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 (weekly) Methodology

## 1. Overview

The Volmex Weekly Active Users (WAU) metric measures the number of unique users who interact with the Volmex protocol over a 7-day rolling window. This metric answers the business question: "How many distinct users are actively using Volmex derivatives products each week?" The calculation uses HyperLogLog data structures to efficiently count unique users across multiple dimensions (project-level, chain-level, user type, and product) while maintaining a rolling 7-day window for each daily timestamp.

## 2. Data sources

The metric relies on the following blockchain data sources:

- **Volmex protocol events**: Parsed `CollateralToken_Collateralized_event` and `CollateralToken_Redeemed_event` logs from Volmex v1 smart contracts deployed across three blockchain networks
- **Multi-chain coverage**: Event logs from Arbitrum, Ethereum, and Polygon networks where Volmex protocol is deployed
- **Event parameters**: User addresses extracted from the `sender` parameter of collateralization and redemption events
- **Transaction metadata**: Block timestamps for temporal aggregation and transaction hashes for event identification
- **Protocol metadata**: Chain identifiers, market sector classification (derivatives), and business segment information (perpetual)

## 3. Step-by-step calculation

### Component A: User activity extraction
- **Purpose**: Identifies all user interactions with the Volmex protocol from raw blockchain events
- **Logic**: Extracts user activity from `CollateralToken_Collateralized_event` logs (when users mint derivative tokens) and `CollateralToken_Redeemed_event` logs (when users redeem tokens) across Arbitrum, Ethereum, and Polygon chains. Each event includes the user address from the `sender` parameter and is classified by interaction type
- **Key fields**: `block_timestamp` for temporal analysis, `user_address` from the event sender, `user_type` classified as 'Minter' or 'Redeemer' based on event type, `chain` identifier, and protocol metadata like `market_sector_id` (derivatives) and `business_segment_id` (perpetual)

### Component B: Daily active user aggregation  
- **Purpose**: Counts unique users per day across different dimensional breakdowns
- **Logic**: Groups user activity by day using timestamp truncation and counts distinct user addresses. Creates separate counts for each combination of chain, user type, and product dimensions. Maintains daily granularity as the foundation for weekly aggregation
- **Key fields**: `timestamp` truncated to day level, `timestamp_grain` set to 'day', dimensional attributes (`chain`, `user_type`, `product_id`), and `user_dau` containing the unique user address

### Component C: HyperLogLog encoding
- **Purpose**: Converts daily user sets into memory-efficient HyperLogLog structures for set operations
- **Logic**: Initializes HyperLogLog data structures from daily user counts using `hll_count.init` with precision parameter 15. This enables efficient merging of user sets across time windows without storing individual user addresses
- **Key fields**: `hll` containing the encoded user set, maintaining all dimensional attributes from the daily aggregation

### Component D: Rolling window construction
- **Purpose**: Creates 7-day sliding windows for weekly active user calculation
- **Logic**: Generates a complete datespine covering all days in the analysis period, then creates join relationships between each day and its preceding 6 days using `generate_timestamp_array`. Uses cross-join logic with `unnest` operations to create all day-to-lookback-day combinations needed for the rolling window
- **Key fields**: `timestamp` as the target date and `joinday` representing each day within the 7-day lookback window

### Component E: Weekly user set merging
- **Purpose**: Combines user sets across 7-day windows to calculate weekly active users
- **Logic**: Joins the rolling window structure with daily HyperLogLog data, then merges HLL structures across each 7-day period using `hll_count.merge_partial`. Extracts final unique user counts using `hll_count.extract` to get the cardinality of the merged set
- **Key fields**: Maintains dimensional attributes while computing `user_wau` as the count of unique users across the 7-day window

### Component F: Dimensional breakdowns
- **Purpose**: Creates separate WAU calculations for different analytical perspectives
- **Logic**: Generates four distinct dimensional views by grouping and aggregating at different levels. Project-level view aggregates across all dimensions, chain-level preserves chain identity, user-type-level preserves user classification, and product-level preserves product segmentation. Sets non-relevant dimension fields to null for each breakdown
- **Key fields**: `dimensions` identifier indicating the breakdown type, relevant dimensional fields populated based on the breakdown level, and `user_wau` containing the weekly unique user count

### Final aggregation
- **Combining components**: Union all dimensional breakdowns into a single result set, maintaining the different analytical perspectives in separate rows identified by the `dimensions` field
- **Currency conversion**: Not applicable for user count metrics
- **Time aggregation**: Daily timestamps with 7-day rolling window logic, where each day represents the WAU for that day plus the preceding 6 days
- **Final calculation**: The `user_wau` field contains the count of unique user addresses that performed any Volmex protocol interaction (minting or redeeming derivative tokens) within the 7-day window ending on each timestamp. Missing days are filled with zeros using the `finalize_metric_user_wau` macro to ensure complete time series coverage.

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

Unique revenue-generating users over a rolling 7-day window.

Volmex

Active users (weekly)

Active users (weekly)

Competitive landscape

Chain breakdown