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Blockchains (L2)

peaq

# peaq Active addresses (weekly) Methodology

## 1. Overview

Weekly active addresses measures the number of unique blockchain addresses that initiated at least one transaction on the Peaq network within a 7-day rolling window. This metric provides insight into user engagement and network activity by tracking how many distinct addresses are actively participating in transactions over weekly periods. The calculation uses a rolling 7-day window approach, where each day's value represents the count of unique addresses that were active during that day plus the previous 6 days.

## 2. Data sources

The calculation uses the following raw blockchain data sources:

- **Peaq Substrate extrinsics**: Transaction records from the Peaq Substrate blockchain, containing transaction metadata and sender identification
- **Peaq EVM events**: Ethereum-compatible event logs from the Peaq EVM execution layer, specifically `Executed` events from the ethereum pallet that contain transaction sender addresses
- **Blockchain timestamps**: Block timestamp data used to organize transactions into daily boundaries for time-based aggregation

## 3. Step-by-step calculation

### Component A: User address extraction
- **Purpose**: Extract all unique addresses that have initiated transactions on the Peaq network
- **Logic**: Combine transaction data from both Peaq blockchain layers. From Substrate extrinsics, extract the `id` field which identifies transaction senders. From Peaq EVM events, filter for ethereum pallet `Executed` events and extract sender addresses from the first element of the event data array. All extracted addresses are classified as 'Transaction sender' user types and tagged with appropriate chain identifiers ('peaq' for Substrate, 'Peaq EVM' for EVM layer)
- **Key fields**: `block_timestamp` for timing, `id` for Substrate senders, `event_data[0]` for EVM senders, `chain` for network identification

### Component B: Daily active user calculation  
- **Purpose**: Aggregate unique addresses by calendar day to create daily active user counts
- **Logic**: Group all transaction sender addresses by calendar day using timestamp truncation to day boundaries. For each day, identify the distinct set of addresses that initiated at least one transaction. This creates a foundation dataset of unique daily active users with their associated metadata including chain, user type, and product information
- **Key fields**: `timestamp` truncated to day granularity, `user_address` for deduplication, `chain` and `user_type` for categorization

### Component C: Weekly aggregation using rolling windows
- **Purpose**: Create 7-day rolling window counts of unique active addresses using efficient probabilistic counting
- **Logic**: For each calendar day, construct a 7-day rolling window spanning from 6 days prior through the current day. Create HyperLogLog sketches for each day's unique addresses to enable efficient merging across the rolling window. Use a datespine to ensure all calendar days are represented, then join with daily user data across the 7-day window periods. Merge HyperLogLog sketches within each rolling window to count distinct addresses while automatically handling overlaps between days. Generate separate breakdowns by project tag, chain, user type, and product dimensions
- **Key fields**: `timestamp` for window boundaries, `hll` sketches for probabilistic counting, `dimensions` for categorization breakdowns

### Final aggregation
- **Combining components**: Select the weekly active user counts from the rolling window aggregation, specifically filtering for 'chain' and 'project_dbt_tag' dimensional breakdowns to focus on overall network-level metrics
- **Currency conversion**: Not applicable for address counting metrics
- **Time aggregation**: Each day represents a 7-day rolling window ending on that date, providing continuous weekly activity measurement without gaps
- **Final calculation**: Rename the `user_wau` field to `active_addresses_weekly` to reflect that unique active addresses are being measured. Apply the finalize metric macro to standardize schema, fill any missing days with zero values, and ensure consistent output formatting across the time series

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

Unique active addresses over a rolling 7-day window.

peaqPEAQ

Active addresses (weekly)

Active addresses (weekly)

Competitive landscape

Chain breakdown