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Blockchains (L1)

Movement

# Movement Active addresses (daily) Methodology

## 1. Overview

Active addresses daily for Movement measures the number of unique wallet addresses that initiated at least one transaction on the Movement blockchain within a 24-hour period. This metric serves as a key indicator of user engagement and network activity, representing actual user participation rather than transaction volume. The calculation uses HyperLogLog algorithms for memory-efficient distinct counting of addresses across multiple dimensional breakdowns.

## 2. Data sources

The calculation relies on the following raw data sources:
- **Movement blockchain transaction records**: Complete transaction history including sender addresses, timestamps, gas usage, and transaction hashes
- **Transaction metadata**: Gas pricing information and transaction execution details from the Movement blockchain
- **Chain availability configuration**: System configuration data that defines valid operational periods for the Movement network

## 3. Step-by-step calculation

### Component A: Transaction data preparation
- **Purpose**: Extract and standardize Movement blockchain transaction data for user identification
- **Logic**: The system retrieves all transactions from Movement blockchain records, extracting key fields including sender addresses, block timestamps, transaction hashes, gas usage, and gas prices. Gas unit prices are normalized by dividing by 10^8 to correct for decimal precision. A special timestamp handling rule applies to genesis transactions: any transactions with timestamps before January 1, 2000 are reassigned to December 5, 2024, while normal transactions are filtered to include only those after January 1, 2000. This ensures proper chronological ordering while handling edge cases in the blockchain's genesis block data.
- **Key fields**: `sender` (wallet address), `block_timestamp` (transaction time), `hash` (transaction identifier), `gas_used` (computational cost), `gas_unit_price` (normalized pricing)

### Component B: Daily user identification
- **Purpose**: Identify unique active addresses per day from transaction activity
- **Logic**: Transaction timestamps are truncated to daily granularity using day-level time buckets. The system extracts distinct sender addresses for each day, treating each unique sender as an active address. Records are enriched with metadata including chain identifier ('movement'), user classification ('Transaction sender'), market sector ('blockchains_l2'), and business segment ('blockchain'). The process uses incremental processing strategy with insert-overwrite logic, looking back a configurable number of days for updates. Chain availability filters ensure only valid operational periods are included in the analysis.
- **Key fields**: `timestamp` (daily bucket), `sender` (active address), `user_type` (classification), `chain` (network identifier)

### Component C: HyperLogLog aggregation
- **Purpose**: Efficiently count distinct active addresses across multiple dimensional breakdowns
- **Logic**: The system applies HyperLogLog sketching with precision level 15 to enable memory-efficient distinct counting of addresses. Daily user records are processed through HLL initialization for each unique address within day/chain/user_type/product combinations. The aggregation creates four distinct dimensional views: project-level aggregation (overall network activity), chain-level breakdown (network-specific counts), user-type breakdown (classification-based counts), and product-level breakdown (application-specific counts). Each dimension uses HLL merge operations to combine sketches and extract final distinct counts through HLL extraction functions.
- **Key fields**: `hll` (HyperLogLog sketch), `user_dau` (distinct active addresses), `dimensions` (breakdown category), `chain_id` (network identifier)

### Final aggregation
- **Combining components**: The final active addresses metric selects records from the user DAU aggregation where the dimension type is either 'chain' or 'project_dbt_tag', effectively providing both overall project-level and chain-specific active address counts. The `user_dau` field from the aggregated data becomes the `active_addresses_daily` metric value.
- **Currency conversion**: No currency conversion is required as this metric counts addresses rather than monetary values
- **Time aggregation**: Daily boundaries are established through timestamp truncation to day-level granularity, with missing days filled with zero values to ensure continuous time series data
- **Final calculation**: The final active addresses count represents the distinct number of unique sender addresses that initiated transactions within each daily period, calculated using HyperLogLog distinct counting methods and filtered by chain availability rules to ensure data quality and operational validity

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

Unique active addresses over a fixed 24-hour window.

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