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

APX Finance is a non-custodial exchange for perpetual contracts built on BNB Chain, Arbitrum, and Base.

APX tokenholders own and govern the protocol. They currently take a cut of the total trading fees paid by traders (revenue). APX's investors include Binance Labs, Kronos Research, etc.

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Derivative exchanges

APX V2

# APX V2 Active users (monthly) Methodology

## 1. Overview

Monthly Active Users (MAU) measures the number of distinct users who performed trading activities on the ApolloX decentralized derivatives platform within any rolling 30-day period. This metric captures user engagement across ApolloX's perpetual trading and "Dumb Mode" prediction markets on multiple blockchains. The calculation uses HyperLogLog data structures for efficient approximate distinct counting across large time windows, providing accurate user counts while maintaining computational efficiency.

## 2. Data sources

The MAU calculation relies on the following blockchain data sources:

- **Arbitrum parsed events**: ApolloX contract events including `OpenMarketTrade`, `CloseTradeSuccessful`, `ExecuteCloseSuccessful`, and `PredictAndBet` from multiple contract versions (ApolloX3 and ApolloX5)
- **Base parsed events**: ApolloX contract events including `OpenMarketTrade`, `CloseTradeSuccessful`, and `ExecuteCloseSuccessful` from ApolloX4 contracts  
- **BSC parsed events**: ApolloX contract events including `OpenMarketTrade`, `CloseTradeSuccessful`, `ExecuteCloseSuccessful`, and `PredictAndBet` from multiple contract versions (ApolloX2, ApolloX3, and ApolloX5)
- **Event metadata**: Block timestamps, transaction hashes, and contract addresses for activity tracking
- **Chain availability data**: Blockchain operational status to filter out periods of downtime or data quality issues

## 3. Step-by-step calculation

### Component A: User activity extraction

- **Purpose**: Identify all trading activities and extract user addresses from ApolloX contract events across supported blockchains
- **Logic**: Parse specific ApolloX contract events that represent trading activities. For each event, extract the user address and classify the user type based on the activity. `OpenMarketTrade`, `CloseTradeSuccessful`, and `ExecuteCloseSuccessful` events classify users as "Perpetual trader" while `PredictAndBet` events classify users as "Dumb Mode trader". Each blockchain has different contract versions deployed at different times, requiring parsing from multiple contract addresses to capture complete activity history.
- **Key fields**: `block_timestamp` for timing, `user` parameter from events as `user_address`, `chain` identifier, and derived `user_type` classification

### Component B: Daily user aggregation  

- **Purpose**: Aggregate user activities into daily distinct user counts using HyperLogLog data structures for efficient processing
- **Logic**: Group all user activities by day using timestamp truncation to daily granularity. For each day, create distinct combinations of timestamp, chain, user type, and product dimensions. Initialize HyperLogLog structures with 15-bit precision to track distinct user addresses efficiently. This creates a foundation of daily active user counts that can be efficiently merged across time periods.
- **Key fields**: `timestamp` truncated to day, `chain` identifier, `user_type` classification, `product_id`, and `user_dau` HyperLogLog structure containing distinct user addresses

### Component C: Rolling window construction

- **Purpose**: Create 30-day rolling windows for each day to enable MAU calculations
- **Logic**: Generate a complete datespine covering all days from the earliest user activity to the present. For each day in the datespine, create a 30-day rolling window by generating an array of the previous 29 days plus the current day. This ensures every day has a complete 30-day lookback period for MAU calculation, even if some days had no activity. Join this window structure with the daily user data to create a comprehensive dataset for MAU aggregation.
- **Key fields**: `timestamp` for the reference day, `joinday` for each day within the 30-day window, and associated dimensional attributes

### Component D: MAU calculation with dimensional breakdowns

- **Purpose**: Calculate distinct users across 30-day rolling windows and provide multiple dimensional views
- **Logic**: For each 30-day window, merge the HyperLogLog structures from all days within the window to count distinct users who were active at any point during that period. Create separate aggregations for different analytical perspectives: project-level (overall MAU), chain-level (MAU by blockchain), user type-level (MAU by trading behavior), and product-level breakdowns. Each breakdown uses HyperLogLog merge operations to maintain accuracy while enabling efficient computation across large time periods.
- **Key fields**: `user_mau` as the final distinct user count, `dimensions` to identify the breakdown type, and dimensional identifiers (`chain_id`, `user_type`, `product_id`) with null values for non-applicable dimensions

### Final aggregation

- **Combining components**: Union all dimensional breakdowns into a single dataset, maintaining separate rows for each dimensional view (project-level, chain-level, user type-level, product-level) of the same time period
- **Time boundaries**: Use daily boundaries with 30-day rolling windows, where each day's MAU represents users active within the previous 30 days including that day
- **Data quality**: Apply chain availability filtering to exclude periods where blockchain data may be incomplete or unreliable
- **Final calculation**: The final MAU value for any given day represents the count of distinct user addresses that performed any trading activity (perpetual trading or prediction betting) on ApolloX contracts within the 30-day rolling window ending on that day, calculated using HyperLogLog merge operations for computational efficiency

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

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

APX V2APX

Active users (monthly)

Active users (monthly)

Competitive landscape

Chain breakdown