An introduction to on-chain metrics

On-chain analysis refers to the process of using blockchain data to analyze and understand the behavior and activity of cryptocurrency networks. When done on the aggregate data from the entire blockchain, this type of analysis can be used to gain insights into a wide range of topics, including market trends, network health, and user behavior. In this article, we will explore the basics of on-chain analytics, and explain some basic metrics to understand the behavior of crypto-native participants as an aggregate. Starting on-chain analysis can be a little challenging and hopefully this article gives you, the reader, the ability to start looking at a few on-chain metrics yourself.

Basic questions

1. What is on-chain analysis and why is it important?

On-chain analysis is a method of using data from blockchain networks to gain insights into the behavior and activity of the network. This type of analysis can be used to understand market trends, network health, and user behavior, providing valuable insights into a cryptocurrency ecosystem’s overall state.

2. How does on-chain analysis work?

On-chain analysis typically involves collecting data from the blockchain and using various tools and techniques to analyze and interpret the data. This can include visualizing the data in charts and graphs, possibly using statistical methods to identify patterns and trends to know the current state of the market, and possibly make predictions about future behavior.

3. Is on-chain ‘analysis’ different from on-chain ‘metrics’?

Not always. On-chain ‘metrics’ are metrics derived from blockchain data and are supposed to be indicative of aggregate activity on the chain. On-chain ‘analysis’ is a broad term and can mean many things. When speaking about on-chain ‘analysis’ one could be referring to analysis of the behavior of a specific participant on the chain, of all holders of a particular token, or of everyone who is part of a particular incident (hack, airdrop, etc). Unless an explicit incident is mentioned, on-chain ‘analysis’ will be general and will involve the use of on-chain ‘metrics’.

4. What are some common applications of on-chain analysis?

There are many potential applications of on-chain analysis, including understanding market trends, predicting price movements, analyzing user behavior, and assessing the health of the network. For example, on-chain analysis can be used to identify patterns in transaction data that may indicate market trends, such as an increase in the number of transactions or a change in the types of transactions being executed on the network. This information can be used by investors and traders to make informed decisions about their investments. On-chain analysis can also be used to study user behavior and track the adoption of different cryptocurrencies and blockchain-based applications. This information can be valuable for researchers and businesses looking to understand the evolution of the blockchain ecosystem and identify new opportunities for growth.

5. Is something similar to on-chain analysis possible for traditional assets?

On-chain analysis is possible in crypto because most cryptocurrencies are built on blockchain technology, which provides a decentralized and transparent ledger of all transactions on the network. Traditional assets such as stocks and bonds are not equivalently transparent, making it difficult to perform similar analyses. If traditional assets are tokenized and made available on a blockchain (public/private), such analyses can be done on them as well. Event specific on-chain analysis is equivalent to a financial audit and can only be performed by someone with complete financial knowledge of the respective assets.

Assumptions and Precursors

There are some assumptions to constructing on-chain analytics and to making predictions about the whole cryptocurrency market via on-chain analytics

  1. “All cryptocurrencies have a high degree of correlation to Bitcoin (BTC) and will follow its trends.”:
    • This is assumed for predicting movements in the broader crypto market. This is true for the most part, but over time we expect and hope the market will decouple from BTC and then this will be less applicable
  2. “When a coin was last moved on-chain is the price and time it was most recently traded”
    • Sometimes a coin may be moved between two wallets owned by the same person, or the coins may remain in exchange wallets while users trade them on the exchange. In both cases we are unable to have accurate information about the past price a coin was moved at. We hope not a lot of coins fit cases like the ones we mentioned and go ahead with our analysis
  3. “All of the factors affecting crypto markets can be covered using on-chain analysis”:
    • This is clearly untrue, but it is not a strict assumption. This means that on-chain metrics are a better indicator of what happens in the market when there is minimal macro involvement. Hence, we must be careful not to base our analysis purely on on-chain metrics and be cognizant of other factors
  4. “Older coins have more significance in the market since more informed, profitable players operate them”:
    • This assumption is the basis of many metrics which seek to know when older coins are being moved. It is true to an extent because
      1. Older players have a tendency to hold onto their coins and only trade them in exigent circumstances
      2. It is self fulfilling since people expect older players to have some bearing on the market
    • I expect this assumption to become less relevant in the far future

There are two main types of blockchain networks: UTXO-based chains and account-based chains. UTXO-based chains, such as Bitcoin and Litecoin, track unspent transaction outputs (UTXOs) to determine the current state of the blockchain. Account-based chains, such as Ethereum and EOS, track balances associated with user accounts instead. The way on-chain metrics are calculated for both the types of chains is slightly different and details can be found here.

