MetaMask Swap aggregates multiple non-custodial exchanges & traders exchange funds directly through their MetaMask wallet.

MetaMask is a non-custodial wallet and DEX aggregator built on Ethereum and other adjacent blockchains. MetaMask was founded by Aaron Davis and launched in 2015.

MetaMask team & investors own and govern the protocol. They currently take all of the total trading fees paid by traders (revenue).

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MetaMask

# MetaMask Average trading volume (per user) Methodology

## 1. Overview

Trading volume average per user measures the mean USD value of trading activity per daily active user on MetaMask's DEX aggregator platform. This metric divides the total daily trading volume by the number of unique daily active users, providing insight into user engagement intensity and average transaction sizes. The calculation aggregates swap activity across BSC, Ethereum, and Polygon chains where MetaMask operates as a DEX aggregator.

## 2. Data sources

- **MetaMask Router swap events**: Parsed `Router_swap_function` events from MetaMask's smart contracts on BSC, Ethereum, and Polygon networks containing swap transaction details
- **Transaction records**: Blockchain transaction data from BSC, Ethereum, and Polygon networks to identify user addresses and transaction context
- **End-of-day token price feeds**: Daily closing prices for token-to-USD conversion
- **Token metadata**: Token contract information including decimal places for amount normalization
- **Denylisted tokens**: Maintained list of excluded token addresses to filter out spam or invalid tokens

## 3. Step-by-step calculation

### Component A: Trade event extraction
- **Purpose**: Identifies and processes all MetaMask swap transactions across supported chains
- **Logic**: Extracts `Router_swap_function` events from MetaMask contracts on BSC, Ethereum, and Polygon. Filters for successful swaps only (status = true) and excludes transactions involving denylisted token addresses. Joins swap events with transaction records to capture the originating user address (`from_address` from transaction data). Standardizes native token addresses by converting null addresses to wrapped native token addresses (WETH on Ethereum, WBNB on BSC, WMATIC on Polygon). Captures key fields including swap amounts, token addresses, aggregator IDs, and user addresses.
- **Key fields**: `block_timestamp`, `user_address`, `token_address`, `amount`, `chain`, and aggregator information

### Component B: Trading volume calculation  
- **Purpose**: Converts raw token amounts to USD values and aggregates daily trading volumes
- **Logic**: Takes the extracted swap amounts and applies decimal correction based on token metadata to normalize amounts to standard units. Converts normalized amounts to USD using end-of-day price feeds matched by date and token address. Aggregates converted amounts by day using timestamp truncation to daily boundaries. Sums all USD-converted swap amounts within each day to calculate total daily trading volume. Applies chain availability filtering to ensure data quality across all supported networks.
- **Key fields**: `timestamp` (daily), `trading_volume` (USD), `chain_id`

### Component C: Daily active user calculation
- **Purpose**: Counts unique users who performed trades each day using HyperLogLog for efficiency
- **Logic**: Identifies unique user addresses from the trade events for each day using timestamp truncation to daily boundaries. Applies HyperLogLog (HLL) data structure with precision 15 for efficient distinct counting across large datasets. Creates multiple dimensional breakdowns including project-level aggregation (`project_dbt_tag`), chain-level breakdowns, user type classifications, and product-level segmentation. Uses HLL merge operations to combine counts across dimensions while maintaining distinct user accuracy. Classifies all users as 'Trader' user type since the data source is swap events.
- **Key fields**: `timestamp`, `user_dau`, `dimensions`, `chain_id`, `user_type`

### Final aggregation
- **Combining components**: Joins daily trading volume with daily active users using inner join on timestamp, specifically using the project-level DAU aggregation (`dimensions = 'project_dbt_tag'`) to match against total trading volume
- **Currency conversion**: All amounts are converted to USD using end-of-day price feeds with decimal correction applied before aggregation
- **Time aggregation**: Both components use daily boundaries created by truncating timestamps to day level, ensuring consistent time alignment
- **Final calculation**: Divides total daily trading volume by daily active users using safe division to handle edge cases where DAU might be zero, resulting in the average trading volume per active user in USD

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

Average trading volume (per user)

Trading volume divided by daily active users.

MetaMask

Average trading volume (per user)

Average trading volume (per user)

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