BTC on-chain data analysis: Has this cycle reached its peak?

Hello everyone, welcome to WEB3 Mint To Be initiated by Mint Ventures. Here, we continue to ask questions and think deeply, clarify facts, explore reality, and find consensus in the WEB3 world. We clarify the logic behind hot topics, provide insights that penetrate the events themselves, and introduce multiple perspectives.

Alex: This episode is a little special because we have discussed many topics about specific tracks or projects before, and also exchanged some cyclical narratives, such as memes. But today we are going to discuss on-chain data analysis, especially on-chain data analysis of BTC. We will take a close look at its working principles, 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 your convenience.

Some data metrics and concepts mentioned in this podcast:

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

Realized Price: Calculated based on the price weighted when Bitcoin last moved on the chain, reflecting the historical cost of Bitcoin on the chain, suitable for evaluating the overall profit/loss status of the market.

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

RUP (Relative unrealized profit): Relative unrealized profit. It is used to measure the ratio of the unrealized profits of all holders in the Bitcoin market to the total market value.

Cointime True Market Mean Price: An on-chain average price indicator based on the Cointime Economics system. It aims to more accurately evaluate the long-term value of BTC by introducing Bitcoin's "time weight". Compared with BTC's current market price and realized market price (Realized Price), the True Market Mean Price under the Cointime system also comprehensively considers the impact of time and is suitable for BTC prices in large cycles.

Shiller ECY: A valuation indicator proposed by Nobel Prize winner in Economics Robert Shiller, it is used to evaluate 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-Earnings Ratio (CAPE) and mainly considers the impact of the interest rate environment.

An opportunity to learn on-chain data analysis

Alex: Our guest today is Colin, a free trader and on-chain data researcher. Please let Colin say hello to our listeners first.

Colin: Hello everyone, first of all, thank you Alex for the invitation. I was a little surprised when I received this invitation, because I am an unknown retail investor, and I don’t have any special titles. I just quietly do my own trading. My name is Colin. I run an account on Twitter called Mr. Berg. I usually share some teaching on chain data, analysis of the current market conditions, and some trading concepts. I have three positionings for myself: the first is an event-driven trader. I usually think about event-driven trading strategies; the second is an analyst of chain data, which is also the content I usually share on Twitter; the third is more conservative. I call myself an index investor. I will choose to allocate part of the funds to the US stock market. Through this part of the funds to invest in Beta, I will reduce the overall volatility of my asset curve, while maintaining a certain defensiveness of the overall position. The above is roughly how I position myself.

Alex: Thank you Colin for introducing yourself. I invited Colin to participate in the program because I saw his on-chain data analysis of Bitcoin on Twitter, which was very inspiring. This is a topic that we have rarely talked about before, and it is also a relatively lacking part in my own section. I read the series of articles he wrote and felt that the logic was clear and meaningful, so I invited him. I would like to remind everyone that today, both my and the guests’ opinions are highly subjective in the program, and the information and opinions may change in the future. Different people may have different interpretations of the same data and indicators. This content is not intended as any investment advice. This program will mention some data analysis platforms, which are only for personal sharing and examples, 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 crypto assets. I just mentioned that Colin is a trader, so under what circumstances did you start to contact and learn on-chain data analysis of crypto assets?

Colin: I think this question should be divided into two parts. First of all, I think that no matter who is around me, as long as they want to enter or have entered the financial market, including myself, the main goal should be to make money and use the profits to improve their quality of life. So my philosophy has always been consistent, that is, I will learn whatever can help me make money. In this way, I can improve the expected value of my overall trading system. In simple terms, I will learn whatever can make money. The second part is that I was exposed to on-chain data by accident at the beginning. About six or seven years ago, I didn’t understand it at all. I looked at this and that. When 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 started to learn and study it. In the later stage of learning, I will combine the knowledge I have learned in other fields, mainly the part of quantitative trading development, and combine it with on-chain data, and then develop some trading models, and finally integrate these models into my own trading system.

Alex: So, since you officially started to get involved in on-chain data analysis, how many years have you been studying and researching it systematically?

