Liquidity providers deposit funds into Ribbon Protocol and receive option premiums from traders & withdrawal fees from other liquidity providers (supply-side fees).

Ribbon Finance is a non-custodial asset management protocol built on Ethereum. Ribbon Finance was founded by Julian Koh & Ken Chan and launched in 2021.

RBN tokenholders own and govern the protocol. They currently take a cut of the total premiums and management fees paid by liquidity providers (revenue).

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Ribbon Finance

# Ribbon Finance Active users (weekly) Methodology

## 1. Overview

Weekly Active Users (WAU) for Ribbon Finance measures the number of unique users who interacted with Ribbon's yield-generating vaults within any 7-day rolling window. This metric captures user engagement across Ribbon's decentralized options and structured products protocol by tracking deposits and withdrawals from various vault types. The calculation uses HyperLogLog sketches for efficient approximate counting of unique users across time periods and provides dimensional breakdowns by chain, user type, and product.

## 2. Data sources

- **Ribbon vault events**: Parsed smart contract events from Ribbon's vault contracts across multiple versions and chains, including `Deposit`, `Withdraw`, and `InstantWithdraw` events
- **Blockchain transaction data**: Ethereum and Avalanche transaction timestamps and block data for event ordering and daily boundary determination
- **Vault contract logs**: Event logs from ThetaVault, EarnVault, and TreasuryVault contracts spanning Ribbon v1 and v2 deployments
- **Chain availability metadata**: Data quality filters and chain-specific availability windows

## 3. Step-by-step calculation

### Component A: User event extraction
- **Purpose**: Identify all user interactions with Ribbon vaults across supported chains
- **Logic**: Extract user addresses from parsed smart contract events across Ribbon's vault ecosystem. The system monitors multiple vault types including ThetaVault (options strategies), EarnVault (yield farming), and TreasuryVault (treasury management) on Ethereum and Avalanche networks. Events are filtered to three key interaction types: deposits (when users add funds), standard withdrawals (scheduled withdrawals), and instant withdrawals (immediate withdrawals with potential penalties). Each event captures the user address from the `account` parameter and classifies users as either 'Depositor' or 'Withdrawer' based on the event type.
- **Key fields**: `block_timestamp` for temporal ordering, `user_address` for unique user identification, `chain` for network classification, `user_type` for interaction categorization

### Component B: Daily active user aggregation
- **Purpose**: Convert raw events into daily unique user counts using probabilistic data structures
- **Logic**: Group user events by daily boundaries using timestamp truncation to day granularity. For each day, chain, user type, and product combination, initialize HyperLogLog sketches with 15-bit precision to enable efficient approximate counting of unique users. This approach allows for memory-efficient storage and fast computation of distinct user counts while maintaining high accuracy. The daily aggregation preserves dimensional attributes including chain identifier, user type classification, and product identifiers for downstream analysis.
- **Key fields**: `timestamp` (daily boundary), `user_dau` (unique user identifier), `hll` (HyperLogLog sketch), `chain_id`, `user_type`, `product_id`

### Component C: Rolling window construction
- **Purpose**: Create 7-day rolling windows to calculate weekly active users
- **Logic**: Generate a comprehensive datespine covering all days from the earliest available data to the current date to ensure no gaps in the time series. For each date in the datespine, create associations with the current day plus the previous 6 days to form complete 7-day windows. This windowing approach ensures that every day has a complete 7-day lookback period for WAU calculation. Join the windowed dates with the daily HLL data to gather all relevant user sketches within each 7-day period.
- **Key fields**: `timestamp` (window end date), `joinday` (individual days within window), `hll` (daily user sketches)

### Final aggregation
- **Combining components**: Merge HyperLogLog sketches across all 7 days within each rolling window using HLL merge operations, then extract the final unique user count from the merged sketch
- **Currency conversion**: Not applicable for user count metrics
- **Time aggregation**: Daily calculation with 7-day rolling windows, using timestamp truncation to maintain consistent daily boundaries
- **Final calculation**: For each day, merge the HyperLogLog sketches from that day and the previous 6 days, then extract the approximate unique user count. Generate separate dimensional views including project-level totals (all chains and products combined), chain-level breakdowns (unique users per blockchain), user-type breakdowns (depositors vs withdrawers), and product-level breakdowns (unique users per vault product). Apply standardized metric formatting and fill missing days with zero values to maintain complete time series continuity.

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

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

Ribbon FinanceRBN

Active users (weekly)

Active users (weekly)

Competitive landscape

Chain breakdown