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# Hyperbridge Active addresses (weekly) Methodology

## 1. Overview

Weekly active addresses measures the number of unique addresses that interacted with the Hyperbridge protocol within a 7-day rolling window. This metric captures user engagement across both the native Hyperbridge parachain and cross-chain interactions with Hyperbridge infrastructure deployed on multiple EVM-compatible blockchains. The calculation uses HyperLogLog sketches for efficient approximate distinct counting and provides breakdowns by individual chain and overall project level.

## 2. Data sources

The calculation draws from two primary data source categories:

- **Hyperbridge chain extrinsics**: Native blockchain transaction data from the Hyperbridge parachain, capturing all on-chain activities and their associated sender addresses
- **EVM transaction data**: Transaction records from eight supported blockchains (Ethereum, Arbitrum, Optimism, Base, BSC, Gnosis, Polygon, and Unichain) that interact with specific Hyperbridge protocol smart contracts
- **Hyperbridge contract addresses**: Predefined smart contract addresses across each supported chain, including Host contracts, Handlers, Intent Gateways, Token Gateways, and other protocol infrastructure components
- **Chain availability filters**: Configuration data that determines which chains are active and should be included in calculations

## 3. Step-by-step calculation

### Component A: Hyperbridge chain user extraction
- **Purpose**: Identifies active users on the native Hyperbridge parachain
- **Logic**: Extracts all extrinsics from Hyperbridge blockchain data and identifies the `id` field (extrinsic signer) as the user address. Each extrinsic represents a user interaction with the Hyperbridge protocol. The data includes `block_timestamp`, `extrinsic_id`, and user classification as 'Transaction sender' type under the 'Hyperbridge Substrate' product category.
- **Key fields**: `block_timestamp`, `extrinsic_id`, `user_address` (from `id` field), `chain` (set to 'hyperbridge')

### Component B: Cross-chain user extraction  
- **Purpose**: Identifies users interacting with Hyperbridge protocol contracts on EVM chains
- **Logic**: Scans transaction data across eight EVM-compatible blockchains (Ethereum, Arbitrum, Optimism, Base, BSC, Gnosis, Polygon, Unichain) and filters for transactions where the `to_address` matches predefined Hyperbridge contract addresses. Each chain has 4-6 specific contract addresses representing different protocol components. The `from_address` of these transactions becomes the user address, classified as 'Transaction sender' type under the 'Hyperbridge App' product category.
- **Key fields**: `block_timestamp`, `transaction_hash`, `user_address` (from `from_address`), `chain`, specific contract addresses per chain

### Component C: Daily active user aggregation
- **Purpose**: Creates daily distinct active user counts across all data sources
- **Logic**: Combines users from both Hyperbridge chain extrinsics and EVM chain transactions, then deduplicates by `timestamp` (truncated to day), `chain`, `user_type`, `product_id`, and `user_address`. This ensures each unique address is counted only once per day per dimensional combination. Users are classified with their chain of activity, interaction type, and associated product identifier.
- **Key fields**: `timestamp`, `chain`, `user_type`, `product_id`, `user_dau` (unique user address)

### Component D: HyperLogLog sketch creation
- **Purpose**: Enables efficient approximate distinct counting for large datasets
- **Logic**: Converts daily user lists into HyperLogLog sketches using precision 15, which allows for approximate distinct counting while maintaining reasonable accuracy. These sketches are grouped by `timestamp`, `chain_id`, `user_type`, and `product_id` dimensions to preserve granular breakdowns.
- **Key fields**: `timestamp`, `chain_id`, `user_type`, `product_id`, `hll` (HyperLogLog sketch)

### Component E: Rolling 7-day window generation
- **Purpose**: Creates continuous 7-day rolling windows for weekly active user calculation
- **Logic**: Generates a complete daily datespine from the earliest data point to yesterday, then creates 7-day windows by joining each day with the current day plus the 6 preceding days. This approach ensures consistent weekly calculations even when daily data has gaps, as the datespine provides continuous time coverage.
- **Key fields**: `timestamp` (window end date), `joinday` (individual days within each window)

### Component F: Weekly active user calculation via HLL merging
- **Purpose**: Calculates weekly active users using HyperLogLog sketch merging
- **Logic**: For each 7-day window, merges the HyperLogLog sketches from all days within the window using `hll_count.merge_partial()`, then extracts the final distinct count using `hll_count.extract()`. This provides an approximation of unique users active within each rolling 7-day period while handling overlapping users efficiently across the window.
- **Key fields**: `timestamp` (window end date), `user_wau` (weekly active users count)

### Component G: Multi-dimensional breakdown
- **Purpose**: Provides weekly active user counts across different analytical dimensions
- **Logic**: Aggregates the weekly active user data across four dimensions: project_dbt_tag (overall project level with null chain/product/user_type), chain (per-blockchain with null product/user_type), user_type (per interaction type with null chain/product), and product_id (per product category with null chain/user_type). Each dimension provides a different analytical perspective on user activity.
- **Key fields**: `dimensions` (breakdown type), `chain_id`, `product_id`, `user_type`, `user_wau`

### Final aggregation
- **Combining components**: The target model selects weekly active user data filtered for 'chain' and 'project_dbt_tag' dimensions, which provide per-blockchain and overall project-level breakdowns of user activity
- **Currency conversion**: Not applicable - this metric counts addresses rather than monetary values
- **Time aggregation**: Uses rolling 7-day windows calculated daily, where each day's value represents the count of unique addresses active in that day plus the preceding 6 days
- **Final calculation**: Renames the `user_wau` field to `active_addresses_weekly` and applies standardization through the `finalize_metric_active_addresses_weekly` macro, which adds schema consistency, fills missing days with zeros, and applies chain availability filters to exclude inactive blockchain networks

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

Unique active addresses over a rolling 7-day window.

HyperbridgeBRIDGE

Archived

Active addresses (weekly)

Active addresses (weekly)

Competitive landscape

Chain breakdown