Colin: I think this is hard to define. In fact, I have never really studied it systematically. Because from the past to now, I have encountered a problem, that is, I have not seen any systematic teaching at all. When I first saw this field, it was probably 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 knew about it. After a while, I came back and saw some more in-depth content. At that time, I was focusing on studying other things. I came back here and saw that this was quite interesting, so I continued to study it. There was no time for systematic learning. I just pieced it together.

Alex: I see. How long did it take you to learn about on-chain data and apply it to your actual investment practice?

Colin: This boundary is difficult to define, but I think it is close to two rounds of Bitcoin cycles... It can't be considered two rounds, it depends on whether you define it from the bull market or the bear market. I started to get in touch with it around 2020 or 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 started to learn.

The value and principles of on-chain data analysis

Alex: I see. We will talk about many specific concepts about on-chain data analysis, including some indexes. What on-chain data observation platforms do you usually use?

Colin: I mainly use one website now, which is Glassnode. Let me briefly explain that it is paid. There are two paid levels. One is the professional version which is more expensive. I remember it cost more than 800 US dollars a month. I forgot the second one, which is about 30 to 40 U a month. It also has a free version, but the free version actually provides very little information. Of course, there are many other websites besides Glassnode. I finally chose it because this website was the most suitable for me when I was screening and researching.

Alex: I see. After reading a lot of information from Colin, I also registered for Glassnode and became their paid member. I feel that their data is indeed very rich and timely. So let's talk about the second question. You just mentioned that you are a trader, and you value its help in investment practice. 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, let's talk about the value and principle of on-chain data analysis. I plan to talk about these two together because they are actually quite simple. Our traditional financial markets, whether trading stocks, futures, bond options, or even real estate, or some commodities, Bitcoin has a fundamental difference from them, which is that it uses blockchain technology. The most important and most often mentioned value of this technology is its transparency. All of this Bitcoin transfer information is open and transparent, so you can directly see on the chain that, for example, 300 Bitcoins are transferred from one address to another, which can be checked on the blockchain browser. Although I have no way of knowing who is behind this string of addresses, it is not important because no single individual can actually affect the price trend and trend of the entire Bitcoin. 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 or that address, I can analyze the flow of their chips by aggregating all the addresses to see whether they have taken profits or stopped losses, their profit and loss situation, at which price they prefer to buy a large amount of Bitcoin or at which price they don’t like to buy Bitcoin. These data are actually visible. This is what I think is the greatest value of Bitcoin chain data analysis compared to other financial markets, because other markets cannot do this.

Alex: This is indeed very important. When we invest in cryptocurrencies, we need to analyze fundamentals just like we do when we look at stocks or other products. As you just said, the on-chain data is transparent and everyone can observe it. If other professional investors look at the on-chain data and you don't, then you are missing an important weapon in your investment.

Difficulties in on-chain data analysis

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

Colin: I think this question is very well asked, and I plan to answer it in two parts. First of all, the first part is relatively easy to solve. There is a difficult point in learning, which is the basic knowledge. For most people, including me at that time, as I mentioned before, it is difficult to find a truly systematic teaching. Of course, I did not ask offline if there are any paid courses of this kind, but if there are, I would not dare to buy them, because I have been trading by myself until now, and I don’t really pay to buy some courses. I have not been exposed to any systematic teaching courses, so in fact, all the content must be explored and explored by myself. There are many types of on-chain data. In the process of research, my own idea is to understand the calculation method and principle behind each indicator I have seen. This is actually a very time-consuming process, because when you only see a certain indicator, it will give you a calculation formula. My idea is to figure out what is behind this calculation formula and why it is designed in this way. After I figure out these indicators, the second thing I have to do is called screening. If you have experience in developing quantitative strategies or have studied indicators, you will know that the correlation between many indicators is very high. A high correlation will cause a problem, that is, it is easy to generate noise in your judgment, or you will over-interpret. For example, suppose I have a system for escaping the top today. This system may have 10 signals from 1 to 10. If the correlation between 1 to 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 make the lights from 1 to 4 light up at the same time, which is actually very troublesome. Because if their correlation is too high, this is an inevitable phenomenon. If 4 out of 10 lights are on today, you say it is very dangerous, but in fact it is not reasonable, because they will light up anyway. If you don't cut them according to the correlation, this phenomenon is very easy to happen. After I have studied the principles of each indicator and data, I can actually know whether their correlation is high or not by looking at the calculation formula directly, and I cut it according to the correlation. For example, if these 5 are highly correlated, I will cut and screen them slightly, and finally select one or two.

