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 Active users (monthly) Methodology

## 1. Overview

MetaMask Monthly Active Users (MAU) measures the number of unique users who executed at least one trade through MetaMask's DEX aggregation service within a rolling 30-day period. This metric answers the business question: "How many distinct users are actively trading through MetaMask each month?" The calculation uses HyperLogLog sketches for memory-efficient approximate distinct counting and provides breakdowns across multiple dimensions including blockchain networks, user types, and products.

## 2. Data sources

The MAU calculation relies on these underlying data sources:

- **Blockchain events**: MetaMask Router `swap` function calls from Ethereum, BSC (Binance Smart Chain), and Polygon networks, capturing when users execute trades through MetaMask's DEX aggregation service
- **Transaction data**: Transaction records from Ethereum, BSC, and Polygon containing user wallet addresses and transaction metadata
- **Token metadata**: Token contract addresses and wrapped native token mappings (ETH to WETH, BNB to WBNB, MATIC to WMATIC) for consistent token identification across chains
- **Data types**: Daily trading activity events, user wallet addresses, token swap amounts, and blockchain timestamps

## 3. Step-by-step calculation

### Component A: Trade event processing
- **Purpose**: Extract and normalize trading activity from MetaMask's DEX aggregation service across multiple blockchains
- **Logic**: The system processes MetaMask Router swap function events from Ethereum, BSC, and Polygon networks. Each swap event is joined with the corresponding blockchain transaction to extract the actual user wallet address (`from_address` from transactions, not the router contract address). Native token addresses are normalized to their wrapped equivalents (ETH becomes WETH, BNB becomes WBNB, MATIC becomes WMATIC) for consistent handling. A denylist filters out known spam or scam token addresses. Only successful swaps are included based on transaction status.
- **Key fields**: `user_address` (wallet initiating the trade), `block_timestamp` (when trade occurred), `chain` (blockchain network), `token_address` (normalized token being traded), `amount` (trade size)

### Component B: Daily active user aggregation
- **Purpose**: Identify distinct users who traded on each calendar day
- **Logic**: Trading events are aggregated to daily granularity using day-level timestamp truncation. For each calendar day, the system extracts all distinct user wallet addresses who executed at least one successful swap. All users are classified as 'Trader' type since MetaMask functions as a DEX aggregator. Chain availability filtering ensures only chains with reliable data are included.
- **Key fields**: `timestamp` (calendar day), `chain` (blockchain network), `user_type` ('Trader'), `user_dau` (distinct user address)

### Component C: HyperLogLog sketch creation
- **Purpose**: Create memory-efficient data structures for approximate distinct user counting across large datasets
- **Logic**: Daily active user data is converted into HyperLogLog sketches using BigQuery's `hll_count.init` function with 15-bit precision. This creates compact probabilistic data structures that can efficiently estimate distinct counts without storing individual user addresses. Sketches are created for each combination of day, chain, user type, and product.
- **Key fields**: `hll` (HyperLogLog sketch), `timestamp` (calendar day), `chain_id`, `user_type`, `product_id`

### Component D: Rolling window construction
- **Purpose**: Build 30-day lookback windows for monthly active user calculation
- **Logic**: A comprehensive datespine is created covering all calendar days from the earliest available data through yesterday to ensure no missing timestamps. For each target date, a 30-day lookback window is constructed (29 previous days plus the current day) using timestamp array generation. This creates a cross-join between each target date and all days in its 30-day window. HyperLogLog sketches are then joined to match each day in each window.
- **Key fields**: `timestamp` (target date for MAU calculation), `joinday` (individual days within the 30-day window), `hll` (daily user sketches)

### Component E: Dimensional breakdowns
- **Purpose**: Calculate MAU across different business dimensions for reporting flexibility
- **Logic**: The system creates four separate MAU calculations: overall project level (all chains and products combined), by individual blockchain network, by user type, and by product identifier. For each breakdown, HyperLogLog sketches within the 30-day windows are merged using `hll_count.merge_partial` and then extracted to get approximate distinct user counts using `hll_count.extract`. Null values are used for non-applicable dimension fields in each breakdown.
- **Key fields**: `dimensions` (breakdown type), `chain_id`, `user_type`, `product_id`, `user_mau` (monthly active users count)

### Final aggregation
- **Combining components**: The four dimensional breakdowns (project-level, by-chain, by-user-type, by-product) are combined using UNION ALL operations to create a single comprehensive dataset
- **Currency conversion**: No currency conversion is applied as this metric counts users, not monetary values
- **Time aggregation**: MAU is calculated as a rolling 30-day window, updated daily. Each date represents the count of distinct users active in the 30 days ending on that date
- **Final calculation**: The final MAU value for each date and dimension combination represents the approximate number of distinct wallet addresses that executed at least one successful trade through MetaMask's DEX aggregation service in the trailing 30-day period, calculated using merged HyperLogLog sketches for memory efficiency

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

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

MetaMask

Active users (monthly)

Active users (monthly)

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Chain breakdown