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

Ronin is a smart contract platform for decentralized applications (a blockchain). Ronin was founded by Sky Mavis and launched in 2021.

RON holders own and govern the protocol. They currently don’t take a cut of the total transaction fees paid by users (revenue). Sky Mavis' investors include a16z, Binance, Animoca Brands, etc.

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Blockchains (L1)

Ronin

# Ronin Active addresses (monthly) Methodology

## 1. Overview

Monthly active addresses measures the number of unique addresses that initiated at least one transaction on the Ronin blockchain within a 30-day rolling window, aggregated monthly. This metric answers the question "How many distinct users are actively transacting on Ronin each month?" For blockchain analysis, active addresses are equivalent to active users since each transaction sender represents a unique user address. The calculation uses HyperLogLog algorithms to efficiently count distinct addresses across overlapping time periods and provides breakdowns by chain, project, user type, and product dimensions.

## 2. Data sources

- **Ronin blockchain transactions**: Raw transaction records from the Ronin blockchain containing transaction senders, timestamps, and transaction hashes
- **Timestamp data**: System-generated date ranges used to create complete daily sequences for rolling window calculations

## 3. Step-by-step calculation

### Component A: User identification and daily activity
- **Purpose**: Identify unique transaction senders and calculate daily active users as the foundation for monthly aggregation
- **Logic**: Extract transaction data from Ronin blockchain transaction records, identifying each `from_address` as a user with classification as 'Transaction sender' under the 'blockchains_l1' market sector and 'blockchain' business segment. Group transactions by day using timestamp truncation to day granularity and count distinct `user_address` values per day to calculate daily active users (DAU). Apply incremental processing logic to only reprocess recent data when updating.
- **Key fields**: `block_timestamp` for temporal grouping, `from_address` renamed to `user_address` for user identification, `transaction_hash` for transaction uniqueness, and computed `timestamp` field truncated to day precision

### Component B: HyperLogLog sketch creation and rolling window preparation
- **Purpose**: Convert daily distinct user counts to HyperLogLog sketches and create a complete date spine for rolling window calculations
- **Logic**: Transform daily active user counts into HyperLogLog sketches using BigQuery's `hll_count.init()` function with precision parameter 15 for efficient distinct counting across overlapping periods. Generate a complete date spine from the earliest transaction date to the current date minus one day to ensure no missing days in rolling window calculations. Create a self-join structure where each day is paired with its previous 29 days to form 30-day windows.
- **Key fields**: `hll` sketches containing encoded distinct user information, `timestamp` for daily boundaries, and `joinday` for window boundary creation

### Component C: Monthly active user calculation with dimensional breakdowns
- **Purpose**: Calculate 30-day rolling monthly active users by merging HyperLogLog sketches and create breakdowns by different dimensions
- **Logic**: For each day, merge HyperLogLog sketches from the current day plus the previous 29 days using `hll_count.merge_partial()` to handle overlapping user counts efficiently. Extract final distinct counts using `hll_count.extract()` to get monthly active user values. Create separate dimensional breakdowns: project-level aggregation across all chains and products, chain-level aggregation for Ronin specifically, user-type-level aggregation for transaction senders, and product-level aggregation (though product_id is null for blockchain transactions). Combine all dimensional breakdowns using UNION ALL operations.
- **Key fields**: `user_mau` containing distinct monthly active user counts, `dimensions` indicating breakdown type ('project_dbt_tag', 'chain', 'user_type', 'product_id'), `chain_id` for chain identification, and dimension-specific fields like `user_type` and `product_id`

### Final aggregation
- **Combining components**: The monthly active users calculation serves as the base, with the final model simply renaming the `user_mau` field to `active_addresses_monthly` since active users are equivalent to active addresses for blockchain metrics. Filtering is applied to only include chain-level and project-level dimensional breakdowns in the final output.
- **Currency conversion**: No currency conversion is required as this metric counts addresses/users rather than monetary values
- **Time aggregation**: Uses 30-day rolling windows calculated daily, but the final output is filtered and aggregated at monthly boundaries. Each timestamp represents the end date of a 30-day period, with monthly partitioning for efficient storage and querying.
- **Final calculation**: The metric represents the distinct count of unique addresses that initiated at least one transaction within any 30-day period ending on each day of the month. The HyperLogLog algorithm ensures accurate distinct counting even when the same address appears in multiple overlapping windows, providing precise monthly active address counts for the Ronin blockchain.

*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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