Here is the first article in a serie dedicated to blockchain, cryptocurrencies and decentralized finance. Entitled Blockchain Lessons (my wife often told me that I know how to be original and surprising…), it aims to analyze its tools and uses-cases in order to better understand the profound digital evolution that is happening.

This introduction is dedicated to On-Chain datas ; let’s first try to define what they are and where to find them, to better understand how to use them.

But before to dive in, three important preliminary notes.

1. This article is informative and should not be considered as financial advice ; I don’t own any bitcoin or cryptocurrency, nor encourage their use for trading or for investing purpose.

2. This is a vulgarization essay : some concepts are simplified for comprehension purpose

3. English is not my native language, feel free to let me know any error / mistake.

PRELIMINARY NOTES

I. What are on-chain datas and where to find them

One of the (many) advantages of blockchain is transparency. In opposition of what was implying by one of the narratives used during the first cycles to criticize Bitcoin (used buy on the darknet, to launder money…), blockchains are transparent by definition ; every transaction (the ‘from’ and ‘to’ addresses, the amount transferred in the transaction, the timestamp, gas price, miner, block size, Smart contract invocation and usage, etc.) is recorded and stored in a chain of blocks, visible by anyone in a decentralized way.

On-chain data refer to all those informations, or put differently, all information written on the blocks of a Blockchain. And, as you already know with the fast growth of the Big Data industry, data is a goldmine when properly aggregated and analyzed.

Blockchains explorer like BTC.com or Blockchain.com for Bitcoin, Etherscan or Ethplorer for Ethereum, Polkascan for Polkadot, etc… are available for raw and basic datas (check all the transaction, the history of an address, of specific coins you just received…) while on-chain data aggregators like Glassnode, Messari, Santiment, CryptoksQuant, Nansen, IntoTheBlock, etc… will use algorithms and analytical tools to provide more insightful datas about the health of a network, it’s elasticity, it’s liquidity and any kind of other informations.

While analyzing datas of fiat currencies is sometimes very imprecise, because results depends on informations that aren’t aggregated, analyzing bitcoin and crypto-currencies datas becomes much more easier and efficient considering the accuracy of the inputs : it is a fundamentals driven approach rather than based on technical analysis, hype or sentiment. 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.

But, how to use those datas effectively and how to read on chain metrics ? Let’s dive in more precisely into the subject with some concrete exemples…

II. How to read on-chain data metrics, and which metrics you should follow

Data analytics is a vast and technical subject, and this article does not intend to provide you with the tools and knowledge to analyze raw data by your own : understanding how metrics work and how they are calculated should be the first thing to do if you want to make accurate conclusions. My objective will be to outline a few important metrics, and explain how to read them.

Here are a few exemples of metrics, what they mean and which conclusions we can make about the underlying network / protocol.

a) Number of active addresses and number of transactions

Let’s start with the simpler ones, but the obvious ones. The number of active addresses and the number of transactions. The Number of active addresses represents unique addresses that were active in the network either as a sender or receiver, and the numbers of transactions represents only successful transactions.

Those two metrics are very useful when it is about to analyze a protocol and it’s real usage. They also have a high correlation with prices ; when the number of active addresses and transactions rise sharply, it usually goes with a rising price. Also, it sometimes can be interesting to check those metrics beforehand, to evaluate if a growth was organic or manipulated.

Note : it is possible, and occasionally more efficient, to filter those data with criteria such as “the total value of the wallet initiating the transaction”, “since how long this wallet was hodling” (to hodl : term derived from a misspelling of “hold” that refers to buy-and-hold strategies in the context of bitcoin and other cryptocurrencies) and others… This allows to have a sociologic point of view and to understand and apprehend behavior of specific type of wallets.

b) Bitcoin NVT signal

The NVTS (NVT Signal) is an amelioration of the NVT ratio introduced by crypto expert Willy Woo back in 2017. The NVT ratio is a simple division between the network total value and the daily transaction value, while the NVT Signal uses a 90 day moving average of the daily transaction volume in replacement of the raw daily transaction volume, in order to improve the ratio to better function as a leading indicator.

Explanation of the metric : by analyzing the relationship between market capitalization and transfer volumes, this metric provides a clear chart indicating whether bitcoin’s price is overvalued or undervalued.

How to read it : When NVT (red line) is high (over 150), this indicates that the network value of bitcoin is outpacing the value being transferred on the network. This could represent legitimate growth stages. When NVT is low (under 45), this means that network value isn’t keeping up with increased usage of the network, potentially representing bearish sentiment.

In general, you should therefore rather sell your bitcoins when the NVTS passes the 150 mark and buy back when it drops below 45.
However, being in an overbought zone does not necessarily mean that we will reach the highest price of this cycle, and the reverse is also true : the metric can stay in these two extremes for a long time, but it still gives a good indication (using trend lines may help).

c) Bitcoin MVRV ratio

MVRV stands for Market Value to Realized Value, and shows the average profit/loss of all the coins currently in circulation according to the current price by dividing the market capitalization of an asset by the realized capitalization (a variation of market cap that values each transaction based on the price when it was last moved, as opposed to its current value).