The first part is actually easy to solve and is not the main difficulty. The second part is the real challenge, which is about the on-chain data. How do you prove your point of view to the people around you or to yourself? I may give a more vulgar example here, but it is easy to understand. I have written in a tweet before that in fact, the quantitative field will tell you that trading is not easy to stick to the old ways. I have given an example before. Suppose there is a very strange trading strategy today. Its entry standard is that if my dog at home barks twice and it rains outside, I will enter the market and go long. As a result, I backtested this strategy 1,000 times and found that the winning rate was 95%, which far beat the market. Does anyone dare to use this strategy? It is actually quite strange. If the dog barks for no reason and it rains outside, you can go long, and the winning rate is still so high. There is actually a term called survivor bias. If you can't give it any logical support today, even if the number of samples is sufficient, this strategy cannot be used. Some people will argue that it has been backtested 1,000 times, and the winning rate is 95%. The backtest results support that this strategy can be used. I just mentioned the so-called survivor bias. Simply put, if I toss a coin 10 times, the probability of getting heads 10 times is actually 1/1024. In other words, on average, when 1,024 people do this, one person will succeed. The situation of throwing heads 4 times in a row is actually the so-called survivor. The other 1,023 people failed when doing this, but we don’t actually see it. We always see those successful cases. Back to Alex’s question just now, that is, where is the so-called main difficulty. Because we mainly look at large-scale consensus and trends, looking back at the history of Bitcoin, the most obvious three cycle tops are the two tops in 2013, 2017 and 2021. This is only 4 samples, which is definitely not enough. Since the number of samples is not enough, if we go back to the old ways today and see where a certain indicator has been in 2013, where a certain indicator has been in 2017, so we have to go there this year, this is unreasonable. Because the number of samples is completely insufficient, if we don't 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 historical samples, I must use the deductive method instead of simply using the inductive method to study. After I finish my research, I draw a conclusion based on the deductive method, and I need time to prove whether my view is right or wrong. If it is right, it means that my previous deductive reasoning process may be reasonable. If it is wrong, then I need to continue to correct the previous deductive logic. But if we just rely on induction today, in fact, most retail investors like to do this the most, thinking that the previous trend is very similar to the current trend, so there should be a surge or plunge in the future, which is actually unreasonable. Back to the first sentence I said at the beginning, I think the biggest difficulty is that I have to prove to others or myself that my inference is correct, so I have to correct my logic and assumptions all the time, and then check whether there are any flaws. Because Bitcoin is too young, there will always be a problem of insufficient samples in the on-chain data analysis. At this time, you actually have to use a simple deductive method in research, and use a logical way to infer it, and then wait for time to prove your judgment. This is the biggest difficulty I have encountered so far.

Key on-chain metrics to watch

Alex: I see. I think it is very inspiring. The question I asked you just now was also some confusion when I started to look 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. Later, I tend to choose the logic of those indicators, which is quite similar to the logic you just mentioned. That is, first of all, I have to look at the calculation logic behind this indicator, and I have to think that this logic makes sense, rather than backtesting and pulling it out and saying that it seems that this indicator is very accurate, and then use this accurate indicator to predict the future. As you said, the reference of the deductive method needs to be greater before it can be used as the main indicator we adopt. So after the experience you just talked about, in your current daily analysis of Bitcoin, which on-chain indicators have you been paying attention to for a long time or do you think are more important?

