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 Average trading volume (per trade) Methodology

## 1. Overview

The AirSwap trading volume average per trade count metric measures the average USD value per trade executed on the AirSwap decentralized exchange aggregator. This metric is calculated by dividing the total daily trading volume by the total daily trade count, providing insight into the typical trade size on the platform. The calculation covers multiple AirSwap contract versions across Ethereum, Arbitrum, Avalanche, BSC, and Polygon networks.

## 2. Data sources

- **Blockchain events**: Parsed swap events from AirSwap smart contracts including `SwapERC20`, `Swap`, `Light_Swap`, and `AirSwapExchange_Filled` events across Ethereum, Arbitrum, Avalanche, BSC, and Polygon
- **Transaction data**: Transaction records from all supported chains to identify user addresses and transaction context
- **Token metadata**: Token information including decimal places and symbols for proper amount calculations and display names
- **External data**: End-of-day token price feeds in USD for volume calculations
- **Chain data**: Chain availability filters to ensure only active blockchain networks are included in metrics

## 3. Step-by-step calculation

### Component A: Trade event extraction
- **Purpose**: Extract and standardize trade events from multiple AirSwap contract versions and chains
- **Logic**: Collect swap events from different AirSwap smart contract versions (v0 through v4) deployed across five blockchain networks. Each contract version has different event schemas and fee structures. Events are filtered by minimum block numbers to exclude test or invalid early transactions. Transaction data is joined to identify the user initiating each trade. Key fields extracted include token addresses, raw token amounts, protocol fees, and transaction metadata.
- **Key fields**: `signerToken`/`makerToken` and `senderToken`/`takerToken` addresses, `signerAmount`/`makerAmount` and `senderAmount`/`takerAmount` values, `protocolFee` or `signerFee` amounts, user addresses from transaction data

### Component B: Volume calculation with anti-manipulation
- **Purpose**: Convert raw token amounts to USD trading volumes with price manipulation protection
- **Logic**: Raw token amounts are adjusted using decimal places from token metadata to get actual token quantities. Both tokens in each trade are converted to USD using end-of-day price feeds matched by date, chain, and token address. To prevent price manipulation attacks, the system takes the lesser USD value between the two tokens in each trade using the `least()` function. Trades are aggregated daily with timestamp truncation. Missing price data results in zero volume for that trade.
- **Key fields**: Decimal-adjusted `token0_amount` and `token1_amount`, USD `price_usd` from price feeds, calculated `trading_volume` as the lesser of the two USD values

### Component C: Trade count aggregation
- **Purpose**: Count unique trades per day across all chains and contract versions
- **Logic**: Using the same processed trade data, count distinct transaction hashes per day to determine the number of unique trades. This approach ensures that complex transactions with multiple swap events are counted as single trades. Trades are grouped by timestamp, chain, and business segment identifiers.
- **Key fields**: Distinct `transaction_hash` counts aggregated as `trade_count`

### Final aggregation
- **Combining components**: Daily trading volume and trade count metrics are joined on timestamp to create matched pairs for each day
- **Currency conversion**: All volume calculations use end-of-day USD prices, ensuring consistent currency conversion timing
- **Time aggregation**: All data is aggregated to daily boundaries using timestamp truncation, with separate aggregation for volume (sum) and count (distinct transaction hashes)
- **Final calculation**: The average volume per trade is computed by dividing total daily trading volume by total daily trade count using safe division that returns null for days with zero trades, then gap-filled with zeros for missing days to ensure complete time series data

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

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