Explanation of the metric : it can helps to time the market tops and bottoms by providing an idea of ​​the average price at which investors bought their BTC, helping to estimate how overvalued or undervalued the asset is.

How to read it : a MVRV value at 2 means that if all holders sell their coins/tokens at the current price they will generate a x2 profit on average : the more this ratio increases, the more people will be willing to sell and take profits. A MVRV between 0 and 1 means that the market may be undervalued on average, meaning most people will be realizing losses if they all sell their holdings.

Awe & Wonder created a derivative of this metric, the MVRV with a z-score, providing a clearer picture of the market cycle with tops and bottoms normalizing around common levels.

d) Bitcoin difficulty ribbon

The Bitcoin difficulty ribbon is another indicator created by Willy Woo, based upon the ideas of Vinny Lingham. This data analyze the impact of miners selling pressure on Bitcoin price action; in other words, it analyze the effect of mining difficulty on price.

Willy Woo explains :

“as new coins are mined into existence, miners sell some of their mined coins to pay for production costs. This produces bearish price pressure.

The weakest miners sell more of their coins to remain operational. When it becomes unsustainable, they capitulate, hashing power and network difficulty reduces (ribbon compression), leaving only the strong, who sell less leaving more room for more bullish price action.

Typically we see this at the end of bear cycles, after miners capitulate, the lack of miner selling pressure allows the price to stabilise and then climb; the classic accumulation bottom“.

Willy Woo, in Introducing the Difficulty Ribbon, signaling the best times to buy Bitcoin, 1st aug. 2019

Explanation of the metric : the metric consists of eight different moving averages (what is a move average) of the difficulty level of mining on the Bitcoin network. When the ribbon compresses or moves negative (lagging drops in the actual difficulty level), it represents inefficient miners leaving the Bitcoin network. 

How to read it : once again, Willy Woo explains in simple ways.

“When the ribbon compresses, or flips negative, these are the best times to buy Bitcoin“.

Willy Woo, in Introducing the Difficulty Ribbon, signaling the best times to buy Bitcoin, 1st aug. 2019

e) Stock-to-Flow model

Definitely one of my favorites! Evoked for the first time by Plan B in march 2019 in the article Modeling Bitcoin Value with Scarcity, the Stock-to-Flow model is a reutilization of the famous S2F model commonly used for commodities such as gold, silver or platinum, as some argue it may be applicable to bitcoin, viewed as a scarce digital resource. It measures the relationship between the currently available stock of a resource and its production rate by dividing the current circulating supply by the newly supplied coins. To do so, Plan B pushed his Stock-to-Flow calculations by integrating the different halvings and therefore, the amount of bitcoins mined over time.

Explanation of the metric : this model suggests that we can project future price by observing the projected stock-to-flow line, which can be calculated as we know the approximate mining schedule of future Bitcoin mining (halving).

How to read it : the effective stock-to-flow value is represented by the red line, and the model variance (lighter red line) represents the projection of this model. The colored line represents the effective price of Bitcoin, the color showing how many days are remaining until the halving. Thanks to this metric, we have a clear view of previous bitcoin cycles and their evolution and timing in relation to the halving. Not enough to conclude a clear pattern but I truly think that, used with other metrics, it can provide useful information to anticipate macro trends.

Note : this model use projections and, as such, can not be qualified as a strict on chain metric.

Those are just a little part of the metrics available, and cannot represent the diversity of data available to analyze the market situation. They can help analyze and anticipate major trends, but markets remains highly volatile and unpredictable.

If you enjoyed this article, let me know, I use other metrics I may present in a part two of this article. If you need advice or consultancy, feel free to contact me.

III. Sources & resources

a) Useful resources

• Glassnode Academy
• Subscribe right now to the Glassnode Insight newsletter if you want your weekly dose of on-chain data analysis.
• Santiment Academy
• Willy Woo’s charts
• Messari

b) Sources

• MVRV and NVT as Key Bitcoin Indicators (DCresearch on Medium)
• Modeling Bitcoin Value with Scarcity (Plan B on Medium)
• On-Chain Data: A New Framework to Evaluate Bitcoin (Yassine Elmandjra for ARK Invest)
• On-chain transactions (Investopedia)
• How Can On-chain Analysis Help Cryptocurrency Traders? (Interdax on Medium)
• An Introduction to «On-chain» Analysis (Sukanya Singh for Blockstar)
• Guide to cryptocurrency valuation : a look into on-chain data (Sid Shekhar for Master The Crypto)
• These crucial on-chain metrics will help you profit the most from DeFi’s high volatility (FX Street)
• BTC Fundamentals: Bitcoin On-Chain Metrics You Need to Know About (Xena Exchange on Medium)