Colin: I have actually talked about this question before. I will try to filter based on correlation. I usually look at a lot of on-chain data indicators, so today I will introduce them from different dimensions, that is, try to divide them into three levels from the part with low correlation.

The first indicator that I will pay attention to for a long time and focus on must be the URPD indicator. It is a chart, which is presented in a row of bar charts. The horizontal axis is the price of Bitcoin and the vertical axis is the number of Bitcoins. Suppose we see a very high and large column at the position of 90,000 today, then we will know that a very large number of Bitcoins are built at this position, that is, the cost of their purchase. The bar chart will show how many Bitcoins they bought at this price. So in fact, based on this matter, we can see at a glance that if there are a lot of accumulations above 100,000, then we can know that many people buy above 100,000. There are two main points of observation in this URPD chart. The first is the simplest chip structure. Suppose today I see that the current market situation is around 87,000, and a very large number of chips have been accumulated above 87,000. According to the data of last week, it should be 4.4 million. Then we know that there is a very large turnover in this range, or someone has bought here. Since someone has bought, it is very likely to form a certain consensus. In this range of large accumulation, it is easy to form an attractive effect on the price, that is, the price is likely to fluctuate in this range, and it is easy to repair after a period of time if it falls, and then rise back. If it rises, the chips below have all become floating profits, then they are easy to sell, do short-term transactions, and then sell the price back. So it is easy to fluctuate in this range. This is the first observation point. The second observation point is that we can observe the process of Bitcoin distribution through URPD. The so-called distribution is that in the early bear market, those chips that bought Bitcoin at a low price, and then they sold the cheap chips in their hands upwards, so I define this process as distribution. Suppose today there are 300,000 more chips at the price of 100,000, and the chips with a cost of 20,000, assuming it is 20,000, just reduced by 300,000, then we can actually see that people with a cost of 20,000 sold 300,000 today, and their average selling price is about 100,000. We can see whether those low-cost chips usually have some drastic changes. Of course, the current price is 100,000 or 90,000, so if they change dramatically, it must be a decrease, not an increase, because the current price range is more than 90,000, not more than 20,000, so it will only decrease, not increase. So we can observe the distribution rate based on this matter, which is roughly what I mean. This is the first indicator that I will pay attention to for a long time.

The second indicator I want to introduce is called RUP, which means Relative Unprofitable Status in Chinese. This indicator actually has only one purpose, which is to help 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 did you earn, or not much, or a lot, roughly this concept. The principle of this indicator is actually very simple, because through the so-called transparent mechanism of blockchain, we can track the purchase price of most chips. We can compare the purchase price of these chips with the current price. Suppose he bought it at 50,000, and the current price is 100,000, we will know that this Bitcoin is currently profitable, so we can calculate how much money it has earned. For example, if there are 10 Bitcoins bought at 50,000, and now it is 100,000, 1 will earn 50,000, and 10 will earn 500,000. We add up all these floating profits and losses, and then standardize this number according to the current market value, then we can get a number between 0 and 1. The range between 0 and 1 is easy to observe. If today's RUP is very high, such as 0.7, 0.68, or 0.75, we know that the overall profitability of the market is very high, which may make more people want to take profits. Therefore, a high RUP is usually regarded as a relative warning.

The third dimension I want to talk about is a fair valuation model of the 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 Bitcoin. The so-called fair value is actually how much a Bitcoin is worth. After looking at so many models, I think the Cointime Price model is the most proven. I have not seen the Chinese translation of this term anywhere else. Simply put, we often hear a name called Cathie Wood, her ARK Invest, and the chain data website, which is the Glassnode I mentioned just now. This concept is mentioned in a document produced by the two parties in cooperation. The biggest feature of this model is that it introduces the concept of time weighting and then calculates the fair value of Bitcoin. The calculated number has two main uses. The first is very simple, which is to buy at the bottom. Suppose today in the bear market, it falls and falls, and finally falls below the valuation given by Cointime Price. As I said just now, this number is actually how much a Bitcoin should be worth. If it falls below this position today, it is equivalent to buying at a very cost-effective position. According to historical backtesting and its logic, we can actually see that whenever the price falls below the Cointime Price, it is actually a very good position to buy at the bottom. The second application is to escape the top. We can monitor the current price and the Cointime Price to see how far it is away. If it deviates too much from the Coin Time Price, we can evaluate whether the market may be close to the top if the deviation is too large. The above three dimensions are the chip structure, profit status and fair valuation model, which are the three indicators and aspects I want to share.

