Collators aggregate parachain transactions block candidates and producing state transition proofs for validators based on those blocks.

Energy Web X is energy blockchain ecosystem powered by Polkadot Relay chain.

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Blockchains (L2)

Energy Web X

# Energy Web X Active addresses (weekly) Methodology

## 1. Overview

Weekly active addresses measures the number of unique addresses that performed at least one transaction on the Energy Web blockchain within a 7-day rolling window. This metric provides insight into the breadth of network usage by tracking distinct addresses that initiate 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 on that day or any of the 6 preceding days.

## 2. Data sources

The metric is built from the following raw data sources:

- **Energy Web blockchain extrinsics**: Transaction records from the Energy Web blockchain containing transaction sender addresses, timestamps, and transaction metadata
- **Energy Web transaction data**: Block-level timestamp information for temporal aggregation and time-series analysis

## 3. Step-by-step calculation

### Component A: User identification
- **Purpose**: Identify unique transaction sender addresses from Energy Web blockchain activity
- **Logic**: Extract transaction sender addresses from Energy Web extrinsics data. Each extrinsic record contains an `id` field representing the transaction sender address and a `block_timestamp` indicating when the transaction occurred. All transaction senders are classified as "Transaction sender" user types and categorized under the "blockchains_l2" market sector and "blockchain" business segment within the broader blockchain category.
- **Key fields**: `id` (transaction sender address), `block_timestamp` (transaction timestamp), user classification metadata

### Component B: Daily active user calculation  
- **Purpose**: Calculate daily distinct active users as the foundation for weekly aggregations
- **Logic**: Group transaction data by day using timestamp truncation to daily granularity. For each day, count the distinct transaction sender addresses that performed at least one transaction. This creates a time series of unique daily active addresses. The calculation uses incremental processing with lookback periods to efficiently update results as new transaction data becomes available.
- **Key fields**: `timestamp` (daily truncated), `user_address` (distinct transaction senders), `timestamp_grain` (day-level indicator)

### Component C: Rolling window construction
- **Purpose**: Create a continuous 7-day rolling window for weekly active address calculation
- **Logic**: Generate a complete datespine covering all days from the earliest transaction date to the current date to ensure no missing days in the time series. For each day in the datespine, create a 7-day window consisting of the current day plus the 6 preceding days. This window construction approach ensures that every day has a complete 7-day lookback period for calculating weekly active addresses, even when some days have no transaction activity.
- **Key fields**: `timestamp` (daily periods), `joinday` (days within each 7-day window)

### Component D: Weekly active user aggregation using HyperLogLog
- **Purpose**: Efficiently calculate distinct users across 7-day rolling windows while handling potential overlaps
- **Logic**: Use HyperLogLog (HLL) probabilistic data structure to efficiently count distinct addresses across the rolling window periods. For each day in the daily active user data, initialize an HLL sketch of the unique addresses. Then, for each 7-day window, merge the HLL sketches from all days within that window to produce an estimate of distinct addresses that were active at any point during the 7-day period. This approach efficiently handles the challenge of counting distinct users across overlapping time periods without double-counting addresses that appear on multiple days within the same window.
- **Key fields**: `hll` (HyperLogLog sketches), `user_wau` (weekly active user counts from HLL extraction)

### Component E: Dimensional breakdowns
- **Purpose**: Generate weekly active user counts across multiple analytical dimensions
- **Logic**: Create separate aggregations for different analytical perspectives: project-level (all Energy Web activity combined), chain-level (Energy Web blockchain specifically), user-type level (transaction senders), and product-level (currently null for Energy Web). Each dimensional breakdown applies the same HLL merge logic but groups results differently to support various analytical use cases. The breakdowns are combined using union operations to create a comprehensive dataset supporting multiple analytical perspectives.
- **Key fields**: `dimensions` (analytical dimension indicator), `chain_id`, `user_type`, `product_id`, `user_wau` (weekly active users for each dimension)

### Final aggregation
- **Combining components**: Select weekly active user counts from the multi-dimensional breakdown, specifically filtering for chain-level and project-level dimensions. Rename the `user_wau` field to `active_addresses_weekly` to align with the target metric definition.
- **Time aggregation**: Each day's value represents a 7-day rolling window ending on that day. The rolling window approach ensures smooth time series without the choppy patterns that would result from non-overlapping weekly periods.
- **Final calculation**: The final active addresses weekly value for any given day equals the distinct count of addresses that performed at least one transaction during the 7-day period ending on that day. Missing days are filled with zeros using the finalization macro to ensure a complete time series for analytical purposes.

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

Unique active addresses over a rolling 7-day window.

Energy Web XEWT

Active addresses (weekly)

Active addresses (weekly)

Competitive landscape

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