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Blockchains (L2)

Shape

# Shape Active users (weekly) Methodology

## 1. Overview

Weekly Active Users (WAU) for the Shape protocol measures the number of unique addresses that initiated at least one transaction on the Shape blockchain within any 7-day rolling window. This metric provides insight into user engagement patterns and protocol adoption by tracking distinct addresses that actively participate in Shape network transactions over weekly periods.

The calculation uses HyperLogLog probabilistic data structures to efficiently compute distinct user counts across rolling 7-day windows, with multiple dimensional breakdowns for comprehensive analysis.

## 2. Data sources

- **Shape blockchain transactions**: Raw transaction records from the Shape L2 blockchain containing transaction metadata including sender addresses, transaction hashes, and block timestamps
- **Token Terminal chain availability configuration**: Internal configuration data that determines valid time periods for data inclusion based on when chains became available for tracking

## 3. Step-by-step calculation

### Component A: User transaction extraction
- **Purpose**: Identifies all unique transaction senders as active users on a daily basis
- **Logic**: Extracts transaction records from Shape blockchain data, treating each transaction's `from_address` (sender) as an active user. Transactions are grouped by day using timestamp truncation to day boundaries. Each unique sender address per day is considered a Daily Active User (DAU). The component classifies all users as "Transaction sender" type and assigns Shape-specific metadata including chain identifier, market sector as "blockchains_l2", and business segment as "blockchain"
- **Key fields**: `block_timestamp` for temporal aggregation, `from_address` as the user identifier, `transaction_hash` for transaction reference, and derived classification fields

### Component B: Daily active user aggregation  
- **Purpose**: Creates daily snapshots of unique active users with HyperLogLog initialization for efficient weekly calculations
- **Logic**: Takes the extracted user transactions and creates distinct daily user counts. For each day, creates HyperLogLog sketches initialized with precision 15 to enable probabilistic distinct counting. Groups users by day, chain, user type, and product dimensions. Applies chain availability filtering to ensure only data from supported periods is included. Uses incremental processing to efficiently update recent periods while maintaining historical data
- **Key fields**: `timestamp` truncated to day boundaries, `user_address` as the unique user identifier, `user_type` for user classification, and `hll` containing the HyperLogLog sketch

### Component C: Weekly rolling window creation
- **Purpose**: Establishes 7-day rolling windows for calculating weekly active users
- **Logic**: Creates a complete date spine from the earliest available data to current date minus one day to avoid incomplete periods. For each date in the spine, generates a 7-day window by creating timestamps for the current day and the previous 6 days using timestamp array generation. Joins this window structure with the daily HyperLogLog data to associate each day's user sketches with all relevant weekly windows. Applies incremental processing with appropriate lookback periods to maintain data consistency
- **Key fields**: `timestamp` for the window end date, `joinday` for individual days within each window, and associated dimension fields

### Component D: Weekly user aggregation
- **Purpose**: Computes distinct weekly active users across multiple dimensional breakdowns  
- **Logic**: Takes the rolling window data and merges HyperLogLog sketches across each 7-day period to count distinct users. Creates four separate dimensional views: project-level (all dimensions aggregated), chain-level (by chain), user-type-level (by user type), and product-level (by product). For each breakdown, merges the HyperLogLog sketches using partial merge operations and extracts the final distinct count. Combines all dimensional breakdowns using union operations to create a unified result set
- **Key fields**: `user_wau` containing the distinct weekly active user count, `dimensions` indicating the breakdown level, and relevant dimension identifiers

### Final aggregation  
- **Combining components**: Unions all dimensional breakdowns (project-level, chain-level, user-type-level, product-level) into a single result set with consistent schema
- **Time aggregation**: Uses 7-day rolling windows where each day represents the count of unique users active in that day plus the previous 6 days
- **Final calculation**: HyperLogLog sketch merging across the 7-day window provides probabilistic distinct count of unique addresses that performed transactions within the weekly period. The finalize metric macro standardizes the output schema and fills any missing time periods with zero values to ensure complete time series data

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

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

ShapeSHAPE

Active users (weekly)

Active users (weekly)

Competitive landscape