How to view data conflicts

Alex: Okay, I have made it very clear just now. Many users may ask a question. The three indicators you just listed may represent different aspects, which is consistent with what you just said that the correlation between them is not that high, so they can be put together as a reference indicator. Then suppose that such indicators have divergent situations in actual application. For example, indicator one may feel that it is currently in a distribution situation, while indicators two and three may show that the current distance to the top does not seem to be that high from a cycle perspective. In this case, how would you deal with the conflicting data?

Colin: I think this is not just in the field of on-chain data analysis, but also in other fields such as technical analysis or macroeconomics, where there may be so-called fighting. In the on-chain field, my personal approach is very simple. I will give different weights to different levels. 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 the low-cost chips in the market. During the bear market, for example, the Bitcoin chips bought at 15,000 or 16,000, have they been distributed? There is a very special phenomenon 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 is from March to April last year. In fact, in terms of profitability, you can definitely see large-scale distributions at that time. But if I only see large-scale distributions today, then my next question is to think about whether they have been distributed? All the criteria for judgment start from this question. If they have a large-scale distribution, but have not yet completed it, then I can tell myself with peace of mind that the bull market has not ended. For example, from March to April last year, Bitcoin rushed to more than 70,000. I was actually quite excited because the bull market finally came and set a new high. As a result, it began to fluctuate for more than half a year. At that time, I could not draw the conclusion that the bottom had been reached by observing these data. At best, it was the first distribution. And a lot of data is also the same. For example, I have published some mid-term analysis and chip structure analysis before. At that time, according to the average cost of short-term holders, his situation was actually different from the end of the real bull market. So I was actually very at ease at that time. Then you say that the data is conflicting, and now he says that he has distributed, so should I flee the top? In fact, no, because the main issue is still the one I just talked about: whether the distribution has ended. Using this issue as the standard for screening each indicator and as the basis for judgment, it is actually very easy to draw this conclusion, that is, even if the distribution has occurred and is still large-scale, I just need to judge whether it has ended. Using this as a criterion can effectively deal with the so-called data conflict problem.

Alex: Let's make a scenario. For example, let's look at URPD. Assume that this indicator has already had two distributions, which is more like what you just said, one in March and April last year, and then there was a peak distribution from December to January at the end of the year. Assuming that it has such a distribution, but the other two valuation indicators may not be so high, when this happens, you just said that you will give them different weights. Then, will you reduce part of the position according to the proportion of the weight, or will you consider the three indicators in a unified way and not adjust the position according to the weight, 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 the real top now, and no one can escape at the highest position. If there is, it would be too great, and I would definitely want to know it. My personal interpretation of the top is that it is a slow process. Although you feel it is very fast if you look at the daily chart, in fact, if you are in the present, for example, if you are at 69,000, the top of the previous cycle, you will not feel that it is the top now. We can only make a judgment based on the data and say that it is possible that the conditions for the formation of the top are now in place. So based on this premise, I will actually take a segmented position. For example, when I think the conditions for the top have gradually matured, once I see a certain indicator giving me a warning during this period, such as a RUP divergence I shared on Twitter before, I will do a corresponding reduction in positions. Of course, the extent of this reduction in positions must be determined in advance from the beginning. It is impossible to say that there is a divergence now and I don’t know how much to reduce. It won’t be like this. I will make a rough plan first. For example, I will divide my position into 4 parts. Once a warning signal of a certain type appears, I will reduce one part first. When the second warning signal appears, I will reduce another part. At the same time, I will plan that the last part of funds must be sold out no matter what. For example, if the bear market is definitely over, but other warning signals have not appeared yet, we need to formulate an extreme, final escape strategy to screen.

