WEB3 Mint To Be: Learn Bitcoin on-chain Data Analysis from Scratch

Host: AlexMint Ventures Research Partner

Guest: Colin, freelance trader, on-chain data researcher

Recording time: 2025.2.15

Hello everyone, welcome to WEB3 Mint To Be. Here, we continuously question and think deeply, clarifying facts, exploring realities, and seeking consensus in the WEB3 world. We aim to clarify the logic behind hot topics, provide insights that penetrate the events themselves, and introduce diverse perspectives.

Statement: The content we discuss in this podcast does not represent the views of the institutions of the guests. The projects mentioned do not constitute any investment advice.

Alex: This episode is a bit special because we have previously discussed many topics about specific tracks or projects, and also exchanged some periodic narratives, such as the memes we talked about before. But today we are going to discuss on-chain data analysis, especially the on-chain data analysis of BTC. We will take a close look at its functioning principles, key indicators, and learn its methodology. In today's program, we will mention many concepts related to indicators and list these concepts at the beginning of the text version for everyone's understanding.

Some data metrics and concepts mentioned in this episode of the podcast:

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

Realized Price: Calculated based on the price weighted at the last on-chain movement of Bitcoin, reflecting the on-chain historical cost of Bitcoin, suitable for assessing 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): a measure of the ratio of the unrealized profit of all Bitcoin holders to the total market capitalization.

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

Shiller ECY: A valuation indicator proposed by Nobel Laureate Robert Shiller, used to assess the long-term return potential of the stock market and measure the attractiveness of stocks relative to other assets. It is an improvement on Shiller's cyclically adjusted price-to-earnings ratio (CAPE) and mainly considers the impact of the interest rate environment.

Opportunity to learn on-chain data analysis

Alex: Today our guest is the free trader and on-chain data researcher Colin. Let's have Colin say hello to our audience.

Colin: Hello everyone, first of all, I would like to thank Alex for the invitation. I was somewhat surprised when I received this invite because I am just an unknown small retail investor and don't have any special titles; I quietly do my own trading. My name is Colin, and I run an account on social media called Mr. Beig, where I mainly share some on-chain data tutorials, analyses of the current market situation, and some trading concepts. I see my role in three aspects: first, I am an event-driven trader, and I usually think about event-driven trading strategies; second, I am an on-chain data analyst, which is also the main content I share on social media; third, I am relatively conservative, and I refer to myself as an index investor. I choose to allocate part of my funds to the large-cap US stocks, using this part of the capital to invest in order to reduce the overall volatility of my asset curve while maintaining a certain level of defensiveness in the overall position. This is roughly how I define my role.

Alex: Thanks to Colin for his self-introduction. I invited Colin to participate in the program because I saw his on-chain data analysis about Bitcoin on social media, which was very inspiring. This is a topic we have discussed less in the past and also a part that I personally lack in my own section. I read the series of articles he wrote and found the logic clear and substantial, so I invited him. I want to remind everyone that today, whether it's my viewpoint or the guest's, there is a strong subjectivity 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 episode is not considered as any investment advice. The program will mention some data analysis platforms, but only as personal sharing and examples, not as business recommendations. This program has not received any commercial sponsorship from any platform. Let's get to the point and talk about on-chain data analysis of crypto assets. Earlier, it was mentioned that Colin is a trader, so under what circumstances did you start to get in touch with and learn about on-chain data analysis of crypto assets?

Colin: I think this question should be divided into two parts to answer. First of all, I believe that for anyone around me who wants to enter or has already 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: I learn whatever can help my profitability. By this means, I enhance the expected value of my overall trading system. To put it simply, I learn whatever can make money. The second part is that I first came across on-chain data purely by accident about six or seven years ago when I had no understanding at all. I would look at this and that. While exploring various fields, I came across some interesting research theories that I wanted to learn. At that time, I also stumbled upon the so-called on-chain data analysis field related to Bitcoin, and I began to study and research it. In later stages, I would combine the knowledge I learned from other fields, mainly in the area of quantitative trading development, and integrate it with on-chain data to develop some trading models, and finally incorporate these models into my own trading system.

Alex: So how many years have you been systematically learning and researching on-chain data analysis since you officially started?

