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

Metavault.Trade is a non-custodial exchange for spot and perpetual contracts built on Polygon. Metavault.Trade was founded by an anonymous team and was launched in May 2022.

MVX tokenholders own and govern the protocol. They currently take a cut of the total trading fees paid by traders (revenue).

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Metavault.Trade

# Metavault.Trade Active users (monthly) Methodology

## 1. Overview

Monthly Active Users (MAU) for Metavault.Trade measures the number of unique users who performed any trading activity on the derivatives platform within a rolling 30-day window. This metric uses a sophisticated HyperLogLog-based approach to efficiently calculate unique user counts across multiple dimensional breakdowns, providing insights into user engagement across different time periods, chains, user types, and products.

## 2. Data sources

- **Polygon blockchain event logs**: Parsed smart contract events from the Metavault V1 Vault contract
- **Trading events**: Specific event types including `IncreasePosition`, `DecreasePosition`, and `Swap` events
- **Transaction metadata**: Block timestamps, transaction hashes, and chain identifiers from Polygon blockchain
- **Smart contract parameters**: User account addresses extracted from event parameters

## 3. Step-by-step calculation

### Component A: User identification and daily aggregation
- **Purpose**: Identifies unique daily active users from trading activities and prepares data for MAU calculation
- **Logic**: The system monitors Metavault V1 Vault smart contract events on Polygon blockchain, specifically tracking `IncreasePosition`, `DecreasePosition`, and `Swap` events. From each event, the `account` parameter is extracted as the user address. All users are classified as 'Trader' type since they're performing derivatives trading activities. Block timestamps are truncated to day granularity to create daily user activity records. The system applies chain availability filters to ensure data quality and consistency.
- **Key fields**: `user_address` (extracted from event `account` parameter), `block_timestamp` (truncated to day), `chain` (always 'polygon'), `user_type` (always 'Trader'), `market_sector_id` ('derivatives'), `business_segment_id` ('perpetual')

### Component B: HyperLogLog initialization for daily users
- **Purpose**: Converts daily user data into HyperLogLog data structures for efficient unique counting across rolling windows
- **Logic**: Daily active user records are processed to initialize HyperLogLog (HLL) data structures with precision parameter 15. This allows for approximate but highly efficient unique user counting when creating rolling windows. The data is grouped by timestamp, timestamp grain, chain, user type, and product ID to preserve all dimensional attributes while preparing for the windowing operation.
- **Key fields**: `hll` (HyperLogLog structure), `timestamp` (day-truncated), `timestamp_grain` ('day'), `chain_id`, `user_type`, `product_id`

### Component C: Date spine and rolling window creation
- **Purpose**: Creates a comprehensive date spine and establishes 30-day rolling windows for MAU calculation
- **Logic**: A date spine is generated covering the entire range from the earliest available data to the previous day using timestamp array generation. For each date in the spine, a 30-day rolling window is created by generating an array of dates from 29 days prior through the current date. This creates a joinable structure where each target date is associated with all dates in its 30-day window, enabling the rolling aggregation of user activity.
- **Key fields**: `timestamp` (target date), `joinday` (each day in the 30-day window)

### Component D: MAU calculation with dimensional breakdowns
- **Purpose**: Calculates monthly active users across multiple dimensional perspectives using HyperLogLog merging
- **Logic**: The system performs four parallel calculations to create different dimensional views. For each breakdown (project-level, chain-level, user-type-level, product-level), it groups the windowed data appropriately and merges HyperLogLog structures within each 30-day window. The HLL merge operation efficiently combines unique user counts across all days in each window, providing approximate but accurate unique user counts. Null values are used for non-applicable dimensions in each breakdown to maintain consistent schema structure.
- **Key fields**: `dimensions` (breakdown type identifier), `user_mau` (extracted unique count from merged HLL), dimensional attributes (`chain_id`, `product_id`, `user_type`)

### Final aggregation
- **Combining components**: All dimensional breakdowns are combined using union operations, creating a single result set with different perspective rows marked by the `dimensions` field
- **Currency conversion**: Not applicable for user counting metrics
- **Time aggregation**: Each row represents a specific date with its corresponding 30-day rolling window user count, providing daily snapshots of MAU
- **Final calculation**: The final MAU value for each date and dimension combination is extracted from the merged HyperLogLog structure using the `hll_count.extract` function, providing the approximate unique user count across the 30-day rolling window. The `finalize_metric_user_mau` macro applies standardization, schema consistency, and zero-filling strategies for missing data points.

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

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

Metavault.TradeMVX

Active users (monthly)

Active users (monthly)

Competitive landscape

Chain breakdown