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Blockchains (L2)

Superseed

# Superseed Active addresses (weekly) Methodology

## 1. Overview

Weekly active addresses measures the number of unique addresses that initiated at least one transaction on the Superseed blockchain within a rolling 7-day period. This metric serves as a key indicator of user engagement and network activity, providing insight into the size of the active user base over weekly time horizons. The calculation uses probabilistic counting techniques (HyperLogLog) to efficiently compute distinct address counts across rolling weekly windows.

## 2. Data sources

- **Superseed transaction records**: Raw blockchain transaction data including transaction hashes, sender addresses, recipient addresses, and block timestamps
- **Block timestamp data**: Precise timing information for when transactions were included in blocks
- **Transaction metadata**: Additional context about transaction types and network routing

## 3. Step-by-step calculation

### Component A: Daily active address extraction
- **Purpose**: Identify unique addresses that were active each day
- **Logic**: Extract transaction records from Superseed blockchain data and identify the sender address (`from_address`) for each transaction. Group transactions by day using block timestamp truncated to daily granularity. Count distinct sender addresses per day to establish the daily active address baseline. Each address is classified as a "Transaction sender" user type and associated with the Superseed chain and blockchain business segment.
- **Key fields**: `block_timestamp` for daily grouping, `from_address` as the active address, `transaction_hash` for transaction identification, `chain` set to 'superseed'

### Component B: HyperLogLog sketch creation
- **Purpose**: Create probabilistic data structures for efficient distinct counting over time windows
- **Logic**: Convert the daily distinct address counts into HyperLogLog sketches to enable efficient approximate distinct counting across longer time periods. Initialize HLL sketches with precision parameter 15 for each day's unique addresses, grouped by timestamp, chain, user type, and product dimensions. This allows for memory-efficient aggregation of distinct counts across rolling time windows without storing individual address values.
- **Key fields**: `hll` sketches containing compressed representations of daily unique addresses, `timestamp` for temporal grouping, dimensional fields for breakdowns

### Component C: Rolling window construction
- **Purpose**: Create 7-day rolling windows for weekly active address calculation
- **Logic**: Build a continuous date spine covering the full time range to ensure no gaps in the time series. For each date, create associations with the previous 6 days plus the current day to form 7-day rolling windows. Join the date spine with the daily HLL sketches to create a comprehensive view where each date has access to HLL sketches for its entire 7-day window. This approach ensures consistent weekly calculations even when some days have no transaction activity.
- **Key fields**: `timestamp` as the primary date, `joinday` representing each day in the 7-day window, `hll` sketches for each day in the window

### Component D: Multi-dimensional aggregation
- **Purpose**: Calculate weekly active addresses across different analytical dimensions
- **Logic**: Aggregate HLL sketches across four different dimensional breakdowns. For chain-level analysis, merge HLL sketches across the 7-day window grouped by chain identifier. For project-level analysis, merge all sketches regardless of chain to get overall project metrics. For user-type analysis, group by user type classification. For product-level analysis, group by product identifier. Each aggregation merges HLL sketches using the HLL merge partial function and extracts the final distinct count using HLL count extract.
- **Key fields**: `dimensions` indicating the breakdown level (chain, project_dbt_tag, user_type, product_id), `user_wau` containing the weekly distinct address count

### Final aggregation
- **Combining components**: The multi-dimensional WAU results are filtered to include only chain-level and project-level breakdowns, then passed through a finalization macro that standardizes the schema and fills missing time periods with zeros
- **Currency conversion**: No currency conversion required as this is a count-based metric
- **Time aggregation**: Uses 7-day rolling windows where each day represents the count of distinct addresses active in that day plus the previous 6 days
- **Final calculation**: The `user_wau` field from the weekly aggregation is renamed to `active_addresses_weekly` to reflect the blockchain context where users are represented by their wallet addresses. The final metric represents the approximate count of unique addresses that sent at least one transaction within the 7-day period ending on each date.

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

Unique active addresses over a rolling 7-day window.

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Active addresses (weekly)

Active addresses (weekly)

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