Liquidity providers deposit funds into Zyberswap and receive trading fees from traders (supply-side fees).

Zyberswap is a non-custodial exchange protocol built on Arbitrum. Zyberswap was founded by anonymous team and launched in 2023.

ZYB tokenholders own and govern the protocol. They currently take a cut of the total trading fees paid by traders (revenue).

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Zyberswap

# Zyberswap Active users (weekly) Methodology

## 1. Overview

Weekly Active Users (WAU) measures the number of unique users who performed trading or flash loan activities on Zyberswap over a 7-day rolling window. This metric answers the business question: "How many distinct users are actively engaging with Zyberswap's DEX functionality on a weekly basis?" The calculation uses HyperLogLog (HLL) sketches for efficient distinct user counting and creates a 7-day rolling window from daily active user data across multiple dimensional breakdowns.

## 2. Data sources

- **Zyberswap v2 trading events**: Parsed `Pair_Swap_event` logs from Zyberswap v2 protocol on Arbitrum containing swap transaction details
- **Zyberswap v3 trading events**: Parsed `AlgebraPool_Swap_event` logs from Zyberswap v3 protocol on Arbitrum containing swap transaction details  
- **Zyberswap v2 flash loan events**: Parsed `SwapFlashLoan_TokenSwap_event` logs from Zyberswap v2 protocol on Arbitrum containing flash loan transaction details
- **Zyberswap v3 flash loan events**: Parsed `AlgebraPool_Flash_event` logs from Zyberswap v3 protocol on Arbitrum containing flash loan transaction details
- **Arbitrum transaction records**: Blockchain transaction data used to extract user addresses and apply timestamp filters
- **Historical activity data**: Previous DAU calculations starting from January 23, 2023

## 3. Step-by-step calculation

### Component A: Daily active user collection
- **Purpose**: Identify and collect all unique users who performed trading or flash loan activities on each day
- **Logic**: Extract user addresses from both trading and flash loan events across Zyberswap v2 and v3 protocols. For trades, capture users from `Pair_Swap_event` (v2) and `AlgebraPool_Swap_event` (v3) logs. For flash loans, capture users from `SwapFlashLoan_TokenSwap_event` (v2) and `AlgebraPool_Flash_event` (v3) logs. Join all events with Arbitrum transaction data to get the actual user address from the `from_address` field. Truncate timestamps to daily granularity and classify all users as 'Trader' type. Apply date filter starting from January 23, 2023. Union distinct results from both activity types to avoid double-counting users who perform multiple activities on the same day
- **Key fields**: `block_timestamp` truncated to day, `chain` (Arbitrum), `user_address` from transaction records, `user_type` (always 'Trader'), `market_sector_id`, `business_segment_id`, `product_id`

### Component B: HLL sketch initialization  
- **Purpose**: Convert daily active user data into HyperLogLog sketches for efficient distinct counting across dimensional combinations
- **Logic**: Take the daily active user data and initialize HLL sketches with precision 15 for each unique combination of timestamp, chain, user type, and product. Group by these dimensions to create base HLL sketches that will be used in the rolling window calculation
- **Key fields**: `timestamp`, `chain_id`, `user_type`, `product_id`, `hll` sketch initialized from `user_dau` field

### Component C: Date spine and window construction
- **Purpose**: Create a continuous date range and establish 7-day rolling windows for WAU calculation
- **Logic**: Generate a date spine from the minimum timestamp in the DAU data to yesterday using daily intervals to ensure no missing dates. For each date in the spine, create joins to generate a 7-day window by connecting each date to the previous 6 days (7 days total including the current date). This creates the foundation for the rolling weekly calculation
- **Key fields**: `timestamp` (target date), `joinday` (each day within the 7-day window)

### Component D: Window aggregation
- **Purpose**: Combine HLL sketches within each 7-day window to calculate unique user counts
- **Logic**: Join the 7-day window structure with the HLL sketches from daily data. For each target timestamp, collect all HLL sketches from the corresponding 7 days. This creates the raw data needed for WAU aggregation across different dimensional breakdowns
- **Key fields**: `timestamp` (target date), `timestamp_grain`, `chain_id`, `user_type`, `product_id`, `hll` sketches from the 7-day window

### Component E: Dimensional breakdowns
- **Purpose**: Calculate WAU separately for different analytical perspectives
- **Logic**: Create four separate dimensional aggregations by merging and extracting HLL sketches. Project-level breakdown nullifies all dimensional fields and merges all HLL sketches to get total WAU. Chain-level breakdown groups by chain and nullifies other dimensions. User-type-level breakdown groups by user type and nullifies other dimensions. Product-level breakdown groups by product and nullifies other dimensions. Each breakdown uses HLL merge and extract functions to get the final distinct user count
- **Key fields**: `dimensions` (breakdown type), `chain_id`, `user_type`, `product_id` (nullified appropriately), `user_wau` (extracted HLL count)

### Final aggregation
- **Combining components**: Union all dimensional breakdowns (project, chain, user type, product) into a single result set with consistent schema
- **Schema finalization**: Apply the `finalize_metric_user_wau` macro to standardize the output format, add metadata fields, and fill missing days with zero values using the 'fill_zeros' strategy
- **Time boundaries**: Use daily timestamps with monthly partitioning for efficient querying and storage
- **Incremental processing**: For incremental updates, apply safety margins by filtering data from 29 days plus the incremental lookback period before the last modified time to ensure data completeness and handle late-arriving data

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

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

ZyberswapZYB

Active users (weekly)

Active users (weekly)

Competitive landscape