, and then found that this indicator’s prediction effect is indeed better.

Simply put, this indicator currently shows that the valuation of the U.S. stock market is already a little too high. One thing needs to be clarified here. 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 something like a spectrum of concepts, which is that it is getting closer and closer to danger areas. In fact, the position we are approaching now is a relatively dangerous position that I think is relatively dangerous. The valuation of the stock market is currently mainly contributed by the hottest topic, that is, AI. Some time ago, there was DeepSeek, which was caught off guard and suddenly caused a wave of downward revisions in the valuation of the U.S. stock market. But in fact, on this point, I am pessimistic in the short to medium term. Because although DeepSeek is a bargaining chip in the long run, it is of course absolutely good for the AI industry, in the short term, I don’t think this valuation effect will end so soon, so I think there is still room for downward revision in the valuation. If the U.S. stock market is not good, Bitcoin, as a younger brother, will naturally not look too good. However, these are all my personal prejudices, personal Bias, for your reference.

Alex：Okay, Colin just talked in great detail, so let’s briefly sort out his views. He believes that this current price range has met many conditions for peaking valuations or peaking prices in the past, including the situation where he just mentioned chip distribution, the situation where the profit ratio has not been achieved, and the fact that he also quoted traditional financial markets Professor Schiller’s ECY indicators, he believes that they currently meet many signs of peaking.

How to get started on chain data analysis

Alex：Today, we have talked a lot about the analysis principles of data on the chain, including how to observe some commonly used data and how to use these data in actual combat. Many of our listeners may not have studied this concept or system in depth before, so suppose a beginner asks you and says Colin, I think what you said today is very attractive to me. I also want to learn this knowledge from the beginning and coach me to make some investments in BTC. What kind of learning advice would you give them to start this period of learning?

Colin：Okay, in fact, I have received dozens of private messages asking 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 consider to be the field of technical analysis. In fact, when most people come to me to ask, they usually hold a line chart and draw some morphology or draw an indicator, MACD, RSI. They use these things to ask me if there is any way to match this thing with the data on the chain. In fact, I must give you a suggestion first. I personally do not recommend that novices start learning in the field of technical analysis. The main reason is very simple, because there are too many genres, and some of the viewpoints in many genres cannot withstand the test of science. Because they are simply induction and have no logic behind it, it is easy to go back to the example I just told about the barking dog and heavy rain. In fact, it is entirely possible that it is a survivor bias, but ordinary novices have no ability to distinguish whether this is really useful or is actually a survivor bias.

My personal suggestion is that on-chain data is a very suitable area for novices, and I will mention the way to learn later. I think the reason why he is suitable for novices is very simple, because first, in fact, most of the retail investors around him, or rather our traders, are not actually full-time traders. Most of them may be high school students, college students or office workers. They actually have their own business. Then if you can’t spend a lot of time doing the so-called tracking, the trading role of online data is actually very suitable for you. Because we mentioned earlier that the level of data observation on the chain is very high, at least at the daily level. Since you are observing the daily level, it means that you are making operations because of the signals on the chain. For example, the frequency of buying or selling is actually very low. You don’t need to make 5 transactions or 10 transactions a day. You may only do it four or five times a year at most, so I think this point is important in observing. In fact, it is very suitable for the life and rest of students or office workers. You don’t have to spend too much time. You may set aside half an hour to an hour every day to observe the alarm messages set. You observe whether there are any different changes in these data.

The second part is how to learn. As I mentioned before, in the process of my own learning, I have not seen any free and systematic teaching to this day. There is a lot of teaching, but it is not systematic. He may give you an article that is very long and introduces one or two indicators in great detail. In fact, I think these articles are great, but the problem is that you still don’t have a structure from 0 to 1, so it’s actually quite painful to learn, but this indicator looks very powerful. Should I learn it and study it in depth? The next indicator also looks very powerful, so which one should I start from? My own method is to make steel by local methods. I am more direct because I didn’t know which one was better and which was worse at first, so I learned everything. I took the principle apart and looked at it, and I went to see what the calculation principle was, why did the author design such a formula, what did he want to see? Can this formula really help him see what he wanted to see? This takes time. After reading all these indicators, you need to filter them. But for novices, this process requires a lot of patience, and you have to really watch it slowly one by one. Because trading is not an easy task. From what I can see at present, whether it is simplified or traditional, the resources that the Chinese area can provide are quite few.

