Liquidity providers deposit funds into liquidity pools with different liquidity management strategies and receive trading fees from traders (supply-side fees).

Maverick is a non-custodial exchange protocol built on  Ethereum, Binance Smart Chain, Base, and zkSync Era. Maverick was founded by Alvin Xu and Bob Baxley in March 2023.

MAV tokenholders own and govern the protocol. Currently, the protocol does not take a cut of the trading fees. Maverick's investors include Pantera Capital, Binance Labs, Coinbase Ventures, and Apollo Crypto.

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Maverick Protocol

# Maverick Protocol Active users (weekly) Methodology

## 1. Overview

Weekly Active Users (WAU) for Maverick measures the number of distinct users who performed trades on Maverick DEX pools within a rolling 7-day window. This metric answers the question: "How many unique users traded on Maverick in the past week?" The calculation uses HyperLogLog (HLL) sketches for efficient approximate distinct counting across multiple blockchain networks and provides breakdowns by project level, chain, user type, and product dimensions.

## 2. Data sources

The metric relies on the following raw data sources:
- **Blockchain events**: Parsed `Pool_Swap_event` logs from Maverick v1 smart contracts deployed across Ethereum, BSC, Base, and zkSync Era networks
- **Transaction data**: Transaction records from Ethereum, BSC, Base, and zkSync Era containing transaction metadata including `from_address` to identify actual users
- **Contract logs**: Event logs from Maverick pool contracts containing swap parameters like `amountIn`, `amountOut`, and `tokenAIn` (trade direction)

## 3. Step-by-step calculation

### Component A: Trade event extraction
- **Purpose**: Extract and process raw swap events from Maverick pools across multiple blockchains
- **Logic**: Parse `Pool_Swap_event` logs from Maverick v1 pool contracts on Ethereum, BSC, Base, and zkSync Era. Each swap event contains the contract address, amounts, and trade direction. Join these events with corresponding transaction records to identify the actual user address (`from_address`) who initiated the swap, as the event log only contains the pool contract address
- **Key fields**: `block_timestamp` for temporal grouping, `user_address` for distinct counting, `chain` for network identification, `account_address` (pool contract), `amount_in` and `amount_out` for trade amounts, `trade_direction` flag

### Component B: Daily user aggregation  
- **Purpose**: Aggregate trade events to identify distinct daily active users and create HLL sketches
- **Logic**: Group all trade events by day using timestamp truncation to day granularity. For each day, identify distinct user addresses who performed trades and create HyperLogLog sketches for efficient approximate distinct counting. Classify all users as 'Trades' user type since this metric focuses on trading activity. Maintain chain and product dimensions for later breakdown analysis
- **Key fields**: `timestamp` (daily), `user_dau` (distinct user addresses), `chain`, `user_type` (always 'Trades'), `product_id`, HLL sketch initialization with 15-bit precision

### Component C: Rolling window construction
- **Purpose**: Create a complete 7-day rolling window for each day to calculate weekly active users
- **Logic**: Generate a continuous datespine covering all days from the earliest trade data to yesterday. For each day in the datespine, create associations with the previous 6 days to form 7-day windows. This ensures no days are skipped even if there were no trades on certain days. Join this window structure with the daily HLL sketches to prepare for weekly aggregation
- **Key fields**: `timestamp` (target day), `joinday` (each day in the 7-day window), HLL sketches from daily aggregation

### Component D: Multi-dimensional WAU calculation
- **Purpose**: Calculate WAU across different dimensional breakdowns
- **Logic**: For each 7-day window, merge HLL sketches across the window period and extract approximate distinct counts. Create separate breakdowns: project-level WAU (overall Maverick activity), chain-level WAU (activity per blockchain), user-type-level WAU (trading activity), and product-level WAU (activity per product). Each breakdown groups by the relevant dimensions while setting non-applicable dimensions to null
- **Key fields**: `dimensions` (breakdown type), `chain_id`, `product_id`, `user_type`, `user_wau` (extracted distinct count)

### Final aggregation
- **Combining components**: Union all dimensional breakdowns (project, chain, user type, product) into a single result set with a common schema structure
- **HLL processing**: Use BigQuery's HLL functions to merge partial sketches across the 7-day window and extract final approximate distinct counts with 15-bit precision
- **Time boundaries**: Calculate WAU for each day based on a rolling 7-day window (current day plus previous 6 days), ensuring continuous daily WAU values
- **Final calculation**: Extract the approximate distinct user count from merged HLL sketches to produce the final WAU metric value, representing unique users who traded within the rolling 7-day period

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

Unique revenue-generating users over a rolling 7-day window.

Maverick ProtocolMAV

Active users (weekly)

Active users (weekly)

Competitive landscape

Chain breakdown