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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Wombat Exchange

# Wombat Exchange Active users (monthly) Methodology

## 1. Overview

Monthly Active Users (MAU) measures the number of unique users who performed at least one trade on Wombat Exchange within a rolling 30-day period. This metric provides insight into user engagement and platform adoption across Wombat's multi-chain decentralized exchange infrastructure. The calculation uses a rolling 30-day window approach, meaning each day's MAU represents users active from that day back to the 29 previous days, ensuring consistent measurement of monthly engagement patterns.

## 2. Data sources

- **Blockchain swap events**: Parsed `Swap` events from dozens of Wombat pool contracts across six blockchain networks (BSC, Arbitrum, Ethereum, Avalanche, Optimism, and Base)
- **Cross-chain events**: `SwapV2` events from `CrossChainPool` contracts enabling inter-chain trading
- **Specialized pool events**: `Swap` events from various specialized pools including stablecoin pools, liquid staking pools, and innovation pools
- **Transaction data**: Transaction records from BSC, Arbitrum, Ethereum, Avalanche, Optimism, and Base networks containing user addresses and transaction metadata
- **Pool contract data**: Contract addresses and configurations for various pool types including standard pools, side pools, dynamic pools, and specialized token pools

## 3. Step-by-step calculation

### Component A: Daily trade activity extraction
- **Purpose**: Identify unique users who performed swaps on each day across all Wombat pools and chains
- **Logic**: Extract all swap events from Wombat's various pool contracts across six blockchain networks. Each swap event is joined with its corresponding blockchain transaction to identify the user address that initiated the trade. Users are classified with user type 'Trades' and grouped by day, chain, and other relevant dimensions. The system processes events from multiple pool types including standard pools, cross-chain pools, side pools, dynamic pools, and dozens of specialized pools for specific token pairs.
- **Key fields**: `user_address` (from transaction `from_address`), `block_timestamp` (truncated to day), `chain`, `user_type`, `product_id`

### Component B: HyperLogLog sketch generation  
- **Purpose**: Convert daily unique users into efficient data structures for scalable unique counting across time windows
- **Logic**: Transform the daily unique user lists into HyperLogLog (HLL) sketches with precision parameter 15. This probabilistic data structure enables memory-efficient storage and combination of unique user counts across multiple days without losing accuracy. Each day's users are aggregated by chain, user type, and product dimensions.
- **Key fields**: `hll` (HyperLogLog sketch), `timestamp`, `chain_id`, `user_type`, `product_id`

### Component C: Rolling window construction
- **Purpose**: Create 30-day rolling windows for each day to capture monthly activity patterns
- **Logic**: Generate a continuous datespine covering all days from the earliest available data to the current date. For each day in the datespine, create a 30-day lookback window by generating timestamps for the current day plus the previous 29 days. This creates a rolling window structure where each day is associated with its relevant 30-day period for user activity measurement.
- **Key fields**: `timestamp` (target date), `joinday` (dates within the 30-day window)

### Component D: Window aggregation
- **Purpose**: Combine daily HLL sketches within each 30-day window to capture all unique users active in that period
- **Logic**: Join the rolling window structure with the daily HLL sketches to collect all user activity data for each 30-day period. This creates a comprehensive view where each target date has associated user activity from its corresponding 30-day lookback window. The result includes all the dimension breakdowns (chain, user type, product) for proper aggregation.
- **Key fields**: `timestamp`, `chain_id`, `user_type`, `product_id`, `hll` (daily sketches within window)

### Component E: Dimension-based aggregation
- **Purpose**: Calculate MAU across four different dimensional breakdowns for comprehensive analytics
- **Logic**: Process the windowed data through four parallel aggregation paths. First, aggregate across all dimensions for overall project-level MAU. Second, group by chain to show MAU per blockchain network. Third, group by user type to show MAU by activity category. Fourth, group by product to show MAU by specific product offerings. Each aggregation merges the HLL sketches within the grouping and extracts the final unique user count.
- **Key fields**: `dimensions` (aggregation level), `chain_id`, `user_type`, `product_id`, `user_mau` (extracted count)

### Final aggregation
- **Combining components**: The four dimensional aggregations are combined using union operations to create a comprehensive dataset with MAU calculated at multiple granularity levels
- **Currency conversion**: Not applicable for user count metrics
- **Time aggregation**: Each day represents a rolling 30-day MAU calculation, with continuous daily updates as new data becomes available
- **Final calculation**: HyperLogLog sketches are merged using `hll_count.merge_partial()` within each dimension group, then unique user counts are extracted using `hll_count.extract()` to produce the final MAU values. Missing days are filled with zeros to ensure complete time series coverage.

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

Unique revenue-generating users over a rolling 30-day window.

Wombat ExchangeWOM

Active users (monthly)

Active users (monthly)

Competitive landscape

Chain breakdown