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

Aptos is a smart contract platform for decentralized applications (a blockchain). Aptos was founded by Mo Shaikh and Avery Ching and launched in 2022.

APT token holders own and govern the protocol. They currently don’t take a cut of the total transaction fees paid by users (revenue). Aptos’ investors include Binance Labs, Jump Crypto, Apollo, etc.

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Blockchains (L1)

Aptos

# Aptos Active users (daily) Methodology

## 1. Overview

Daily active users (DAU) for Aptos measures the number of unique addresses that initiated at least one transaction on the Aptos blockchain within a 24-hour period. This metric answers the business question "How many unique users are actively transacting on Aptos each day?" and serves as a key indicator of network adoption and user engagement. The calculation approach identifies unique transaction sender addresses per day from raw blockchain transaction data.

## 2. Data sources

The methodology uses the following underlying data sources:

- **Aptos blockchain transaction records**: Complete transaction history from the Aptos blockchain containing sender addresses, timestamps, transaction hashes, gas usage, and gas prices
- **Genesis block metadata**: Hardcoded timestamp of October 12, 2022 to handle edge cases where transaction timestamps may be invalid (pre-2000 dates)
- **Chain availability metadata**: Information about when the Aptos blockchain was operational to ensure data quality filtering

## 3. Step-by-step calculation

### Component A: Transaction data preparation
- **Purpose**: Extract and prepare all Aptos transaction records for analysis
- **Logic**: Pull all transaction records from raw Aptos blockchain data, filtering for valid timestamps after January 1, 2000. Handle special case of genesis block transactions by applying a hardcoded timestamp of October 12, 2022 UTC for any transactions with timestamps before 2000 (which indicate invalid genesis block data). Extract key fields including `block_timestamp`, transaction `hash`, `sender` address, `gas_used`, and `gas_unit_price`. Convert gas prices from smallest units to standard units by dividing by 10^8. Combine normal transactions with genesis block transactions using a union operation.
- **Key fields**: `block_timestamp` for timing, `sender` for user identification, `hash` for transaction uniqueness, `gas_used` and `gas_unit_price` for transaction cost analysis

### Component B: Daily user identification
- **Purpose**: Identify unique active users per day from transaction sender data
- **Logic**: Truncate transaction timestamps to daily boundaries using day-level granularity. Extract distinct `sender` addresses within each day to count unique users. Apply metadata categorization by assigning each user the type 'Transaction sender', chain identifier 'aptos', market sector 'blockchains_l1', and business segment 'blockchain'. Set `product_id` to null as this is a chain-level metric rather than product-specific. Handle incremental processing by looking back additional days from the maximum partition date to ensure completeness.
- **Key fields**: `timestamp` truncated to day level, `user_dau` containing the unique sender address, `user_type` categorization, chain and sector identifiers

### Final aggregation
- **Combining components**: The daily user identification component directly processes the prepared transaction data without additional joining, as both components work with the same underlying transaction records
- **Currency conversion**: No currency conversion required as this metric counts users rather than monetary values
- **Time aggregation**: Daily boundaries are established by truncating transaction timestamps to day level, creating discrete 24-hour periods for user counting
- **Final calculation**: Count distinct sender addresses per day where each unique address that initiated at least one transaction during the day represents one daily active user. Apply chain availability filtering to exclude periods when the Aptos blockchain was not operational, ensuring data quality and accurate user counts only for valid operational periods.

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

Unique revenue-generating users over a fixed 24-hour window.

AptosAPT

Active users (daily)

Active users (daily)

Competitive landscape