Trades are conducted between a signer and a sender & AirSwap takes a fee (revenue) which is assessed on the signer amount.

AirSwap is a non-custodial exchange built on multiple chains that allows direct peer-to-peer trading. AirSwap was co-founded by Don Mosites & Michael Oved and launched in 2017.

AST tokenholders own and govern the protocol. Revenue is currently distributed to a contributor tips pool and a voter rewards pool according to a set split. AirSwap DAO has not raised funds and the company that originally launched AirSwap is no longer operating.

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AirSwap

# AirSwap Active users (monthly) Methodology

## 1. Overview

Monthly Active Users (MAU) for AirSwap measures the number of unique users who executed trades on the AirSwap decentralized exchange protocol within any 30-day rolling window. This metric tracks trader engagement across all AirSwap protocol versions (v0 through v4) and supported blockchains (Ethereum, Arbitrum, Avalanche, BSC, and Polygon). The calculation uses HyperLogLog algorithms for efficient unique user counting and provides breakdowns by chain, user type, and product dimensions.

## 2. Data sources

- **AirSwap protocol events**: Parsed swap and trade events from AirSwap smart contracts across five protocol versions:
  - v0: `AirSwapExchange` `Filled` events on Ethereum
  - v1: `Swap` events on Ethereum  
  - v2: `Light` contract `Swap` events on Ethereum and BSC
  - v3: `Swap` events on Ethereum, Avalanche, BSC, and Polygon
  - v4: `SwapERC20` events on Ethereum, Arbitrum, Avalanche, BSC, and Polygon
- **Transaction data**: Transaction records from Ethereum, Arbitrum, Avalanche, BSC, and Polygon to identify user addresses
- **Event data**: Blockchain event logs containing token addresses, trade amounts, fees, and contract addresses
- **Chain availability data**: Chain-specific availability filters and metadata

## 3. Step-by-step calculation

### Component A: Trade event extraction
- **Purpose**: Extract and standardize trade events from all AirSwap protocol versions and supported chains
- **Logic**: Parse swap events from AirSwap smart contracts across five different protocol versions, each with distinct event schemas. Extract key trade data including signer and sender token addresses, trade amounts, protocol fees, and contract addresses. Different versions use varying fee structures - v4 and v3 use `protocolFee`, v2 uses `signerFee`, v1 has no fees, and v0 uses a fixed structure. Apply block number filters where needed (v0 starts from block 4916553). Join with transaction records to identify the actual user address (`from_address`) executing each trade. Classify all users as 'Trader' type and categorize the business as 'DEX Aggregator' within the 'exchanges' market sector.
- **Key fields**: `user_address` (from transaction `from_address`), `block_timestamp`, `chain`, `token0_address`, `token1_address`, `token0_amount`, `token1_amount`, `fee_tier`

### Component B: Daily active user calculation  
- **Purpose**: Identify unique users who executed trades on each day
- **Logic**: Group trade events by day using `timestamp_trunc(block_timestamp, day)`. For each day, identify unique `user_address` values who executed at least one trade. Set `timestamp_grain` to 'day' and `user_type` to 'Trader' for all records. Apply chain availability filters to ensure only valid chain data is included. This creates the foundation dataset of daily active users.
- **Key fields**: `timestamp` (daily), `chain`, `user_type`, `product_id`, `user_dau` (unique user address)

### Component C: HyperLogLog initialization
- **Purpose**: Convert daily user data into HyperLogLog sketches for efficient unique counting
- **Logic**: Transform the daily active user dataset into HyperLogLog sketches using `hll_count.init()` with precision 15. Group by timestamp, chain, user type, and product dimensions. This creates compressed probabilistic data structures that enable efficient merging across time windows while maintaining accuracy for unique user counting.
- **Key fields**: `timestamp`, `chain_id`, `user_type`, `product_id`, `hll` (HyperLogLog sketch)

### Component D: Rolling window creation
- **Purpose**: Generate all day combinations within 30-day rolling windows
- **Logic**: Create a datespine covering all days from the earliest trade date to yesterday. For each day in the datespine, generate an array of the previous 30 days (including the current day) using `generate_timestamp_array()`. This creates a cartesian product where each target date is associated with all days in its 30-day lookback window. Join this with the daily HyperLogLog data to create the rolling window structure needed for MAU calculation.
- **Key fields**: `timestamp` (target date), `joinday` (days within the 30-day window)

### Component E: Dimensional aggregation
- **Purpose**: Calculate MAU across different dimensional breakdowns
- **Logic**: Merge HyperLogLog sketches using `hll_count.merge_partial()` within each 30-day window, then extract the final unique count using `hll_count.extract()`. Create four separate dimensional views: project-level (all dimensions null), chain-level (grouped by `chain_id`), user type-level (grouped by `user_type`), and product-level (grouped by `product_id`). Each breakdown calculates the number of unique users who were active within the 30-day window ending on each target date.
- **Key fields**: `dimensions` (breakdown type), `chain_id`, `user_type`, `product_id`, `user_mau` (unique user count)

### Final aggregation
- **Combining components**: Union all dimensional breakdowns into a single result set, with each row representing MAU for a specific dimension combination on a specific date
- **Currency conversion**: Not applicable - this metric counts users rather than monetary values  
- **Time aggregation**: Uses 30-day rolling windows where each day's MAU includes all unique users active in the previous 30 days (inclusive of the target date)
- **Final calculation**: Extract unique user counts from merged HyperLogLog sketches, apply the `finalize_metric_user_mau` macro to standardize the schema and data types, and fill missing periods with zeros to ensure complete time series coverage

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

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

AirSwapAST

Archived

Active users (monthly)

Active users (monthly)

Competitive landscape

Chain breakdown