GuShiio.com Ox or bear, that’s a question.
Moderator: Research Partner, AlexMint Ventures
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.
Cointime True Market Mean Price: An on-chain average price indicator based on the Cointime 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 Price, the True Market Mean Price under the Cointime 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 a good solution, not a major difficulty. The second part is the real challenge, which is about the data on the chain. how do you prove your point to the people around you or to yourself? I may give a more vulgar example here, but it is easy to understand. I’ve written on tweets before, but in fact, this part of the quantitative field will tell you that trading is not easy. Well, I have given an example of this before. suppose there is a very strange trading strategy today, and its entry standard is that if my dog barks twice and then it is raining outside, then I will go in and do more. As a result, I tested back according to this strategy for 1000 times and found that the winning rate was 95%, which still far beat the market. Does anyone dare to use this strategy? In fact, it’s strange that the dog barks inexplicably, and then it’s raining outside, so you can do more, and then the winning rate is still so high. This actually has a noun called survivor deviation. If you can’t give it any logical support today, even if there are enough samples, this strategy can’t be used. Some people will retort that it has been tested 1000 times and the winning rate is 95%. The result of the back test is that it supports that this strategy can be used. Well, I just mentioned the so-called survivor bias. To put it simply, if I even lose a coin 10 times, the probability that 10 times is positive is actually 1×1024. In other words, on average, one person succeeds when every 1024 people are doing this, and four times in a row. This situation is actually the so-called survivor, and the other 1023 people fail when they do it. We don’t actually see it. What we see are always successful cases. Going back to the question of Alex just now, it is 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 most obvious top of the three cycles is the two tops of 2013, 2017 and 2021, so adding one plus only four samples is absolutely not enough. Since the sample size is not enough, it is unreasonable for us to go there again today to see where a certain indicator has been in 2013 and 2017, so we have to go there this year. Because the number of samples is not enough, if we do not give it logic to do research at this time, your theory is very easy to make mistakes. One of the main problems is that in the face of such a small number of samples in history, I have to study it by deduction rather than induction alone. After I finished my research, according to the deductive method, I came to the conclusion that I needed time to prove whether my opinion was right or wrong. If it is right, 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 it is only based on induction today, in fact, most retail investors like to do this most, thinking that the previous trend is very similar to the current trend, so it should soar or plummet later, 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 to myself that my inference is correct, so I have to revise my logic and assumptions all the time. And then check to see if there are any flaws. Because Bitcoin is too young, you will always face the problem of insufficient sample size in data analysis on the chain. at this time, you have to use a purely deductive method and deduce it in a logical way. and then wait time to prove your judgment. This is the biggest difficulty I have encountered so far.
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 Cointime 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 Cointime 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 Cointime Price, it is actually a very good bottom-hunting position. The second application is escape from top, where we can monitor the distance between the current price and the price of Cointime Price. If it deviates too much from Coin Time Price, we can evaluate whether the 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：Well, I’ve actually received dozens of private messages asking similar questions so far. My personal advice is always the same. First of all, I have two main strengths, the first is the data on the chain, and the second I think is in the field of technical analysis. In fact, when most people come to ask me, they usually take a diagram, draw some morphology or draw an indicator, MACD, RSI, ah, they use these things to ask me if there is a way to match this thing with the data point of view on the chain. In fact, I must first give a suggestion, I personally do not recommend that beginners start to learn in the field of technical analysis. The main reason is very simple, because there are too many schools, and then some of the views in many schools cannot stand the test of science. Because they are simple induction, and there is no logic behind it, it is easy to go back to the example of dog barking plus heavy rain that I just mentioned. In fact, it is entirely possible that it is a survivor deviation, but the average novice does not have the ability to tell whether it really works or is actually a survivor bias. My personal advice is that the data on the chain is a very suitable area for beginners, and I’ll talk about it later. I think the reason why he is suitable for novice is very simple, because first of all, most of the retail investors around him, 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 jobs. Well, if you can’t spend a lot of time on the so-called marking, the trading role of the data on the chain is very suitable for you. Because as we mentioned earlier, the level of data observation on the chain is very large, at least at the daily line level, then since you are observing the daily line level, it means that you are operating because of the signal on the chain. 