How Can On-chain Analysis Help Cryptocurrency Traders?
On-chain analysis (or blockchain analysis) is an emerging field which examines the fundamental factors of cryptocurrencies to improve investment and trading decisions.
The development and application of these ideas are promising for traders who want to leverage public blockchain information, with the potential to enhance their trading strategies and position management.
Perhaps the most dominant form of analysis in cryptocurrency trading is the study of price action, i.e., technical analysis. But utilising the wealth of information provided by public blockchains like Bitcoin and Ethereum can provide a unique perspective that is impossible for traditional assets and can complement other analyses.
What is On-chain Analysis?
Cryptocurrency is the first asset class where investor activity can be extracted from massive data sets through each crypto-asset’s public ledger, which captures every on-chain action in history.
Since blockchains are a treasure trove of open, incorruptible financial data, we can pinpoint measures of economic activity in these networks. Through the collection and study of this data, we can measure sentiment and investor behaviour.
On-chain analysis is a fundamentals driven approach rather than based on hype, sentiment or technical analysis. This type of analysis can be focused exclusively on one crypto-asset by looking at historical trends or can be used to compare different crypto-assets to identify undervalued/overvalued coins.
We can think of the actual value of a cryptocurrency like BTC as made up of two parts: speculative value and utility value. On-chain analysis is a vital tool that helps you separate the speculative value of a cryptocurrency from its utility value. By examining, for instance, user adoption and miner activity using blockchain data, we can see whether the current price is justified by the fundamentals.
A Brief History of On-chain Analysis
On-chain analysis can trace its history back as far as 2011, when coin days destroyed was created as a valuation metric for bitcoin and was the first indicator to make use of the age of bitcoin addresses.
One of the first widely used, on-chain metrics that was developed for cryptocurrencies was the Network Value to Transaction (NVT) ratio, popularised by CoinMetrics, Chris Burniske and Jack Tatar. The NVT ratio was created in summer 2017 to determine the utility value of a cryptocurrency, specifically how much the market is willing to pay for the transactional utility of the blockchain.
By comparing the value of the network with the volume of transactions recorded on the blockchain, we can identify when a cryptocurrency is overvalued. When the value of the network is not justified by the volume of transactions, the NVT ratio is relatively high. When considering the transaction volume, if the network value is unusually low then it may suggest that a higher valuation is justified. The NVT ratio is often compared with the Price-Earnings ratio for equities and is applied in a similar way to find coins to buy, hold or sell.
It wasn’t long until the NVT ratio was iterated upon further. Other researchers made improvements to the metric so it was a more accurate assessment whether the network value is justified by economic activity taking place on the blockchain.
For example, the Network Value to Transaction ratio Signal (or NVTS) was developed by taking the 90-day moving average of transaction volume. More recently, CoinMetrics have refined the ratio further by using the free float supply in their calculations. These gradual improvements demonstrate how the methods of fundamentally valuing a cryptocurrency are continually evolving.
Another on-chain metric emerged from the dissatisfaction with simplistic measures from technical analysis (such as price/volume) and other concepts borrowed from traditional markets, such as market capitalisation. Market capitalisation is widely used by many comparison websites to rank cryptocurrencies.
But since cryptocurrencies are more similar to money or commodities (rather than a company stock), market capitalisation is an inexact and misleading measure. Market capitalisation ranks can be gamed through different methods of issuance — for instance, a token can be created with a circulating supply of 1 trillion and a few coins sold at $1 means the market cap is $1 trillion — but the coin could only have a trading volume of $3.
Because of the weakness with market capitalisation and the pitfalls of applying traditional metrics bluntly to cryptocurrencies, a new set of tools have been developed that can help traders to more accurately and precisely assess the health of blockchain networks.
A lot of these metrics rely on the concept of UTXOs (Unspent Transaction Outputs) in Bitcoin, which can be tracked to see when a wallet last moved their coins or how long an address has held coins for. The age, size and number of UTXOs transferred on a particular blockchain can provide reliable signals and have been developed into on-chain metrics, such as realised capitalisation, HODL waves and percentage of supply in profit/loss.
Realised capitalisation emerged as a way without any of the drawbacks of market capitalisation used to analyse coins fundamentally using blockchain data. Created by Nic Carter and Antoine Le Calvez, realised capitalisation aggregates all UTXOs and each UTXO is assigned a price based on the last time they were moved.
Further work built on the realised capitalisation metric, such as the Market Value to Realised Value (MVRV) ratio which was developed in October 2018 by Mahmud Marov and David Puell. The MVRV ratio can be thought of as an oscillator that has historically respected certain thresholds that suggest bitcoin is over- or under-valued. The metric has been tweaked with variations such as the MVRV z-score, the long-term holder/short-term holder MVRV ratio and application to account-based blockchains like Ethereum as well.
Ethereum as the second largest blockchain network, differs from Bitcoin and some altcoins as it is based on an account model rather than a UTXO model. In the UTXO model, a UTXO calculates a cumulative sum along each coin’s path to a final address, the ledger records who owns what and when, and addresses can have multiple UTXOs.
