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

Fantom is a smart contract platform for decentralized applications (a blockchain). Fantom was founded by Dr. Ahn Byung Ik and launched in 2019.

FTM holders own and govern the protocol. They currently take a cut of the total transaction fees paid by users (revenue). Fantom’s investors include DHVC, Arrington XRP Capital, 8 decimal Capital, etc.

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# Fantom Median transaction fee Methodology

## 1. Overview

The Fantom transaction fee median metric measures the median (50th percentile) transaction fee paid by users on the Fantom blockchain on a daily basis. This metric provides insight into the typical cost of executing transactions on Fantom, serving as a key indicator of network accessibility and usage costs. The calculation involves analyzing all transactions within each day, computing individual transaction fees based on gas usage and pricing, and determining the median fee amount in USD.

## 2. Data sources

- **Fantom blockchain transaction records**: Raw transaction data from the Fantom blockchain containing gas usage information, effective gas prices, block timestamps, transaction hashes, and sender/receiver addresses
- **End-of-day token price feeds**: Price data for WFTM (Wrapped Fantom Token) used to convert native fee amounts to USD equivalents
- **WFTM token metadata**: Information about the WFTM token contract (`0x21be370d5312f44cb42ce377bc9b8a0cef1a4c83`) which serves as the fee denomination token for Fantom transactions

## 3. Step-by-step calculation

### Component A: Transaction data extraction
- **Purpose**: Extract all Fantom blockchain transactions with complete gas and pricing information
- **Logic**: Pull all transaction records from Fantom blockchain data, including block timestamps, transaction hashes, gas usage amounts, and effective gas prices. Filter for transactions with valid gas information and convert gas prices from wei to WFTM units by dividing by 10^18. Include sender and receiver addresses along with block numbers for complete transaction context.
- **Key fields**: `block_timestamp` for timing, `transaction_hash` for unique identification, `gas_used` for actual gas consumption, `effective_gas_price` for the price per unit of gas, `from_address` and `to_address` for transaction participants

### Component B: Individual fee calculation
- **Purpose**: Calculate the actual fee paid for each transaction
- **Logic**: For each transaction, multiply the `gas_used` by the `effective_gas_price` to determine the total fee paid in WFTM. Handle cases where gas information might be missing by using a default value of zero to avoid excluding transactions from the median calculation. This ensures all valid transactions contribute to the statistical calculation.
- **Key fields**: `fee_amount` representing the total transaction fee in WFTM units, derived from gas consumption and pricing data

### Component C: Daily median calculation
- **Purpose**: Determine the median transaction fee for each day
- **Logic**: Group all transactions by day using timestamp truncation to daily boundaries. For each day, collect all individual transaction fees and calculate the 50th percentile using approximate quantile functions with 100 buckets. This median represents the typical transaction fee that half of all users paid above and half paid below on that specific day.
- **Key fields**: `timestamp` truncated to day granularity, `transaction_fee_median` as the 50th percentile of all fees for that day

### Final aggregation
- **Combining components**: The daily median fees in WFTM are passed through currency conversion processes
- **Currency conversion**: Convert median fees from WFTM to USD using end-of-day price feeds for the WFTM token, ensuring consistent valuation timing with the daily aggregation boundaries
- **Time aggregation**: Data is aggregated to daily granularity with timestamps representing the start of each day
- **Final calculation**: The final metric represents the median transaction fee in USD that users paid on the Fantom blockchain for each day, providing a normalized measure of typical transaction costs across time periods

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

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