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# Etherex Active users (weekly) Methodology

## 1. Overview

The EthereX Weekly Active Users (WAU) metric measures the number of unique users who actively interact with the EthereX protocol within rolling 7-day windows. This metric provides insight into user engagement and platform adoption by counting users who perform trading activities or flash loan operations. The calculation uses HyperLogLog data structures for efficient computation and provides breakdowns across multiple dimensions including chain, product version, and user type.

## 2. Data sources

- **Parsed EthereX v2 swap events**: `Pair_Swap_event` logs from EthereX v2 pair contracts on Linea blockchain, capturing trading activities between token pairs
- **Parsed EthereX v3 swap events**: `Pool_Swap_event` logs from EthereX v3 pool contracts on Linea blockchain, capturing trading activities in concentrated liquidity pools  
- **Parsed EthereX v3 flash loan events**: `Pool_Flash_event` logs from EthereX v3 pool contracts on Linea blockchain, capturing flash loan borrowing activities
- **Linea transaction records**: Transaction data providing user addresses (`from_address`) who initiated the protocol interactions
- **Chain availability filters**: Configuration data determining which chains and time periods have reliable data coverage

## 3. Step-by-step calculation

### Component A: Daily active user identification

- **Purpose**: Extract and consolidate all unique users who interact with EthereX protocol each day
- **Logic**: Combine users from two activity types - trading and flash loans. For trading activities, extract users from both EthereX v2 `Pair_Swap_event` logs and v3 `Pool_Swap_event` logs on Linea. For flash loan activities, extract users from v3 `Pool_Flash_event` logs. Join each event with corresponding transaction data to get the actual user address from the `from_address` field. Group activities by day using timestamp truncation and classify all users as 'Trader' type. Use UNION DISTINCT to combine trading and flash loan users while avoiding double-counting users who perform both activities on the same day.
- **Key fields**: `block_timestamp` for daily grouping, `user_address` from transaction `from_address`, `chain` identifier, `product_id` (Constant Product or Concentrated Liquidity), `user_type` (always 'Trader')

### Component B: HLL preprocessing for efficient aggregation

- **Purpose**: Convert daily user sets into HyperLogLog data structures for memory-efficient unique user counting across time windows
- **Logic**: Take the daily active users and initialize HLL structures with precision 15 for each combination of day, chain, user type, and product. This preprocessing step enables efficient merging of user sets across multiple days without storing individual user addresses.
- **Key fields**: `timestamp` (daily), `hll` (HyperLogLog structure containing user set), dimensional grouping fields (`chain`, `user_type`, `product_id`)

### Component C: Rolling 7-day window generation

- **Purpose**: Create 7-day rolling windows ensuring continuous time series without gaps
- **Logic**: Generate a complete datespine covering all days from the earliest data to yesterday. For each day in the datespine, create associations with that day plus the 6 preceding days (total 7 days) to form rolling windows. This approach ensures that even days with no activity are included in the calculation and that the WAU for each day reflects users active in the current day plus the previous 6 days.
- **Key fields**: `timestamp` (target day), `joinday` (each of the 7 days in the rolling window)

### Component D: HLL aggregation within windows

- **Purpose**: Merge HLL structures across each 7-day window and extract unique user counts
- **Logic**: For each target day, take the HLL structures from all 7 days in that day's rolling window. Merge these HLL structures using `hll_count.merge_partial` to combine user sets while maintaining uniqueness. Extract the final unique user count using `hll_count.extract`. This produces the weekly active user count for each combination of day, chain, user type, and product.
- **Key fields**: `user_wau` (extracted unique user count from merged HLL), dimensional grouping maintained from previous steps

### Component E: Dimensional breakdown creation

- **Purpose**: Generate WAU metrics across four different dimensional views
- **Logic**: Create four separate breakdowns from the windowed data: project-level (aggregating across all dimensions), chain-level (grouping by chain), user type-level (grouping by user type), and product-level (grouping by product version). Each breakdown nullifies the non-relevant dimensional fields and aggregates HLL structures accordingly. Combine all breakdowns using UNION ALL to create a comprehensive dimensional view.
- **Key fields**: `dimensions` (indicating breakdown type), dimension-specific fields (`chain_id`, `product_id`, `user_type`), `user_wau` (aggregated count)

### Final aggregation

- **Combining components**: Union all dimensional breakdowns into a single result set, with each row representing WAU for a specific day and dimensional combination
- **Schema standardization**: Apply the `finalize_metric_user_wau` macro to standardize column names, data types, and add metadata fields like data source identifier ('etherex')
- **Gap handling**: Fill missing days with zero values to maintain continuous time series using 'fill_zeros' strategy
- **Chain filtering**: Apply chain availability filters to exclude time periods where data coverage may be incomplete or unreliable
- **Final calculation**: The final WAU value represents the count of unique users who were active within the 7-day rolling window ending on each date, broken down by the specified dimensions

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

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

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Active users (weekly)

Active users (weekly)

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