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 Transaction count Methodology

## 1. Overview

The Fantom transaction count metric measures the daily number of unique transactions executed on the Fantom blockchain. This metric provides insights into network activity and usage by counting distinct transactions per day, regardless of their complexity, gas usage, or value transferred. The calculation aggregates all on-chain transactions into daily totals, applying data quality checks and gap-filling logic to ensure a complete time series.

## 2. Data sources

The calculation relies on the following raw data sources:

- **Fantom blockchain transaction records**: Complete transaction history from the Fantom blockchain including transaction hashes, block timestamps, block numbers, sender and recipient addresses, gas usage, and gas prices
- **Token metadata**: Wrapped Fantom (WFTM) token information including the contract address `0x21be370d5312f44cb42ce377bc9b8a0cef1a4c83` used for gas fee calculations in related metrics

## 3. Step-by-step calculation

### Component A: Transaction data preparation
- **Purpose**: Extract and standardize all Fantom blockchain transactions with relevant metadata
- **Logic**: The system retrieves all transaction records from Fantom blockchain data, including block timestamps, transaction hashes, block numbers, and address information. Each transaction is enriched with standardized metadata including chain identifier, market sector classification, and business segment categorization. Gas usage and effective gas price information is extracted and decimal-adjusted for potential use in related calculations, though not directly used in the transaction count metric itself.
- **Key fields**: Essential fields include `block_timestamp` for temporal grouping, `transaction_hash` for unique identification, `block_number` for ordering, `from_address` and `to_address` for transaction participants, and `gas_used` and `effective_gas_price` for fee analysis

### Component B: Daily transaction aggregation
- **Purpose**: Group transactions by day and count unique transactions to produce daily totals
- **Logic**: Transaction timestamps are truncated to daily boundaries using day-level granularity, removing hours, minutes, and seconds. Transactions are then grouped by the truncated timestamp along with chain and classification metadata. The system counts distinct transaction hashes within each day to ensure no double-counting occurs, even if transactions appear multiple times in the raw data due to processing or storage patterns.
- **Key fields**: The aggregation produces `timestamp` (daily boundary), `timestamp_grain` (set to 'day'), `chain_id` (set to 'fantom'), classification fields (`market_sector_id`, `business_segment_id`), and the core `transaction_count` metric representing unique daily transactions

### Final aggregation
- **Combining components**: The daily transaction counts are processed through a finalization macro that validates the data schema, ensures proper metadata assignment, and applies gap-filling logic
- **Time aggregation**: Daily boundaries are established using timestamp truncation, creating consistent 24-hour periods aligned to UTC day boundaries
- **Data quality assurance**: The finalization process includes schema validation, identifier verification, and automated gap detection. Missing days in the time series are filled with zero transaction counts to maintain continuity
- **Chain availability filtering**: Final results are filtered based on chain availability rules to ensure data is only included for periods when the Fantom network was operational and data collection was reliable
- **Final calculation**: The metric represents the count of distinct transaction hashes occurring within each daily time period on the Fantom blockchain, with systematic gap-filling and quality assurance applied to create a complete daily time series

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

Confirmed transactions added to the blockchain.

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Transaction count

Transaction count

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