So my suggestion here is that if you want to study a certain indicator, if you can find the original author’s article, that’s the best. Try not to read other people’s articles. The original author himself is definitely the person who understands that indicator the most. If you really can’t find it, at least read through his formula. The Glassnode website just mentioned has a column called Weekly onchain. They will publish a weekly report based on some different indicators, not fixed indicators, and share the current market situation in a form similar to a weekly report every week and why they think the current market situation is like this. Then you can see a variety of indicators from above. You can capture and study each indicator, and there will be a large library of learning materials. There are some teaching on my Twitter, which is not systematic. If you are interested, you can also check it out.

Alex：It’s still quite systematic. I’ve been following your updates. It seems that I have written more than ten articles. Basically, every 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 person you are a trader. Today, we spent a lot of space talking about the help of data on the chain to trading. But in fact, when you trade, in addition to the analysis of data indicators on the chain, do you refer to some other factors? For example, macro, for example, some fundamental events in Bitcoin may be advancing like the U.S. state finances and even national finances ‘reserves of Bitcoin. In addition to on-chain data analysis, what weight will other indicators serve as a reference for your trading?

Colin：Okay, I think this question is very profound. First of all, in terms of my system, the data part on the chain can actually be regarded as an independent system for my position allocation. 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, for example, about 1.5 times or 1.3 times. This is a system, and the main basis for trading decisions in this system is data on the chain. On-chain data will provide me with a framework in the general direction. I will know whether it is the early, middle or late stage of the market, whether it is a bull market or a bear market. It provides the benefits of a general direction guidance.

As for other parts, as I mentioned earlier, my other strength is technical analysis. There is actually no way to talk too much about this part because it is too complex. Many genres and some premises and assumptions must be explained clearly first. If you don’t explain it clearly, it will be easy to mislead others. For the technical analysis part, I will use it to make short orders to medium term trading orders. The main role of technical analysis in my own trading system is to refine the final entry point. Assuming that I have confirmed today that I want to make a certain opportunity, where will I finally enter the trading opportunity? I will find a way to refine my entry point through technical analysis. Let me give an example casually. This is not a financial suggestion. Assuming that Ethereum from 2000 to 2600 can enter, I think it will definitely rise in the future. So suppose I am God and I know it will go up, so of course I just 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 make an evaluation every time, so there is no way to get an exact data, but I will have a set of benchmarks for measurement.

Next, there is the macro level. I am more concerned about the supply chain of the global market and the decisions of the Federal Reserve, because in fact, the United States still has relatively great influence in the financial market, and their expectations of raising interest rates will affect the risk market. Have a very serious impact. For example, if the CPI data is not very good recently, the risk market will make a corresponding pricing, because the market prices in advance. When trading expectations, it is impossible for them to wait until the interest rate cuts are really raised before they rise, nor can they wait until the interest rate rises before falling. There will be an advance expectation. Those futures traders or option traders will make a pricing based on the overall judgment of the market. So this part is also something I will pay more attention to, but my macro is not as in-depth as my technical analysis or on-chain data. This is my relative weakness.

Finally, there is the news or fundamentals mentioned by Alex just now, the so-called strategic reserve news. This part actually goes back to what I said at the beginning, which is that I will design some event-driven trading strategies. This is to make some trading opportunities with high certainty based on specific events. Let me give an example. Like in late May last year, there was a senior ETF analyst named Eric at Bloomberg. The market paid close attention to his post. He suddenly posted a post at 3 a.m. East Eighth District Time. The post said that the probability of passing the Ethereum ETF was adjusted to 75%. At that time, the entire market originally expected that the Ethereum ETF would not pass. As soon as his news came out, Ethereum increased by 20% within 24 hours, and the value of the increase directly exceeded Solana, which was very impressive. After this kind of news appeared, the first thing I actually thought of was to start preparing to find time to cut in and make an event-driven trade, which is to prepare to go long Solana and short ETH. This background is actually very simple, because the whole world knows that the ETF is going to pass and it is a very big plus, so Ethereum immediately pulls the offer. This is very simple. The real thing to think about is who will be next? In terms of the market environment at that time, Litecoin and Dogcoin had actually not as high as Solana’s. At that time, my first target was Solana, and then about a week after that time, I began to lay out trading opportunities for Solana’s long short strategy against ETH. Simply put, you use the contract to go long Solana, and then short ETH to increase the price of the exchange rate between the two. I think the next person expected to hype is Solana, because Ethereum is already a definite fact. Assuming Ethereum really passes, Solana will inevitably receive a wave of related increases.

