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 Trade count Methodology

## 1. Overview

The AirSwap trade count metric measures the daily number of unique trading transactions across all AirSwap protocol versions and supported blockchain networks. This metric counts distinct transactions containing swap events, providing insight into daily trading activity and user engagement on the AirSwap decentralized exchange aggregator platform.

## 2. Data sources

The calculation relies on several key data sources:

- **AirSwap swap events**: Parsed blockchain events from multiple AirSwap contract versions (v0 through v4) across Ethereum, Arbitrum, Avalanche, BSC, and Polygon networks, including `SwapERC20`, `Swap`, `Light_Swap`, and `AirSwapExchange_Filled` events
- **Transaction records**: Transaction data from Ethereum, Arbitrum, Avalanche, BSC, and Polygon networks containing transaction hashes, user addresses, and timestamps
- **Token metadata**: Token information including symbols and decimal places for proper amount normalization
- **End-of-day price feeds**: Daily USD price data for tokens to enable volume-based filtering

## 3. Step-by-step calculation

### Component A: Raw trade extraction
- **Purpose**: Extract all AirSwap swap events across multiple protocol versions and blockchain networks
- **Logic**: The system queries parsed swap events from AirSwap contracts versions v0 through v4 deployed on Ethereum, Arbitrum, Avalanche, BSC, and Polygon. Each protocol version uses different event signatures and field names. The events are joined with transaction records to obtain user addresses (`from_address`) and ensure complete trade information. Block number filters are applied to exclude early test transactions for older versions.
- **Key fields**: Transaction hash (`transaction_hash`), log index (`log_index`), contract address (`address`), user address (`user_address`), token addresses (`signerToken`/`senderToken` or `makerToken`/`takerToken`), token amounts (`signerAmount`/`senderAmount` or `makerAmount`/`takerAmount`), protocol fees, and block timestamp

### Component B: Daily trade aggregation and USD conversion
- **Purpose**: Aggregate trades by day, normalize token amounts, and convert to USD values
- **Logic**: Trades are grouped by day using timestamp truncation. Token amounts are converted from raw values to proper decimal representation by dividing by 10 raised to the token's decimal places. The system joins with token metadata to obtain symbols and creates trading pair names. USD conversion is performed by joining with end-of-day price feeds for both tokens in each swap. To filter out potential price manipulation or erroneous data, the system takes the lesser USD value between the two tokens in each trade pair.
- **Key fields**: Daily timestamp (`timestamp`), transaction hash, token amounts corrected for decimals (`token0_amount`, `token1_amount`), trading pair symbols (`account_name`), and USD-converted trading volume (`trading_volume`)

### Final aggregation
- **Combining components**: The daily aggregated trade data is used directly for counting, with all necessary joins and transformations already completed in the previous step
- **Currency conversion**: USD conversion occurs during trade aggregation using end-of-day price feeds, with the minimum USD value between token pairs used for volume calculations
- **Time aggregation**: Trades are aggregated at daily boundaries using timestamp truncation to day level
- **Final calculation**: The metric counts distinct transaction hashes per day using the `approx_count_distinct` function, grouped by timestamp, chain, market sector, business segment, and product identifiers. The `finalize_metric_trade_count` macro is applied with a fill_zeros strategy to ensure consistent daily data points, filling any missing days with zero trade counts. Finally, a chain availability filter is applied to ensure only data from supported chains is included in the results.

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

Number of trades executed on a decentralized exchange.

AirSwapAST

Archived

Trade count

Trade count

Competitive landscape

Chain breakdown