Today, the vast majority of cryptocurrencies on the market rely on open public blockchains to validate and store transactional data. As a result, anybody can access the data “on-chain” at any time and location.

The on-chain analysis is the process of determining market sentiment by analyzing data from a blockchain ledger. Specifically, it entails looking at transaction data and crypto wallet balances, which are important in determining whether or not to invest. After all, it is safe to assume that an investment in a token is not a wise idea if it is not traded and the great bulk of its circulating supply is owned by a small number of big holders, known as whales.

Utilizing the vast amounts of data made available by public blockchains such as Bitcoin and Ethereum may provide a fresh viewpoint that is not available with conventional assets, and it can supplement the findings of other analyses.

In this article, we’ll delve deeper into what on-chain analysis is all about and try to comprehend how it can benefit cryptocurrency investors.

What is an On-chain Analysis?

An on-chain analysis technique uses data from public blockchains to help traders improve their crypto trading tactics.

All of the major transactions that take place on a particular public blockchain network are typically included in the on-chain data. Transaction information includes the sender and receiver addresses, the value of the transaction, the transaction fee, and the remaining funds at a particular address. In addition, timestamps, miners’ fee rewards, and smart contract codes are also in this block data.

History of On-chain analysis

Bitcoin’s on-chain analysis can be traced back to the creation of coin days destroyed in 2011, which was the first indicator to leverage age as a metric for valuing the currency.

The Network Value to Transaction (NVT) ratio, popularized by CoinMetrics, Chris Burniske, and Jack Tatar, was one of the first commonly used on-chain measures for cryptocurrencies. In addition, there is an NVT ratio, which was developed in the summer of 2017 to measure the utility value of a cryptocurrency, especially its transactional utility.

We can tell whether a cryptocurrency is overpriced by comparing its network value to the number of transactions recorded on the blockchain. The NVT ratio is high when the number of transactions doesn’t justify the network’s value. Conversely, if the network value is extremely low, it may signal that a more significant price is warranted when considering the transaction volume. The Price-Earnings ratio is sometimes used to compare the NVT ratio to stocks, and the two ratios may also be used to determine if a coin is an excellent investment to purchase, hold, or sell.

It didn’t take long until the NVT ratio was updated. Others improved the statistic to reflect better the economic activities taking place on the blockchain, allowing for a more accurate evaluation of network value.

For instance, the Network Value to Transaction ratio Signal, also known as NVTS, was created by measuring the moving average of transaction volume over the course of 90 days. In addition, CoinMetrics has recently improved the ratio by considering the free float supply while doing their calculations. For example, these incremental steps show how a cryptocurrency’s underlying value might evolve over time.

On-chain metrics have evolved from the discontent with simple indicators from technical analysis (such as volume) and other notions adopted from conventional markets like market capitalization, such as price/volume. Many cryptocurrency comparison websites utilize market capitalization as a ranking factor.

Because cryptocurrencies are more like money or commodities than corporate stock, market capitalization is an inaccurate and misleading metric. Market capitalization levels may be subverted using various techniques of issuance — For example, suppose a project has 1 trillion tokens in circulation, and a few of them sell for $1, the market cap is $1 trillion, even though the currency may only trade a few hundred dollars worth of units.

Traders are using a new set of techniques to better understand the health of blockchain networks because of the weaknesses of market capitalization and the risks of applying standard measures to cryptocurrencies.

It is possible to trace how long a wallet has kept its funds using the concept of UTXOs (Unspent Transaction Outputs) in Bitcoin. As a result of this, on-chain indicators such as realized capitalization, HODL waves, and the proportion of supply in profit/loss have been developed to offer reliable signals.

As an alternative to using market capitalization to analyze coins, realized capitalization has arisen as a means to leverage blockchain data to do so without any of the drawbacks. The recognized capitalization algorithm, developed by Nic Carter and Antoine Le Calvez, assigns a value to each UTXO depending on the last time it was transferred.

In October 2018, Mahmud Marov and David Puell created the Market Value to Realized Value (MVRV) ratio, which builds on the realized capitalization statistic. An oscillator, the MVRV ratio, indicates if bitcoin is overvalued or undervalued by respecting certain thresholds in the past. MVRV z-score, long-term holder to short-term holder MVRV ratio, and account-based blockchains like Ethereum are all metric variations.

Ethereum, the second-largest blockchain network, varies from Bitcoin and several altcoins because it is built on an account model rather than a UTXO model. A UTXO tracks each coin’s journey to a final address; the ledger records who owns what and when, and the addresses may include several UTXOs.

