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Bloom Trading Bot

# Bloom Trading Bot Average fees per user (AFPU) Methodology

## 1. Overview

Average Fees Per User (AFPU) measures the average daily fee revenue generated per active user on the BloomTradingBot protocol. This metric is calculated by dividing daily fee revenue (in USD) by the number of daily active users. AFPU helps assess the revenue efficiency and monetization effectiveness of the trading bot platform, answering the business question: "How much fee revenue does each user generate on average per day?"

## 2. Data sources

The calculation relies on these raw data sources:
- **Solana native transfer events**: Transfer transactions on the Solana blockchain, specifically native SOL transfers to BloomTradingBot fee collection addresses
- **Transaction data**: Solana transaction records containing signer information to identify unique users
- **End-of-day price feeds**: Daily SOL/USD price data for currency conversion
- **Token metadata**: SOL token information (address: `So11111111111111111111111111111111111111112`) for proper token identification

## 3. Step-by-step calculation

### Component A: Fee collection identification
- **Purpose**: Identify all fee payments made to the BloomTradingBot protocol
- **Logic**: Extract Solana native transfer events where the recipient address is the BloomTradingBot fee collection address (`7HeD6sLLqAnKVRuSfc1Ko3BSPMNKWgGTiWLKXJF31vKM`). Filter out transactions where the signer is the fee collection address itself to exclude internal transfers. Each transfer represents a fee payment from a user to the protocol.
- **Key fields**: `block_timestamp` for timing, `transaction_signer` for user identification, `amount` for fee value, `to_address` for recipient validation

### Component B: Fee aggregation and USD conversion
- **Purpose**: Calculate total daily fee revenue in USD
- **Logic**: Convert raw transfer amounts from lamports to SOL by dividing by 1e9 (Solana's standard conversion factor). Aggregate these SOL amounts by day using daily time boundaries. Convert the aggregated SOL fees to USD using end-of-day price feeds. Group the results by timestamp, chain, product identifier, and token address.
- **Key fields**: `timestamp` (daily truncated), `chain` (always 'solana'), `token_address` (SOL token), `fees` (USD value)

### Component C: Daily active user counting
- **Purpose**: Count unique users who paid fees each day
- **Logic**: Extract unique transaction signers from the same fee transaction dataset. Each signer represents a user who interacted with the protocol and paid fees, classified as 'Trader' user type. Use HyperLogLog functions to efficiently count distinct users across multiple dimensions including project-level, chain-level, user-type-level, and product-level breakdowns. Aggregate counts by day using the same time boundaries as fee calculations.
- **Key fields**: `timestamp` (daily truncated), `user_type` (always 'Trader'), `signer` (unique user identifier), `user_dau` (distinct user count)

### Final aggregation
- **Combining components**: Join the daily fee totals with daily active user counts on matching timestamp and dimensional attributes (chain, product_id). The join ensures both metrics are calculated for the same time periods and dimensional breakdowns.
- **Currency conversion**: Fees are already converted to USD using end-of-day SOL price feeds before the division calculation
- **Time aggregation**: Both numerator (fees) and denominator (users) use consistent daily time boundaries (timestamp truncated to day level)
- **Final calculation**: Divide the daily fee total by the daily active user count to produce the average fees per user. Apply zero-filling strategy for days with no activity, ensuring continuous time series data with AFPU values of zero for inactive periods.

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

Bloom Trading Bot

Average fees per user (AFPU)

Average fees per user (AFPU)

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