Colin: I think this is hard to define, actually I have never really studied it systematically. Because from the past to now, I have encountered a problem, which is that I have not really seen any systematic teaching. From the very beginning when I first saw this field, it was probably several years ago, I noticed it back then, but did not delve deeper into research, just read a couple of articles to understand this thing. After a while, I came back to see some more in-depth content, at that time I was focusing on researching other things, and then came back here, finding this quite interesting, so I continued to study it. There hasn’t been a time for systematic learning, just piecing things together.

Alex: Got it. How long have you been applying what you've learned from on-chain data to your actual investment practice?

Colin: This boundary is quite difficult to define, but I think it's close to two Bitcoin cycles... but it can't really be considered two cycles, it depends on whether you define it from a bull market or a bear market. I started getting in touch around 2020 or 2019, but at that time there was no practical application because I was hesitant; I wasn't very familiar with this thing back then, but I had already started learning.

The value and principles of on-chain data analysis

Alex: Understood. We will discuss many specific concepts related to on-chain data analysis next, including some indices. Which on-chain data observation platforms do you usually use?

Colin: I primarily use one website, which is Glassnode. To briefly explain, it requires a subscription. There are two paid tiers; one is the professional version, which is quite expensive, I remember it costs over 800 USD per month. The second one, I forget a bit, is about thirty to forty USD per month. It also has a free version, but the information available in the free version is actually very limited. Of course, besides Glassnode, there are many other options. I ultimately chose it because, in the initial filtering and research phase, this website matched my preferences the best.

Alex: I understand. After reviewing a lot of information from Colin, I registered for Glassnode myself and became a paid member. I do feel that their data is very rich, and the timeliness is also quite good. Now, let's talk about the second question. You mentioned that you are a trader, and what you value is its help to practical investment. So, what is the core value of on-chain data analysis in your investments? What is the underlying principle? Please introduce it to us.

Colin: Alright. First, let's talk about the value and principles of on-chain data analysis. I plan to combine these two topics because they are quite simple. In our traditional financial markets, whether it's trading stocks, futures, options, real estate, or some raw materials, Bitcoin has a fundamental difference from them, which is that it uses blockchain technology. The most important and commonly mentioned value of this technology is its transparency. All the transfer information of Bitcoin is public and transparent, so you can directly see on-chain, for example, 300 Bitcoins being transferred from one address to another, which can be checked on a blockchain explorer. Although I cannot know who is behind this string of addresses, that is not important, because no single individual can affect the price trend and movement of Bitcoin as a whole. Therefore, normally when we study on-chain data, we look at the overall market, its trends, and the consensus and behavior of the crowd. Even if I do not know who is behind this address or that address, I can analyze the flow of chips by aggregating all the addresses to see whether they have taken profits or stopped losses, how their profits are doing, how their losses are doing, and at which price levels they are more inclined to buy large amounts of Bitcoin or which price levels they dislike buying Bitcoin. This data is actually all visible. This is what I believe is the greatest value of on-chain data analysis for Bitcoin compared to other financial markets, because other markets cannot do this.

Alex: Indeed, this point is very important. Just like when we make crypto investments, we need to analyze the fundamentals just like we do with stocks or other products. As you just mentioned, on-chain data is transparent and everyone can observe it. If other professional investors are looking at on-chain data and you are not, it is equivalent to having one very important weapon less than others in your investment.

The difficulties of on-chain data analysis

Alex: When you actually perform 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. The first part is relatively easier to address, which is a more difficult point in learning, namely foundational knowledge. For most people, including myself back then, it is hard to find a truly systematic teaching. Of course, I did not inquire offline about whether there are any paid courses of this kind, but even if there were, I probably wouldn’t dare to buy them because I have been trading until now, and I actually don’t pay for courses. I have not encountered any systematic teaching courses, so all content has to be explored and discovered by oneself. There are many types of on-chain data, and during my research, my philosophy is to clarify the calculation methods and principles behind every indicator I have seen. This is actually a very time-consuming process because when you see a certain indicator, it gives you a calculation formula. My idea is to delve into what this calculation formula is really thinking and why it is designed this way. After I understand these indicators, the next thing to do is called filtering. If someone has experience in developing quantitative strategies or has studied indicators, they will know one thing: many indicators have very high correlation. High correlation can cause a problem where it is easy to introduce noise in judgment, or you may overinterpret it. For example, suppose I have a topping-out system today, and this topping-out system might have 10 signals numbered from 1 to 10. If the correlation between signals 1 to 4 is too high, it will cause a problem. For instance, the price of Bitcoin today...