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Origin Protocol

# Origin Protocol Active users (weekly) Methodology

## 1. Overview

Weekly Active Users (WAU) for Origin Protocol measures the number of unique users who interacted with any Origin Protocol product within a 7-day rolling window. This metric captures user engagement across Origin's multi-token ecosystem including Origin Dollar, OETH, superOETHb, and OS tokens on Ethereum, Base, Arbitrum, and Sonic chains. The calculation uses HyperLogLog probabilistic data structures for memory-efficient distinct user counting and provides dimensional breakdowns by project, blockchain, user activity type, and product.

## 2. Data sources

The calculation relies on the following raw blockchain data sources:

- **Ethereum event logs**: Parsed `Mint` and `Redeem` events from Vault and OETHVault contracts, `Zap` events from ZapperLidoARM and OETHZapper contracts, and `RedeemClaimed` events from LidoARM contract
- **Base event logs**: Parsed `Mint` and `Redeem` events from superOETHbVault contract and `Zap` events from superOETHbZapper contract  
- **Sonic event logs**: Parsed `Mint` and `Redeem` events from OSVault contract and `Zap` events from OSZapper contract
- **Ethereum function call traces**: Successfully executed Flipper contract functions including `sellOusdForDai`, `sellOusdForUsdt`, `sellOusdForUsdc`, `buyOusdWithDai`, `buyOusdWithUsdc`, and `buyOusdWithUsdt`
- **Arbitrum function call traces**: Successfully executed wOETH `burn` functions
- **Chain availability data**: Metadata defining which chains are available for analysis during specific time periods

## 3. Step-by-step calculation

### Component A: User activity identification
- **Purpose**: Extract all user interactions with Origin Protocol products across supported blockchains
- **Logic**: Parse blockchain event logs and function call traces to identify user addresses engaging with Origin Protocol contracts. Events include token minting, redemption, and zapping activities from vault contracts. Function calls capture trading activities through Flipper contracts and token burning operations. Each interaction is classified by user activity type such as "Origin Dollar minter", "OETH redeemer", "ARM depositor", "Flipper: Origin Dollar seller", etc. The system processes data from Ethereum, Base, Arbitrum, and Sonic chains
- **Key fields**: `block_timestamp` for timing, `user_address` for user identification, `chain` for blockchain, `user_type` for activity classification, `product_id` for product association, `transaction_hash` for deduplication

### Component B: Daily active user aggregation  
- **Purpose**: Calculate daily distinct active users from the identified user activities
- **Logic**: Group user activities by day using timestamp truncation to day granularity. Take distinct user addresses within each day, chain, user type, and product combination to eliminate duplicate activities by the same user on the same day. Apply chain availability filters to ensure only valid chain data for specific time periods is included
- **Key fields**: `timestamp` truncated to day, `timestamp_grain` set to "day", `chain`, `user_type`, `product_id`, `user_dau` containing distinct user addresses

### Component C: HyperLogLog initialization
- **Purpose**: Convert daily active user data into memory-efficient HyperLogLog sketches for scalable distinct counting
- **Logic**: Initialize HyperLogLog data structures with precision 15 for each daily user set. This probabilistic data structure enables efficient merging of user sets across time windows without storing actual user addresses. Group by timestamp, chain, user type, and product to maintain dimensional granularity
- **Key fields**: `hll` containing HyperLogLog sketch of daily users, maintaining all dimensional grouping fields

### Component D: Date spine and rolling window creation
- **Purpose**: Ensure complete date coverage and create 7-day rolling windows for weekly aggregation
- **Logic**: Generate a complete date spine from the earliest user activity date to the current date minus one day using timestamp arrays. For each date in the spine, create associations with the current date plus the previous 6 days to form 7-day rolling windows. This ensures no missing days in the final output and enables consistent weekly calculations
- **Key fields**: `timestamp` for the target date, `joinday` for each day within the 7-day window

### Component E: Weekly active user calculation
- **Purpose**: Merge HyperLogLog sketches across 7-day windows to calculate weekly distinct users
- **Logic**: Join the rolling window structure with daily HLL sketches, matching on dates within each 7-day period. Merge HyperLogLog sketches using `hll_count.merge_partial` to combine user sets across the 7-day window, then extract final counts using `hll_count.extract`. This produces weekly active user counts while preserving dimensional breakdowns
- **Key fields**: `user_wau` containing weekly active user count, along with dimensional fields

### Component F: Dimensional breakdowns
- **Purpose**: Create separate aggregations for different analytical dimensions
- **Logic**: Generate four distinct dimensional views by grouping and merging HLL sketches differently. Project-level breakdown merges all dimensions for total protocol users. Chain-level breakdown maintains chain separation while merging other dimensions. User-type-level breakdown separates by activity type. Product-level breakdown maintains product separation. Each breakdown nullifies unused dimensional fields and combines results using union operations
- **Key fields**: `dimensions` indicating breakdown type, dimensional fields populated or nullified based on breakdown level, `user_wau` for each dimensional combination

### Final aggregation
- **Combining components**: Union all dimensional breakdowns into a single result set with standardized schema
- **Time aggregation**: Daily time boundaries with 7-day rolling windows, ensuring consistent weekly calculations across all days
- **Data quality**: Apply incremental processing with safety margins and lookback periods to handle late-arriving data and ensure data freshness
- **Final calculation**: Weekly active users represent the distinct count of users who performed any activity within a 7-day rolling window, calculated using probabilistic HyperLogLog merging for scalability and memory efficiency

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

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

Origin ProtocolOGN

Active users (weekly)

Active users (weekly)

Competitive landscape

Chain breakdown