Validators process transactions and receive fees from users (supply-side fees).

Stellar is a distributed ledger for transfer and store of value (a blockchain). Stellar was founded by Jed McCaleb launched in 2014.

XLM holders own and govern the protocol. They currently take all of the total transaction fees paid by users (revenue).

github

twitter

discord

etherscan

website

Blockchains (L1)

Stellar

# Stellar Earnings Methodology

## 1. Overview

Stellar earnings represents the net financial performance of the Stellar blockchain network, calculated as the difference between revenue generated from transaction fees and expenses incurred through token incentives. This metric answers the business question: "What is Stellar's daily net profitability?" The calculation follows standard financial accounting: Earnings = Revenue - Expenses.

## 2. Data sources

The calculation relies on several key data sources:
- **Stellar transaction records**: Complete transaction data including fee amounts charged for each transaction and success status
- **Stellar operations records**: Historical blockchain operations data, specifically inflation operations that distributed XLM tokens (discontinued around 2020)
- **Stellar effects records**: Detailed effects of blockchain operations, particularly account credit events that resulted from inflation operations
- **End-of-day token price feeds**: XLM token prices for USD conversion using closing prices
- **Token metadata**: Information identifying XLM as Stellar's native token with proper decimal handling

## 3. Step-by-step calculation

### Component A: Revenue from transaction fees
- **Purpose**: Calculate daily revenue generated from Stellar network transaction fees
- **Logic**: Extract the `fee_charged` field from all Stellar transactions where `successful` equals true. Filter out any transactions with null fee values. Convert fee amounts from stroops (Stellar's smallest unit) to XLM by dividing by 10,000,000 (1e7). Group transactions by daily timestamp boundaries and sum all fees for each day. Convert daily XLM fee totals to USD using end-of-day price feeds
- **Key fields**: `fee_charged` (transaction fee in stroops), `successful` (transaction status flag), `block_timestamp` (for daily aggregation)

### Component B: Expenses from token incentives
- **Purpose**: Calculate daily expenses from XLM token distributions through inflation mechanisms
- **Logic**: Identify inflation operations from operations records by filtering for `operation_type` equal to 'inflation'. Join these operations with effects records to find corresponding `account_credited` events where `asset_type` equals 'native' (XLM). Extract the `amount` field representing XLM credited to accounts. Group by daily timestamp boundaries and sum all token incentive amounts for each day. Convert daily XLM incentive totals to USD using end-of-day price feeds. Note that inflation operations were historically discontinued around 2020, so recent data will show zero expenses
- **Key fields**: `operation_type` (to identify inflation operations), `effect_type` (to find account credits), `asset_type` (to filter native XLM), `amount` (incentive amount in XLM)

### Final aggregation
- **Combining components**: Join daily revenue and expense calculations by timestamp to create matched pairs for each day
- **Currency conversion**: Both revenue and expenses are converted to USD using end-of-day XLM price feeds, ensuring consistent currency denomination
- **Time aggregation**: All calculations use daily timestamp boundaries with consistent truncation to day granularity
- **Final calculation**: Subtract daily expenses from daily revenue to produce net earnings (Revenue - Expenses = Earnings). Apply zero-fill strategy to ensure complete time series without gaps, where missing days are filled with zero values rather than nulls

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

Net profit (also profits, net income, profitability) — revenue remaining after subtracting expenses.

StellarXLM

Earnings

Earnings

Competitive landscape