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 addresses (daily) Methodology

## 1. Overview

Active addresses daily for Aptos measures the number of unique addresses that initiated at least one transaction on the Aptos blockchain within a given day. This metric answers the fundamental question of daily user activity by counting distinct transaction senders, providing insight into network adoption and usage patterns. The calculation treats active users and active addresses as equivalent concepts for blockchain networks, since each user is represented by their wallet address.

## 2. Data sources

- **Aptos blockchain transaction data**: Complete transaction records from the Aptos network including sender addresses, transaction hashes, block timestamps, gas usage, and gas prices
- **Table metadata**: Incremental processing metadata used to optimize data loading and track the last modified timestamps for efficient updates

## 3. Step-by-step calculation

### Component A: Transaction fact preparation
- **Purpose**: Extract and standardize all Aptos transaction data with proper genesis handling
- **Logic**: Retrieves all transaction records from Aptos blockchain data, focusing on sender addresses and block timestamps. Applies special handling for genesis blocks by normalizing any transactions with timestamps before January 1, 2000 to October 12, 2022 (the hardcoded genesis date). Converts gas unit prices from the raw blockchain format by dividing by 10^8 to get the proper decimal representation. Filters out any extremely old timestamps to ensure data quality
- **Key fields**: `block_timestamp` for daily aggregation boundaries, `sender` addresses for counting unique users, `transaction_hash` for transaction identification, `gas_used` and `gas_unit_price` for transaction cost analysis

### Component B: Daily user identification
- **Purpose**: Identify unique sender addresses per day and classify them as transaction senders
- **Logic**: Groups transaction data by day using timestamp truncation to day granularity. Extracts distinct sender addresses for each day, classifying each as a "Transaction sender" user type. Associates each address with metadata including the Aptos chain identifier, business segment classification as "blockchain", and market sector as "blockchains_l1". Applies incremental processing logic to only reprocess recent days based on the last modification timestamp plus a lookback window
- **Key fields**: `timestamp` truncated to day boundaries, `user_dau` containing the sender address, `user_type` set to "Transaction sender", `chain` identifier set to "aptos"

### Component C: HyperLogLog aggregation with dimensional breakdowns
- **Purpose**: Efficiently count distinct addresses across multiple dimensional views using probabilistic data structures
- **Logic**: Initializes HyperLogLog sketches for each sender address to enable scalable distinct counting. Creates four dimensional breakdowns: project-level aggregation combining all addresses across the entire Aptos ecosystem, chain-level breakdown (though Aptos is a single chain), user-type level breakdown separating different types of users, and product-level breakdown for different applications or services. Uses HLL merge operations to combine sketches within each dimension while maintaining accuracy for distinct address counting
- **Key fields**: `hll` sketches containing compressed address information, `dimensions` indicating the aggregation level, `chain_id` for chain-specific breakdowns, `user_type` and `product_id` for classification-based breakdowns

### Final aggregation
- **Combining components**: Merges HyperLogLog sketches using probabilistic union operations to calculate distinct address counts across all dimensional breakdowns, then extracts the final count estimates from the merged sketches
- **Currency conversion**: Not applicable for this metric as it measures address counts rather than monetary values
- **Time aggregation**: Uses daily boundaries defined by timestamp truncation, with incremental processing to efficiently update only recent time periods
- **Final calculation**: Extracts distinct address counts from merged HyperLogLog sketches and renames the `user_dau` field to `active_addresses_daily` to reflect that for blockchain networks, active users are equivalent to active addresses. Applies schema finalization to ensure consistent output format and fills any missing days with zero values to maintain continuous time series data

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

Unique active addresses over a fixed 24-hour window.

AptosAPT

Active addresses (daily)

Active addresses (daily)

Competitive landscape