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

HMX is a non-custodial exchange for perpetual contracts built on Arbitrum. HMX was founded by Shu Wei, Rockman, and Taoree and was launched in 2022.

HMX tokenholders own and govern the protocol. They currently take a cut of the total trading fees paid by traders (revenue). HMX's investors include Coral DeFi, Compound Capital Partners, Cryptomind, etc.

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Derivative exchanges

# HMX Active users (monthly) Methodology

## 1. Overview

Monthly Active Users (MAU) measures the number of unique users who have been active on the HMX protocol within any 30-day rolling window. This metric tracks user engagement in HMX's derivatives trading platform by identifying traders who have opened or closed perpetual positions. The calculation uses HyperLogLog probabilistic data structures to efficiently compute unique user counts across overlapping time windows, providing dimensional breakdowns by project, chain, user type, and product.

## 2. Data sources

- **HMX smart contract events**: Parsed `LogIncreasePosition` and `LogDecreasePosition` events from HMX's TradeService contract on Arbitrum blockchain
- **Arbitrum blockchain data**: Transaction logs and event data from the Arbitrum network containing HMX protocol interactions
- **Event parameters**: User addresses (`primaryAccount` field) from HMX trading events that identify individual traders

## 3. Step-by-step calculation

### Component A: User activity extraction
- **Purpose**: Identifies unique user addresses engaged in HMX trading activities
- **Logic**: Extracts user addresses from HMX TradeService contract events on Arbitrum, specifically capturing `LogIncreasePosition` events (when users open or increase positions) and `LogDecreasePosition` events (when users close or reduce positions). Each event contains a `primaryAccount` field identifying the trader's address. Users are classified as 'Trader' type within the derivatives market sector and perpetual business segment.
- **Key fields**: `block_timestamp` for timing, `primaryAccount` for user identification, event type for activity classification

### Component B: Daily active users aggregation  
- **Purpose**: Converts raw user activity into daily unique user counts
- **Logic**: Groups user activity by day using timestamp truncation to day boundaries, then deduplicates user addresses within each day to count distinct active users. This creates a daily active user dataset with dimensions including chain, user type, market sector, business segment, and product identification.
- **Key fields**: `timestamp` (truncated to day), `user_address` for deduplication, `chain` and `user_type` for dimensional analysis

### Component C: HyperLogLog sketch preparation
- **Purpose**: Converts daily user sets into probabilistic data structures for efficient set operations
- **Logic**: Transforms daily active user data into HyperLogLog sketches using 15-bit precision. This allows efficient merging of user sets across time windows without storing individual user addresses. Each sketch represents the unique users active on a specific day with specific dimensional attributes.
- **Key fields**: `hll` sketch containing compressed user set data, dimensional attributes preserved for grouping

### Component D: Rolling window construction
- **Purpose**: Creates 30-day rolling windows for MAU calculation
- **Logic**: Generates a complete datespine covering all days from the earliest available data to yesterday, ensuring no gaps in the time series. For each date in the datespine, creates a lookback window covering that date plus the previous 29 days (total 30 days). This approach uses timestamp array generation to create join keys for the 30-day periods.
- **Key fields**: `timestamp` for the reference date, `joinday` for the window boundaries

### Component E: Window aggregation
- **Purpose**: Merges user activity across 30-day periods to calculate unique monthly active users
- **Logic**: Joins the rolling window structure with the HyperLogLog sketches, grouping by the window end date and dimensional attributes. Merges all HyperLogLog sketches within each 30-day window using probabilistic set union operations, then extracts the final unique user count. This process efficiently handles overlapping time periods and deduplicates users who were active on multiple days within the same window.
- **Key fields**: Window `timestamp`, merged `hll` sketches, dimensional grouping fields

### Component F: Dimensional breakdown
- **Purpose**: Creates multiple views of MAU data across different analytical dimensions
- **Logic**: Generates separate breakdowns of the MAU data by project level (overall HMX), chain level (Arbitrum), user type level (Trader), and product level. Each breakdown nullifies non-relevant dimensions while preserving the appropriate grouping level. All breakdowns are combined using union operations to create a comprehensive dimensional dataset.
- **Key fields**: `dimensions` field indicating breakdown type, relevant dimensional fields, nullified irrelevant dimensions

### Final aggregation
- **Combining components**: All dimensional breakdowns are unioned together to create a single comprehensive dataset containing MAU calculations at multiple granularity levels
- **Time aggregation**: Uses 30-day rolling windows with daily granularity, ensuring complete coverage through datespine methodology
- **Schema standardization**: Applies the `finalize_metric_user_mau` macro to standardize output schema, handle missing data with zero-filling strategy, and apply any additional business logic or filtering rules
- **Final calculation**: The result provides unique monthly active user counts across different dimensional cuts, calculated using probabilistic HyperLogLog algorithms for efficient processing of large-scale user activity data

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

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

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Active users (monthly)

Active users (monthly)

Competitive landscape

Chain breakdown