Some people may say, will your idea stand the test? I dare not say 100%, but one of the most obvious examples is in January 2024. I don’t know how many people discovered that on the day the Bitcoin ETF was passed, Ethereum skyrocketed, and the exchange rate also skyrocketed directly at that time. If I remember correctly, the exchange rate of ETH to BTC rose by about 30 percentage points within 24 hours. Many people have questions, what has Ethereum got to do with Bitcoin ETF passed? The next hype is Ethereum. So this is one of the so-called event-driven transactions. Going back to Alex’s question, I think focusing on news or fundamentals is too difficult to quantify, so I personally would prefer to design some event-driven strategies to deal with these opportunities that may have room for operating inefficiently pricing in the market.

Alex：I understand. Thanks to Colin for his very logical and organized explanation. He made it very clear about the thinking behind each operating strategy, including what scenarios might apply. It can be seen that he has a very rich toolbox and knows what tools to use in what scenarios, rather than making a very vague decision based on his feelings.

Daily life of online data researchers

Alex：So coming to the last question, as a trader and a chain data analyst, what is your typical working day like? In addition to focusing on online data, what more information or tools might you use?

Colin：Okay, this question is quite interesting, because actually my usual day is quite boring and boring. My schedule is not normal, but I try to keep myself awake when U.S. stocks open. The reason is simple, because the U.S. stock market is usually the time when the U.S. stock market opens when the liquidity is best on the Crypto market. If my physical strength still permits, I will look for any 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 to catch up on my sleep, because in fact, the chance of missing a market during the day is relatively low, and the chance of missing a market at night is relatively high, and it is more valuable to watch the market. In fact, you can find that every weekend or weekday, during the day during Asian time, it is actually quite boring when the market is volatile in most cases. Even if it is across there, there is not much trading volume and the liquidity is very poor, so this is why try to stay awake in the middle of the night. After I usually get up, I get up in the morning. In addition to observing, as Alex said, that is, I will definitely observe whether there is any change in the data on the chain, I will observe and record some additional data that I want to see. In addition to the k-chart chart, I will regularly scan all the transactions that I usually pay attention to, and I will also manually record the net inflows and outflows of Bitcoin and Ethereum ETF in the United States, as well as the market. Volatility, panic and greed index, I will take a look at it, because it is another quantified indicator to measure market sentiment. There is also the position in the contract market. If there is an extreme surge or slump today, I may still look at the liquidation volume, which is liquidation. I will record all these data, and I am quite sensitive to these data. The rest of the data is to see if there are any additional events. Once it happens, I want to see if there have been any changes in this data.

Normally, what is fixed is what I just talked about. The positions in the contract market, market volatility, indicators of panic and greed, and net inflows and outflows of ETFs are about this. There is another data that I also like to read, which is whether there is a premium or discount on the contract quotations of Coinbase compared to mainstream exchanges, such as Binance and OKX. This is also an emotional indicator that I personally think can be quantified. Emotions are aimed at the sentiment of US funds, that is, the sentiment of people in the United States. For example, if Coinbase’s premium is obvious, it means that their buying may be relatively strong. This happened very obviously when Trump was elected. If there is any change in these numbers, I actually observe it every day. I have to maintain this sensitivity and once I find it, I start to think about whether it is for no reason or whether there are actually some trading opportunities.

In addition to the above time when recording these data, I will also keep an eye on the disk at other times, because as I mentioned earlier, technical analysis is one of the few small strengths that I can talk about. I will spend a short period of time, say a few hours, watching, and then observe whether the trading plan I plan and revise every day reaches the position I expected. If it is close or has already reached it, I will focus on the disk and see the data I want to see. Or whether there is any deviation from the trading plan and needs to be corrected. I have two screens, and I have Twitter on the other screen and run my own Mr. Berger account on Twitter. The part other than trading is actually quite boring. I occasionally go out for a run, but the frequency is not very high. The purpose is to keep me moving. I don’t have to exercise all day. I still spend the rest of the time mainly with my family. So my day is actually quite boring, and there is nothing particularly eye-catching, because trading is actually my job, so I am not much different from an ordinary office worker or student. I mainly work, then get off work, eat, sleep, and that’s about it.

Alex：I understand, Colin just talked about his day’s work. The amount of information and the workload of his brain power are quite large, but he may have fixed and modularized it, so the brain can do a series of important tasks without special activation every day, including data follow-up and so on. He is accustomed to what to do at each time period and has a very clear arrangement so that he can enter a state faster. One thing we can also observe is that Colin himself is very curious about trading, investment, and the business world. What he gets from it is not just money. 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 to Colin for coming to the show today to share with us so many thoughts and very systematic explanations about online data analysis, investment, and trading. I hope we can invite Colin to tell us more in future shows. Knowledge in other aspects. Thanks Colin.

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

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