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 four or five times a year at most, so I think this is in observation. In fact, it is very in line with the life of students or office workers. You don’t have to spend too much time, you may set aside half an hour to an hour a day to observe these alarms and see if there are any different changes in the data. The second part is how to learn. As I mentioned earlier, I have not seen any free and systematic teaching in the process of learning. There is a lot of teaching, but it is not systematic. He may give you an article, which is very long, introducing one or two indicators, very detailed. In fact, I think these articles are very good, but the problem is that you still do not have a structure from 0 to 1, so it is actually quite painful to study. That is, this sign looks very strong, so should I study it or not? The next indicator also looks great, so which one should I start to learn? My own practice is indigenous steelmaking. I am more direct, because I don’t know which one is good or bad at the beginning, so I learn all of them. I take the principle away from each one, and I go to see what the principle of calculation is, why the author designs such a formula, what he wants to see, does this formula really help him see what he wants to see? This takes time. After reading all these indicators, you have to sift through them. But for beginners, this process requires a lot of patience and needs to be seen one by one. Because trading is not an easy thing to do. As far as I can see, there are very few resources available to the Chinese area, whether in simplified or traditional Chinese. So my suggestion on my side is that if you want to study a certain indicator, if you can find the original author’s article, that is the best, try not to read other people’s, the original author is absolutely the person who understands that indicator best. If you really can’t find it, at least read his formula. The Glassnode website just mentioned has a column called Weekly onchain. Every week, according to some different indicators, not fixed indicators, they will send a weekly report to share what the current market situation is like and why they think it is like this. Then you can see a variety of indicators from above, you can grasp each index to study, 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 take a look at it.
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：OK, I think this question is very deep. First of all, as far as my system is concerned, the data part of the chain can actually be thought of as an independent system for my position configuration. I will have a relatively large long tail of the so-called spot configuration, and even I will make it a little bit leveraged at the bottom of the bear market, for example, 1.5 times or 1.3 times. This is a system in which the main trading decisions are based on data on the chain. The data on the chain will provide me with a framework for a general direction, and I will know whether it is the early, middle or late stage of the market, a bull market or a bear market, which provides the benefits of a general direction. In other parts, as I mentioned earlier, my other strong point is the part of technical analysis. As a matter of fact, there is no way to talk too much about this part, because it is too complicated, and many schools and some premises have to be explained clearly first. if it is not explained clearly, it will easily mislead others. Part of the technical analysis, I will use it to make short to medium-term trading orders are possible. The main role of technical analysis in my own trading system is to refine the final entry point, that is, assuming that I have confirmed today that I am going to do a certain opportunity, where am I going to enter this trading opportunity? I will find a way to refine my entry point by means of technical analysis. Let me cite an example casually. This is not a financial suggestion. Assuming that 2000 to 2600 of Tai Fong can enter the market, I think it will certainly rise later. Then suppose I am God, I know it will go up, of course, just buy it. But because I am not God, I will find a way to get a more satisfactory entry point through technical analysis in this area. As for what this number is, I have to make an assessment every time, so there is no way to get an exact data, but I will have a set of benchmarks to measure. Then there is the macro level. I am more concerned about the supply chain of the global market and the decision-making of the Federal Reserve of the United States, because the United States still has a great influence in the financial market at present. Their expectations of raising and lowering interest rates will have a very serious impact on the risk market. For example, if the recent CPI data is not very good, the risk market will make a corresponding pricing, because the market is priced in advance, and it is impossible for them to rise after the real interest rate cut or fall after the real interest rate rise. There is an expectation in advance that those futures traders or option traders will make a pricing according to the overall judgment of the market. So I will pay more attention to this part, but my macro is not as deep as my technical analysis or chain data, this is my relative weakness. Finally, there is the news or fundamentals mentioned by Alex just now, the so-called strategic reserve. This part actually goes back to what I said at the beginning that I prefer to do, that is, I will design some event-driven trading strategies. This is to make some trading opportunities with high certainty for specific events. Let me cite an example. For example, around late May last year, Bloomberg had a senior ETF analyst named Eric, and the market paid close attention