Validators process transactions and receive fees from users (supply-side fees).

Ethereum is a smart contract platform for decentralized applications (a blockchain). Ethereum was founded by Vitalik Buterin, Anthony Di Iorio, Mihai Alisie, etc. and launched in 2015.

ETH holders own and govern the protocol. They currently take a cut of the total transaction fees (base fee) paid by users (revenue). Ethereum’s investors include KR1, Block Ventures, 8 Decimal Capital, etc.

governance

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etherscan

website

Blockchains (L1)

Ethereum

# Ethereum Active addresses (weekly) Methodology

## 1. Overview

The Ethereum weekly active addresses metric measures the number of unique addresses that initiated at least one transaction on the Ethereum network within a rolling 7-day period. This metric provides insight into user engagement and network activity by tracking distinct wallet addresses that actively participated in network transactions over weekly timeframes.

The calculation uses HyperLogLog approximation for memory-efficient distinct counting across large datasets and implements a rolling window approach to provide continuous weekly measurements.

## 2. Data sources

The metric relies on the following raw data sources:

- **Ethereum transaction records**: Complete transaction history from the Ethereum blockchain, including sender addresses, transaction hashes, block timestamps, and transaction metadata
- **Blockchain transaction logs**: Raw Ethereum network transaction data that captures all on-chain activity and user interactions

## 3. Step-by-step calculation

### Component A: User identification from transactions
- **Purpose**: Identifies unique user addresses from Ethereum transaction activity
- **Logic**: Extracts all transaction sender addresses from Ethereum transaction records. Each `from_address` in a transaction represents a user who actively initiated a transaction on the network. The component enriches each user record with metadata including chain identifier ('ethereum'), market sector ('blockchains_l1'), business segment ('blockchain'), and user type classification ('Transaction sender')
- **Key fields**: `from_address` as the user identifier, `block_timestamp` for temporal analysis, `transaction_hash` for uniqueness, and classification metadata

### Component B: Daily active user aggregation  
- **Purpose**: Creates daily snapshots of unique active addresses
- **Logic**: Groups user addresses by day using timestamp truncation to day boundaries. Deduplicates addresses within each day to count each unique address only once per day, regardless of how many transactions they initiated. Maintains dimensional metadata for downstream aggregations
- **Key fields**: `timestamp` (truncated to day), `user_address` for distinct counting, and dimensional attributes like `chain`, `user_type`, and `product_id`

### Component C: HyperLogLog initialization for weekly windows
- **Purpose**: Prepares data for memory-efficient distinct counting across rolling windows
- **Logic**: Converts daily user address lists into HyperLogLog sketches using BigQuery's `hll_count.init()` function with precision parameter 15. This probabilistic data structure enables efficient approximate distinct counting when combining multiple days of data across rolling windows
- **Key fields**: `hll` sketch containing compressed user address information, `timestamp` for window construction

### Component D: Rolling 7-day window construction
- **Purpose**: Creates continuous 7-day rolling windows for weekly active user calculation
- **Logic**: Uses a datespine approach to generate all possible dates within the analysis period. For each date, creates joins to the previous 6 days to form 7-day windows. This ensures continuous time series coverage without gaps, even for days with no transaction activity. Joins HLL sketches from daily aggregations to each window period
- **Key fields**: Window `timestamp` representing the end date of each 7-day period, `joinday` for historical day inclusion in windows

### Component E: Weekly active user calculation
- **Purpose**: Computes distinct user counts across 7-day rolling windows
- **Logic**: Merges HLL sketches across each 7-day window using `hll_count.merge_partial()` to combine probabilistic distinct counts from multiple days. Extracts final approximate distinct counts using `hll_count.extract()`. This approach efficiently handles large-scale distinct counting while maintaining reasonable memory usage
- **Key fields**: `user_wau` containing the weekly active user count for each time period

### Component F: Dimensional breakdowns
- **Purpose**: Creates multiple analytical perspectives of weekly active users
- **Logic**: Aggregates weekly active user counts across different dimensional cuts including chain-level aggregation (for cross-chain analysis), project-level aggregation (for ecosystem-wide metrics), user-type breakdowns, and product-specific views. Each breakdown maintains the same HLL-based calculation methodology while grouping by different dimensional attributes
- **Key fields**: `dimensions` indicating the breakdown type, dimension-specific identifiers, and aggregated `user_wau` counts

### Final aggregation
- **Combining components**: Selects weekly active user counts from the dimensional breakdown that matches 'chain' and 'project_dbt_tag' dimensions, representing both chain-specific and project-wide perspectives
- **Currency conversion**: Not applicable for this address-counting metric
- **Time aggregation**: Maintains daily resolution for the rolling 7-day windows, with each day representing the count of unique addresses active in the trailing 7-day period
- **Final calculation**: Renames `user_wau` to `active_addresses_weekly` and applies metric finalization including zero-filling for periods with no activity, ensuring continuous time series coverage

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

Unique active addresses over a rolling 7-day window.

EthereumETH

Active addresses (weekly)

Active addresses (weekly)

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