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 (weekly) Methodology

## 1. Overview

Weekly Active Users (WAU) measures the number of unique users who executed at least one trade on AirSwap protocols within a rolling 7-day period. This metric provides insight into AirSwap's user engagement and trading activity across multiple blockchain networks and protocol versions. The calculation uses HyperLogLog probabilistic counting to efficiently handle large-scale distinct user counting across a rolling window approach.

## 2. Data sources

The WAU calculation relies on several key data sources:

- **AirSwap protocol events**: Parsed swap events from AirSwap smart contracts across multiple versions (v0 through v4) including `Swap`, `SwapERC20`, and `Filled` events
- **Multi-chain coverage**: Trade events from Ethereum, Arbitrum, Avalanche, BSC, and Polygon networks
- **Transaction data**: Blockchain transaction records from all supported chains to identify user addresses
- **Protocol metadata**: AirSwap contract addresses, version information, and protocol configuration data

## 3. Step-by-step calculation

### Component A: Trade event collection
- **Purpose**: Aggregate all AirSwap trading activity across multiple protocol versions and chains
- **Logic**: Collect parsed swap events from AirSwap smart contracts spanning versions v0 through v4. Events include `SwapERC20` events from v4 contracts, `Swap` events from v1 and v3 contracts, `Light_Swap` events from v2 contracts, and `AirSwapExchange_Filled` events from v0 contracts. Each event captures token addresses, trade amounts, fee information, and protocol metadata. Join with transaction data to identify the user address from the transaction `from_address` field. Filter events by minimum block numbers where applicable and apply incremental processing for recent data.
- **Key fields**: `block_timestamp`, `transaction_hash`, `user_address`, `token0_address`, `token1_address`, `token0_amount`, `token1_amount`, `chain`, `version`

### Component B: Daily active user identification  
- **Purpose**: Identify unique users who traded each day across all AirSwap protocols
- **Logic**: Group trade events by day using timestamp truncation to day granularity. Extract distinct user addresses for each day, classified as "Trader" user type. Maintain dimensional breakdown by chain, user type, market sector, business segment, and product. Each user is counted once per day regardless of number of trades executed.
- **Key fields**: `timestamp` (day-truncated), `timestamp_grain`, `chain`, `user_type`, `product_id`, `user_dau`

### Component C: HyperLogLog sketch creation
- **Purpose**: Create probabilistic data structures for efficient distinct counting across time windows
- **Logic**: Initialize HyperLogLog sketches with precision 15 for each day's unique users. Group sketches by timestamp, chain, user type, and product dimensions. HyperLogLog enables efficient memory usage and fast approximate distinct counting when merging across multiple days.
- **Key fields**: `timestamp`, `chain_id`, `user_type`, `product_id`, `hll` (HyperLogLog sketch)

### Component D: Rolling window generation
- **Purpose**: Create 7-day rolling windows for each calendar day to capture weekly activity periods
- **Logic**: Generate a complete date spine covering the full time range from earliest trade to current date minus one day. For each day in the spine, create a 7-day window including the current day plus the previous 6 days. This approach ensures consistent 7-day periods even when some days have no trading activity. Join the date spine with daily HLL sketches to create the rolling window structure.
- **Key fields**: `timestamp` (target date), `joinday` (each day in 7-day window), `hll` (daily sketches)

### Component E: Multi-dimensional aggregation
- **Purpose**: Calculate WAU across different analytical dimensions while maintaining consistent methodology
- **Logic**: Aggregate the 7-day window data into four distinct breakdowns. Project-level aggregation merges all HLL sketches across chains, user types, and products for overall AirSwap WAU. Chain-level aggregation maintains chain separation while merging across user types and products. User type aggregation separates trader categories while merging across chains and products. Product-level aggregation maintains product separation while merging across chains and user types. Each aggregation uses HyperLogLog merge operations to combine sketches and extract final distinct counts.
- **Key fields**: `dimensions` (breakdown type), `chain_id`, `user_type`, `product_id`, `user_wau` (distinct count)

### Final aggregation
- **Combining components**: Union all dimensional breakdowns (project, chain, user type, product) into a single result set with standardized schema
- **Time aggregation**: Maintain daily granularity with each record representing WAU for a specific day's 7-day trailing window
- **Missing day handling**: Apply zero-filling strategy for days with no trading activity to ensure complete time series
- **Final calculation**: The final `user_wau` value represents the approximate distinct count of users who executed at least one trade within the 7-day period ending on the specified timestamp, calculated using HyperLogLog probabilistic counting with precision 15

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

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

AirSwapAST

Archived

Active users (weekly)

Active users (weekly)

Competitive landscape

Chain breakdown