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

Gnosis Chain is a smart contract platform for decentralized applications (a blockchain). Gnosis was founded by Martin Köppelmann and Stefan George and launched in 2015.

GNO holders own and govern the protocol. They currently take a cut of the total transaction fees paid by users (revenue). 

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Blockchains (L1)

Gnosis

# Gnosis Active addresses (monthly) Methodology

## 1. Overview

Monthly active addresses for Gnosis measures the number of unique wallet addresses that sent at least one transaction on the Gnosis blockchain within a 30-day rolling window. This metric provides insights into user adoption and network activity by tracking distinct transaction senders over monthly periods. The calculation uses a rolling 30-day window approach, meaning each day's value represents the count of unique addresses that were active in the previous 30 days (including the current day).

## 2. Data sources

The methodology relies on the following raw data sources:
- **Gnosis blockchain transaction logs**: Complete transaction records from the Gnosis network including transaction metadata, sender and recipient addresses, timestamps, gas usage, and transaction types
- **Transaction data fields**: Specific transaction attributes including `from_address` (transaction sender), `block_timestamp`, `transaction_hash`, `block_number`, and gas-related metrics

## 3. Step-by-step calculation

### Component A: Daily active address identification
- **Purpose**: Identifies unique wallet addresses that sent transactions each day on Gnosis
- **Logic**: Extracts all transactions from Gnosis blockchain data and identifies the sender address (`from_address`) for each transaction. Timestamps are truncated to day level to group transactions by calendar day. Duplicate addresses within the same day are eliminated to count each address only once per day. All identified addresses are classified as 'Transaction sender' user type and assigned to the 'blockchains_l1' market sector and 'blockchain' business segment.
- **Key fields**: `from_address` (transaction sender address), `block_timestamp` (transaction timestamp), `user_type` (set to 'Transaction sender'), `market_sector_id`, `business_segment_id`

### Component B: HyperLogLog initialization for efficient counting
- **Purpose**: Prepares daily address data for efficient distinct counting across rolling windows
- **Logic**: Applies HyperLogLog algorithm to daily active address data to enable memory-efficient counting of distinct addresses across large datasets. Each day's unique addresses are processed into HLL sketches with precision parameter 15, allowing for approximate but highly accurate distinct counts. The data is grouped by timestamp, chain, user type, and product dimensions to enable multi-dimensional analysis.
- **Key fields**: `timestamp` (daily timestamp), `chain_id` (set to 'gnosis'), `hll` (HyperLogLog sketch), `user_type`, `product_id`

### Component C: Rolling window generation
- **Purpose**: Creates 30-day rolling windows for each day to calculate monthly active addresses
- **Logic**: Generates a complete datespine covering all days from the earliest transaction date to the current date to ensure no missing days in the calculation. For each day in the datespine, creates a 30-day lookback window (29 days prior plus the current day) by joining with all relevant historical days. This windowing approach ensures that each day's monthly active address count includes all unique addresses that were active in the previous 30 days.
- **Key fields**: `timestamp` (current date), `joinday` (each day within the 30-day window), window boundaries

### Component D: Multi-dimensional aggregation
- **Purpose**: Calculates monthly active addresses across different analytical dimensions
- **Logic**: Aggregates the 30-day rolling windows using HyperLogLog merge operations to count distinct addresses across four dimensions. For 'project_dbt_tag' dimension, merges all HLL sketches regardless of chain or product to get project-level totals. For 'chain' dimension, groups by chain identifier to get chain-specific counts. For 'user_type' dimension, groups by user type classification. For 'product_id' dimension, groups by product identifier. Each aggregation uses HLL merge and extract operations to produce accurate distinct address counts.
- **Key fields**: `dimensions` (aggregation level), `chain_id`, `product_id`, `user_type`, `user_mau` (distinct address count)

### Final aggregation
- **Combining components**: The monthly active user counts from the multi-dimensional aggregation are filtered to include only 'chain' and 'project_dbt_tag' dimensions, then renamed from `user_mau` to `active_addresses_monthly` to reflect that these represent unique wallet addresses rather than users
- **Currency conversion**: No currency conversion is applied as this metric counts addresses rather than monetary values
- **Time aggregation**: Uses 30-day rolling windows calculated daily, where each day represents the count of unique addresses active in the trailing 30-day period
- **Final calculation**: The final metric value is the distinct count of wallet addresses that sent at least one transaction on Gnosis within the 30-day rolling window, calculated using HyperLogLog merge and extract operations for computational efficiency and accuracy

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

Unique addresses active over a rolling 30-day window.

GnosisGNO

Active addresses (monthly)

Active addresses (monthly)

Competitive landscape