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Blockchains (L1)

Movement

# Movement Active addresses (monthly) Methodology

## 1. Overview

Monthly active addresses measures the number of unique addresses that have sent at least one transaction on the Movement blockchain within a rolling 30-day window. This metric provides insight into user engagement and adoption by tracking distinct addresses that actively participate in on-chain transactions. The calculation uses a rolling window approach where each day's MAU represents the count of unique addresses that transacted in that day plus the previous 29 days.

## 2. Data sources

- **Movement blockchain transaction records**: Raw transaction data from the Movement blockchain containing transaction metadata, sender addresses, timestamps, gas usage, and pricing information
- **Movement blockchain genesis data**: Special handling for early blockchain transactions with timestamps before the official genesis, which are reassigned to the actual genesis timestamp of December 5, 2024
- **System metadata**: Chain availability data used to determine valid date ranges for Movement blockchain analytics

## 3. Step-by-step calculation

### Component A: Daily active user identification
- **Purpose**: Identify unique addresses that sent transactions each day
- **Logic**: Extract all transaction records from Movement blockchain data and identify the `sender` address for each transaction. Filter transactions to include only those after January 1, 2000, with special handling for pre-genesis transactions (those before this date are reassigned to December 5, 2024 UTC as the genesis timestamp). Truncate all timestamps to day granularity and identify distinct sender addresses per day.
- **Key fields**: `sender` (transaction originator address), `block_timestamp` (truncated to day), `transaction_hash`, `gas_used`, `gas_unit_price` (converted from 8 decimal places)

### Component B: HyperLogLog sketch creation
- **Purpose**: Create approximate distinct count sketches for efficient MAU calculation
- **Logic**: For each day, create HyperLogLog sketches of the unique sender addresses using precision parameter 15. Group addresses by day, user type (labeled as 'Transaction sender'), chain identifier ('movement'), and other dimensional attributes. This probabilistic data structure enables efficient computation of distinct counts across large datasets.
- **Key fields**: `timestamp` (daily), `chain_id`, `user_type`, `hll` (HyperLogLog sketch)

### Component C: Rolling window construction  
- **Purpose**: Build complete 30-day rolling windows for MAU calculation
- **Logic**: Create a datespine covering all days from the minimum available date to yesterday. For each day in the datespine, generate a 30-day lookback window (current day plus previous 29 days) using date array generation. Join this window structure with the daily HLL sketches to ensure complete coverage of the rolling period, even when no transactions occurred on specific days.
- **Key fields**: `timestamp` (target date), `joinday` (dates within the 30-day window)

### Component D: Monthly active user aggregation
- **Purpose**: Calculate MAU by merging HLL sketches within rolling windows
- **Logic**: For each day and its 30-day window, merge all HyperLogLog sketches from the window period using HLL merge operations. Extract the final distinct count from the merged sketch to get the approximate count of unique addresses. Create separate aggregations for different dimensional breakdowns: project-level (project_dbt_tag), chain-level, user-type-level, and product-level.
- **Key fields**: `user_mau` (extracted HLL count), `dimensions` (aggregation level), `chain_id`, `user_type`, `product_id`

### Final aggregation
- **Combining components**: Select only the chain-level and project-level dimensional aggregations from the MAU calculations, filtering out user-type and product-level breakdowns. Rename the `user_mau` field to `active_addresses_monthly` to reflect the specific metric being calculated.
- **Time aggregation**: Each day represents a rolling 30-day window MAU calculation, with daily updates as new transaction data becomes available
- **Final calculation**: The final metric value represents the approximate count of unique addresses that sent at least one transaction within the most recent 30-day period for each date, using HyperLogLog probabilistic counting for scalability and performance

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

Unique addresses active over a rolling 30-day window.

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Active addresses (monthly)

Active addresses (monthly)

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