Lenders deposit funds into Moonwell and receive interest payments from borrowers (supply-side fees).

Moonwell is a non-custodial lending protocol built on multiple blockchains.

WELL tokenholders own and govern the protocol. They currently take a cut of the total interest + liquidation fees paid by borrowers (revenue).

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Lending

Moonwell

# Moonwell Active users (weekly) Methodology

## 1. Overview

Weekly Active Users (WAU) measures the number of unique users who interacted with Moonwell lending protocol within any 7-day period. This metric provides insight into user engagement and protocol adoption by counting distinct wallet addresses that performed lending activities (deposits, withdrawals, borrows, repayments) across supported blockchain networks. The calculation uses a rolling 7-day window approach with HyperLogLog approximation for efficient computation across large datasets.

## 2. Data sources

- **Blockchain events**: Parsed compound fork CToken events from Base and Optimism networks, specifically `Borrow`, `Mint`, `Redeem`, and `RepayBorrow` events
- **Market configuration**: Moonwell protocol market definitions including official contract addresses and underlying token mappings
- **Token metadata**: Token symbols, decimal places, and identifiers for both underlying tokens and market tokens
- **Chain availability**: Network-specific data availability windows to ensure accurate cross-chain calculations

## 3. Step-by-step calculation

### Component A: User activity identification
- **Purpose**: Identify all unique daily active users from lending protocol interactions
- **Logic**: Extract user addresses from parsed CToken events across Base and Optimism chains. Filter events to include only interactions with verified Moonwell market contracts. Classify users into four types based on their actions: Borrower (from `Borrow` events using `borrower` field), Depositor (from `Mint` events using `minter` field), Withdrawer (from `Redeem` events using `redeemer` field), and Repayer (from `RepayBorrow` events using `payer` field). Group interactions by day, chain, and user type to create daily active user records
- **Key fields**: `block_timestamp` for daily grouping, `user_address` for distinct counting, `chain` for network identification, `user_type` for activity classification

### Component B: HyperLogLog sketch creation
- **Purpose**: Convert daily active user data into efficient approximate counting structures
- **Logic**: Transform daily user lists into HyperLogLog sketches using 15-bit precision for memory-efficient distinct counting. Group by timestamp, chain, user type, and product dimensions. This enables efficient merging across time windows without storing individual user addresses
- **Key fields**: `timestamp` for daily boundaries, `hll` sketch containing compressed user data, dimensional grouping fields for chain, user type, and product

### Component C: Rolling window construction  
- **Purpose**: Create complete 7-day rolling windows for WAU calculation
- **Logic**: Generate a continuous date spine covering the full analysis period without gaps. For each date, create a 7-day lookback window including the current day plus the 6 preceding days. Join window dates with available daily HLL sketches to ensure complete coverage even when some days have no activity
- **Key fields**: `timestamp` for the WAU date, `joinday` for the component days in each window, `hll` sketches from matching daily data

### Component D: Weekly active user aggregation
- **Purpose**: Calculate weekly active users by merging daily sketches across 7-day windows
- **Logic**: For each 7-day window, merge all available HLL sketches using HyperLogLog merge operations. Extract the final distinct count to produce the weekly active user number. This approach ensures users active on multiple days within the week are counted only once
- **Key fields**: `user_wau` as the final weekly active user count, dimensional fields for breakdown categories

### Component E: Dimensional breakdowns
- **Purpose**: Provide WAU metrics across multiple analytical dimensions
- **Logic**: Calculate separate WAU values for four different breakdowns: project-level (overall Moonwell protocol), chain-level (Base vs Optimism), user-type level (Borrower, Depositor, Withdrawer, Repayer), and product-level (future extensibility). Each breakdown aggregates the appropriate dimensional groupings while setting non-relevant dimensions to null
- **Key fields**: `dimensions` field indicating breakdown type, `chain_id`, `user_type`, `product_id` for dimensional filtering

### Final aggregation
- **Combining components**: Union all dimensional breakdowns into a single output table with standardized schema. Each record represents WAU for a specific date and dimensional combination
- **Time aggregation**: Daily WAU calculations using 7-day rolling windows, ensuring consistent time boundaries across all dimensions
- **Schema standardization**: Apply the `finalize_metric_user_wau` macro to ensure consistent field naming, data types, and missing value handling with zero-fill strategy for days without activity
- **Final calculation**: Weekly active users equal the distinct count of user addresses active within any 7-day period ending on each date, calculated using HyperLogLog approximation for computational efficiency

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

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

MoonwellWELL

Active users (weekly)

Active users (weekly)

Competitive landscape

Chain breakdown