to his post. He suddenly posted more than 3: 00 a. M. in East eighth District time saying that the pass probability of ethernet ETF was adjusted to 75%. At that time, the whole market expected that ethernet ETF would not pass. As soon as his news came out, Ethernet Square increased by 20% within 24 hours, and the increase in value directly exceeded that of Solana. The first thing that comes to mind after news like this is to start preparing to find time to cut into an event-driven trade, to be long Solana and short ETH at the same time. This background is actually very simple, because the whole world knows that ETF is about to pass, and it is a very good thing, so it is very simple to pull the plate at Ethernet Square right away. The real thing to think about is who’s next? In terms of the market environment at that time, in fact, the support or voice of Leiter coin and dog coin was not as high as that of Solana. 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 for ETH. To put it simply, it is to use the contract to do long Solana, and then short ETH, to do the price rise of the exchange rate between the two. I think the next expected hype is Solana, because Ethernet Fong is already a confirmed fact. Assuming that Tai Fong has really passed, then Solana is bound to receive a wave of related increases. Some people may say, does your idea stand up to 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 found that on the day when the bitcoin ETF was passed, Ethernet Fong skyrocketed, and at that time the exchange rate also skyrocketed directly. If I remember correctly, the exchange rate of ETH against BTC rose by about 30% in 24 hours. Many people have questions, Bitcoin ETF has passed, what is the matter with etheric place? The next hype is Ethernet Square. So this is one of the so-called event-driven transactions. Going back to the question of Alex, I find it too difficult to quantify the focus on news or fundamentals, so I am more inclined to design some event driven strategy to deal with these opportunities that may have market inefficient pricing.
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：Well, this is an interesting question, because my usual day is actually quite boring and boring. My schedule is not very normal, but I try to keep myself awake during the opening of US stocks. The reason is very simple, because the US stock market usually opens when the Crypto market side is the most liquid. If my physical strength still permits, I will look for any short-term trading opportunities at this time. This is actually a habit that has been formed several years ago. If I am really tired during the day, I will sleep a little, because in fact, the chance of missing the market during the day is relatively low, the chance of missing the market at night will be higher, and the Kanban is also more valuable. In fact, you can find that every weekend or during the day on weekdays, during the day of Asian time, in fact, most of the market shocks are boring, even over there, there is no trading volume, and the liquidity is very poor. So that’s why you 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 I will definitely observe any changes in the data on the chain, I will observe and record some extra data that I want to see. In addition to the k chart, I will regularly scan the targets of the transactions I normally follow, all those coins, and I will manually record the net inflows and outflows of Bitcoin and ETF in the United States, as well as the volatility of the market. I will take a look at the panic and greed index, because it is another quantified indicator of market sentiment. Then there is the position in the contract market. If there is an extreme surge or collapse today, I may also look at the liquidation volume, that 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, and once it happens, I want to see if there is any change in the data. Normally speaking, the fixed ones are the ones I just mentioned, the positions in the contract market, the volatility of the market, the indicators of panic and greed, and the net inflow and outflow of ETF. Another data I quite like to look at is whether there is a premium or discount for the contracts quoted by Coinbase relative to mainstream exchanges, such as Binance, such as OKX. This is also an emotional indicator that I personally think can be quantified. Emotion is aimed at the mood of US funds, that is, the mood of people on the other side of the United States. For example, if the premium of Coinbase is obvious, it means that their buying may be strong, which was very obvious when Trump was elected. If there are any changes in these numbers, I actually observe them every day. I want to maintain this sensitivity, and as soon as I find out, I begin to wonder whether there is no reason for this or whether there are some trading opportunities in it. In addition to the time it takes to record these data, I will 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 use as a mouth. I will spend a short period of time, for example, a few hours on the mark, and then see if my daily planned and revised trading plan has reached my desired position. If I am close or have reached it, I will concentrate on the disk right now and see the data I want to see. Or whether there is any deviation from the trading plan, it needs to be corrected. I have two screens, and on the other, I run my own Mr. Berg account on Twitter with Twitter on. The part outside the transaction 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 no exercise all day. I still spend the rest of my time with my family. So my day is actually quite boring, and there is nothing particularly noticeable, because trading is actually my job, so I am not very different from ordinary office workers or students, mainly at work, and then get off work, eat, sleep, probably like this.
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.
