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Lending

Prime Numbers

# Prime Numbers Active users (weekly) Methodology

## 1. Overview

Weekly Active Users (WAU) for Primenumbers measures the number of unique users who performed any lending activity within a rolling 7-day period. This metric tracks user engagement across the Primenumbers V1 lending protocol on XDC blockchain, providing insights into weekly user adoption and retention. The calculation uses HyperLogLog probabilistic counting to efficiently aggregate distinct users across multiple dimensional breakdowns while maintaining accuracy for large user sets.

## 2. Data sources

- **XDC blockchain event logs**: Parsed events from the Primenumbers V1 LendingPool contract on XDC blockchain
- **Lending protocol events**: Specific event types including `Borrow`, `FlashLoan`, `Repay`, `Deposit`, and `Withdraw` events
- **Transaction metadata**: Block timestamps and transaction hashes for temporal analysis
- **Contract interaction data**: User addresses and lending action classifications from smart contract events

## 3. Step-by-step calculation

### Component A: User activity extraction
- **Purpose**: Identifies all users who interact with the Primenumbers lending protocol and classifies their activity types
- **Logic**: Extracts user addresses from five types of lending events on XDC blockchain. Each event type creates a different user classification: `Borrow` events identify Borrowers, `FlashLoan` events identify Borrowers (using the `initiator` field), `Repay` events identify Repayers, `Deposit` events identify Depositors, and `Withdraw` events identify Withdrawers. Each interaction includes the block timestamp, transaction hash, and standardized metadata including chain identifier as 'xdc', market sector as 'lending', business segment as 'lending', and product identifiers
- **Key fields**: `user_address` (the wallet performing the action), `block_timestamp` (when the action occurred), `user_type` (classification based on lending action), `product_id` (varies by event type - 'PrimeFi Lending' for borrows, 'primenumbersv1' for other actions), `chain` (always 'xdc')

### Component B: Daily active user aggregation
- **Purpose**: Calculates the distinct count of users who performed any lending activity each day
- **Logic**: Takes all user interactions and groups them by day using timestamp truncation to the day level. For each day, counts distinct user addresses while preserving dimensional attributes like user type, product ID, and chain. Creates a foundation dataset of daily unique active users that serves as input for the weekly rolling window calculation
- **Key fields**: `timestamp` (day-level truncated date), `user_dau` (unique user address for that day), `user_type` (preserved from activity classification), `product_id` (preserved from source events), `chain` (preserved as 'xdc')

### Component C: Rolling window construction
- **Purpose**: Creates 7-day rolling windows to capture weekly activity patterns without gaps in the time series
- **Logic**: Generates a complete date spine covering the full date range from the earliest user activity to the previous day. For each date in the spine, creates joins to the previous 6 days plus the current day (exactly 7 days total). Uses HyperLogLog initialization on the daily user data to enable efficient distinct counting across the weekly windows. This ensures every day has a complete 7-day lookback window even if some days had no activity
- **Key fields**: `timestamp` (the end date of each 7-day window), `joinday` (each of the 7 days included in that window), `hll` (HyperLogLog sketch of users for efficient distinct counting)

### Component D: Multi-dimensional breakdowns
- **Purpose**: Calculates WAU across different analytical dimensions to support various reporting needs
- **Logic**: Creates four separate dimensional views of the weekly active users data. Project-level breakdown aggregates all users regardless of other dimensions. Chain-level breakdown groups by blockchain (though only XDC for Primenumbers). User-type breakdown groups by lending activity classification (Borrower, Repayer, Depositor, Withdrawer). Product-level breakdown groups by specific product identifiers within the protocol. Each breakdown uses HyperLogLog merge operations to combine the probabilistic user counts across the 7-day windows
- **Key fields**: `dimensions` (identifies the breakdown type), `user_wau` (distinct user count for that dimension and time window), dimension-specific fields like `chain_id`, `user_type`, or `product_id`

### Final aggregation
- **Combining components**: All dimensional breakdowns are combined using union operations to create a single comprehensive dataset with multiple analytical views
- **Currency conversion**: Not applicable for user count metrics
- **Time aggregation**: Each row represents a 7-day rolling window ending on the specified date, calculated daily to provide a continuous time series
- **Final calculation**: The final WAU value is extracted from the merged HyperLogLog sketches using `hll_count.extract()`, providing an approximate but highly accurate count of distinct users who were active within each 7-day period. Missing dates are filled with zeros 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.

Prime NumbersPRFI

Active users (weekly)

Active users (weekly)

Competitive landscape

Chain breakdown