Liquidity providers deposit collateral and mint volatility tokens. Traders can then purchase these volatility tokens to express views on market volatility.

Volmex enables VIX-like indices for crypto assets and trading functionality on Ethereum. Volmex was founded by Cole Kennelly and launched in 2021.

The Volmex team and investors own and govern the protocol. They currently charge a minting and redemption fee (revenue). Volmex’s investors include Three Arrows Capital, Robot Ventures, CMS Holdings etc.

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Volmex

# Volmex Active users (monthly) Methodology

## 1. Overview

Monthly Active Users (MAU) for Volmex measures the number of unique users who interacted with the Volmex derivatives protocol within any 30-day rolling period. This metric captures user engagement across the protocol's core functions of minting (collateralizing) and redeeming tokens on Arbitrum, Ethereum, and Polygon chains. The calculation uses HyperLogLog probabilistic counting for efficient aggregation and provides breakdowns by chain, user type, and product dimensions.

## 2. Data sources

The calculation relies on the following raw data sources:
- **Blockchain events**: Parsed `CollateralToken_Collateralized_event` and `CollateralToken_Redeemed_event` logs from Volmex protocol smart contracts across Arbitrum, Ethereum, and Polygon chains
- **Event data**: Transaction timestamps, transaction hashes, user addresses (senders), and blockchain context from protocol interactions
- **Chain metadata**: Chain identifiers and availability information for data quality filtering

## 3. Step-by-step calculation

### Component A: User interaction extraction
- **Purpose**: Identifies all user interactions with the Volmex protocol across supported chains
- **Logic**: Extracts user data from two key event types on each chain. `CollateralToken_Collateralized_event` logs capture users who mint tokens by providing collateral, while `CollateralToken_Redeemed_event` logs capture users who redeem their collateralized tokens. Each event provides the user address from the `sender` field, transaction timestamp, and chain context. Users are classified into two types: 'Minter' for collateralization events and 'Redeemer' for redemption events. All interactions are standardized with market sector as 'derivatives' and business segment as 'perpetual'
- **Key fields**: `block_timestamp` for timing, `transaction_hash` for uniqueness, `sender` address for user identification, `chain` for blockchain context, `user_type` for interaction classification

### Component B: Daily active user aggregation  
- **Purpose**: Converts raw user interactions into daily active user counts
- **Logic**: Groups all user interactions by day using timestamp truncation to daily granularity. For each day, counts distinct user addresses within each combination of chain, user type, market sector, business segment, and product. This creates the base Daily Active Users (DAU) dataset that serves as input for the rolling MAU calculation. Chain availability filters are applied to ensure data quality and completeness
- **Key fields**: `timestamp` truncated to day, `user_address` for distinct counting, dimensional grouping fields including `chain`, `user_type`, `product_id`

### Component C: HyperLogLog initialization
- **Purpose**: Prepares efficient probabilistic counting structures for large-scale user aggregation
- **Logic**: Converts the daily active user data into HyperLogLog sketches using `hll_count.init` with a precision parameter of 15. This allows efficient approximate counting of unique users across large datasets without storing individual user addresses. Each daily record is converted to an HLL sketch grouped by timestamp, chain, user type, and product dimensions
- **Key fields**: `hll` sketch containing compressed user count information, maintaining all dimensional groupings from the DAU data

### Component D: Rolling window creation
- **Purpose**: Establishes 30-day rolling windows for MAU calculation using datespine methodology
- **Logic**: Creates a complete datespine covering all days from the earliest data through yesterday to ensure no missing timestamps. For each day in the datespine, generates an array of the previous 29 days plus the current day (30 total days) to create the rolling window. This approach ensures consistent MAU calculation even when some days have zero activity. The datespine is joined with HLL data to create windows that contain all user activity within each 30-day period
- **Key fields**: `timestamp` for the reporting date, `joinday` for each day within the 30-day window, `hll` sketches for all days in the window

### Component E: Dimensional MAU calculation
- **Purpose**: Aggregates 30-day rolling unique user counts across multiple dimensional breakdowns
- **Logic**: For each 30-day window, merges all HLL sketches within that period using `hll_count.merge_partial` and extracts the final unique user count with `hll_count.extract`. This process is repeated for four different dimensional views: project-level (overall totals), chain-level (breakdown by blockchain), user type-level (breakdown by Minter vs Redeemer), and product-level (breakdown by specific products). Each breakdown nullifies irrelevant dimensions while preserving the relevant grouping dimension
- **Key fields**: `user_mau` containing the final unique user count for each dimension, `dimensions` indicating the breakdown type, dimensional fields like `chain_id`, `user_type`, `product_id`

### Final aggregation
- **Combining components**: All dimensional breakdowns are combined using UNION ALL to create a comprehensive dataset with multiple views of the same underlying MAU metric. Each record represents the unique user count for a specific day and dimensional breakdown
- **Time aggregation**: Uses 30-day rolling windows where each day's MAU represents unique users active in that day plus the previous 29 days. Window boundaries are calculated using timestamp arithmetic to ensure consistent 30-day periods
- **Final calculation**: The MAU value for any given day represents the approximate count of unique user addresses that had at least one interaction with the Volmex protocol within the 30-day period ending on that day, calculated using HyperLogLog probabilistic counting for efficiency and accuracy at scale

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

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

Volmex

Active users (monthly)

Active users (monthly)

Competitive landscape

Chain breakdown