website

twitter

github

discord

Lending

Dolomite

# Dolomite Active users (weekly) Methodology

## 1. Overview

Weekly Active Users (WAU) measures the number of unique users who interacted with the Dolomite protocol within a 7-day rolling window. This metric answers the business question "How many unique users are actively engaging with Dolomite's margin trading platform on a weekly basis?" The calculation uses a rolling window approach combined with HyperLogLog probabilistic counting to efficiently track unique users across multiple dimensions including chains, user types, and products.

## 2. Data sources

The calculation relies on the following raw data sources:
- **Dolomite protocol events**: Parsed smart contract event logs from Dolomite's margin trading protocol across Arbitrum, Base, Ink, Berachain, and Ethereum networks
- **Contract interactions**: Eight specific event types from DolomiteMargin contracts including `LogDeposit`, `LogWithdraw`, `LogSell`, `LogBuy`, `LogTrade`, `LogTransfer`, `LogLiquidate`, and `LogVaporize` events
- **Blockchain transaction data**: Block timestamps and transaction hashes from the underlying blockchain networks
- **User address extraction**: Account owner addresses and taker addresses from protocol events to identify unique users
- **Chain metadata**: Network identifiers to distinguish activity across different blockchain networks

## 3. Step-by-step calculation

### Component A: User interaction extraction
- **Purpose**: Identify and classify all user interactions with the Dolomite protocol
- **Logic**: Extract user addresses from eight different types of Dolomite protocol events across five blockchain networks. Each event type maps to a specific user classification: `LogDeposit` events identify Depositors, `LogWithdraw` events identify Withdrawers, `LogSell` and `LogBuy` events identify Traders, `LogTrade` events capture Traders via the taker address, `LogTransfer` events identify Margin Users, and both `LogLiquidate` and `LogVaporize` events identify Liquidators. The system processes events from Arbitrum, Base, Ink, Berachain, and Ethereum networks
- **Key fields**: `block_timestamp` for temporal tracking, `transaction_hash` for deduplication, `chain` for network identification, `user_type` for activity classification, and extracted user addresses (`accountOwner`, `takerAccountOwner`, `accountOneOwner`, `solidAccountOwner`)

### Component B: Daily active user aggregation  
- **Purpose**: Aggregate user interactions to daily unique users with dimensional attributes
- **Logic**: Group all user interactions by day using timestamp truncation to the day level. Create distinct combinations of daily timestamp, chain, user type, market sector, business segment, product, and user address. This produces a daily active user dataset where each record represents one unique user's activity on a specific day with their associated attributes
- **Key fields**: `timestamp` truncated to day granularity, `timestamp_grain` set to 'day', `chain` for network identification, `user_type` for activity classification, `product_id` for product segmentation, and `user_address` as the unique user identifier

### Component C: HyperLogLog initialization
- **Purpose**: Convert daily user sets into probabilistic data structures for efficient weekly aggregation
- **Logic**: Transform the daily active user data into HyperLogLog sketches using the `hll_count.init` function with precision parameter 15. Group by timestamp, chain, user type, and product to create HLL sketches for each dimensional combination. This enables efficient approximate counting of unique users when merging across multiple days
- **Key fields**: `timestamp` for daily grouping, `chain_id` for network segmentation, `user_type` for activity classification, `product_id` for product segmentation, and `hll` containing the probabilistic user count sketch

### Component D: Rolling window construction
- **Purpose**: Create a complete 7-day rolling window framework for WAU calculation  
- **Logic**: Generate a complete datespine covering all days from the earliest user activity to yesterday. For each day in the datespine, create associations with the previous 6 days to form 7-day windows. This involves generating timestamp arrays and using cross joins to create day-to-window mappings. The approach ensures no gaps in the time series and provides complete coverage for the rolling window calculation
- **Key fields**: `timestamp` representing the end date of each 7-day window, `joinday` representing each individual day within the window for joining with daily user data

### Component E: Window data aggregation
- **Purpose**: Combine daily HLL sketches within each 7-day window
- **Logic**: Join the rolling window framework with the daily HLL data to collect all user sketches within each 7-day period. For each window end date and dimensional combination, gather the HLL sketches from all 7 days in the window. This creates a dataset where each record contains the HLL sketches for one 7-day period
- **Key fields**: `timestamp` as the window end date, dimensional attributes (`chain_id`, `user_type`, `product_id`), and `hll` sketches from each day in the window

### Component F: Dimensional breakdown calculation
- **Purpose**: Calculate WAU metrics across four different dimensional views
- **Logic**: Create separate aggregations for different business perspectives: project-level WAU (all users regardless of other dimensions), chain-level WAU (users grouped by blockchain network), user-type-level WAU (users grouped by activity type like Depositor or Trader), and product-level WAU (users grouped by product). For each dimensional view, merge the HLL sketches using `hll_count.merge_partial` and extract the final unique user count using `hll_count.extract`. Set unused dimensions to null for each breakdown
- **Key fields**: `timestamp` for time tracking, `dimensions` indicating the breakdown type, relevant dimensional fields (`chain_id`, `user_type`, `product_id`) with nulls for unused dimensions, and `user_wau` containing the final weekly active user count

### Final aggregation
- **Combining components**: Union all four dimensional breakdowns into a single result set containing project-level, chain-level, user-type-level, and product-level WAU metrics
- **Currency conversion**: Not applicable for user count metrics
- **Time aggregation**: Uses 7-day rolling windows where each day represents the WAU for the 7-day period ending on that date
- **Final calculation**: The final `user_wau` value represents the approximate count of unique users who interacted with Dolomite protocol within the 7-day window, calculated using HyperLogLog probabilistic counting with 15-bit precision for efficient aggregation across large user sets

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

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

DolomiteDOLO

Active users (weekly)

Active users (weekly)

Competitive landscape

Chain breakdown