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.

github

twitter

discord

etherscan

website

Exchanges (DEX)

Wombat Exchange

# Wombat Exchange Average trading volume (per user) Methodology

## 1. Overview

Trading volume average per user measures the mean daily trading volume per daily active user on Wombat Exchange. This metric indicates the average trading activity intensity by dividing the total USD trading volume across all Wombat pools and chains by the number of unique addresses that conducted trades on each day. It helps assess user engagement depth and the concentration of trading activity among the user base.

## 2. Data sources

The calculation relies on several core data sources:

- **Swap events**: Parsed `Swap` and `SwapV2` events from 43+ Wombat Exchange trading pool contracts across BSC, Arbitrum, Ethereum, Avalanche, Optimism, and Base blockchains
- **Fee tier events**: `SetHaircutRate` events from the same pool contracts that track dynamic fee tier changes over time
- **Transaction records**: Blockchain transaction data from all six supported chains to link swap events to user wallet addresses
- **Token metadata**: Token contract information including decimal places and symbols for proper amount conversion and trading pair identification
- **End-of-day price feeds**: Daily USD price data for all tokens traded on Wombat Exchange to enable volume conversion to USD

## 3. Step-by-step calculation

### Component A: Raw trade data extraction
- **Purpose**: Collect all trading activity from Wombat Exchange across supported chains
- **Logic**: Extract `Swap` and `SwapV2` events from 43+ pool contracts spanning BSC, Arbitrum, Ethereum, Avalanche, Optimism, and Base. Each event captures the tokens being swapped, amounts, and pool contract address. Handle special wrapped token conversions by mapping certain addresses to the WOM token address. Join with blockchain transaction data to identify the user wallet address that initiated each trade.
- **Key fields**: `fromToken` and `toToken` addresses, `fromAmount` and `toAmount` values, pool contract `address`, user `from_address`, `block_timestamp`

### Component B: Fee tier calculation  
- **Purpose**: Track dynamic fee rates for each trading pool over time
- **Logic**: Start with hardcoded initial fee tiers for each pool contract, ranging from 0.002% to 0.25%. Capture subsequent fee changes through `SetHaircutRate` events, converting the `value` parameter from 18-decimal wei format to decimal rate. Forward-fill missing days so every pool has a fee tier for every day since launch.
- **Key fields**: Pool contract `address`, `fee_tier` rate, effective `timestamp`

### Component C: Trading volume calculation
- **Purpose**: Convert raw swap data into standardized USD trading volume
- **Logic**: Process the extracted swap events by correcting token amounts using decimal places from token metadata. Convert both tokens in each swap to USD using end-of-day price feeds matched by date and chain. To prevent manipulation from artificially inflated token prices, use the lesser USD value between the two tokens in each swap. Aggregate all trades to daily level, grouping by trading pair and pool attributes. Join with fee tier data to associate the appropriate rate with each trade.
- **Key fields**: Daily `trading_volume` in USD, `chain`, `account_address` (pool), `token0_address`, `token1_address`, `fee_tier`

### Component D: Daily active user counting
- **Purpose**: Count unique addresses that traded on Wombat Exchange each day
- **Logic**: Extract user addresses from the same transaction data used for trading volume. Group by day and count distinct user addresses using HyperLogLog aggregation for efficiency. Aggregate across multiple dimensions including project-wide totals, per-chain breakdowns, and by user activity type. For this metric, use the project-wide total that combines unique users across all chains and pools.
- **Key fields**: `user_dau` count, aggregation `dimensions`, `timestamp`

### Final aggregation
- **Combining components**: Join the daily trading volume totals with the project-wide daily active user counts on the timestamp field. Sum trading volume across all pools and chains for each day, while taking the maximum DAU value (since it's already aggregated at the project level).
- **Currency conversion**: All volume calculations use end-of-day USD prices matched by token address, chain, and date to ensure consistent valuation timing
- **Time aggregation**: Both volume and user data are aggregated to daily boundaries using date truncation of block timestamps
- **Final calculation**: Divide the total daily trading volume by the daily active user count using safe division to handle edge cases where user count might be zero. Apply standardization macro for consistent formatting and gap-filling with zero values for missing dates.

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

Average trading volume (per user)

Trading volume divided by daily active users.

Wombat ExchangeWOM

Average trading volume (per user)

Average trading volume (per user)

Competitive landscape