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​​An Introduction To On-Chain Analysis

What is it? What are the different data types? And how to use it

A magnifying glass looking at a trial of shoe marks
Taking a look at on-chain analysis

This research was sponsored by Luno Global, a platform that allows users to buy, save and manage cryptocurrencies.

On-chain analysis has gained considerable popularity over the last few years. It can be a very powerful tool for analyzing a network’s fundamentals. However, interested parties must use it correctly, and do so within its limitations.

This article provides an introduction to on-chain analysis. That includes:

  • What makes on-chain data unique?
  • What data is available?
  • How to use it
  • Its limitations in analyzing the market

The underlying blockchains used by bitcoin and other cryptocurrencies provide us with a very unique data source not available for other financial assets. Since these are public ledgers, every transaction conducted on-chain is recorded and transparent.

Furthermore, transactions are immutable once they are recorded on the blockchain.

What data types are there in on-chain analysis?

There are different types of on-chain data. I will classify them as follows:

  • Simple on-chain data (raw on-chain data)
  • Entity data
  • Aggregated on-chain data
  • Cycle indicators (which are often hybrid metrics)

Simple On-Chain Data

Some of the most simple metrics used to gauge network developments are the number of active addresses, the total wallets with a certain amount of bitcoin held, the amount of transactions happening on-chain, and the number of wallets with a non-zero balance (see Graph 1 for this figure).

It’s important to note that the number of addresses is different from the number of users, since one user or organization (for example, an exchange) can own multiple bitcoin addresses. As a result, simply looking at transfers of bitcoin between wallets can be misleading.

Let’s assume one person has 10 bitcoin in one address and splits it up into 10 new one-bitcoin addresses. They might do this for security reasons. Looking at the raw on-chain data, we would falsely conclude that one person has sold their 10 bitcoin and that 10 new smaller holders have appeared. That is where entity data comes into play.

Graph 1: Bitcoin Number of Addresses with a Non-Zero Balance (Source: Glassnode)

Entity Data

Entity data is, broadly speaking, information leveraged to tie wallets, for example those with specific sending or receiving patterns, to a single entity, which can be a person or an organization.

Coming back to the example of the person splitting up 10 bitcoin into 10 wallets, if attributed correctly, entity data would show us that it was only a user splitting up his holdings.

The most important use case of entity data is identifying whether certain types of entities are buying, selling, or holding; or whether bitcoin balances on exchanges are declining or increasing. For example, it can give us an indication of whether long-term holders are accumulating.

It is important to distinguish entity data from other kinds of on-chain data because the steps needed to compile (gather) it have some implications for data reliability, particularly in the short-term, which makes this compiling more difficult.

In spite of this, entity data is a very important source of information in on-chain analysis.

Compiling entity data requires experience. Pulling it from a node is more challenging than gathering raw on-chain data. So we have to rely on third-party information providers for entity data, which is different from other kinds of on-chain data, which, in theory, could be pulled from a full node by anyone with some technical knowledge.

How are entities identified? That is where data providers come into play. They use heuristics, patterns, algorithms, clustering methods, etc. to identify addresses that potentially belong to one entity.

This means that entity data might be unreliable in the short-term, as a bitcoin transfer between wallets only relies on a few data points. But with time, more and more data points are coming in, and entity data is also becoming more reliable. While the method might be imperfect, it can give us valuable insights into on-chain trends.

One of the metrics which falls into that category is bitcoin exchange balances, as pictured in Graph 2. Unfortunately, this data is also often shared when unusually large inflows or outflows appear in the short-term and it is taken as a trading indicator.

But due to the nature of this data, it’s unreliable in the short-term. What may look like an unusually large exchange outflow might simply be an exchange transferring bitcoin to a new wallet that has not been labeled yet by data providers. With more data coming in, this will normally be corrected by data providers a few days later.

In fact, large outflows turn out more often than not to be internal transfers.

If you see this data shared, you should be skeptical, particularly if large moves occur. If a significant inflow or outflow takes place, it could potentially be a false signal, so it would be unwise to make any trades based solely on such information.

For the interested reader, a more detailed description of how data providers identify exchange wallets can be found in the technical paper “Entity Flow Data Generation Process” by CryptoQuant at the bottom of the linked page. Rafael Schultze-Kraft, co-founder and CTO of Glassnode, goes into more detail on how to use and treat exchange flow data in the article “Bitcoin On-Chain Exchange Metrics: The Good, The Bad, The Ugly.”

Graph 2: Bitcoin Exchange Reserves (Source: cryptoquant.com)

Aggregated On-Chain Data

While these simple metrics provide some insights into the fundamentals and developments of the network, there are aggregated on-chain metrics that add more information on what is taking place.

One relatively easy-to-understand, but very useful resource, is the HODL Waves chart (see Graph 3). Simplistically, it shows what fraction of bitcoin has been last moved during a certain time frame, for example the percentage of coins last moved between 1 and 2 years ago.

Amongst others, the HODL Waves chart can give us an idea of whether long-term holders are selling or holding their bitcoin, and whether users are holding their coins for increasingly longer time frames.

Graph 3: Bitcoin HODL Waves (Source: checkonchain.com)

Cycle Indicators

Last, but not least, there are on-chain indicators designed to provide an indication of where bitcoin stands within a cycle, for example whether it is overvalued or entering a buy zone.

