IoTeX is the blockchain platform for Real-World AI. Since 2017, its foundational infrastructure has delivered verified, real-time data from the real world to AI systems and decentralized applications. Powering 100+ projects and 40M devices across mobility, robotics, energy, health, and more, IoTeX enables developers to build next-gen AI models and applications that deliver real-world impact. The IoTeX tech stack provides the data, identity, and verification layers to fulfill the surging demand for real-world data in the new AI era. Trusted by partners like Google, Samsung, IEEE, ARM, and Nordic Semiconductor, IoTeX is uniquely positioned to capture the multi-trillion-dollar AI and data economy.

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Blockchains (L1)

IoTeX

# IoTeX Active addresses (monthly) Methodology

## 1. Overview

Monthly active addresses measures the count of unique addresses that initiated at least one transaction on the IoTeX blockchain within a rolling 30-day window. This metric provides insight into blockchain usage and user engagement by tracking distinct transaction senders over time. The calculation uses a 30-day rolling window approach, meaning each day's value represents the number of unique addresses that were active in the current day plus the previous 29 days.

## 2. Data sources

- **IoTeX transaction records**: Raw blockchain transaction data containing sender addresses (`from_address`), timestamps, and transaction hashes
- **Chain availability configuration**: Data quality filters that ensure complete and reliable blockchain data coverage

## 3. Step-by-step calculation

### Component A: Transaction sender extraction
- **Purpose**: Identifies all unique addresses that sent transactions on IoTeX
- **Logic**: Extracts transaction sender addresses from IoTeX blockchain transaction records. Each transaction record provides the `from_address` field which identifies who initiated the transaction. Addresses are normalized to lowercase to ensure consistent counting. Records are tagged with blockchain metadata including chain identifier ('iotex'), market sector ('blockchains_l1'), and user classification ('Transaction sender').
- **Key fields**: `block_timestamp` for daily grouping, `from_address` as the unique user identifier, `transaction_hash` for transaction tracking

### Component B: Daily active user aggregation  
- **Purpose**: Creates daily snapshots of unique active addresses
- **Logic**: Groups transaction senders by day using timestamp truncation to day boundaries. Applies distinct counting to identify unique addresses active each day. Each daily record represents one unique address that was active on that specific day, with duplicates removed within each day.
- **Key fields**: `timestamp` truncated to day, `user_dau` containing the unique address, `user_type` for categorization

### Component C: HyperLogLog sketch creation
- **Purpose**: Enables memory-efficient distinct counting across large datasets
- **Logic**: Converts daily active addresses into HyperLogLog probabilistic data sketches using precision level 15. HyperLogLog allows approximate distinct counting with minimal memory usage, crucial for handling large address datasets efficiently. Each day's unique addresses are encoded into a compact HyperLogLog sketch that can be merged with other sketches.
- **Key fields**: `hll` containing the HyperLogLog sketch for each day's addresses

### Component D: Rolling 30-day window construction
- **Purpose**: Creates the rolling window framework for monthly active user calculation
- **Logic**: Generates a comprehensive datespine covering all historical dates, then creates join relationships between each day and its preceding 29 days. This produces a mapping where each date is associated with all dates in its 30-day window. The datespine ensures no dates are skipped even when no transactions occurred, maintaining continuity in the time series.
- **Key fields**: `timestamp` for the reference date, `joinday` for each day within the 30-day window

### Component E: Monthly active user calculation
- **Purpose**: Aggregates unique addresses across 30-day rolling windows
- **Logic**: Merges HyperLogLog sketches from all days within each 30-day window using HyperLogLog merge operations. This combines the probabilistic sketches to produce an approximate count of distinct addresses active across the entire window. The merged sketches are then extracted to produce the final monthly active address count for each day.
- **Key fields**: `user_mau` containing the final monthly active address count

### Component F: Dimensional breakdown
- **Purpose**: Provides the metric across different analytical dimensions
- **Logic**: Creates separate breakdowns of the monthly active addresses metric by different dimensions: 'chain' level (IoTeX blockchain), 'project_dbt_tag' level (project-wide aggregation), 'user_type' level (transaction senders), and 'product_id' level (individual products). Non-applicable dimensions are set to null for each breakdown. Results from all dimensional breakdowns are combined using union operations.
- **Key fields**: `dimensions` indicating the breakdown level, dimension-specific fields like `chain_id` or `user_type`

### Final aggregation
- **Combining components**: The dimensional breakdowns are unioned together to create a comprehensive dataset with multiple analytical perspectives of the same underlying monthly active address metric
- **Currency conversion**: Not applicable - this metric counts addresses rather than monetary values
- **Time aggregation**: Each day represents a rolling 30-day window ending on that date, providing continuous monthly snapshots
- **Final calculation**: The final metric value is the HyperLogLog-approximated count of distinct addresses that were active as transaction senders within the rolling 30-day period ending on each date

*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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