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

Mina is a smart contract platform for decentralized applications (a blockchain). Mina was founded by Evan Shapiro & Izaak Meckler and launched in 2017.

Mina’s investors include Pantera Capital, FTX Ventures, Circle Ventures, etc.

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Blockchains (L1)

Mina Protocol

# Mina Protocol Active addresses (monthly) Methodology

## 1. Overview

Monthly active addresses measures the number of unique addresses that have sent at least one transaction on the Mina blockchain within a rolling 30-day window, calculated on a daily basis. This metric provides insight into user engagement and blockchain adoption by tracking how many distinct addresses interact with the network each month. The calculation uses a rolling window approach where each day represents the count of unique addresses that were active in the preceding 30 days.

## 2. Data sources

The calculation relies on:
- **Mina transaction data**: Raw transaction records from the Mina Protocol blockchain containing sender addresses, timestamps, and transaction metadata
- **Transaction metadata**: Block timestamps and transaction hashes for temporal aggregation and deduplication

## 3. Step-by-step calculation

### Component A: User address extraction
- **Purpose**: Identifies unique user addresses from blockchain transactions
- **Logic**: Extracts sender addresses from raw Mina transaction data along with block timestamps and transaction hashes. Each transaction sender is classified as a "Transaction sender" user type and associated with the Mina chain, blockchain layer 1 market sector, and blockchain business segment. The `sender` field represents the user address that initiated each transaction.
- **Key fields**: `block_timestamp` for temporal analysis, `transaction_hash` for transaction identification, `sender` as the user address

### Component B: Daily active user calculation
- **Purpose**: Calculates the number of distinct active addresses per day
- **Logic**: Groups transactions by calendar day using timestamp truncation and counts distinct sender addresses for each day. This creates daily snapshots of unique addresses that performed transactions, establishing the foundation for rolling monthly calculations. Each day captures all unique addresses that sent transactions within that 24-hour period.
- **Key fields**: `timestamp` truncated to day granularity, `user_address` for distinct counting, `chain` and other metadata fields

### Component C: HyperLogLog sketch creation
- **Purpose**: Creates efficient data structures for approximate distinct counting across time windows
- **Logic**: Converts daily distinct user counts into HyperLogLog sketches with precision parameter 15. These probabilistic data structures enable efficient merging of user counts across overlapping time periods without storing individual addresses. Each daily sketch represents the unique addresses active on that specific day.
- **Key fields**: `hll` sketch containing compressed distinct address information, `timestamp` for temporal alignment

### Component D: Rolling window generation
- **Purpose**: Creates consistent 30-day lookback windows for each calendar day
- **Logic**: Generates a datespine covering all dates from the earliest transaction data to the most recent complete day. For each date in the spine, creates a 30-day window by joining with all dates in the preceding 30 days (including the current date). This ensures every day has a complete 30-day historical context, even when some intermediate days had no transactions.
- **Key fields**: `timestamp` for the reference date, `joinday` for each day within the 30-day window

### Component E: Monthly active user aggregation
- **Purpose**: Calculates rolling 30-day unique user counts using overlapping time windows
- **Logic**: For each day's 30-day window, merges the HyperLogLog sketches from all days within that window and extracts the final distinct count. This provides an approximate count of unique addresses that were active at any point during the 30-day period. The calculation is broken down by multiple dimensions: project level (project_dbt_tag), chain level, user type level, and product level.
- **Key fields**: `user_mau` representing the monthly active user count, `dimensions` indicating the breakdown level, dimension-specific fields like `chain_id` and `user_type`

### Final aggregation
- **Combining components**: Selects monthly active user counts specifically for chain-level and project-level dimensions, filtering out user type and product breakdowns
- **Currency conversion**: Not applicable for address counting metrics
- **Time aggregation**: Uses rolling 30-day windows calculated daily, providing a smoothed view of monthly activity that updates continuously
- **Final calculation**: Renames `user_mau` to `active_addresses_monthly` to reflect that each unique sender address represents an active address on the blockchain. The final metric represents the number of distinct addresses that sent at least one transaction within the most recent 30-day period for each reported day.

*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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