Infrastructure

Maestro

# Maestro Average fees per user (AFPU) Methodology

## 1. Overview

Average fees per user (AFPU) measures the average amount of fees paid per active user on the Maestro platform per day. This metric provides insight into user spending behavior and revenue efficiency by dividing total daily fees collected by the number of unique users who paid fees that day. The calculation uses native SOL transfers to Maestro's fee collection wallet on Solana to identify both fee payments and active users.

## 2. Data sources

The AFPU calculation relies on the following raw data sources:
- **Solana native transfers**: Native SOL transfer events on the Solana blockchain, specifically transfers sent to Maestro's fee collection addresses
- **Transaction data**: Solana transaction records containing transaction signer information to identify users making fee payments
- **End-of-day price feeds**: Daily SOL token price data for converting native token amounts to USD values
- **Fee collection addresses**: Hardcoded Maestro wallet addresses that receive fee payments (`MaestroUL88UBnZr3wfoN7hqmNWFi3ZYCGqZoJJHE36`)

## 3. Step-by-step calculation

### Component A: Fee data extraction
- **Purpose**: Identifies and extracts fee payments made to Maestro on Solana
- **Logic**: Filters Solana native SOL transfers where the recipient address matches Maestro's fee collection wallet address `MaestroUL88UBnZr3wfoN7hqmNWFi3ZYCGqZoJJHE36`. Excludes transfers where the sender is also the fee collection address to avoid counting internal wallet movements. Captures the transfer `amount`, `transaction_signer` (user), and `block_timestamp` for each fee payment.
- **Key fields**: `transaction_signer` identifies the user, `amount` contains the fee value in lamports, `block_timestamp` provides timing, and `to_address` filters for Maestro fee collection

### Component B: Daily fees calculation
- **Purpose**: Aggregates fee payments into daily USD amounts
- **Logic**: Groups fee payment data by day using timestamp truncation. Converts lamport amounts to SOL by dividing by 1e9 (since 1 SOL = 1 billion lamports). Sums all fee payments per day to get total daily fees in SOL. Applies USD conversion using end-of-day price feeds to convert SOL amounts to USD values. Sets the token address as SOL's canonical address `So11111111111111111111111111111111111111112` for price lookup.
- **Key fields**: `timestamp` truncated to day granularity, `fees` as the aggregated USD amount, `chain_id` set to 'solana'

### Component C: Daily active users calculation  
- **Purpose**: Counts unique users who paid fees each day
- **Logic**: Groups fee payment data by day and counts distinct `transaction_signer` addresses to identify unique users. Uses HyperLogLog approximation algorithm with precision 15 for scalable distinct counting across large datasets. Creates user breakdowns by project level, chain, user type ('Trader'), and product dimensions. Classifies all fee-paying users as 'Trader' user type since they are actively trading on the platform.
- **Key fields**: `user_dau` as the distinct count of users, `timestamp` for daily grouping, `user_type` set to 'Trader'

### Final aggregation
- **Combining components**: Uses the `calculate_arpu` macro to divide the daily fees metric by the daily active users metric, matching records by date, chain, and other dimensional attributes. Both metrics are joined on their common timestamp and dimensional keys.
- **Currency conversion**: All fee amounts are converted to USD using end-of-day SOL price feeds before the AFPU calculation
- **Time aggregation**: Calculations are performed at daily granularity, with timestamp truncation to day boundaries
- **Final calculation**: AFPU equals total daily fees in USD divided by the count of distinct users who paid fees that day. The `finalize_metric_afpu` macro standardizes the output schema, fills missing days with zero values, and applies data quality validation to ensure consistent time series data.

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

Maestro

Average fees per user (AFPU)

Average fees per user (AFPU)

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