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Tutorial 2 - Introduction to On-chain Activity
This tutorial provides an introduction to the concept of on-chain activity, and how it can be used to track market cycles.
The first suite of metrics you will be introduced to fall into the category called On-chain Activity. These metrics describe the health and growth of the Bitcoin network user-base, and help describe Bitcoins Network Effects. Metrics in the On-chain Activity category can be used to assess whether the network effects and user-base are expanding, or contracting.
The definition of Network Effects is as follows:
An increase in the number of network participants, results in an increase in the value, or utility of the network.
When a user interacts with their wallet, they are usually sending coins from one address, to another. This could be a withdrawal or deposit to their exchange, or a payment to or from their personal wallet. The metric below shows how many addresses on the Bitcoin network were 'active' each day. More active addresses is often a sign of healthy network usage, whereas declining activity may signal a declining user-base.
Similar to Active Addresses, the New Addresses metric will track when an address that has never received coins before, enters the network. Most Bitcoin wallet software will generate a new address for every transaction, so you will note this metric has a similar behaviour to Active Addresses. We can identify peaks and troughs of in On-chain activity, which often correlate with Bitcoin bull and bear market cycles, respectively. Active and New Address metrics can generally be interpreted within the following framework:
- Higher values means the network was more active that day, signalling more active users, exchanges, and entities are interacting with their wallets. This is often a signal of increasing network effects.
- Lower values means the network was less active that day, with potentially fewer users, less exchange activity, and fewer entities interacting with their wallets. This is often a signal of decreased network effects.
💡 Hint: Metrics associated with on-chain activity often have significant daily variability. This is due to the effects of week-days vs weekends, trading hours in different geographies, and other seasonality influences. It is therefore helpful to apply a 7-day or 14-day moving average, or median to smooth out this daily noise and observe the underlying trends.
Transaction Counts measure the number of confirmed transactions each day. This includes all transaction types, with some key examples being payments, exchange deposits and withdrawals, custodial multi-signature storage, and lightning network channel opening/closing. Generally speaking, more demand for transactions shows growing utilisation of the network, however this can also be influenced by technological advances such as transaction batching by exchanges (more details in this report).
Transfer Volume measures the total amount of BTC value sent across the Bitcoin network each day. When lots of value is being settled by the Bitcoin network, it typically indicates an elevated utilization and suggests greater demand for coins in the network. Conversely, when less volume is being transferred over the Bitcoin network, it may suggest declining utility and lower demand for coins in the network. Note, this metric can be also be viewed in USD denominated volume, which will tend to naturally increase as the market capitalisation (and coin price) grows.
![Bitcoin: Total Transfer Volume [BTC] (14d Exponential Moving Average)](https://3560207530-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2F-LrnqJQEBJtavka04Vo6%2Fuploads%2FzfW4PUF00GJoQXJJ9c6D%2Fimage.png?alt=media&token=3f85f73d-ea47-4e58-8d12-5a206fcf5419)
Active Addresses provides us with a tool to visualise the growth, or decline of Bitcoin network effects, and utilization. The Bitcoin network has historically seen a surge in user growth during the excitement of bull markets, and conversely, a decline in attention and network usage during bearish markets.
With this general observation, we can now look at identifying these periods of user growth, and decline on the chart.
- 1.High, increasing, and accelerating trends generally indicate an influx of activity, new users, and attention, as more people interact with their Bitcoin wallets. This often associated with more bullish momentum in the market.
- 2.Low, declining or sideways trends can generally be interpreted as a relatively Low Demand for the network utility. This often associated with more bearish momentum in the market.
This guide shows how we can identify changes in Bitcoin market structure by observing the utilisation, and activity across the network. Thus, we can help define bullish or bearish conditions based on the performance of various onchain activity metrics.
Topics for Discussion:
- Using onchain activity to identify bulls and bears.
- Mapping the steady growth of adoption over time.
- Identifying which cohorts are driving the market.
- Looking for signals of market Tops and Bottoms.
These on-chain activity metrics are just the tip of the icerberg. With a Glassnode Advanced plan, we open up more opportunities to assess onchain activity, and understand which entities are active on the network, and where they may be sending their coins. Some example Advanced metrics include:
- Number of Non-Zero Addresses shows us the overall growth in the user-base and whether we are seeing sustained growth, or a reduction in network effects.
- Exchange Inflow and Outflow Volume captures the coin volume flowing in and out of exchanges. We can inspect inflows and outflows separately, or net them out to assess overall impact.
- Percent Supply in Profit allows us to assess the overall profitability of the network. When a large cross-section of the market is in a profit or loss, it can signal market exuberance, or fear, respectively.
- Spent Output Age Bands provides insight into the age of coins that are being spent. Statistically speaking, older coins are more likely to stay dormant. So when many older coins do get spent, we can identify this behaviour.