Our Data Source

We use Glassnode as our source for on-chain analytics. There are other providers in the market as well, but we have found Glassnode to be reliable and helpful in terms of the content they put out to increase the understanding of on-chain metrics. The Glassnode academy and their YouTube channel is a great source for guidelines and weekly sessions on what is going on in the market. Our source for all of the graphs in this article is Glassnode.

Some terminology: 

  • An LTH is a Long Term Holder. An STH is a Short Term Holder. Glassnode considers an approximate threshold of 155 days to be the distinction between a STH and LTH. This becomes important because older coins have more significance in the market since they are assumed to be operated by more informed, profitable players. 
  • The realized market capitalization is calculated by adding the values each coin was last moved at together.

Important on-chain metrics

Now that we have clarified what on-chain analysis is, we can delve into some of the well-known metrics which cover all of the core concepts. We will clarify how a metric is constructed, what it is supposed to track/indicate, and the nuances of its readings. Some of the metrics not covered here (dormancy, illiquid supply, binary CDD, etc) are derivatives of the ones we mention.

Spent Output Profit Ratio

The Spent Output Profit Ratio (SOPR) measures the profitability of the coins being spent on the chain. It is possible to know the profitability of each coin being spent (moved to another wallet) on the chain since there is a record for when it was last moved. The SOPR looks at the coins being spent (moved) in the specified time period (1 day, 1 hour) and has a value of 1 if the coins being spent are not profitable or lossy (net). A value greater than 1 indicates profitability and less than 1 indicates loss. 

In the image below, you can see the plots of the LTH SOPR, STH SOPR, general SOPR and the price of BTC on a day time-frame. Since STHs trade often, their value stays very near neutral, but LTHs are another story. It is clear that LTHs have been capitulating at losses since the first drop in BTC price in May ‘22. They have been taking sustained losses and this metric indicates that a recovery in the markets is necessary because they may cash out if this downturn lasts much longer.

Net Unrealised Profit/Loss ratio

While the SOPR tells us the profitability of coins being currently spent, the Net Unrealised Profit/Loss (NUPL) ratio tells us the potential for profit/loss were holders to sell now. The neutral value for NUPL is 0, and a value greater than 0 means net unrealised profitability, while a value less than 0 indicates net unrealised loss. The formula for STH-NUPL is

Here we can see that the general NUPL has taken quite a dive since the FTX crisis in Nov ‘2. We can see that it is inching closer to 0 faster than the price is recovering, so in the next section let us check the relationship between SOPR and NUPL to determine if people are selling at losses. Also note that the LTH-NUPL is closer to the general NUPL than the STH-NUPL. This is because LTHs have more tokens and hence affect the general NUPL more.

Coin Days Destroyed

Coin Days are a concept to track the age of coins and Coin Days Destroyed (CDD) is a metric to know when older coins are being spent. One coin accumulates one “coin day” each day it spends in a wallet. For eg. 0.5 BTC in wallet for 100 days would have accumulated 50 coin days. These “coin days” are destroyed when the coin is moved to another wallet. The core assumption when using CDD to make inferences about the market is that people take note when older coins move and so it will affect market participant behavior. 

We can see quite a lot of coin-days were destroyed after the FTX crisis. The entity-adjusted version of this metric removes transactions between wallets belonging to the same entities from consideration during construction. The quality of this metric is highly dependent on if Glassnode will be able to correctly label all the wallets belonging to an entity. The eCDD-90 metric takes the 90 day rolling sum of the CDD and entity-adjusts it. The CDD metric and its variations can be used to determine when LTHs start spending in the market (CDD trends higher) and if they are abstaining from participating (CDD trends lower). More importantly, it can also be used to know if STH spending is large enough to move the market, which is something you would miss out on if you filter by the holding time of coins.

Spent Output Age Band

The Spent Output Age Band (SOAB) presents a heatmap-like percentage distribution of the age of the spent outputs. The darker areas represent older coins and we can see that the majority of the flow is dominated by younger coins. As with previous metrics, we can see the movement of older coins marks important points in the market, eg: movement in early July ‘22 marks the end of the downturn at the time.


