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BullX

# BullX Active users (monthly) Methodology

## 1. Overview

BullX Monthly Active Users (MAU) measures the number of unique users who actively traded through the BullX protocol within rolling 30-day periods. The metric identifies active traders by tracking fee payments to BullX's fee collection addresses on Solana. Users are considered active when they pay trading fees, indicating actual protocol usage. The calculation uses HyperLogLog probabilistic data structures to efficiently compute distinct user counts across large datasets and multiple dimensional breakdowns.

## 2. Data sources

The calculation relies on the following raw data sources:
- **Solana native transfers**: Native SOL token transfers between addresses on the Solana blockchain, including transfer amounts, timestamps, and participant addresses
- **Transaction metadata**: Solana transaction records containing transaction signers (the users initiating transactions) and block timestamps
- **Fee collection addresses**: Two specific BullX protocol addresses that collect trading fees: `F4hJ3Ee3c5UuaorKAMfELBjYCjiiLH75haZTKqTywRP3` and `9RYJ3qr5eU5xAooqVcbmdeusjcViL5Nkiq7Gske3tiKq`

## 3. Step-by-step calculation

### Component A: Fee transaction identification
- **Purpose**: Identifies BullX trading activity by capturing fee payments to protocol addresses
- **Logic**: Filters Solana native SOL transfers where the recipient address matches one of the two BullX fee collection addresses. Excludes self-transfers where the transaction signer is also a fee collection address to avoid counting internal protocol operations. Captures the transaction signer as the user identifier, transfer amount as the fee paid, and block timestamp for temporal analysis.
- **Key fields**: `transaction_signer` (the trading user), `to_address` (fee collection address), `amount` (fee amount), `block_timestamp` (transaction timing), `transaction_id` (unique transaction identifier)

### Component B: Daily active user calculation  
- **Purpose**: Aggregates fee-paying users into daily active user counts
- **Logic**: Groups fee transactions by day using timestamp truncation to the day level. Extracts distinct transaction signers per day as the daily active users, classifying all users as "Trader" type since they're identified through fee payments. Associates users with the Solana chain and maintains product identification for potential future segmentation.
- **Key fields**: `timestamp` (truncated to day), `user_dau` (distinct user identifier), `user_type` (always "Trader"), `chain` (always "solana")

### Component C: HyperLogLog sketch preparation
- **Purpose**: Converts daily user lists into probabilistic data structures for efficient distinct counting at scale
- **Logic**: Initializes HyperLogLog sketches for each day's user set using precision parameter 15. Groups data by timestamp, chain, user type, and product to maintain dimensional breakdowns. HyperLogLog enables approximate distinct counting with high accuracy while using minimal memory.
- **Key fields**: `hll` (HyperLogLog sketch containing user identifiers), maintaining all dimensional fields for later aggregation

### Component D: Rolling window generation
- **Purpose**: Creates 30-day rolling windows for each day to calculate monthly active users
- **Logic**: Generates a datespine covering all days from the first data point to yesterday. For each day in the datespine, creates a 30-day lookback window by cross-joining with the 29 preceding days plus the current day. This produces combinations of target dates and their corresponding 30-day windows for MAU calculation.
- **Key fields**: `timestamp` (target date for MAU calculation), `joinday` (each day within the 30-day window)

### Component E: Window data assembly
- **Purpose**: Combines rolling window definitions with actual user data
- **Logic**: Joins the rolling window structure with daily HyperLogLog sketches to associate each target date with the user data from its 30-day window. Maintains all dimensional fields (chain, user type, product) to support multiple breakdowns in the final aggregation.
- **Key fields**: `timestamp` (target MAU date), `hll` (user sketches for days in the window), dimensional identifiers

### Component F: Dimensional MAU breakdowns
- **Purpose**: Calculates monthly active users across multiple dimensional cuts
- **Logic**: Creates four separate MAU calculations: overall project-level MAU (aggregating across all dimensions), chain-level MAU (breaking down by blockchain), user-type-level MAU (breaking down by user classification), and product-level MAU (breaking down by product segment). For each breakdown, merges HyperLogLog sketches across the appropriate grouping and extracts the final distinct user count.
- **Key fields**: `dimensions` (breakdown type), `user_mau` (final distinct user count), dimensional identifiers set to null for non-relevant breakdowns

### Final aggregation
- **Combining components**: Unions all dimensional MAU breakdowns into a single result set, with each row representing MAU for a specific date and dimensional combination
- **Currency conversion**: Not applicable as this metric counts users rather than monetary values
- **Time aggregation**: Uses rolling 30-day windows ending on each calendar date, providing daily MAU values that each represent activity over the preceding month
- **Final calculation**: Extracts distinct user counts from merged HyperLogLog sketches using the `hll_count.extract()` function, providing the final MAU value for each date and dimensional breakdown

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

Unique revenue-generating users over a rolling 30-day window.

BullX

Active users (monthly)

Active users (monthly)

Competitive landscape