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).

governance

github

twitter

discord

etherscan

website

Derivative exchanges

Metavault.Trade

# Metavault.Trade Active users (weekly) Methodology

## 1. Overview

Weekly Active Users (WAU) measures the number of unique users who interact with Metavault's derivatives trading platform within any 7-day period. This metric answers the business question "How many distinct users are actively trading on Metavault each week?" The calculation uses HyperLogLog data structures to efficiently count distinct users across rolling 7-day windows, broken down by multiple dimensions including chain, user type, and product.

## 2. Data sources

- **Polygon blockchain events**: Parsed event logs from Metavault smart contracts capturing all trading activities
- **Position events**: `IncreasePosition` and `DecreasePosition` events representing users opening, modifying, or closing derivative positions
- **Swap events**: `Swap` events representing users performing token swaps within the platform
- **Transaction data**: Blockchain timestamps from Polygon blocks to establish when activities occurred
- **User addresses**: Account addresses extracted from event parameters to identify unique users
- **Chain metadata**: Blockchain network identifiers and availability information

## 3. Step-by-step calculation

### Component A: User activity identification
- **Purpose**: Identifies all user trading activities from blockchain events
- **Logic**: Extracts user addresses from three types of Metavault smart contract events on Polygon: `IncreasePosition` events when users open or increase derivative positions, `DecreasePosition` events when users close or reduce positions, and `Swap` events when users perform token swaps. Each event provides the user's `account` address and block timestamp. All users are classified as 'Trader' type within the 'derivatives' market sector and 'perpetual' business segment.
- **Key fields**: `account` address as user identifier, `block_timestamp` for timing, event types for activity classification

### Component B: Daily user aggregation  
- **Purpose**: Converts raw events into daily distinct user counts using efficient data structures
- **Logic**: Groups user activities by day using daily timestamp boundaries created from block timestamps. Creates HyperLogLog sketches initialized with a precision of 15 bits for each combination of date, chain, user type, and product. This enables memory-efficient storage and processing of distinct user counts across large datasets while maintaining accuracy for cardinality estimation.
- **Key fields**: `timestamp` truncated to day boundaries, `user_dau` containing user addresses, `hll` sketches for distinct counting

### Component C: Time series backbone
- **Purpose**: Creates complete daily time series to ensure no gaps in the weekly rolling calculations
- **Logic**: Generates a datespine of consecutive daily timestamps from the earliest date in the user data through yesterday. This ensures that even days with no user activity are represented in the final dataset, preventing gaps that would distort weekly aggregations. For incremental updates, the datespine starts from a lookback period before the last modification time.
- **Key fields**: `timestamp` for each calendar day in the analysis period

### Component D: Rolling window construction
- **Purpose**: Creates 7-day sliding windows for weekly user calculations
- **Logic**: For each date in the datespine, generates joins to the 6 preceding days plus the current day, creating a 7-day window. Uses cross-joins with generated timestamp arrays to create all combinations of target dates and their respective 7-day lookback periods. This approach ensures that weekly calculations can be performed for every single day, with each day's WAU representing users active in the 7 days ending on that date.
- **Key fields**: `timestamp` as the window end date, `joinday` as each day within the 7-day window

### Component E: Multi-dimensional breakdowns
- **Purpose**: Calculates WAU across different analytical dimensions
- **Logic**: Merges HyperLogLog sketches within each 7-day window and extracts the final distinct user count. Creates four separate dimensional breakdowns: project-level WAU (aggregated across all dimensions), chain-level WAU (grouped by blockchain), user-type-level WAU (grouped by user classification), and product-level WAU (grouped by specific products). Each breakdown maintains its dimensional grouping while setting other dimensions to null.
- **Key fields**: `dimensions` indicating the breakdown type, `chain_id`, `user_type`, and `product_id` for dimensional grouping, `user_wau` containing the distinct user count

### Final aggregation
- **Combining components**: The four dimensional breakdowns are combined using union operations to create a single dataset containing WAU metrics across all analytical dimensions
- **HyperLogLog processing**: HLL sketches are merged using `hll_count.merge_partial` across the 7-day windows, then final distinct counts are extracted using `hll_count.extract`
- **Time aggregation**: Daily boundary logic ensures consistent 24-hour periods, with 7-day rolling windows providing overlapping weekly metrics for every day
- **Final calculation**: The weekly active user count represents the number of distinct user addresses that performed any trading activity (position changes or swaps) within each 7-day period ending on the specified date

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

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

Metavault.TradeMVX

Active users (weekly)

Active users (weekly)

Competitive landscape

Chain breakdown