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

Viction is a smart contract platform for decentralized applications (a blockchain). Viction was founded by Ninety Eight and launched in 2018.

VIC holders own and govern the protocol. They currently don't take a cut of the total transaction fees paid by users (revenue). Ninety Eight's investors include Binance Labs, The Spartan Group, Multicoin Capital, etc.

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Blockchains (L1)

Viction

# Viction Active addresses (monthly) Methodology

## 1. Overview

Monthly active addresses measures the number of unique wallet addresses that initiated at least one transaction on the Viction blockchain within a 30-day rolling window. This metric provides insight into user engagement and adoption trends by tracking distinct addresses that actively participate in blockchain transactions over monthly periods. The calculation treats transaction senders as active users, making this a core indicator of blockchain utilization and ecosystem health.

## 2. Data sources

- **Viction blockchain transactions**: Native transaction records from the Viction blockchain containing sender addresses, transaction hashes, block timestamps, and transaction metadata
- **End-of-day timestamp boundaries**: Daily and monthly timestamp truncation for proper time-based aggregation
- **Chain availability configuration**: Data quality filters to exclude periods when blockchain data may be incomplete or unreliable

## 3. Step-by-step calculation

### Component A: User identification
- **Purpose**: Extract and classify active user addresses from raw transaction data
- **Logic**: Process Viction blockchain transaction records to identify transaction senders as active users. Each transaction's `from_address` field represents an active wallet address. The system applies metadata classification, marking these addresses as "Transaction sender" user type within the "blockchains_l1" market sector and "blockchain" business segment. All transactions are timestamped using block timestamp for temporal analysis.
- **Key fields**: `block_timestamp` for timing, `from_address` as the active user address, `transaction_hash` for uniqueness, and classification fields for market segmentation

### Component B: Daily active user calculation
- **Purpose**: Count distinct active addresses per day to establish the foundation for monthly calculations
- **Logic**: Aggregate the user identification data by truncating timestamps to daily boundaries and counting distinct user addresses per day. The system maintains dimensional breakdowns by chain, user type, and product while ensuring incremental processing efficiency. Each day produces a count of unique addresses that initiated transactions during that 24-hour period.
- **Key fields**: `timestamp` truncated to day level, `user_address` for distinct counting, dimensional classification fields for breakdowns

### Component C: Monthly active user computation using HyperLogLog
- **Purpose**: Calculate 30-day rolling monthly active users efficiently using probabilistic counting
- **Logic**: Create HyperLogLog sketches for each daily active user count to enable efficient distinct counting across time windows. Generate a complete datespine covering all days in the analysis period, then construct 30-day rolling windows by joining each day with the previous 29 days of data. For each day, merge HyperLogLog sketches from all days in the 30-day window to produce an accurate count of distinct addresses active during that period. The system creates multiple dimensional breakdowns including project-level aggregations ('project_dbt_tag'), chain-level breakdowns, user type breakdowns, and product-level segmentation.
- **Key fields**: `hll` sketches for efficient distinct counting, `timestamp` for window boundaries, dimensional fields for various breakdowns, `user_mau` as the final monthly active user count

### Final aggregation
- **Combining components**: The monthly active user data flows directly into the final model, where the `user_mau` field is renamed to `active_addresses_monthly` to reflect the specific metric being calculated
- **Currency conversion**: Not applicable for this address-based metric
- **Time aggregation**: Uses 30-day rolling windows calculated daily, with monthly timestamp grain for reporting purposes
- **Final calculation**: Filter the MAU data to include only 'chain' and 'project_dbt_tag' dimensional breakdowns, rename the user count field to active addresses, apply the finalize metric standardization macro with zero-filling strategy, and filter results based on chain availability to ensure data quality. The result is a time series of monthly active address counts with standardized schema and complete temporal coverage.

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

Unique addresses active over a rolling 30-day window.

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Archived

Active addresses (monthly)

Active addresses (monthly)

Competitive landscape