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 Average trading volume (per trade) Methodology

## 1. Overview

Trading volume average per trade count measures the average USD value per trade on Wombat Exchange, a multi-chain decentralized exchange. This metric is calculated by dividing the total daily trading volume by the number of unique trades, providing insight into the typical trade size on the platform. The calculation aggregates data from swap events across BSC, Arbitrum, Ethereum, Avalanche, Optimism, and Base networks.

## 2. Data sources

- **Wombat Exchange Swap events**: Parsed `Swap` and `SwapV2` events from multiple pool contracts across BSC, Arbitrum, Ethereum, Avalanche, Optimism, and Base blockchains
- **Transaction records**: Blockchain transaction data from each supported network to identify user addresses
- **Fee tier events**: `SetHaircutRate` events from Wombat pool contracts containing dynamic fee updates
- **Token price feeds**: End-of-day token price data for USD conversion
- **Token metadata**: Token information including decimal places and symbols for proper unit conversion

## 3. Step-by-step calculation

### Component A: Trade event extraction
- **Purpose**: Identifies all swap transactions across Wombat's multi-chain pool contracts
- **Logic**: Extracts `Swap` and `SwapV2` events from various pool types including CrossChainPool, Pool, SidePool, DynamicPool, and specialized pools like FraxEthPool, WmxWOMPool across five blockchain networks. Events include `fromToken`, `toToken`, `fromAmount`, `toAmount` fields along with transaction metadata. Applies token address normalization for specific wrapped tokens by converting them to the standard WOM token address. Joins with transaction data to obtain user addresses and applies incremental loading logic for efficient processing.
- **Key fields**: `fromToken`, `toToken`, `fromAmount`, `toAmount`, `transaction_hash`, `log_index`, `block_timestamp`, `address` (pool contract)

### Component B: Volume calculation with price conversion
- **Purpose**: Converts raw token amounts to USD values with manipulation protection
- **Logic**: Processes token amounts by dividing by the appropriate decimal divisor based on token metadata to get actual token quantities. Joins with end-of-day token price feeds to convert amounts to USD values for both tokens in each swap. Applies manipulation protection by taking the lesser USD value between the two tokens in each trade to prevent inflated volumes from price discrepancies. Groups trades by day and sums the USD amounts to get daily trading volume per pool contract.
- **Key fields**: `token0_amount`, `token1_amount`, `trading_volume` (USD), `timestamp`, `account_address`

### Component C: Fee tier application
- **Purpose**: Applies dynamic fee rates to each pool contract
- **Logic**: Uses a dimension table containing initial hardcoded fee tiers for each pool contract ranging from 0.00002 to 0.0025. Updates these rates with values from `SetHaircutRate` events that modify fee tiers over time. Forward-fills fee tier values across dates to ensure each day has an applicable rate. Joins fee tier data with trade records based on timestamp, chain, and pool contract address.
- **Key fields**: `fee_tier`, `timestamp`, `chain`, `account_address`

### Component D: Trade count aggregation
- **Purpose**: Counts the number of unique trades per day
- **Logic**: Uses the same processed trade data from the volume calculation but counts distinct `transaction_hash` values instead of summing amounts. Groups by timestamp, chain, and business dimensions to get daily trade counts. Applies the same data quality filters and incremental processing logic as the volume calculation to ensure consistency between numerator and denominator.
- **Key fields**: `transaction_hash`, `trade_count`, `timestamp`, `chain_id`

### Final aggregation
- **Combining components**: The trading volume metric and trade count metric are calculated separately using the same underlying trade data, then joined on timestamp to ensure alignment. Both metrics use identical filtering and grouping logic to maintain consistency.
- **Currency conversion**: All token amounts are converted to USD using end-of-day price feeds, with the lesser value between token pairs used to prevent manipulation from price discrepancies
- **Time aggregation**: Data is aggregated to daily boundaries using timestamp truncation, with missing days filled with zeros using the fill_zeros strategy
- **Final calculation**: The average trading volume per trade is calculated by dividing the total daily trading volume by the daily trade count using safe division to handle cases where trade count might be zero, resulting in the average USD value per trade on Wombat Exchange

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

Wombat ExchangeWOM

Average trading volume (per trade)

Average trading volume (per trade)

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