Liquidity providers deposit funds into Voltz and receive trading fees from traders (supply-side fees).

Voltz Protocol is a non-custodial Automated Market Maker (AMM) for Interest Rate Swaps deployed on Ethereum.

Voltz DAO owns and governs the protocol. They do not currently take a cut of the total trading fees paid by traders (revenue). Voltz’s investors include Framework, Fabric Ventrues etc.

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Voltz

# Voltz Active users (monthly) Methodology

## 1. Overview

Monthly Active Users (MAU) measures the number of unique users who interacted with the Voltz protocol within any 30-day rolling window. This metric provides insight into the protocol's user engagement and adoption trends by tracking distinct users across different types of interactions (trading, depositing, withdrawing) on both Ethereum and Arbitrum networks. The calculation uses a rolling 30-day window approach with HyperLogLog data structures for efficient distinct user counting across large datasets.

## 2. Data sources

- **Voltz V1 protocol events**: Parsed smart contract events from Ethereum and Arbitrum networks capturing user interactions
  - **Arbitrum events**: VAMM (Virtual Automated Market Maker) `Swap`, `Mint`, and `Burn` events
  - **Ethereum events**: FCM `FullyCollateralisedSwap` events and VAMM `Swap`, `Mint`, and `Burn` events
- **Blockchain transaction data**: Transaction timestamps and hash identifiers for event ordering and deduplication
- **Chain metadata**: Network identifiers and availability filters for cross-chain data consistency

## 3. Step-by-step calculation

### Component A: User extraction from blockchain events
- **Purpose**: Identifies and classifies users based on their interactions with Voltz protocol smart contracts
- **Logic**: Parses blockchain events from Voltz V1 contracts on Ethereum and Arbitrum networks. Extracts user addresses from different event parameters based on interaction type. For `Swap` events, extracts the `recipient` address and classifies as "Trader". For `Mint` events, extracts the `owner` address and classifies as "Depositor". For `Burn` events, extracts the `owner` address and classifies as "Withdrawer". For `FullyCollateralisedSwap` events, extracts the `trader` address and classifies as "Trader". Each record includes the blockchain timestamp, transaction hash for deduplication, chain identifier, and user type classification
- **Key fields**: `block_timestamp` for temporal analysis, `user_address` for unique user identification, `user_type` for interaction classification, `chain` for network segmentation, `transaction_hash` for deduplication

### Component B: Daily active user aggregation
- **Purpose**: Calculates daily unique active users by removing duplicates within each day
- **Logic**: Groups the extracted user events by day using timestamp truncation to create daily boundaries. Removes duplicate users within each day by selecting distinct combinations of date, chain, user type, and user address. This ensures that if a user performs multiple transactions of the same type on the same day, they are only counted once. Preserves dimensional breakdown by chain, user type, and product for downstream analysis
- **Key fields**: `timestamp` truncated to day granularity, `user_dau` containing unique daily active user addresses, dimensional fields for segmentation

### Component C: Rolling 30-day window generation  
- **Purpose**: Creates a comprehensive datespine and rolling window structure for MAU calculation
- **Logic**: Generates a complete date spine covering the entire historical period to ensure no days are skipped in the rolling calculation. For each day in the datespine, creates a 30-day lookback window by generating a series of dates from 29 days prior to the current day. Uses cross join logic to associate each target date with its corresponding 30-day window of dates. This structure enables efficient aggregation of unique users across rolling monthly periods without gaps
- **Key fields**: `timestamp` for target dates, `joinday` for dates within each 30-day window

### Component D: HyperLogLog aggregation for efficient distinct counting
- **Purpose**: Efficiently calculates unique user counts across large datasets using probabilistic data structures
- **Logic**: Initializes HyperLogLog sketches for daily user data to enable efficient merging across time periods. Joins the rolling window structure with daily user data to associate each day's users with all relevant 30-day windows. Merges HyperLogLog sketches across each 30-day window to calculate approximate distinct user counts. This approach provides scalable distinct counting without needing to store and deduplicate all individual user addresses
- **Key fields**: `hll` containing HyperLogLog sketches, merged counts representing unique users in each 30-day window

### Component E: Multi-dimensional breakdown
- **Purpose**: Provides MAU metrics across different analytical dimensions for flexible reporting
- **Logic**: Calculates MAU at four different aggregation levels. Project-level aggregation combines all chains, user types, and products into a single MAU count. Chain-level aggregation groups by individual blockchain networks. User-type aggregation groups by interaction types (Trader, Depositor, Withdrawer). Product-level aggregation groups by specific Voltz products. Each breakdown uses null values for unused dimensions and combines results using union operations
- **Key fields**: `dimensions` indicating the aggregation level, `user_mau` containing the final distinct user count, dimensional identifiers or null values

### Final aggregation
- **Combining components**: The four dimensional breakdowns are combined using union operations to create a unified result set with multiple aggregation levels in a single table
- **Time aggregation**: Each day represents the unique user count for the 30-day period ending on that date, providing rolling monthly metrics updated daily
- **Data completeness**: Missing days are filled with zero values to ensure complete time series coverage for visualization and analysis
- **Final calculation**: The metric represents the approximate distinct count of users who performed any tracked interaction (swap, mint, burn) with Voltz protocol during each 30-day rolling window, segmented by various dimensions for analytical flexibility

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

Unique revenue-generating users over a rolling 30-day window.

Voltz

Active users (monthly)

Active users (monthly)

Competitive landscape

Chain breakdown