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Blockchains (L2)

Settlus

# Settlus Active addresses (monthly) Methodology

## 1. Overview

The Settlus active addresses monthly metric measures the number of unique wallet addresses that initiated at least one transaction on the Settlus blockchain within a rolling 30-day period, aggregated on a monthly basis. This metric provides insight into user engagement and adoption of the Settlus L2 blockchain ecosystem. The calculation uses a probabilistic counting approach with HyperLogLog algorithms to efficiently track unique addresses across the rolling window, then maps monthly active users (MAU) directly to active addresses since each user is identified by their unique wallet address.

## 2. Data sources

The calculation relies on the following data sources:

- **Settlus transaction records**: Raw blockchain transaction data containing transaction hashes, block timestamps, and sender addresses from the Settlus L2 blockchain
- **Token Terminal chain availability configuration**: Internal configuration data that determines which chains and time periods have reliable data coverage for accurate metric reporting

## 3. Step-by-step calculation

### Component A: User identification from transactions
- **Purpose**: Extract unique user addresses from Settlus blockchain transactions and classify them by user type
- **Logic**: Process all Settlus transaction records to identify unique sender addresses (`from_address`) for each transaction. Each transaction sender is classified as a "Transaction sender" user type and associated with the Settlus chain, which is categorized under the "blockchains_l2" market sector and "blockchain" business segment. The `block_timestamp` field is used to track when each address was active.
- **Key fields**: `block_timestamp`, `transaction_hash`, `from_address` (mapped to `user_address`), `user_type`, `chain`, `market_sector_id`, `business_segment_id`

### Component B: Daily active user calculation
- **Purpose**: Calculate daily active users by identifying unique addresses that transacted on each day
- **Logic**: Group transaction data by day using timestamp truncation to day granularity. For each day, identify distinct user addresses that initiated at least one transaction. This creates a daily snapshot of unique active addresses on the Settlus network.
- **Key fields**: `timestamp` (truncated to day), `timestamp_grain`, `user_address` (as `user_dau`), `chain`, `user_type`, `product_id`

### Component C: Monthly active user aggregation with HyperLogLog
- **Purpose**: Calculate monthly active users using a 30-day rolling window with probabilistic counting for memory efficiency
- **Logic**: Initialize HyperLogLog sketches for each day's unique users to enable memory-efficient counting of distinct addresses across large datasets. Create a datespine covering all days from the earliest transaction date to yesterday, then generate a rolling 30-day window by joining each day with the previous 29 days. For each day in the window, merge the HyperLogLog sketches to count unique addresses that were active within that 30-day period. This creates monthly active user counts using a rolling window approach rather than calendar month boundaries.
- **Key fields**: `timestamp`, `timestamp_grain`, `chain_id`, `user_type`, `product_id`, `hll` (HyperLogLog sketch), `user_mau` (extracted count)

### Component D: Dimensional breakdowns
- **Purpose**: Create MAU calculations across different analytical dimensions
- **Logic**: Generate separate aggregations for different analytical perspectives: project-level aggregation (project_dbt_tag dimension), chain-level aggregation, user-type-level aggregation, and product-level aggregation. Each breakdown groups the HyperLogLog sketches by the relevant dimension and merges them to produce unique user counts. Non-applicable dimensional fields are set to NULL for each breakdown.
- **Key fields**: `dimensions` (indicating the breakdown type), `chain_id`, `product_id`, `user_type`, `user_mau`

### Final aggregation
- **Combining components**: Union all dimensional breakdowns (project_dbt_tag, chain, user_type, product_id) into a single dataset containing MAU calculations across all analytical perspectives
- **Currency conversion**: Not applicable for this address-based metric
- **Time aggregation**: Monthly boundaries are applied using timestamp truncation to month granularity, with a 30-day rolling window approach rather than strict calendar months
- **Final calculation**: Map the `user_mau` values directly to `active_addresses_monthly` since each user is uniquely identified by their wallet address. Filter to include only chain and project_dbt_tag dimensions for the active addresses metric. Apply the finalize_metric macro to standardize the output format, fill missing time periods with zeros, and ensure consistent schema structure. Finally, apply chain availability filters to ensure only periods with reliable data coverage are included in the final metric output.

The final metric represents the count of unique Settlus wallet addresses that initiated at least one transaction within a rolling 30-day window, providing a monthly view of blockchain user engagement and adoption.

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

Unique addresses active over a rolling 30-day window.

Settlus

Active addresses (monthly)

Active addresses (monthly)

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