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 (daily) Methodology

## 1. Overview

MetaMask user DAU (Daily Active Users) measures the number of unique users who execute trades through MetaMask's DEX aggregator functionality on any given day. This metric captures trading activity across Ethereum, BSC, and Polygon chains, identifying users as "active" when they successfully complete swap transactions through MetaMask router contracts. The calculation uses HyperLogLog algorithms for efficient approximate distinct counting across multiple dimensional breakdowns.

## 2. Data sources

The metric relies on several blockchain data sources:
- **Swap function call events**: Parsed `swap` function calls from MetaMask router contracts on Ethereum, BSC, and Polygon networks
- **Transaction records**: Complete transaction data from Ethereum, BSC, and Polygon blockchains to identify actual user addresses
- **Token metadata**: Information about wrapped native token addresses for each supported chain
- **Denylist data**: Hardcoded list of token addresses representing spam or invalid tokens to be excluded from analysis

## 3. Step-by-step calculation

### Component A: Trade extraction
- **Purpose**: Extract all successful trading activity from MetaMask DEX aggregator contracts
- **Logic**: Query parsed `swap` function calls from MetaMask router contracts across three supported chains (Ethereum, BSC, Polygon). Filter for successful transactions only (`status = true`) and exclude transactions involving denylisted token addresses. Handle native token swaps by converting null addresses to the appropriate wrapped native token address for each chain (WETH for Ethereum, WBNB for BSC, WMATIC for Polygon). Join swap events with blockchain transaction data to obtain the originating user address (`from_address`) rather than just the contract interaction address
- **Key fields**: `block_timestamp`, `transaction_hash`, `user_address`, `chain`, `tokenFrom`, `amount`, `status`

### Component B: Daily user identification  
- **Purpose**: Identify unique users who traded on each day
- **Logic**: Truncate all trade timestamps to daily boundaries using day-level granularity. Group trades by day, chain, and classify all users as "Trader" user type since this metric specifically measures trading activity. Extract distinct `user_address` values for each day-chain combination to identify unique daily active users. Apply chain availability filtering to ensure only data from chains with sufficient data quality is included
- **Key fields**: `timestamp` (daily), `chain`, `user_type`, `user_address`

### Component C: HyperLogLog aggregation
- **Purpose**: Create scalable approximate distinct counting across multiple dimensional views
- **Logic**: Initialize HyperLogLog sketches for each daily user count using precision parameter 15 for balance between accuracy and storage efficiency. Create separate dimensional breakdowns: project-level (all chains combined), chain-level (breakdown by individual chains), user-type level (breakdown by user classification), and product-level (breakdown by product categories). For each dimension, merge HLL sketches appropriately and extract final distinct counts
- **Key fields**: `hll` (HyperLogLog sketch), `user_dau` (extracted count), `dimensions`, `chain_id`, `user_type`, `product_id`

### Final aggregation
- **Combining components**: Union all dimensional breakdowns into a single result set, with null values for non-applicable dimension columns in each breakdown
- **Currency conversion**: Not applicable - this metric counts users rather than monetary values
- **Time aggregation**: Daily boundaries established by truncating timestamps to day level, with zero-filling applied for missing days to ensure continuous time series
- **Final calculation**: Apply `finalize_metric_user_dau` macro to standardize the schema format and implement zero-filling strategy for days with no trading activity, then apply final chain availability filtering to ensure data quality standards

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

Unique revenue-generating users over a fixed 24-hour window.

MetaMask

Active users (daily)

Active users (daily)

Competitive landscape

Chain breakdown