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

Compound is a non-custodial lending protocol built on Ethereum. Compound was founded by Robert Leshner & Geoffrey Hayes and launched in 2017.

 COMP tokenholders own and govern the protocol. They currently take a cut of the total interest paid by borrowers (revenue). Compound’s investors include a16z, Paradigm, Polychain, etc.

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Lending

Compound

# Compound Active users (monthly) Methodology

## 1. Overview

Monthly Active Users (MAU) measures the number of unique users who have interacted with the Compound protocol within any 30-day period. The metric captures all lending and borrowing activities across Compound v2 and v3 deployments on Ethereum, Arbitrum, Base, and Polygon. The calculation uses HyperLogLog probabilistic data structures to efficiently estimate distinct user counts across large datasets and provides breakdowns by chain, user interaction type, and product.

## 2. Data sources

The calculation relies on parsed blockchain event data from Compound protocol smart contracts:

- **Compound v2 Ethereum events**: Parsed `CEther_Borrow`, `CEther_Mint`, `CEther_Redeem`, and `CEther_RepayBorrow` events capturing ETH lending interactions
- **Compound v2 ERC20 events**: Parsed `CErc20_Borrow`, `CErc20_Mint`, `CErc20_Redeem`, and `CErc20_RepayBorrow` events for ERC20 token lending
- **Compound v2 delegator events**: Parsed `CErc20Delegator_Borrow`, `CErc20Delegator_Mint`, `CErc20Delegator_Redeem`, and `CErc20Delegator_RepayBorrow` events for delegated token contracts
- **Compound v3 multi-chain events**: Parsed `Comet_SupplyCollateral`, `Comet_Supply`, `Comet_WithdrawCollateral`, and `Comet_Withdraw` events from Ethereum, Arbitrum, Base, and Polygon deployments

Each event contains user addresses, transaction timestamps, and interaction context needed for user activity tracking.

## 3. Step-by-step calculation

### Component A: User interaction extraction
- **Purpose**: Identifies and categorizes all user interactions with Compound protocol
- **Logic**: Extracts user addresses from parsed blockchain events across all supported chains and protocol versions. Maps different event parameters to user addresses - `borrower` and `src` parameters identify users initiating withdrawals/borrows, while `minter`, `payer`, and `dst` parameters identify users making deposits/repayments. Categorizes each interaction by user type: Borrower (borrow, withdraw events), Depositor (mint, supply events), Withdrawer (redeem, withdraw collateral events), and Repayer (repay, supply events). Includes chain identifier, timestamp, and market classification for each interaction.
- **Key fields**: `block_timestamp` for timing, `user_address` for user identification, `user_type` for interaction categorization, `chain` for blockchain identification

### Component B: Daily active user aggregation  
- **Purpose**: Calculates daily distinct active users using probabilistic counting
- **Logic**: Groups user interactions by day using timestamp truncation to daily granularity. Creates HyperLogLog sketches with 15-bit precision to estimate distinct user counts for each combination of date, chain, user type, and product. This probabilistic approach efficiently handles large user sets while maintaining accuracy. Applies chain availability filters to ensure only valid chain data is included.
- **Key fields**: `timestamp` truncated to day level, `user_dau` containing user addresses for distinct counting, `timestamp_grain` set to 'day'

### Component C: Monthly rolling window creation
- **Purpose**: Establishes 30-day sliding windows for MAU calculation
- **Logic**: Generates a complete datespine covering all days from the earliest user activity to yesterday. For each day in the datespine, creates a 30-day lookback window by generating an array of 30 consecutive days ending on that date. Joins this datespine with the daily HyperLogLog data to create a comprehensive window dataset that includes all combinations of target dates and their corresponding 30-day historical periods.
- **Key fields**: `timestamp` for target date, `joinday` for historical dates within each window, `hll` containing daily user sketches

### Component D: MAU calculation and dimensional breakdowns
- **Purpose**: Computes final MAU estimates across multiple dimensional cuts
- **Logic**: Merges HyperLogLog sketches within each 30-day window using `hll_count.merge_partial()` to combine daily user estimates. Extracts final MAU counts using `hll_count.extract()` to convert probabilistic sketches to numeric estimates. Creates four separate dimensional views: project-level (all chains and user types combined), chain-level (grouped by blockchain), user-type-level (grouped by interaction type), and product-level (grouped by product identifier). Each breakdown aggregates the merged HyperLogLog data at the appropriate granularity.
- **Key fields**: `user_mau` containing final distinct user estimates, `dimensions` indicating breakdown type, `chain_id`, `user_type`, `product_id` for dimensional grouping

### Final aggregation
- **Combining components**: Unions all dimensional breakdowns into a single result set, with null values for non-applicable dimension fields in each breakdown
- **Time aggregation**: Uses daily timestamp boundaries with 30-day rolling windows, ensuring consistent MAU measurement across all time periods
- **Final calculation**: The final MAU value represents the estimated number of distinct users who interacted with Compound protocol within any 30-day period, calculated using HyperLogLog probabilistic counting for scalable distinct user estimation across large datasets and multiple blockchain networks

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

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

CompoundCOMP

Active users (monthly)

Active users (monthly)

Competitive landscape

Chain breakdown