Overcollateralized nodes custody and transfer funds between blockchains and receive burn & mint fees from traders (supply-side fees).

Ren is a non-custodial interoperability protocol built on Ethereum and other blockchains. Ren was founded by Tayiang Zhang & Loong Wang and launched in 2017. Ren is currently only in redeeming-mode, due to their exposure to the effects of Alameda Research's failure.

REN tokenholders own and govern the protocol. They do not currently take a cut of the total mint/burn fees paid by traders (revenue). Ren's investors include Polychain, FBG Capital, BlockVC, etc.

governance

github

twitter

discord

etherscan

website

Bridges

# Ren Active users (monthly) Methodology

## 1. Overview

Monthly Active Users (MAU) for Ren protocol measures the number of unique users who interact with Ren's cross-chain bridge over a rolling 30-day period. This metric captures users who either deposit assets to create wrapped tokens (Depositors) or withdraw/burn wrapped tokens (Withdrawers) across multiple blockchain networks. The calculation uses HyperLogLog data structures for efficient unique user counting and provides dimensional breakdowns by blockchain, user type, and product category.

## 2. Data sources

The calculation relies on the following raw data sources:
- **Ren protocol gateway events**: Parsed `LogMint` and `LogBurn` events from BTCGateway contracts on Ethereum, BSC, Fantom, and Polygon networks
- **Ren protocol gateway events**: Parsed `LogMint` and `LogBurn` events from ETHGateway contracts on Arbitrum, Avalanche, and Optimism networks  
- **Blockchain transaction data**: Block timestamps and transaction hashes from all supported chains
- **Contract event logs**: Raw event parameters including user addresses and transaction details

## 3. Step-by-step calculation

### Component A: User activity extraction
- **Purpose**: Identifies all user interactions with Ren protocol bridge contracts across multiple chains
- **Logic**: Processes gateway contract events from seven blockchain networks (Ethereum, Arbitrum, Avalanche, BSC, Fantom, Polygon, Optimism). Extracts user addresses from `LogMint` events (representing deposits/wrapping) and `LogBurn` events (representing withdrawals/unwrapping). Maps different parameter names across chains (`to`, `_to`) to standardized user address fields. Classifies users as 'Depositor' for mint events or 'Withdrawer' for burn events. Associates each interaction with block timestamp, transaction hash, chain identifier, and market classification as 'bridges' sector with 'mint_and_burn' business segment.
- **Key fields**: `block_timestamp` for timing, `user_address` for unique user identification, `user_type` for deposit/withdrawal classification, `chain` for blockchain network, `transaction_hash` for deduplication

### Component B: Daily active user aggregation  
- **Purpose**: Converts raw user interactions into daily unique user counts using efficient data structures
- **Logic**: Groups user activity by calendar day (truncating timestamps to day granularity), chain, user type, and product category. Creates HyperLogLog sketches for each unique user address within each day-dimension combination. Uses HyperLogLog initialization with 15-bit precision to enable memory-efficient counting of distinct users while maintaining accuracy. Preserves dimensional attributes (chain, user_type, product_id) for downstream breakdown analysis.
- **Key fields**: `timestamp` truncated to day, `hll` containing HyperLogLog sketch of unique users, dimension fields for grouping

### Component C: Rolling 30-day window construction
- **Purpose**: Creates overlapping 30-day periods for each date to calculate rolling monthly active users
- **Logic**: Generates a complete date spine covering the full range of available data to ensure no gaps in the time series. For each date in the spine, creates a 30-day lookback window by generating timestamps from 29 days prior through the current date. Joins this expanded date range with daily HyperLogLog data to collect all user activity sketches within each 30-day period. Handles missing days gracefully by including only available data within each window.
- **Key fields**: `timestamp` for the window end date, `joinday` for dates within the 30-day lookback period, daily `hll` sketches for aggregation

### Component D: Dimensional MAU breakdowns
- **Purpose**: Calculates unique monthly active users across different analytical dimensions
- **Logic**: Creates four separate dimensional views of MAU data. For overall project-level MAU, merges all HyperLogLog sketches regardless of chain, user type, or product. For chain-level breakdown, groups by individual blockchain networks while merging across user types. For user type analysis, separates Depositors from Withdrawers while combining across chains. For product-level view, maintains product categories while combining other dimensions. Uses HyperLogLog merge operations to combine sketches and extract final unique user counts for each dimension combination.
- **Key fields**: `dimensions` indicating breakdown type, relevant dimension fields (chain_id, user_type, product_id), `user_mau` containing final unique user count

### Final aggregation
- **Combining components**: Unions all dimensional breakdown results into a single output table, with irrelevant dimension fields set to null for each breakdown type
- **Time aggregation**: Uses rolling 30-day windows calculated daily, where each date represents the MAU for the preceding 30-day period ending on that date
- **HyperLogLog processing**: Applies HyperLogLog merge operations to combine daily user sketches within each 30-day window, then extracts the final estimated unique user count
- **Final calculation**: The metric value represents the estimated number of unique users who performed at least one deposit or withdrawal transaction with Ren protocol within the 30-day period ending on each date, broken down by the specified dimensions

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

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

RenREN

Active users (monthly)

Active users (monthly)

Competitive landscape

Chain breakdown