On the other hand, the account model makes it more challenging to determine the age of individual currencies like Ethereum and ERC-20 tokens since incoming and outgoing coins are mingled in account balances. Ethereum and other account-based cryptocurrencies are not immediately comparable to metrics used in Bitcoin (and other blockchains like Bitcoin Cash and Litecoin). Further effort is required to carry these models across from UTXO-based cryptocurrencies.

How does on-chain analysis work?

An asset’s HODL status, its market capitalization, and its future prospects are all criteria that may be used for on-chain research.

Market Capitalization

A cryptocurrency’s value is determined by its market capitalization. To calculate the overall worth of a network, you multiply the cryptocurrency’s price by the total supply. In addition to calculating the network’s net value, we can also use market capitalization to assess the crypto asset’s market size, adoption, and risks.

Hold Status

Analysts utilize a statistic known as the HODL wave to assess the current market trend. If traders are HODLing or swiftly dumping an asset, then the HODL wave alerts analysts. In addition, it influences the market’s sentiment and the HODLers’ outlook, i.e., whether they believe the price will fall or climb.

Using coin concentration metrics, it is also possible to identify the concentration of “whales” and significant investors in the network. For example, if a few addresses own a substantial proportion of a token, the whales and large-scale investors may influence the market by dumping tokens. Therefore, it’s necessary to analyze the concentration of large token holders to minimize cryptocurrency investment risks.

Future Prospects of a Cryptocurrency

You may look at a crypto asset’s future open interest to see whether or not investors are becoming more engaged in it in the long run. These include the connection between a token’s price and Bitcoin’s and the overall volume of trades.

Investing in cryptocurrencies that are more intimately correlated to Bitcoin price drops might help investors reduce their exposure to risk by tying the value of their token or altcoin to the price of Bitcoin. At the same time, it may serve as a warning indicator for high-net-worth individuals and large institutions by indicating when specific tokens or currencies are coming into or going out of circulation on a particular exchange.

How to Use On-chain Analysis for Crypto?

It is possible for on-chain analysts to build more accurate pictures of the crypto market based on solid facts and a fundamentals-driven approach than on hype, thanks to cryptocurrency and blockchain data’s transparency.

Predict future market movements

On-chain analysis helps traders improve their tactics and better forecast future market moves by monitoring investor behavior and network health in real-time. Crypto traders, for example, may forecast whether or not interest in a specific cryptocurrency will increase or diminish by considering the number of active addresses and the number of transactions. The price of a cryptocurrency often rises in tandem with an increase in the number of active addresses and transactions.

Study investor behaviors

In addition, on-chain data might provide information about specific investment habits. On-chain analysts, for example, may look at how long an address has been holding a cryptocurrency and how many people are HODLing the coin. It’s possible that as the number of people HODLing the cryptocurrency grows, so does its supply. On the other hand, if demand remains steady, on-chain research suggests that the price of that coin will rise. As a bonus, it demonstrates faith in the asset’s long-term success as well.

What are the drawbacks of using on-chain analysis?

On-chain analysis, despite its potential, is still in its infancy. Given the lack of historical data, its application may develop, or new patterns may be identified that lead to the establishment of new metrics as the sector evolves.

When comparing the on-chain parameters of different crypto-assets, careful consideration is needed. This is because not all blockchains are created equal; for example, Bitcoin is focused on the aim of digital gold, while Ethereum’s blockchain is utilized for a wider variety of applications. Nevertheless, if on-chain metrics are improving, this is a positive sign in general.

The following are a few limitations of on-chain analysis:

• Only a decade’s worth of Bitcoin history may be used to support historical analysis (and even less data for more recently launched crypto-assets). It is possible that specific measurements may lose their validity over time or that their interpretation may alter in light of conflicting data.

• On-chain throughput metrics may be distorted by layer 2 scaling solutions like the Lightning Network, sidechains for BTC, Plasma, and zkRollups for Ethereum, which might change the way transaction volume is measured. The way these metrics are interpreted may evolve in response to shifts in on-chain activities.

• The on-chain analysis may not be helpful for scalpers and short-term traders since these indicators are more useful for longer-term market cycles. However, short-term traders may profit from more detailed data that may be accessed by operating their complete node or by mixing on-chain insights with order book data and technical analysis. For example, order book data may be compared to on-chain positions to identify critical support and resistance zones. Technical signals may also be utilized to execute a trade based on blockchain analysis.

Closing thoughts

The on-chain analysis is similar to studying a company’s fundamentals to have a better understanding of its value and usefulness. Rather than relying on traditional financial statements to assess the health of a company, on-chain analysis of digital assets uses data from transaction logs, chain metrics, and wallets. While still in its infancy, the enormous volume of publicly accessible data on crypto-assets makes it a particularly attractive application of data science and machine learning.