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Blockchains (L2)

Shape

# Shape Active addresses (monthly) Methodology

## 1. Overview

Active addresses monthly measures the number of unique addresses that initiated at least one transaction on the Shape L2 network within a 30-day rolling window, aggregated to monthly boundaries. This metric helps understand user engagement and adoption on the Shape blockchain by tracking distinct wallet addresses that actively participate in network activity. The calculation uses HyperLogLog data structures to efficiently count distinct addresses across large datasets while maintaining accuracy.

## 2. Data sources

- **Shape blockchain transactions**: Raw transaction records from the Shape L2 network containing transaction metadata, sender addresses, timestamps, and transaction hashes
- **Transaction sender data**: Address information from the `from_address` field representing the wallet addresses that initiated transactions on the Shape network

## 3. Step-by-step calculation

### Component A: User fact table creation
- **Purpose**: Establishes the base dataset of users (transaction senders) from Shape blockchain transactions
- **Logic**: Extracts transaction records from Shape blockchain data, casting transaction timestamps, hashes, and sender addresses to proper data types. Each transaction sender is classified as a "Transaction sender" user type within the "blockchains_l2" market sector and "blockchain" business segment. The chain identifier is set to "shape" for all records.
- **Key fields**: `block_timestamp` (transaction timestamp), `from_address` (transaction sender address), `transaction_hash` (unique transaction identifier)

### Component B: Daily active user aggregation  
- **Purpose**: Calculates daily distinct active users by identifying unique addresses that sent transactions each day
- **Logic**: Groups transaction data by daily timestamp boundaries using timestamp truncation to day granularity. Extracts distinct sender addresses for each day, creating a record for each unique address-day combination. Applies incremental processing with lookback periods to handle late-arriving data.
- **Key fields**: `timestamp` (daily boundary), `user_address` as `user_dau` (distinct daily active user address), `chain` (Shape network identifier)

### Component C: Monthly active user calculation with HLL
- **Purpose**: Calculates 30-day rolling monthly active users using HyperLogLog data structures for efficient distinct counting
- **Logic**: Initializes HyperLogLog sketches for daily user counts to enable efficient distinct user aggregation. Creates a comprehensive datespine covering all days from the earliest transaction date to yesterday. For each day in the datespine, generates a 30-day lookback window by creating join relationships to the previous 29 days. Merges HLL sketches across the 30-day window to calculate distinct users over the rolling period. Breaks down results by multiple dimensions including chain-level and project_dbt_tag-level aggregations.
- **Key fields**: `user_mau` (monthly active users from HLL extraction), `dimensions` (breakdown category), `chain_id` (for chain-level breakdown)

### Final aggregation
- **Combining components**: Selects monthly active user data specifically for chain-level and project_dbt_tag-level dimensions, renaming `user_mau` to `active_addresses_monthly` to align with the metric definition
- **Currency conversion**: Not applicable as this metric counts addresses rather than monetary values
- **Time aggregation**: Uses monthly timestamp boundaries with 30-day rolling windows to calculate monthly active address counts, ensuring continuous coverage even for periods with missing transaction days
- **Final calculation**: Applies the finalize metric macro to standardize the schema, fill missing monthly periods with zero values, and apply chain availability filters to ensure data consistency across different network availability periods

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

Unique addresses active over a rolling 30-day window.

ShapeSHAPE

Active addresses (monthly)

Active addresses (monthly)

Competitive landscape