Aleph Zero EVM is powered by Arbitrum's AnyTrust protocol which uses a data availability committee (DAC) to store raw transaction data.

Aleph Zero EVM is an Ethereum layer 2 powered by Arbitrum Orbit tech stack. This dashboard does not include Aleph Zero WASM data.

AZERO tokenholders own and govern the protocol.

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Blockchains (L2)

Aleph Zero EVM

# Aleph Zero EVM Active addresses (monthly) Methodology

## 1. Overview

Monthly active addresses measures the number of unique addresses that sent at least one transaction on the Aleph Zero EVM blockchain within any 30-day rolling period. This metric treats blockchain addresses as user proxies, providing insight into network usage and adoption trends. The calculation uses a rolling 30-day window approach, meaning each day represents the count of unique addresses that were active in the current day plus the previous 29 days.

## 2. Data sources

The metric relies on the following raw data sources:

- **Aleph Zero EVM blockchain transaction records**: Complete transaction history including sender addresses, receiver addresses, transaction hashes, and execution details
- **Block timestamp data**: Precise timing information for when each transaction was included in a block
- **Gas pricing and usage data**: Transaction execution costs including gas price and gas usage amounts for fee calculations
- **Transaction metadata**: Additional transaction details such as effective gas prices and receipt information

## 3. Step-by-step calculation

### Component A: Transaction data extraction
- **Purpose**: Extract and standardize raw transaction data from the Aleph Zero EVM blockchain
- **Logic**: Pull transaction records from Aleph Zero EVM blockchain data, capturing essential fields like block timestamps, transaction hashes, sender addresses (`from_address`), receiver addresses (`to_address`), gas pricing, and gas usage. Calculate Layer 2 gas fees using the formula: gas used multiplied by gas price divided by 1e18 to convert from wei to ETH units. Classify all transactions as belonging to the 'Aleph Zero EVM' chain with market sector 'blockchains_l2' and business segment 'blockchain'
- **Key fields**: `block_timestamp` for timing, `from_address` for sender identification, `transaction_hash` for uniqueness, `gas_price` and `gas_used_amount` for fee calculations

### Component B: Daily active user identification
- **Purpose**: Create daily snapshots of unique transaction senders
- **Logic**: Process transaction data to identify unique active addresses per day. Truncate block timestamps to day-level granularity using timestamp truncation. Extract sender addresses (`from_address`) as the unique identifiers for daily active users. Classify all senders as 'Transaction sender' user type. Apply date filtering to limit analysis to relevant time periods (up to '2025-09-17'). Maintain dimensional attributes like chain, market sector, business segment, and product identifiers for downstream aggregation
- **Key fields**: `timestamp` (day-truncated), `user_dau` (sender address), `user_type` classification, dimensional attributes

### Component C: Rolling 30-day MAU calculation
- **Purpose**: Calculate monthly active users using a 30-day rolling window approach
- **Logic**: Build a comprehensive date spine covering all days from the first transaction to the current date. For each day in the date spine, create a 30-day rolling window by generating timestamp arrays that include the current day plus the previous 29 days. Use HyperLogLog (HLL) data structures to efficiently count unique addresses across these rolling windows, initializing HLL sketches for daily data and merging them across the 30-day periods. Aggregate unique address counts across multiple dimensional breakdowns including chain-level (grouped by chain), project-level (aggregated across all chains), user-type-level (grouped by transaction sender type), and product-level (grouped by product identifier). Apply HyperLogLog extraction functions to get final unique counts from the merged sketches
- **Key fields**: `timestamp` for window boundaries, `hll` for HyperLogLog sketches, `user_mau` for extracted unique counts, `dimensions` for aggregation levels

### Final aggregation
- **Combining components**: Select the relevant dimensional breakdowns (chain-level and project-level) from the MAU calculations and rename the `user_mau` field to `active_addresses_monthly` to align with metric naming conventions
- **Currency conversion**: Not applicable - this is a count-based metric measuring unique addresses rather than monetary values
- **Time aggregation**: Uses rolling 30-day windows where each day represents the count of unique addresses active in that day plus the previous 29 days. The rolling approach provides smooth trend analysis without month-end boundary effects
- **Final calculation**: The final metric value is the count of unique addresses that sent at least one transaction within any 30-day rolling period, calculated using HyperLogLog algorithms for scalable and accurate unique counting across large datasets

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

Unique addresses active over a rolling 30-day window.

Aleph Zero EVMAZERO

Archived

Active addresses (monthly)

Active addresses (monthly)

Competitive landscape

Chain breakdown