Liveliness builds on Coin Days Destroyed and indicates when more coin days are being destroyed than are being created. The formula for Liveliness is

For a very active coin, coin days will be spent frequently and the value for liveliness would be closer to 1 than less active coins. A decrease in liveliness means accumulation is happening, and an increase in liveliness means more people are transacting. Of course, this ‘increase’ and ‘decrease’ is relative to the chain’s normal values. In the highlighted period below you can see that the entity adjusted liveliness went down, while the regular liveliness had stabilized. This meant that while it may seem like there was activity on chain, it was not meaningful activity since it was between wallets owned by the same entities.

Network Value to Transactions Ratio

The Network Value to Transactions (NVT) Ratio aims to determine when the value of the network does not match the transaction volume on it. It is the ratio of the market cap to the transaction volume.

The NVT can be considered equivalent to the price to earnings (PE) ratio used in traditional markets. It compares the speculative value of any cryptocurrency network and its utilitarian value, which makes it a good metric to compare different networks to each other. It can also be used to determine market tops/bottoms when used in conjunction with other indicators. In the figure below you can see how important the entity adjusted metric is since the regular metric is subject to fluctuations.

Market Value to Realized Value Ratio

The Market Value to Realized Value ratio is calculated by dividing the current market capitalization of BTC (or the coin in consideration) by its realized market capitalization. This metric aims to determine when the market value of BTC (or the coin in consideration) is above its “true” value, or when there is money to be made by selling it. NUPL and MVRV are closely related by the following formula

We have included MVRV for the sake of inclusivity, but NUPL conveys the same information, and can be varied for time of holding of the coins.

Miner Metrics

Different metrics are available to know what miners are doing on-chain. It is meaningful to know their behavior because not only are they large holders of particular coins, they act as a constant sell pressure for coins because they need liquidity. Two of them are

Balance in Miner wallets: We can see the distribution of tokens by different miners and know if there is a significant shift

Miner transfers to exchanges: We can see if a miner might be trying to selling some of their token reserves

Confluence Between Metrics

It is very important to look at these metrics in conjunction with one another. Individually a metric is susceptible to giving false signals, but collectively they covered each other’s flaws. One example we can give is the NUPL and SOPR confluence:

We can look at NUPL to see the potential moves in the market, e.g. if NUPL is very negative for LTHs, there is the possibility that they will start selling. If the LTH-NUPL then starts moving to 0, we can look at the LTH-SOPR to see if they are realizing losses. That would be confirmation for capitulation by LTHs. Once that is done (BTC price will go down) however, if the LTH-NUPL is 0, that means there is no more potential for LTHs to sell, and will mark a market bottom. The same phenomenon applies on the upward side as well: if LTH-NUPL is very positive, LTHs are profitable; once it starts moving to 0, look at LTH-SOPR to see realized profits, and once the NUPL is at 0, it will mark a market top.

We can see that the LTHs were pushed into lossy territory (via NUPL) during the price drop at June end and have been selling at losses (via SOPR) since then. They inched back to 0 NUPL (neutral value) in August but were pushed into unrealized loss again during the start of September. This pressure has sustained on the LTHs since then and has only gotten worse with the FTX crash. Fortunately, now that the inflation rate is a bit under control again and the Fed has become a bit dovish with the latest 50 bp rate hike, we should hope the macro stays optimistic for a while.


In this article we covered what on-chain metrics are and explained some important metrics. We explained the core assumptions made when using on-chain metrics for understanding market behavior and the conditions under which they can fail. It is important to note that on-chain metrics have been indicating a market bottom for a few months now and yet crypto markets have not bounced back. This is because of the heavy effect on global macro market effects on crypto markets and you must keep assumption 3 in mind. 

You should now have an understanding of the kind of insights on-chain analytics can provide and can now attempt to use them yourself. This article intends to provide a base for your journey in on-chain analytics and enable you to understand on-chain metrics for Layer-1s (Bitcoin here). On-chain analytics have a variety of applications in analyzing DeFi and NFT growth as well, and you can explore them using Dune analytics and Nansen. We use them extensively to analyze the growth of such projects and context-specific on-chain investigations as well.

I have posted analyses of the crypto-market combining on-chain metrics, derivatives and options data in the past, which you can use as reference. If you would like to get more on-chain updates like this, or commentaries on the market you can follow me on Twitter. If you found this article interesting, please do go through others in our blog.

Author – Harshvardhan Walia, Research Associate, Woodstock

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