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Trojan

# Trojan Active users (weekly) Methodology

## 1. Overview

Weekly Active Users (WAU) for Trojan measures the number of unique users who paid fees to the protocol over a 7-day rolling window. This metric captures user engagement by tracking distinct transaction signers who interacted with Trojan's fee collection mechanism. The calculation uses HyperLogLog (HLL) data structures for efficient distinct user counting across multiple dimensional breakdowns.

## 2. Data sources

- **Solana native transfers**: Native SOL transfer events on the Solana blockchain, specifically transfers directed to Trojan's fee collection address
- **Transaction metadata**: Solana transaction records containing signer information and block timestamps
- **Blockchain timestamps**: Block-level timestamp data for temporal aggregation and window calculations

## 3. Step-by-step calculation

### Component A: Fee transaction identification
- **Purpose**: Identifies all fee payments made to Trojan protocol
- **Logic**: Filters Solana native SOL transfers where the recipient address matches Trojan's designated fee collection address (`9yMwSPk9mrXSN7yDHUuZurAh1sjbJsfpUqjZ7SvVtdco`). Captures the transaction signer as the user identifier, block timestamp for temporal grouping, and transfer amount. All fee transactions are classified as originating from 'Trader' user types and use SOL token address (`So11111111111111111111111111111111111111112`)
- **Key fields**: `block_timestamp` for daily grouping, `transaction_signer` as user identifier, `to_address` for fee address filtering, `amount` for transaction value

### Component B: Daily active user calculation
- **Purpose**: Aggregates unique users per day who paid fees
- **Logic**: Groups fee transactions by day using timestamp truncation and extracts distinct transaction signers as daily active users. Each day produces a set of unique user addresses who interacted with the protocol. Users are classified as 'Trader' type and associated with the 'solana' chain identifier
- **Key fields**: `timestamp` truncated to day level, `user_dau` containing unique signer addresses, `user_type` set to 'Trader', `chain` identifier

### Component C: HyperLogLog conversion
- **Purpose**: Converts daily user sets into HLL data structures for efficient merging
- **Logic**: Transforms each day's distinct user set into an HLL data structure with precision 15. This allows for approximate but highly efficient distinct counting when merging multiple days of user data. Groups by timestamp, chain, user type, and product dimensions while initializing HLL structures
- **Key fields**: `hll` containing the HyperLogLog representation of daily users, maintaining dimensional groupings for chain, user type, and product

### Component D: Rolling window construction  
- **Purpose**: Creates 7-day rolling windows for weekly user calculation
- **Logic**: Generates a datespine covering all dates from the earliest fee transaction to yesterday. For each date in the datespine, creates joindays spanning the current day plus the previous 6 days (7 days total). Joins this window structure with daily HLL data to collect all user data within each 7-day period
- **Key fields**: `timestamp` as the window end date, `joinday` representing each day within the 7-day window, `hll` data for each day in the window

### Component E: Weekly user aggregation
- **Purpose**: Merges 7 days of user data to calculate unique weekly users
- **Logic**: For each 7-day window, merges all HLL structures within that period using `hll_count.merge_partial()` to combine user sets from multiple days. Extracts the final distinct count using `hll_count.extract()` to produce the weekly unique user count. This process accounts for users who may have been active on multiple days within the week, counting them only once
- **Key fields**: `user_wau` containing the final weekly unique user count, dimensional groupings preserved for breakdown analysis

### Component F: Dimensional breakdowns
- **Purpose**: Generates WAU metrics across multiple dimensional cuts
- **Logic**: Creates four separate breakdowns of the weekly user data: (1) Project-level aggregation combining all dimensions, (2) Chain-level breakdown (though Trojan operates only on Solana), (3) User type breakdown (currently only 'Trader' type), (4) Product-level breakdown for different product offerings. Each breakdown aggregates HLL data across the irrelevant dimensions while preserving the target dimension
- **Key fields**: `dimensions` indicating the breakdown type, dimension-specific fields (`chain_id`, `user_type`, `product_id`), `user_wau` for each dimensional slice

### Final aggregation
- **Combining components**: Union all dimensional breakdowns into a single result set, with each row representing a different dimensional view of weekly user activity
- **Currency conversion**: Not applicable for user count metrics
- **Time aggregation**: Uses 7-day rolling windows calculated daily, with each day representing the end of a weekly period
- **Final calculation**: The `user_wau` field contains the count of unique users who paid fees within each 7-day rolling window, calculated using HyperLogLog merge operations for efficient distinct counting across temporal and dimensional groupings

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

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

Trojan

Active users (weekly)

Active users (weekly)

Competitive landscape