Liquidity providers deposit funds into Everclear and receive transfer fees from users (supply-side fees).

Everclear is a cross-chain liquidity network that enables fully non-custodial transfers between EVM-compatible chains and L2 systems. Everclear was co-founded by  Arjun Bhuptani, Rahul Sethuram, and Layne Haber and launched in 2021.

Everclear team & investors own and govern the protocol. They do not currently take a cut of the total transfer fees paid by users (revenue).

github

twitter

discord

etherscan

website

Bridges

Everclear

# Everclear Active users (weekly) Methodology

## 1. Overview

The Connext Weekly Active Users (WAU) metric measures the number of unique users who interacted with the Connext protocol within any 7-day rolling window. This metric uses HyperLogLog (HLL) probabilistic data structures to efficiently count distinct users across multiple blockchain networks and provides dimensional breakdowns by chain, user type, and product. The calculation employs a rolling window approach where each day's WAU represents users active in that day plus the preceding 6 days.

## 2. Data sources

- **Connext v1 protocol events**: Parsed `TransactionFulfilled` events from TransactionManager contracts across Arbitrum, Avalanche, BSC, Ethereum, Fantom, Gnosis, Optimism, and Polygon networks
- **Connext v2 protocol events**: Parsed `XCalled` events from ConnextDiamond contracts on Arbitrum, BSC, Ethereum, Gnosis, Optimism, and Polygon networks  
- **Multi-version contract events**: Additional `TransactionFulfilled` events from TransactionManagerV2 and TransactionManagerV3 contracts on BSC, Fantom, and Polygon for comprehensive coverage
- **Blockchain transaction data**: Block timestamps and transaction hashes from each supported network to establish temporal context

## 3. Step-by-step calculation

### Component A: Daily active user extraction
- **Purpose**: Identifies unique users active each day across all supported chains and Connext protocol versions
- **Logic**: Extracts user addresses from cross-chain bridge transaction events, with different extraction methods for protocol versions. For v1 events, uses the `user` parameter from `TransactionFulfilled` events. For v2 events, extracts `params.originSender` from `XCalled` events. Truncates block timestamps to day level and removes duplicates to ensure each user address appears only once per day per chain
- **Key fields**: `user_address` as the unique user identifier, `block_timestamp` truncated to day level, `chain` for network identification, `user_type` classified as 'Trader', and dimensional attributes like `product_id`

### Component B: HyperLogLog sketch initialization  
- **Purpose**: Creates efficient probabilistic data structures for scalable distinct user counting across large datasets
- **Logic**: Initializes HLL sketches for each day's unique user set, grouping by timestamp, chain, user type, and product dimensions. Uses HLL precision parameter of 15 bits for balance between accuracy and storage efficiency. Each sketch represents the set of unique users for that specific day and dimension combination
- **Key fields**: `hll` sketches containing compressed user sets, dimensional grouping keys for `chain_id`, `user_type`, and `product_id`

### Component C: Rolling window datespine creation
- **Purpose**: Generates complete time series coverage and creates 7-day rolling windows for WAU calculation
- **Logic**: Creates a datespine spanning from the earliest available data to the current date minus one day. For each date in the spine, generates an array of the current date plus the preceding 6 days to form 7-day windows. This ensures every possible day has its complete 7-day lookback period represented, preventing gaps in the time series that could cause incorrect WAU calculations
- **Key fields**: `timestamp` as the reference date, `joinday` representing each day within the 7-day window

### Component D: Window-based HLL merging
- **Purpose**: Combines daily user sets within each 7-day window to calculate weekly unique user counts
- **Logic**: Joins the rolling window datespine with daily HLL sketches, matching each window day with its corresponding daily user data. Merges HLL sketches within each 7-day window using `hll_count.merge_partial()` to combine user sets while maintaining uniqueness. Extracts final distinct counts using `hll_count.extract()` to get the approximate number of unique users in each 7-day period
- **Key fields**: Merged `hll` sketches representing weekly user sets, extracted `user_wau` counts as final weekly active user numbers

### Final aggregation
- **Combining components**: Creates four separate dimensional views by grouping merged HLL data differently - overall project level (all dimensions nulled), by individual chain, by user type, and by product ID. Each view maintains the same WAU calculation logic but provides different analytical perspectives
- **Currency conversion**: Not applicable for user count metrics
- **Time aggregation**: Uses daily boundaries with 7-day rolling windows, where each day's WAU includes users active in that day plus the preceding 6 days. Incremental processing uses monthly partitions with configurable lookback periods to handle updates efficiently  
- **Final calculation**: The final WAU value for any given day represents the count of distinct users who were active on that day or any of the 6 preceding days, calculated using merged HyperLogLog sketches for scalable approximate counting across multiple blockchain networks and protocol versions

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

Unique revenue-generating users over a rolling 7-day window.

EverclearCLEAR

Active users (weekly)

Active users (weekly)

Competitive landscape

Chain breakdown