Infrastructure

Maestro

# Maestro Active users (weekly) Methodology

## 1. Overview

Weekly Active Users (WAU) for Maestro measures the number of unique users who have interacted with Maestro's services within any 7-day rolling window. This metric captures user engagement by tracking fee-paying activities, providing insights into the platform's user retention and activity patterns. The calculation uses probabilistic counting via HyperLogLog sketches to efficiently handle large user datasets while maintaining accuracy.

## 2. Data sources

The WAU calculation is built upon the following raw data sources:

- **Solana native transfers**: Native SOL token transfer records from the Solana blockchain, specifically transfers directed to Maestro's fee collection addresses
- **Solana transaction data**: Complete transaction records including transaction signers, timestamps, and amounts to identify user-initiated fee payments
- **Maestro fee collection addresses**: Hardcoded list of known Maestro fee collection addresses (`MaestroUL88UBnZr3wfoN7hqmNWFi3ZYCGqZoJJHE36`) that receive user fees
- **Blockchain transaction metadata**: Transaction timestamps, signers, and amounts to establish user activity patterns

## 3. Step-by-step calculation

### Component A: Fee-based user identification
- **Purpose**: Identifies unique users through their fee-paying activities to Maestro services
- **Logic**: Extracts Solana native transfers where the recipient is a known Maestro fee collection address and the sender is not a fee collection address (to exclude internal transfers). Each transfer represents a user interaction with Maestro services. The `transaction_signer` field identifies the user, `block_timestamp` provides timing, and `amount` captures the fee value. All fees are in native SOL tokens (`So11111111111111111111111111111111111111112`). Users are classified as 'Trader' type, indicating trading activity.
- **Key fields**: `transaction_signer` as user identifier, `block_timestamp` for timing, `amount` for fee value, `chain` set to 'solana'

### Component B: Daily active user aggregation
- **Purpose**: Converts fee-paying activities into daily unique user counts using probabilistic counting
- **Logic**: Groups fee transactions by daily timestamp boundaries using `timestamp_trunc` on `block_timestamp`. For each day, identifies distinct users (`transaction_signer` values) who paid fees. Applies HyperLogLog initialization with precision 15 to create probabilistic user count sketches. Maintains dimensional breakdowns by `chain`, `user_type` ('Trader'), and `product_id`. Each day produces an HLL sketch representing that day's unique active users.
- **Key fields**: `timestamp` (daily truncated), `timestamp_grain` ('day'), `user_dau` (unique signer), `hll` (probabilistic count sketch)

### Component C: Seven-day rolling window creation
- **Purpose**: Constructs 7-day rolling windows for each day to capture weekly user activity patterns
- **Logic**: Creates a complete datespine covering all days from the earliest available data to yesterday to ensure no dates are skipped. For each date in the spine, generates a 7-day lookback window consisting of the current day plus the previous 6 days. Uses cross join operations to create all combinations of target dates and their corresponding 7-day windows. This ensures every possible 7-day period is captured for WAU calculation.
- **Key fields**: `timestamp` (target date), `joinday` (each day within the 7-day window)

### Component D: Window-based user aggregation
- **Purpose**: Combines daily HLL sketches across 7-day windows to calculate weekly unique users
- **Logic**: Joins the 7-day windows with daily HLL data to collect all user sketches that fall within each window period. For each target date, gathers HLL sketches from all 7 days in that date's window. Maintains all dimensional attributes (`chain_id`, `user_type`, `product_id`) during the join to preserve breakdown capabilities. This creates the foundation for merging user counts across the weekly periods.
- **Key fields**: `timestamp` (window end date), `hll` (daily user sketches), dimensional attributes for grouping

### Component E: Multi-dimensional WAU calculation
- **Purpose**: Calculates WAU across four different dimensional breakdowns for comprehensive analysis
- **Logic**: Creates four separate aggregation views: overall project level (all dimensions NULL), by chain, by user type, and by product ID. For each dimensional breakdown, groups data by the target dimensions and merges HLL sketches using `hll_count.merge_partial` across the 7-day windows. Extracts final unique user counts using `hll_count.extract`. Sets unused dimensions to NULL to maintain consistent schema across breakdowns. Each breakdown provides a different analytical perspective on weekly user activity.
- **Key fields**: `dimensions` (breakdown type), `user_wau` (final unique count), dimensional grouping fields

### Final aggregation
- **Combining components**: Unions all four dimensional breakdowns (project level, chain, user type, product ID) into a single comprehensive dataset using `UNION ALL` operations
- **Currency conversion**: Not applicable as this metric counts users rather than monetary values
- **Time aggregation**: Uses 7-day rolling windows where each day represents the WAU for that day plus the previous 6 days, providing daily WAU snapshots
- **Final calculation**: Applies the `finalize_metric_user_wau` macro to standardize schema, apply consistent formatting, and fill missing days with zero values to ensure complete time series data for reporting and analysis

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

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

Maestro

Active users (weekly)

Active users (weekly)

Competitive landscape

Chain breakdown