Liquidity providers deposit funds into Wombat Exchange and receive WOM token rewards.

Wombat Exchange is a non-custodial exchange protocol built on BNB Chain. Wombat Exchange was founded by Alex Lee and launched in April 2022.

WOM tokenholders own and govern the protocol. They currently receive all trading fees paid by traders (revenue). Wombat Exchange’s investors include Binance Labs, Animoca Brands and Hailstone Ventures.

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Exchanges (DEX)

Wombat Exchange

# Wombat Exchange Active users (weekly) Methodology

## 1. Overview

Weekly Active Users (WAU) for Wombat measures the number of unique users who performed at least one trade on the Wombat protocol within a rolling 7-day window. This metric provides insight into user engagement and activity patterns across Wombat's decentralized exchange functionality. The calculation uses a rolling window approach where each day's WAU represents the count of distinct users who traded within that day and the previous 6 days. The metric is calculated using HyperLogLog data structures for efficient distinct user counting across multiple dimensional breakdowns.

## 2. Data sources

- **Wombat smart contract events**: Parsed swap events from various Wombat pool types across 6 blockchain networks (BSC, Arbitrum, Ethereum, Avalanche, Optimism, and Base)
- **Pool contract types**: Events from `CrossChainPool_SwapV2`, `Pool_Swap`, `SidePool_Swap`, `DynamicPool_Swap`, and specialized pools like `AxlUsdcPool_Swap`, `BnbxPool_Swap`, `EthxPool_Swap`, etc.
- **Transaction records**: Blockchain transaction data from all supported chains to identify user addresses initiating trades
- **Token metadata**: Token address mappings to handle unsupported wrapped token conversions
- **Chain availability data**: Configuration data to filter results based on chain data availability periods

## 3. Step-by-step calculation

### Component A: Trade event extraction
- **Purpose**: Identify all swap transactions across Wombat pools and extract user activity
- **Logic**: The system collects swap events from over 40 different Wombat pool contracts across 6 blockchain networks. Each swap event contains details about the tokens being traded (`fromToken`, `toToken`), amounts (`fromAmount`, `toAmount`), and the pool address. The events are joined with transaction records to identify the user address (`from_address`) who initiated the trade. Special handling applies to unsupported wrapped tokens, which are mapped to the WOM token address. All trading activity is classified under the 'Trades' user type within the 'DEX Aggregator' business line.
- **Key fields**: `user_address` (from transaction initiator), `block_timestamp` (for daily grouping), `chain` (blockchain network), `fromToken` and `toToken` (traded asset addresses), `fromAmount` and `toAmount` (trade volumes)

### Component B: Daily active user aggregation
- **Purpose**: Convert raw trade events into daily unique user counts using efficient data structures
- **Logic**: Trading events are aggregated by day using `timestamp_trunc` on the `block_timestamp` field. For each day, the system identifies all unique user addresses who performed trades, grouping by chain, user type, and product dimensions. The unique users are stored in HyperLogLog (HLL) data structures initialized with precision 15, which allows for memory-efficient approximate distinct counting. Chain availability filters are applied to ensure only valid data periods are included in the calculation.
- **Key fields**: `timestamp` (truncated to day), `user_dau` (unique user addresses), `chain` (blockchain identifier), `user_type` (always 'Trades'), HLL sketches for efficient distinct counting

### Component C: Datespine and rolling window creation
- **Purpose**: Create a complete time series and establish 7-day rolling windows for WAU calculation
- **Logic**: A datespine is generated covering all days from the earliest available data to the current date minus one day. For each day in the datespine, a 7-day window is created by joining with the current day plus the previous 6 days. This ensures that every day has a complete 7-day lookback period, even if some days within that window had no trading activity. The join creates multiple rows per target day, with each row representing one day within the 7-day window.
- **Key fields**: `timestamp` (target day for WAU calculation), `joinday` (each day within the 7-day window), window boundaries spanning 7 consecutive days

### Component D: HLL aggregation within windows
- **Purpose**: Combine daily HLL sketches within each 7-day window to calculate distinct users
- **Logic**: For each 7-day window, the daily HLL sketches are merged using `hll_count.merge_partial` to create a combined sketch representing all unique users who traded within that 7-day period. The merged HLL sketch is then extracted using `hll_count.extract` to produce the final distinct user count. This process preserves the approximate distinct counting properties while efficiently handling overlapping users across multiple days within each window.
- **Key fields**: Merged HLL sketches, extracted distinct user counts, maintained dimensional groupings

### Final aggregation
- **Combining components**: The WAU calculation produces four dimensional breakdowns: project-level (aggregated across all dimensions), by chain (preserving chain breakdown), by user type (preserving user type), and by product (preserving product breakdown). Each breakdown uses the same underlying HLL aggregation but groups at different dimensional levels.
- **Currency conversion**: Not applicable as this is a user count metric rather than a financial value
- **Time aggregation**: The metric uses daily boundaries with each day representing a 7-day rolling window. Time zones are handled through UTC timestamp truncation to day level.
- **Final calculation**: The final WAU value for each day equals the distinct count of users who performed at least one trade within that day or any of the previous 6 days, calculated using HyperLogLog approximate distinct counting across the merged 7-day window of daily user sets.

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

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

Wombat ExchangeWOM

Active users (weekly)

Active users (weekly)

Competitive landscape

Chain breakdown