In contrast, the account model means that individual coins are not as easily tracked since incoming and outgoing coins are mixed into account balances, so some metrics such as coin age for Ethereum and ERC-20 tokens is slightly more difficult to obtain. Some metrics applied to Bitcoin (and other UTXO-based cryptocurrencies like Bitcoin Cash and Litecoin) are not directly applicable to Ethereum or other similar blockchains, and more work is needed to bring these models over to account-based cryptocurrencies.
How Can On-chain Metrics Help Crypto-traders?
The major benefit of on-chain metrics is that they illuminate investor behaviour and network health in real-time.
Two important on-chain metrics to watch are: the number of active addresses and the number of transactions which are two proxies for the demand for (and usage of) a blockchain network. When the number of active addresses and transactions rise sharply, these conditions usually correlate with a rising price.
While simple proxies are made available by on-chain data, a lot of metrics estimate certain ratios to provide information about the market and investor behaviour. For instance, we can think of market capitalisation as the participation of all traders, while realised capitalisation can be thought as the participation of long-term investors.
When these two metrics diverge significantly, then it could indicate an overheated market and heavy reliance on short-term speculation. Periodically, the divergence between these two metrics waxes and wanes, helping traders to find optimal zones to buy and sell.
Similarly, we can also gain insight into investor behaviour by examining the length of time an address has not moved bitcoins using the UTXO set and aggregate these to see how many investors are HODLing. If a rising number of investors are HODLing, then we can presume that circulating supply is lower, which should increase the price if demand is constant and also points to confidence in the asset’s future performance.
In short, on-chain metrics provide a fascinating insight into the real-time state of a blockchain network. By being aware of these metrics, traders can capitalise on the opportunities these tools present and enhance their analysis of the market.
Obtaining On-chain Data
The Do-It-Yourself approach is the best way to collect on-chain data for a cryptocurrency which means you have to run a node.
By running a node, you are storing and maintaining a copy of the ledger yourself, as well as ensuring that your transactions are broadcast, checking incoming transactions relayed by other nodes and that the consensus rules are being followed. This means you can query blockchain data faster than a third party from the console.
Analytical websites are an alternative for obtaining on-chain analytics. As the industry has matured, a host of data and analytics platforms have sprung up to serve cryptocurrency traders and investors. Often, machine learning is also used to identify different entities, such as miners or exchanges.
A few examples of where you can obtain blockchain data are listed below:
CoinMetrics
CoinMetrics provides freely available data for data on 37 crypto-assets, including on-chain metrics and correlations. A pioneer in the use and application of on-chain metrics, you can also follow their blog for their take on novel approaches in this field.
Look Into Bitcoin
A free resource to visualise models for Bitcoin’s market cycles and on-chain metrics.
Glassnode
You can access basic on-chain metrics for free on Glassnode and some indicators have a lag under the free membership. You have to pay for advanced indicators and high frequency time series data (for example, granular data for the hourly timeframe).
IntoTheBlock
IntoTheBlock offers a wide range of analytical tools, including on-chain analysis for a variety of crypto-assets, as well as order book data and sentiment analysis.
Santiment/Sanbase
Santiment delivers an impressive suite of on-chain metrics, explanations and visualisations. There’s also a lot of work going on here with developing new on-chain metrics and optimising existing ones.
As with most other data providers, there are free and paid membership options. The Sanbase spreadsheet plug-in allows you to fetch on-chain data using Google Sheets.
CQ.Live
CQ.Live should be in each trader’s arsenal, as it is a useful tool to analyse flows to exchanges, miner flows and inter-entity flows.
The website also provides data on other on-chain metrics for Bitcoin, Ethereum, and stablecoins.
What are the Limitations of On-chain Analysis?
Despite the promise of on-chain analysis, it is still in its formative stages and given the limited back history of data, the use of the metrics may evolve over time, or new trends may be highlighted that lead to the creation of new metrics as the industry matures.
Comparing on-chain metrics across multiple crypto-assets requires careful interpretation. Not all blockchains are made equal, for instance, Bitcoin is focused on the goal of digital gold while Ethereum’s blockchain is used for a wider range of applications. But, in general, if on-chain metrics are growing, then this is a positive sign.
Some limitations of on-chain analysis are listed below:
- There’s under a decade of data to back historical analysis for Bitcoin (and even less data for more recently launched crypto-assets). Some metrics may become less reliable over time or their interpretation may change in light of conflicting data.
- Layer 2 scaling solutions (such as the Lightning Network and sidechains for BTC, Plasma and zkRollups for Ethereum) have the potential to reshape transaction volume as we currently measure it on-chain, skewing indicators that rely on on-chain throughput. The interpretation of these metrics may adjust over time to account for changes in on-chain activity.
- On-chain analysis may not have much value for scalpers/short-term traders, as these metrics usually produce actionable signals for longer-term market cycles. Nevertheless, short-term traders may benefit from more granular data that is accessible when running their own full node or by combining on-chain insights with order book data and technical analysis. For instance, on-chain positions can be compared with order book data to identify important support and resistance zones. Technical signals can also be used to enter a trade based on blockchain analysis.
Disclaimer: This blog article should not be taken as financial or investment advice.
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