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 Average trading volume (per user) Methodology

## 1. Overview

This metric calculates the average daily trading volume per daily active user for the AirSwap protocol. It measures how much volume each active trader generates on average, providing insight into user engagement and trading intensity. The calculation divides the total daily trading volume in USD by the number of unique daily active users (traders) for each day.

## 2. Data sources

The calculation relies on several key data sources:

- **Blockchain events**: Parsed swap events from AirSwap smart contracts across multiple versions (v0 through v4) deployed on Ethereum, Arbitrum, Avalanche, BSC, and Polygon networks
- **Transaction data**: Transaction records from each blockchain network to identify trader addresses  
- **External data**: Token metadata containing symbols and decimal precision, and end-of-day token price feeds in USD
- **Contract versions**: Multiple AirSwap protocol iterations including legacy AirSwapExchange `Filled` events, Light protocol `Swap` events, and current SwapERC20 `SwapERC20` events

## 3. Step-by-step calculation

### Component A: Raw trade extraction
- **Purpose**: Captures all swap transactions across AirSwap protocol versions and chains
- **Logic**: Extracts swap events from AirSwap contracts including `SwapERC20` events (v4), `Swap` events (v1-v3), `Light_Swap` events (v2), and legacy `AirSwapExchange_Filled` events (v0). Each event contains signer/maker and sender/taker token addresses and amounts, plus protocol fees. Events are joined with transaction data to identify the trader address from the transaction `from_address` field. Block number filters exclude pre-deployment transactions for each version
- **Key fields**: `signerToken`/`senderToken` addresses, `signerAmount`/`senderAmount` values, `protocolFee` or hardcoded fee values, transaction `from_address` as user identifier

### Component B: Trading volume calculation  
- **Purpose**: Converts raw token amounts to standardized USD trading volume
- **Logic**: Token amounts are normalized by dividing by 10^decimals using token metadata. Each normalized amount is multiplied by the corresponding end-of-day USD price. To prevent price manipulation, the final trading volume takes the lesser of the two token USD values (minimum of token0_usd_value and token1_usd_value). Volumes are aggregated daily by summing all trade volumes within each day boundary
- **Key fields**: `token0_amount` and `token1_amount` after decimal correction, `price_usd` from price feeds, final `trading_volume` as the minimum USD value

### Component C: Daily active user counting
- **Purpose**: Counts unique traders per day across all chains and versions
- **Logic**: Identifies distinct trader addresses per day from the `user_address` field extracted from transaction records. Uses HyperLogLog (HLL) probabilistic data structure for efficient approximate counting when aggregating across multiple dimensions. Users are categorized as 'Trader' user type. The final count aggregates all users at the project level using the 'project_dbt_tag' dimension
- **Key fields**: `user_address` for distinct counting, `timestamp` truncated to day boundaries, `user_dau` as final count

### Final aggregation
- **Combining components**: Trading volume metrics and user DAU metrics are joined on matching timestamp (day) and the project-level dimension filter
- **Currency conversion**: All trading volumes are already converted to USD using end-of-day price feeds during the volume calculation step
- **Time aggregation**: Both components use daily boundaries created by truncating timestamps to day level
- **Final calculation**: The metric divides the sum of daily trading volume by the maximum daily active users using safe division to handle potential zero DAU cases, producing the average trading volume per user in USD

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

Average trading volume (per user)

Trading volume divided by daily active users.

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Average trading volume (per user)

Average trading volume (per user)

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