Options buyers pay premium (supply-side fees) to liquidity providers who collaterize these option positions and cover possible profits.

Premia is an options trading protocol built on Ethereum & adjacent blockchains. Premia was founded by Anon Powered team and launched in 2021.

PREMIA tokenholders own and govern the protocol. They take a protocol fee (revenue) from options trading.

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Premia

# Premia Active users (weekly) Methodology

## 1. Overview

Weekly Active Users (WAU) measures the number of unique users who interact with Premia's decentralized options protocol within any 7-day rolling window. This metric captures user engagement across all supported blockchain networks and differentiates between user types (depositors vs traders) to provide granular insights into protocol adoption. The calculation uses HyperLogLog probabilistic counting to efficiently aggregate unique users across multiple dimensions while maintaining accuracy at scale.

## 2. Data sources

- **Blockchain event logs**: Parsed event logs from Premia v1 Pool contracts deployed on Arbitrum, Ethereum, Fantom, and Optimism networks
- **Pool interaction events**: Four core event types that represent all user interactions with Premia pools:
  - `Deposit` events capturing liquidity provider deposits
  - `Withdrawal` events capturing liquidity provider withdrawals  
  - `Purchase` events capturing options purchases by traders
  - `Sell` events capturing options sales by traders
- **Event parameters**: User addresses extracted from the `user` parameter field in each event log
- **Block metadata**: Block timestamps used to establish daily boundaries for user activity

## 3. Step-by-step calculation

### Component A: User activity identification
- **Purpose**: Extract and classify all user interactions with Premia pools across supported blockchain networks
- **Logic**: Parse event logs from Premia v1 Pool contracts on Arbitrum, Ethereum, Fantom, and Optimism. Extract user addresses from the `user` parameter field of `Deposit`, `Withdrawal`, `Purchase`, and `Sell` events. Classify users into two types based on event interaction: users who interact via `Deposit` or `Withdrawal` events are classified as 'Depositor' (liquidity providers), while users who interact via `Purchase` or `Sell` events are classified as 'Trader' (options traders). Include blockchain network identifier, market sector ('derivatives'), business segment ('option'), and transaction metadata
- **Key fields**: `block_timestamp`, `transaction_hash`, `chain`, `user_type` (Depositor/Trader), `user_address`

### Component B: Daily active user aggregation  
- **Purpose**: Calculate daily unique active users using probabilistic counting for efficient aggregation
- **Logic**: Group user activities by day (truncating block timestamps to daily boundaries), chain, user type, market sector, business segment, and product. Apply distinct counting to user addresses within each group to identify unique daily active users. Initialize HyperLogLog sketches with precision 15 for each user group to enable efficient probabilistic distinct counting in downstream aggregations. Filter results based on chain availability configuration
- **Key fields**: `timestamp` (daily), `timestamp_grain` ('day'), `chain`, `user_type`, `product_id`, `user_dau` (HLL sketch)

### Component C: Rolling window construction
- **Purpose**: Create 7-day rolling windows to capture weekly active user patterns without missing data gaps
- **Logic**: Generate a complete datespine covering all days from the earliest user activity to the current date. For each day in the datespine, create associations with the 6 preceding days to form 7-day windows. This ensures every day has a complete 7-day lookback window even if some days had no user activity. Handle incremental updates by applying appropriate lookback periods to capture late-arriving data
- **Key fields**: `timestamp` (window end date), `joinday` (each day within the 7-day window)

### Component D: Weekly user aggregation
- **Purpose**: Calculate unique users across each 7-day rolling window using HyperLogLog merge operations
- **Logic**: For each day's 7-day window, join the daily HLL sketches from all days within that window. Merge the HLL sketches using HyperLogLog merge operations to calculate the approximate distinct count of unique users who were active at any point during the 7-day period. This preserves user uniqueness across the full window while maintaining computational efficiency. Apply incremental processing filters to handle data updates
- **Key fields**: `timestamp`, `chain_id`, `user_type`, `product_id`, merged HLL sketches

### Component E: Dimensional breakdown creation
- **Purpose**: Generate WAU metrics across multiple analytical dimensions for comprehensive reporting
- **Logic**: Create four separate dimensional views of the weekly active user data:
  - **Project-level**: Aggregate all users across all chains, user types, and products for overall protocol WAU
  - **Chain-level**: Break down WAU by individual blockchain network (Arbitrum, Ethereum, Fantom, Optimism)  
  - **User type-level**: Separate WAU counts for 'Depositor' vs 'Trader' user segments
  - **Product-level**: Break down WAU by specific product identifiers within the protocol
Each breakdown extracts the final distinct count from merged HLL sketches and sets unused dimension fields to NULL
- **Key fields**: `dimensions` (breakdown type), `chain_id`, `user_type`, `product_id`, `user_wau` (final count)

### Final aggregation
- **Combining components**: Union all dimensional breakdown views into a single result set using UNION ALL operations, with NULL values in unused dimension columns enabling consistent schema across breakdowns
- **Time aggregation**: Daily boundary logic using timestamp truncation to day granularity, with 7-day rolling windows calculated via timestamp arithmetic  
- **Final calculation**: Extract final unique user counts from merged HyperLogLog sketches using HLL extract functions. Apply the finalize_metric_user_wau macro to standardize output schema, add project metadata, and fill missing days with zero values to ensure 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.

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Archived

Active users (weekly)

Active users (weekly)

Competitive landscape

Chain breakdown