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

Venus is a non-custodial lending protocol built on BNB chain. Venus was founded by Joselito Lizarondo and launched in 2020.

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

governance

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Lending

Venus

Venus | Data breakdown

# Venus Active users (monthly) Methodology

## 1. Overview

Venus Monthly Active Users (MAU) measures the number of unique users who performed any action on the Venus lending protocol within a rolling 30-day period. This metric tracks user engagement across Venus's multi-chain lending platform by counting distinct addresses that executed borrowing, depositing, withdrawing, or repayment transactions. The calculation uses HyperLogLog probabilistic counting for efficient aggregation across multiple dimensions including total protocol activity, individual chains, user action types, and product segments.

## 2. Data sources

- **Venus protocol smart contract events**: Parsed events from Venus lending contracts across 8 blockchain networks including BSC, Ethereum, Arbitrum, Base, OpBNB, Optimism, Unichain, and zkSync Era
- **Core lending events**: `Borrow`, `Mint` (deposit), `Redeem` (withdraw), and `RepayBorrow` events from VToken contracts, VBep20Delegate contracts, and native BNB contracts (vBNB)
- **Transaction metadata**: Block timestamps and transaction hashes for each lending event
- **Chain availability data**: Information about when Venus protocol became active on each blockchain network

## 3. Step-by-step calculation

### Component A: User event extraction
- **Purpose**: Identify and normalize all user interactions with Venus lending protocol across supported chains
- **Logic**: Extract parsed smart contract events from Venus protocol contracts on 8 different blockchains. For BSC, capture events from both `VBep20Delegate` contracts (for ERC-20 tokens) and `vBNB` contracts (for native BNB). For other chains (Ethereum, Arbitrum, Base, OpBNB, Optimism, Unichain, zkSync Era), capture events from `VToken` contracts. Classify each event by action type: `Borrow` events identify borrowers, `Mint` events identify depositors, `Redeem` events identify withdrawers, and `RepayBorrow` events identify repayers. Extract the user address from each event (`borrower`, `minter`, `redeemer`, or `payer` depending on event type).
- **Key fields**: `block_timestamp` for timing, `transaction_hash` for uniqueness, `chain` identifier, `user_type` classification (Borrower/Depositor/Withdrawer/Repayer), `user_address` for the acting user, `market_sector_id` and `business_segment_id` both set to 'lending'

### Component B: Daily active user calculation  
- **Purpose**: Calculate daily unique active users grouped by relevant dimensions
- **Logic**: Group the normalized user events by day using timestamp truncation to create daily boundaries. For each day, count distinct user addresses grouped by chain, user type, and product identifier. Apply chain availability filtering to exclude data from before Venus launched on each respective blockchain network. This creates a daily snapshot of unique users who performed any lending action on that specific day.
- **Key fields**: `timestamp` truncated to day boundary, `timestamp_grain` set to 'day', `chain`, `user_type`, `product_id`, `user_dau` containing the distinct user address

### Component C: HyperLogLog sketch creation
- **Purpose**: Convert daily user counts into probabilistic data structures for efficient unique counting across time windows
- **Logic**: Take the daily active user data and initialize HyperLogLog sketches for each combination of day, chain, user type, and product. HyperLogLog allows efficient approximate counting of unique users when merging across multiple days without storing individual user addresses. Use precision parameter of 15 bits for the HyperLogLog sketches to balance accuracy with storage efficiency.
- **Key fields**: `hll` containing the HyperLogLog sketch initialized from daily user addresses, maintaining all grouping dimensions from the DAU calculation

### Component D: Rolling window construction
- **Purpose**: Create 30-day rolling windows for MAU calculation using a datespine approach
- **Logic**: Generate a complete datespine covering all days from the earliest Venus data through yesterday. For each target date in the datespine, create a 30-day lookback window by generating dates from 29 days prior through the target date. Join this window with the daily HyperLogLog sketches to capture all users active during each 30-day period. This approach ensures no gaps in the time series even if certain days had no user activity.
- **Key fields**: `timestamp` for the target date, `joinday` for each day in the 30-day window, maintaining all dimensional breakdowns (chain, user type, product)

### Component E: Multi-dimensional aggregation
- **Purpose**: Calculate MAU across different dimensional breakdowns
- **Logic**: Create four separate aggregations from the windowed data: total protocol MAU (project level), MAU by individual chain, MAU by user action type, and MAU by product. For each aggregation, merge the HyperLogLog sketches across the appropriate dimensions within each 30-day window, then extract the final unique user count. Set non-relevant dimension fields to null for each breakdown type (e.g., chain_id is null for the total protocol breakdown).
- **Key fields**: `dimensions` indicating breakdown type, `user_mau` containing the final extracted unique user count, dimensional fields set to actual values or null based on breakdown type

### Final aggregation
- **Combining components**: Union all four dimensional breakdowns (total, by chain, by user type, by product) into a single result set with consistent schema structure
- **Time aggregation**: Apply monthly partitioning boundaries and use incremental processing with configurable lookback periods and safety margins to handle late-arriving data
- **Final calculation**: Extract the approximate unique user count from merged HyperLogLog sketches, representing the number of distinct users who performed any Venus lending action within each rolling 30-day period. Apply the finalize_metric_user_mau macro to standardize output format and fill any gaps with zero values where appropriate.

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

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

VenusXVS

Active users (monthly)

Active users (monthly)

Competitive landscape

Chain breakdown