On-chain data analysis expert Colin interprets BTC indicators, revealing investment strategies and challenges.

Main Text

Host: Research Partner at a Certain Research Institution

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.

Disclaimer: The content discussed in this episode of the podcast does not represent the views of the institutions of the guests, and the projects mentioned do not constitute any investment advice.

Host: This episode is a bit special, as we have previously discussed many topics related to specific tracks or projects, and exchanged some cyclical narratives, such as when we talked about memes. But today we are going to discuss on-chain data analysis, especially the on-chain data analysis of BTC. We will closely examine its operating 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 better 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 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 all holders' unrealized profits in the Bitcoin market to the total market capitalization.

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

Shiller ECY: An 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, improved from Shiller's price-to-earnings ratio indicator (CAPE), primarily considering the impact of the interest rate environment.

Opportunity to learn on-chain data analysis

Host: Today we have invited the guest Colin, a freelance trader and on-chain data researcher. Let's ask Colin to say hello to our listeners.

Colin: Hello everyone, first of all, thank you to the host for the invitation. I was a bit surprised when I received this invitation because I am just an unknown small retail investor with no special title, quietly doing my own trading. My name is Colin, and I run an account on Twitter called Mr. Bag, where I mainly share some educational content on on-chain data, analysis of the current market situation, and some trading concepts. I see my role in three main aspects: the first is as an event-driven trader, where I think about event-driven trading strategies; the second is as an on-chain data analyst, which is also the main content I share on Twitter; the third is rather conservative, as I call myself an index investor, where I choose to allocate part of my funds to the major U.S. stock market, using this allocation to reduce the overall volatility of my asset curve while maintaining a certain level of defensiveness in my overall position. That’s roughly how I see myself.

Host: Thank you, Colin, for your self-introduction. I invited Colin to participate in the program because I saw his enlightening analysis of on-chain data for Bitcoin on Twitter. This is a topic we haven't discussed much before, and it is also a part that is lacking in my own area. I read the series of articles he wrote and found his logic clear and substantial, so I decided to invite him. I want to remind everyone that both my and the guest's opinions today are very subjective and that the information and viewpoints may change in the future. Different people may have different interpretations of the same data and indicators. The content of this episode does not serve as any investment advice. The program will mention some data analysis platforms, but only as a personal sharing and examples, not as commercial recommendations. This program has not received any commercial sponsorship from any platform. Let's get into the main topic and talk about on-chain data analysis of crypto assets. As I mentioned earlier, Colin is a trader, so under what circumstances did you start to engage with and learn about on-chain data analysis of crypto assets?

Colin: I believe this question should be broken down into two parts. First, I think 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 improve their quality of life through profits. Therefore, my philosophy has always been consistent: I will learn whatever can help me profit. In this way, I aim to enhance the expected value of my overall trading system; simply put, I will learn anything that can make money. The second part is that my initial exposure to on-chain data was purely accidental. About six or seven years ago, I had no understanding at all; I just looked at this and that. While exploring various fields, I came across some interesting research theories that I wanted to learn about. 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 the later stages of my learning, I combined the knowledge I acquired from other fields, mainly focusing on quantitative trading development, and integrated it into on-chain data to develop some trading models, which I ultimately incorporated into my own trading system.

Host: 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. In fact, I have never really studied it systematically. Because from the past to now, I have encountered a problem, which is that I have completely not seen any systematic teaching. From the very beginning when I first saw this field, it was probably several years ago. At that time, I noticed it, but I didn't delve into it; I just read two or three articles to understand this thing. After a while, I came back to see some more in-depth content, and at that time I was focusing on studying other things, then I came back here, found this quite interesting, and continued to study it. There hasn't been a systematic learning period, it's just piecing things together.

Host: I see. How long has it been since you started learning about on-chain data and applying it to your actual investment practices?

Colin: This boundary is quite difficult to define, but I think it's close to the two cycles of Bitcoin... 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 involved around 2020 or 2019, but at that time there were no practical applications because I was hesitant and not very familiar with it yet, though I had already started learning.

The value and principles of on-chain data analysis

Host: Understood. Next, we will discuss many specific concepts related to on-chain data analysis, including some indexes. Which on-chain data observation platforms do you usually use?

Colin: The main website I use now is Glassnode. Let me 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 dollars a month. The second one, I kind of forgot, is around thirty to forty U a month. It also has a free version, but the information available on the free version is actually very limited. Of course, besides Glassnode, there are many others, but I ultimately chose it because during my initial filtering and research, this website matched my preferences the best.

Host: I understand. After looking at a lot of information from Colin, I also registered for Glassnode and became a paid member. I do feel that their data is very rich, and the timeliness is also quite good. Now let's discuss the second question. You mentioned that you are a trader, and you value its assistance in 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 it's actually quite simple. In our traditional financial markets, whether 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 cited 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, that 300 Bitcoins are transferred from one address to another, which can be found on the blockchain explorer. Although I cannot know who is behind this string of addresses, it doesn't matter because no single individual can influence the overall price trend and trajectory of 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 address or that address, I can analyze the flow of these chips by aggregating all the addresses to see whether they have taken profits or stopped losses, their profit situation, loss situation, and which price levels they tend to buy large amounts of Bitcoin or dislike buying Bitcoin at certain price levels. This data is actually visible. I believe this is the greatest value of Bitcoin on-chain data analysis compared to other financial markets, as other markets cannot achieve this.

Host: This point is indeed very important. Just like when we invest in cryptocurrencies, 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 can be observed by everyone. If other professional investors are looking at on-chain data and you are not, it is equivalent to having one less important weapon in your investment arsenal compared to others.

The challenges of on-chain data analysis

Host: 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. The first part is relatively easier to solve, which is a challenging point in learning, namely foundational knowledge. For most people, including myself at that time, as I mentioned earlier, it is very difficult to find a truly systematic teaching. Of course, I did not inquire offline about whether there are paid courses of this kind, but even if there were, I probably wouldn't dare to buy them, because throughout my trading experience, I haven't been willing to pay for courses. I have not been exposed to any systematic teaching courses, so all the content has to be explored and discovered on my own. There are many types of on-chain data, and during the research process, 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 figure out what is being thought behind this calculation formula and why it is designed this way. After I have clarified all these indicators, the next thing I need to do is a process called filtering. If someone has experience in developing quantitative strategies or has researched indicators, they will know one thing: the correlation of many indicators is very high. Too high correlation can cause a problem, which is that you can easily generate noise in interpretation or over-interpret. For example, let's say I have a system for escaping peaks, and this escape peak system might have 10 signals from 1 to 10. If the correlation from 1 to 4 is too high, it can lead to a problem. For instance, if Bitcoin's price behaves or changes in a certain way today, it might directly cause signals 1 to 4 to light up simultaneously, which is actually quite troublesome. Because if their correlation is too high, this is an inevitable situation.