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

StakeWise is a non-custodial staking protocol built on Ethereum. StakeWise was founded by Dmitri Tsumak & Kirill Kutakov and launched in 2021.

SWISE tokenholders own and govern the protocol. They currently take a cut of the total return on investment (revenue). StakeWise’s investors include Blockdaemon, Boldstart Ventures, Collider, etc.

github

twitter

discord

etherscan

website

Liquid staking

StakeWise

# StakeWise Active users (weekly) Methodology

## 1. Overview

StakeWise Weekly Active Users (WAU) measures the count of unique users who interacted with StakeWise liquid staking protocol within any 7-day rolling window. This metric answers the business question: "How many distinct users were active on StakeWise during the past week?" The calculation uses HyperLogLog data structures to efficiently aggregate unique users across multiple dimensions while handling the computational complexity of rolling window deduplication.

## 2. Data sources

- **Beacon chain events**: Ethereum beacon chain deposit contract `DepositEvent` logs capturing validator stake deposits
- **Transaction data**: Ethereum transaction traces linking deposit events to originating user addresses
- **Beacon chain data**: Ethereum beacon chain block withdrawal records containing validator exit information
- **StakeWise contract events**: Parsed events from StakeWise v1 and v3 smart contracts including `DepositAdded`, `Deposited`, and `ExitQueueEntered` events across solo and pool staking products
- **Token transfer events**: Mint and burn events for StakeWise liquid staking tokens (sETH2 on Ethereum, sGNO on Gnosis) representing pool staking activity
- **Reference data**: StakeWise validator indexes mapped to deposit accounts and migration transaction records

## 3. Step-by-step calculation

### Component A: Daily active user identification
- **Purpose**: Identify all unique users who interacted with StakeWise on each day
- **Logic**: Users are identified from three primary interaction types. First, contract event users are extracted from StakeWise v1 and v3 smart contract events including solo staking deposits, pool deposits, and exit queue entries, categorizing users as either "Depositor" or "Withdrawer" types. Second, solo staking withdrawers are identified from beacon chain withdrawal data by matching validator indexes to StakeWise-controlled validators and filtering for non-zero withdrawal amounts. Third, pool v1 users are captured from liquid staking token transfers where mint events (transfers from null address) identify depositors and burn events (transfers to null address) identify withdrawers, excluding migration transactions. All users are classified by product type (solostaking or poolstaking) and user type (Depositor or Withdrawer)
- **Key fields**: `block_timestamp` for daily grouping, `user_address` for unique user identification, `chain` for cross-chain support, `product_id` for product segmentation, `user_type` for interaction classification

### Component B: HyperLogLog daily aggregation  
- **Purpose**: Convert daily active users into HyperLogLog sketches for efficient set operations
- **Logic**: Daily active users are grouped by day, chain, user type, and product, then converted to HyperLogLog sketches using `hll_count.init` with precision parameter 15. This approach enables efficient unique user counting across overlapping time windows without storing individual user addresses
- **Key fields**: `timestamp` truncated to day boundaries, `hll` containing the HyperLogLog sketch of daily users, dimensional fields for grouping

### Component C: Rolling window construction
- **Purpose**: Create 7-day rolling windows for WAU calculation
- **Logic**: A datespine generates consecutive daily timestamps covering the entire data range. For each target date, a 7-day window is constructed by creating an array of the target date plus the previous 6 days using `generate_timestamp_array` with 1-day intervals. This approach ensures complete temporal coverage without gaps in the final WAU time series
- **Key fields**: `timestamp` as the target date, `joinday` representing each day within the 7-day window

### Component D: Window aggregation
- **Purpose**: Merge daily HLL sketches within each 7-day window
- **Logic**: Daily HLL sketches are joined to their respective 7-day windows, then merged using `hll_count.merge_partial` which combines multiple HLL sketches while preserving uniqueness properties. The merged sketch represents all unique users who were active within the 7-day period
- **Key fields**: Window `timestamp`, merged `hll` sketch containing unique users across the 7-day period, dimensional grouping fields

### Component E: Dimensional breakdowns
- **Purpose**: Calculate WAU across multiple analytical dimensions
- **Logic**: Four separate dimensional views are created. Project-level WAU aggregates all users regardless of chain, product, or user type. Chain-level WAU groups by blockchain network. User-type level separates depositors from withdrawers. Product-level WAU distinguishes between solo staking and pool staking users. Each breakdown uses `hll_count.extract` to convert the merged HLL sketch into the final unique user count, with unused dimensions set to null values
- **Key fields**: `user_wau` as the final unique user count, `dimensions` indicating the breakdown type, relevant dimensional fields with nulls for unused dimensions

### Final aggregation
- **Combining components**: All dimensional breakdowns are combined using `union all` to create a comprehensive dataset containing WAU values across all analytical dimensions
- **Currency conversion**: Not applicable for user count metrics
- **Time aggregation**: Daily WAU values represent 7-day rolling windows, with each day showing unique users active in that day plus the previous 6 days
- **Final calculation**: The HyperLogLog extract operation produces the final unique user count representing distinct users active within each 7-day rolling window, segmented across project, chain, user type, and product dimensions

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

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

StakeWiseSWISE

Active users (weekly)

Active users (weekly)

Competitive landscape

Chain breakdown