Many of those cycle indicators are hybrid metrics. A hybrid metric is a metric that combines market and on-chain data into one indicator.

One of the oldest of these hybrid metrics is the Bitcoin Market-Value-to-Realized-Value (MVRV) Ratio, created by bitcoin analysts Murad Mahmudov and David Puell (see Graph 4). The MVRV Ratio divides market capitalization (market value) by realized value (realized capitalization).

Market capitalization is calculated by multiplying the total bitcoin in existence by the price of one bitcoin.

To obtain realized capitalization, each UTXO (unspent transaction output) is multiplied by the price it had when it was last moved.

For example, if 100 bitcoin had a price of $500 each when last transferred, they would be assigned a value of $50,000 (100BTC*$500). This is done for all bitcoin that have been mined. Aggregated, they form the realized capitalization.

Realized capitalization, intuitively speaking, accounts for both lost coins and those in the hands of users. Both reduce the value of realized capitalization, while for market capitalization, all units of bitcoin have the same price.

When the MVRV drops below a value of 1, realized capitalization is higher than market capitalization. This is an indication that bitcoin’s price might be undervalued (holders vs. speculators). If the MVRV ratio reaches a much higher value, it could indicate that bitcoin is overvalued.

However, it is important to remember that the past performance of an indicator does not guarantee future results. In some instances, it might give false signals. As a result, it is best to avoid basing your trading strategy solely on one indicator.

Graph 4: Bitcoin: Market Value to Realized Value Ratio (MVRV) (Source: Glassnode)

The MVRV ratio is only one of many on-chain indicators that might provide an indication of whether bitcoin is overvalued or undervalued.

Due to the scope of this article, only some of the on-chain metrics are explored here, and the metrics provided should only be seen as a starting point.

For those wanting to take a deeper look into what on-chain metrics exist and how they might be used, the crypto analyst Shaitan has written extensively about the use of on-chain metrics and hybrid metrics in the paper “Bitcoin Data Analytics — Measuring Economic Activity and Assesing Market Sentiment Using On-Chain Data.”

Further, for anyone wanting to learn more about on-chain analysis, I recommend taking a look at the websites of data providers. By perusing these sites, interested parties can get a better grasp of the data and how to use it.

Before concluding this article, I would like to provide a few more remarks on how to use on-chain data. While some traders might use on-chain analysis successfully for short-term trading, this approach shines when it comes to fundamental analysis, which focuses on the medium- to long-term.

It can provide investors with insights into the health of the network, the direction the network is taking, and where bitcoin stands in a cycle. It could be used to identify good entrance or exit points for the investor with a medium to long-term horizon.

As with any other resource used to analyze the markets, on-chain analysis has its limitations. For example, it does not account for derivatives markets, which are becoming increasingly important.

Thus, it should be used in combination with other data sources, such as trading data from exchanges and derivatives data. More importantly now than ever, macro developments should also be taken into consideration. I comment on the use of on-chain analysis and why it continues to have its place, even in the current macro environment, in the article “Is On-Chain Analysis Dead?”

Below you will find some of the data sources to start out and six guiding questions to help when evaluating on-chain data.

Where To Find On-Chain Data?

If you want to start out in on-chain analysis and get a feeling for the data, there are some great on-chain data sources provided for free by on-chain analysts Checkmate, Tsypruyan, and Willy Woo:

  1. Checkonchain
  2. Tsypruyan
  3. Woobull

Besides these free resources, there are prominent data providers such as Glassnode, CryptoQuant, Bytetree, Santiment, and Coinmetrics, which have very extensive on-chain data sets.

While the advanced metrics are often behind a subscription service, some basic metrics are provided free of charge.

Further, these data providers often provide detailed descriptions of the indicators, tutorials, and their own analyses, so it is worthwhile to check out their websites:

  1. Glassnode
  2. CryptoQuant
  3. Bytetree
  4. Santiment
  5. Coinmetrics

How To Critically Evaluate On-Chain Data

The questions I outline below are from the article “Bullish underlying on-chain trend and a short-term skeptical outlook for Bitcoin,” which I published on June 18, 2021.

When you see an on-chain chart posted on Twitter or somewhere else, I find the following questions useful in getting an idea of how relevant it is and what it means in the context of the current market bitcoin:

  1. What, exactly, does this chart show?
  2. Is the information reliable? The newest entity data might not be accurate.
  3. Is the chart bullish or bearish?
  4. How relevant is the chart? Is the change significant enough to affect prices? Has any new trend existed for long enough to be notable?
  5. If it is relevant, will this affect bitcoin in the short-, medium- or long-term?
  6. Is there other data supporting this chart or speaking against it? Charts are useful, but usually only in combination with other information sources.

As always, the motto “Don’t trust! Verify!” applies to any chart out there, which includes the charts in this article.

This content is for educational purposes only. It does not constitute trading advice. Past performance does not indicate future results. Do not invest more than you can afford to lose. The author of this article may hold assets mentioned in the piece.

If you found this content engaging, and have an interest in commissioning content of your own, check out Quantum Economics’ Analysis on Demand service.



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Jan Wüstenfeld

Jan Wüstenfeld

On-chain Analyst for Quantum Economics, Bitcoin Research, Data Analysis, Twitter: @JanWues; E-Mail: on-chain@wuestenfeld.eu