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BullX

# BullX Active users (weekly) Methodology

## 1. Overview

The BullX Weekly Active Users (WAU) metric measures the number of unique traders who have paid fees on the BullX platform within any 7-day period. This metric provides insight into the platform's user engagement and trading activity trends. The calculation uses a rolling 7-day window approach with HyperLogLog aggregation for efficient unique user counting across multiple dimensions.

## 2. Data sources

- **Solana native transfers**: Native SOL transfer events on the Solana blockchain, specifically transfers to BullX fee collection addresses
- **Transaction records**: Solana transaction metadata including transaction signers and timestamps
- **BullX fee accounts**: Hardcoded fee collection addresses (`F4hJ3Ee3c5UuaorKAMfELBjYCjiiLH75haZTKqTywRP3` and `9RYJ3qr5eU5xAooqVcbmdeusjcViL5Nkiq7Gske3tiKq`) that receive trading fees from platform users

## 3. Step-by-step calculation

### Component A: Fee payment extraction
- **Purpose**: Identify trading activity by capturing fee payments to BullX
- **Logic**: Extract all Solana native SOL transfers where the recipient is one of the BullX fee collection addresses. Filter out any transfers that originate from the fee accounts themselves to avoid counting internal transfers. Each transfer represents a trading transaction where a user paid fees to the platform.
- **Key fields**: `block_timestamp` for timing, `transaction_signer` for user identification, `amount` for fee value, and `to_address` for fee account validation

### Component B: Daily active user calculation
- **Logic**: Group fee payment transactions by day and transaction signer to identify unique daily active users. Each unique signer who made at least one fee payment on a given day is counted as a daily active user. All users are classified as 'Trader' user type since fee payment indicates trading activity.
- **Key fields**: `timestamp` (truncated to day), `signer` as `user_dau`, `chain` (always 'solana'), `user_type` (always 'Trader')

### Component C: HyperLogLog initialization  
- **Purpose**: Prepare daily user data for efficient weekly aggregation
- **Logic**: Convert daily active user data into HyperLogLog sketches for memory-efficient cardinality estimation. Group daily users by timestamp, chain, user type, and product dimensions, then initialize HLL sketches with precision parameter 15 for each group.
- **Key fields**: `hll` containing the HyperLogLog sketch of daily users

### Component D: 7-day window creation
- **Purpose**: Create rolling 7-day periods for WAU calculation
- **Logic**: Generate a complete datespine covering all days from the earliest data to yesterday. For each day in the datespine, create join keys for the 7 days ending on that date (current day plus 6 previous days). This ensures every day has a complete 7-day lookback window even if some days have no activity.
- **Key fields**: `timestamp` for the window end date, `joinday` for each day within the 7-day window

### Component E: Window data assembly
- **Purpose**: Combine daily HLL sketches into 7-day windows
- **Logic**: Join the 7-day window structure with the daily HLL data, matching each window day to its corresponding daily user sketch. This creates multiple records per window end date - one for each day within the 7-day period that has user activity.
- **Key fields**: `timestamp` (window end date), `hll` (daily user sketches within the window)

### Component F: Dimensional breakdowns
- **Purpose**: Calculate WAU across different business dimensions
- **Logic**: Create four separate views of weekly active users:
  - Project-level: Overall platform WAU (all dimensions nullified except timestamp)
  - Chain-level: WAU broken down by blockchain (currently only Solana)
  - User-type-level: WAU by user classification (currently only 'Trader')
  - Product-level: WAU by product ID (if products are segmented)
- **Key fields**: `dimensions` indicating the breakdown type, relevant dimension fields, `user_wau` containing the unique user count

### Final aggregation
- **Combining components**: Merge HLL sketches for each 7-day window using HyperLogLog merge operations, then extract the final cardinality estimate. Union all dimensional breakdowns into a single result set.
- **Time aggregation**: Each record represents WAU for the 7-day period ending on the specified timestamp
- **Final calculation**: The `user_wau` field contains the estimated count of unique users who were active (paid fees) within the 7-day window ending on each date. The calculation leverages HyperLogLog's ability to merge probabilistic sketches for efficient unique counting across time periods.

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

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

BullX

Active users (weekly)

Active users (weekly)

Competitive landscape