Stakers deposit funds into Swell and receive a return on investment in the form of staking rewards (supply-side revenue).

Swell is a non-custodial liquid staking protocol built on Ethereum. Swell was founded by Daniel Dizon and launched in 2023.

SWELL tokenholders own and govern the protocol. They currently don’t take a cut of the total return on investment (revenue).

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Swell

# Swell Active users (weekly) Methodology

## 1. Overview

Weekly Active Users (WAU) for Swell measures the number of unique users who interacted with the Swell liquid staking protocol within any 7-day rolling window. This metric tracks user engagement by identifying depositors who stake ETH through Swell's smart contracts. The calculation uses HyperLogLog probabilistic data structures to efficiently count distinct users across rolling 7-day periods, providing approximate but highly scalable distinct user counts.

## 2. Data sources

The WAU calculation relies on the following data sources:

- **Ethereum blockchain event logs**: Parsed `ETHDepositReceived` events from Swell's liquid staking smart contracts
- **Swell V1 contract events**: Events from the original `swETH` contract capturing ETH deposits and user addresses
- **Swell V2 contract events**: Events from the upgraded `swETHV2` contract capturing ETH deposits and user addresses  
- **Chain availability metadata**: Data quality filters that ensure only complete and reliable blockchain data periods are included in calculations

## 3. Step-by-step calculation

### Component A: User interaction extraction
- **Purpose**: Identifies all user addresses that have interacted with Swell's liquid staking protocol
- **Logic**: Extracts user addresses from `ETHDepositReceived` events emitted by both Swell V1 (`swETH`) and V2 (`swETHV2`) smart contracts on Ethereum. These events are triggered when users deposit ETH to receive liquid staking tokens. The `from` parameter in these events identifies the depositor address. Each interaction is classified with user type as "Depositor", market sector as "liquid_staking", and includes transaction metadata like `block_timestamp`, `transaction_hash`, and `log_index`.
- **Key fields**: `block_timestamp` for timing, `user_address` for user identification, `chain` set to "ethereum", `user_type` set to "Depositor"

### Component B: Daily active user aggregation  
- **Purpose**: Calculates daily distinct active users using probabilistic counting
- **Logic**: Groups user interactions by day using `timestamp_trunc(block_timestamp, day)` and creates HyperLogLog data structures to efficiently count distinct users. The HLL approach allows scalable approximate distinct counting without storing individual user addresses. Users are aggregated across multiple dimensions including chain, user type, market sector, business segment, and product. Chain availability filters are applied to ensure only reliable data periods are included.
- **Key fields**: `timestamp` (day-truncated), `user_dau` (user address for distinct counting), `chain`, `user_type`, `product_id`

### Component C: Datespine generation
- **Purpose**: Creates a complete time series backbone to ensure no gaps in WAU calculations
- **Logic**: Generates a continuous array of daily timestamps from the earliest data date through yesterday using `generate_timestamp_array`. This datespine ensures that even days with no user activity are included in the 7-day rolling window calculations. For incremental processing, the datespine starts from a calculated lookback period to handle late-arriving data.
- **Key fields**: `timestamp` covering every day in the analysis period

### Component D: Rolling window creation
- **Purpose**: Establishes 7-day rolling windows for WAU calculation
- **Logic**: For each day in the datespine, creates a 7-day window by generating timestamps for the current day plus the 6 preceding days using `generate_timestamp_array(timestamp_sub(datespine.timestamp, interval 6 day), timestamp, interval 1 day)`. This approach uses `UNNEST` to create multiple rows per day, with each row representing one day within the 7-day window. The result is joined with daily HLL data to associate each window day with its corresponding user activity data.
- **Key fields**: `timestamp` (the end date of the 7-day window), `joinday` (each individual day within the window)

### Component E: WAU calculation by dimension
- **Purpose**: Merges HyperLogLog data across 7-day windows and calculates distinct user counts for different dimensional breakdowns
- **Logic**: Groups the windowed data and merges HyperLogLog structures using `hll_count.merge_partial(hll)` to combine user sets from all 7 days in each window. The merged HLL is then extracted using `hll_count.extract()` to get the approximate distinct user count. This process is repeated for four dimensional breakdowns: project-level (all dimensions aggregated), by chain, by user type, and by product. Each breakdown nullifies irrelevant dimension fields and groups by the remaining relevant dimensions.
- **Key fields**: `user_wau` (final weekly active user count), `dimensions` (breakdown type), dimension-specific fields like `chain_id`, `user_type`, `product_id`

### Final aggregation
- **Combining components**: The four dimensional breakdowns are combined using `UNION ALL` operations to create a single result set containing WAU metrics at different granularities
- **Currency conversion**: Not applicable for user count metrics
- **Time aggregation**: Uses 7-day rolling windows where each day represents the WAU for that day and the 6 preceding days, providing daily WAU values
- **Final calculation**: The `finalize_metric_user_wau` macro standardizes the output schema, applies zero-filling for missing data points, and adds metadata fields. Incremental processing ensures only recent data is recomputed, with a safety margin to handle late-arriving blockchain data

*This methodology was automatically generated by Token Terminal's methodology analyzer.*

Unique revenue-generating users over a rolling 7-day window.

SwellSWELL

Active users (weekly)

Active users (weekly)

Competitive landscape

Chain breakdown