We currently only track the Squeeth product. Overcollateralized borrowers mint Squeeth (oSQTH), a power perpetual token that tracks the price of ETH squared.

Opyn builds derivatives and options infrastructure. Opyn was founded by Nick Sawinyh, Zubin Koticha and Alexis Gauba and launched in 2019.

Opyn team & investors own and govern the protocol. They do not currently charge a fee for Squeeth mints (revenue). Opyn’s investors include Paradigm, Dragonfly Capital, etc.

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Opyn

# Opyn Active users (weekly) Methodology

## 1. Overview

Weekly Active Users (WAU) for Opyn measures the number of unique addresses that interact with the Opyn options protocol within any 7-day rolling window. This metric provides insight into user engagement and adoption of Opyn's decentralized options trading platform. The calculation uses HyperLogLog (HLL) probabilistic data structures to efficiently compute distinct user counts across overlapping time windows and multiple dimensional breakdowns.

## 2. Data sources

The calculation relies on the following raw blockchain data sources:

- **Ethereum event logs**: Parsed events from Opyn Controller contracts capturing vault operations including `OpenVault`, `MintShort`, and `BurnShort` activities
- **Ethereum event logs**: Parsed events from FlashZen contracts for bull strategy operations including `FlashDeposit` and `FlashWithdraw` activities
- **Ethereum event logs**: Parsed events from CrabStrategy contracts (both v1 and v2) for crab strategy operations including `FlashDeposit` and `FlashWithdraw` activities  
- **Ethereum function call traces**: Transaction traces from ShortHelper contracts capturing `openShort` and `closeShort` function calls
- **Blockchain transaction data**: Ethereum transaction records providing timestamps and transaction hashes for all user activities

## 3. Step-by-step calculation

### Component A: User activity extraction

- **Purpose**: Identifies and classifies all user interactions with Opyn contracts
- **Logic**: Extracts user addresses from various Opyn contract events and function calls on Ethereum. Each activity is classified by type including Vault Openers (from `OpenVault` events), Bull Depositors and Withdrawers (from FlashZen `FlashDeposit` and `FlashWithdraw` events), Crab Depositors and Withdrawers (from CrabStrategy `FlashDeposit` and `FlashWithdraw` events), Short Minters and Burners (from Controller `MintShort` and `BurnShort` events, plus ShortHelper function calls). All activities are tagged with market sector as 'derivatives' and business segment as 'option'
- **Key fields**: `block_timestamp` for timing, `user_address` for unique user identification, `user_type` for activity classification, `chain` for blockchain identification, `transaction_hash` for deduplication

### Component B: Daily active users calculation

- **Purpose**: Converts raw user activities into daily distinct user counts
- **Logic**: Truncates all activity timestamps to day granularity and deduplicates users within each day. Each unique user-day combination becomes a Daily Active User (DAU) record. Users are preserved across their different activity types and chains to maintain dimensional breakdown capability
- **Key fields**: `timestamp` truncated to day, `user_dau` containing the unique user address, `user_type`, `chain`, and `product_id` for dimensional analysis

### Component C: HyperLogLog initialization

- **Purpose**: Prepares efficient data structures for distinct counting across time windows  
- **Logic**: Initializes HyperLogLog sketches for each DAU record using 15-bit precision. This probabilistic data structure enables efficient merging and distinct counting operations across overlapping time windows without storing individual user addresses
- **Key fields**: `hll` containing the HyperLogLog sketch representation of the user set, grouped by `timestamp`, `chain`, `user_type`, and `product_id`

### Component D: Rolling window generation

- **Purpose**: Creates 7-day rolling windows for WAU calculation
- **Logic**: Generates a complete datespine covering all dates from the earliest user activity to the current date minus one day. For each date in the spine, creates associations with the current date plus the 6 preceding days using timestamp array generation. This ensures every possible 7-day window is represented and handles missing days without gaps
- **Key fields**: `timestamp` representing the window end date, `joinday` representing each day within the 7-day window

### Component E: Window aggregation

- **Purpose**: Merges user data within each 7-day rolling window
- **Logic**: Joins the rolling window structure with the HLL-encoded daily user data. For each 7-day window ending on a specific date, collects all HLL sketches from the 7 constituent days. This creates the foundation for distinct user counting across the weekly time period
- **Key fields**: `timestamp` for window identification, `hll` containing daily user sketches to be merged, dimensional fields (`chain_id`, `user_type`, `product_id`)

### Component F: Dimensional breakdowns

- **Purpose**: Calculates WAU across different analytical dimensions
- **Logic**: Creates four separate dimensional views by grouping and merging HLL sketches. Project-level breakdown merges all sketches regardless of other dimensions. Chain-level breakdown groups by `chain_id` only. User type breakdown groups by `user_type` only. Product breakdown groups by `product_id` only. Each breakdown uses HLL merge operations to combine user sketches within the grouping, then extracts the final distinct count
- **Key fields**: `dimensions` indicating the breakdown type, relevant dimensional fields, `user_wau` containing the final distinct user count

### Final aggregation

- **Combining components**: All dimensional breakdowns are combined using UNION ALL operations to create a comprehensive dataset with multiple analytical perspectives
- **Currency conversion**: Not applicable for user count metrics
- **Time aggregation**: Uses daily timestamp boundaries with 7-day rolling windows. Each date represents the end of a 7-day period
- **Final calculation**: The final WAU value is extracted from merged HyperLogLog sketches using `hll_count.extract(hll_count.merge_partial(hll))`, providing an approximate distinct count of users active within each 7-day rolling window. Missing periods are filled with zeros 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.

OpynOSQTH

Active users (weekly)

Active users (weekly)

Competitive landscape

Chain breakdown