Alex: I understand. We will gradually exit the market and reduce our positions based on different warning signals.

Colin: Yes.

Judgment and basis for BTC's position in this cycle

Alex: I see. I have been following your Twitter account recently. You also practice your trading according to the indicators just mentioned, including the concepts behind these indicators. Now let's look at Bitcoin. It has been fluctuating in the range of 91,000 to 109,000 for almost three months. There are quite a lot of differences in the market about this price range. Unlike in December and January, everyone thinks that this bull market is far from over and will reach 150,000, 200,000 or even 300,000. There are many positive views. There are big differences in the market at present. Some people think that the top of BTC in this round is around 100,000, but some people think that BTC has not reached its peak in this cycle, and there will still be a main uptrend in 2025. So based on your current comprehensive judgment, what is your opinion? Where is BTC in this round of our big cycle? And what are the data sources that support your judgment?

Colin: Before answering this question, I may need to give a precaution. I am actually very bearish on 2025. I think BTC is currently in a state where the top is formed. In fact, I know that many people, including some participants around me, did not have good returns during the so-called special bull market in 2024, because the overall market performance in 2024 is different from every previous cycle. The most obvious point is that there is no altcoin season. This hurts many people, including some non-professional traders around me. They also came to participate in this market. In fact, they suffered a lot of losses on altcoins. Why is this the case? Let's take a look back at 2024. There was an altcoin market at the beginning of the year, and the second one appeared in November last year, when Trump was elected as the US president. Compared with our previous cycles, these two altcoin markets actually have a big and obvious point, that is, their sustainability is actually not very good. Even in the market in November and December last year, altcoins did not rise across the board at all. It was a very obvious sector rotation. At that time, there was a Defi sector, and after the rise, it switched to old coins, such as XRP, and then Litecoin, etc. The sector rotation was very obvious. From this incident, we can see that this round of bull market in 2024, if everyone thinks it is a bull market, this cycle is actually very different from the previous ones. There is also a theory that there must be a so-called alt season before the end of the bull market. In fact, I personally think that you can't say that the bull market will end only when the alt season appears. This is obviously not strongly correlated. We can't use this as a judgment on whether the bull market is over. As mentioned earlier, there is a shortcoming in on-chain data analysis, that is, the number of samples is never enough. If we simply use historical conditions to extrapolate today's market, it is actually a practice of carving a boat to find a sword, which is not very good. If you want to carve a boat to find a sword, the tops of 13, 17, and 21 should appear around the end of the year, according to the time.

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

How to get started with on-chain data analysis

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

: : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : So my suggestion here is that if you want to study a certain indicator, if you can find the article of the original author, it is best, try not to read other people's. The original author himself is definitely the person who understands the most about that indicator. If you really can't find it, at least you have to read his formula. The Glassnode website mentioned just now has a column called Weekly onchain. They will send a weekly report to share the current market situation in a form similar to weekly reports and why they think the current market situation is like this. Then you can see various indicators from above. You can grasp each indicator and study it, and there will be a large library of learning materials. There are some teachings on my Twitter, which cannot be called systematic. If you are interested, you can also take a look.

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

: : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : Some people may say, can your idea stand the test? I dare not say 100%, but there is the most obvious example. In January 2024, I don’t know how many people found that the day when the Bitcoin ETF passed, Ethereum soared, and the exchange rate also soared directly. If I remember correctly, the exchange rate of ETH against BTC rose by about 30 percentage points within 24 hours. Many people have questions, if Bitcoin ETF passed, what’s the matter with Ethereum? The next hype is Ethereum. So this is one of the so-called event-driven transactions. Back to the Alex question, I think it’s too difficult to quantify the part of the news or fundamentals, so I personally would be more inclined to design some event driven strategies to deal with these opportunities that may have inefficient pricing in the market.

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

Daily life of on-chain data researchers

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

: : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 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 make me move, not to exercise all day, and the rest of my time is to accompany 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 very different from ordinary office workers or students. I am mainly working, then getting off work, eating, and sleeping. This is probably what it is.

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

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

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