Liquidity providers deposit funds into zkSwap and receive trading fees from traders (supply-side fees). Traders receive token rewards for their trades through the Swap2Earn incentive program (token incentives).

zkSwap Finance is a non-custodial Swap-to-Earn DeFi exchange protocol built on zkSync Era (originally a fork of Uniswap). zkSwap Finance was founded by pseudonymous ThunderNoah, etc., and launched in 2023. 

ZF token holders own and govern the protocol via Snapshot vote. They currently take a cut of the total trading fees paid by traders (revenue).

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zkSwap Finance

# zkSwap Finance Average trading volume (per trade) Methodology

## 1. Overview

The zkSwap trading volume average per trade count metric measures the average USD value traded per transaction on the zkSwap decentralized exchange. This metric provides insight into the typical trade size on the platform by dividing the total daily trading volume by the number of unique trades. It helps assess user trading patterns and market depth on zkSwap's automated market maker (AMM) protocol deployed on zkSync Era.

## 2. Data sources

- **Blockchain events**: Parsed zkSync Era `Pair_Swap_event` logs from zkSwap v1 exchange contracts capturing individual trade executions with token amounts
- **Transaction data**: zkSync Era transaction records providing user addresses and transaction metadata
- **Trading pair data**: `Factory_PairCreated_event` logs identifying valid trading pairs and their token compositions
- **Token metadata**: Token information including decimal places and symbols for amount normalization and pair naming
- **External data**: End-of-day token price feeds for USD conversion

## 3. Step-by-step calculation

### Component A: Raw trade extraction
- **Purpose**: Extract individual swap transactions from zkSwap exchange contracts
- **Logic**: Parse `Pair_Swap_event` logs from zkSwap v1 contracts on zkSync Era. Each event contains `amount0In`, `amount0Out`, `amount1In`, and `amount1Out` fields representing the token flows in the swap. Calculate net token movements by computing `token0_amount` as the difference between inbound and outbound amounts for token0, and similarly for `token1_amount`. Take absolute values to represent trade volumes regardless of swap direction.
- **Key fields**: `transaction_hash`, `log_index`, `block_timestamp`, token amount fields, contract addresses

### Component B: Trading pair identification
- **Purpose**: Map swap events to their corresponding token pairs and extract metadata
- **Logic**: Use `Factory_PairCreated_event` logs to identify valid trading pairs and their token compositions. Join swap events with trading pair data to obtain token addresses, decimal places, and fee tiers. Create human-readable pair names by combining token symbols.
- **Key fields**: `account_address` (pair contract), `token0_address`, `token1_address`, `token0_decimals`, `token1_decimals`, `fee_tier`

### Component C: Volume calculation with anti-manipulation
- **Purpose**: Convert token amounts to USD values while filtering out price manipulation
- **Logic**: Normalize token amounts by dividing by their respective decimal places to convert from blockchain units to human-readable amounts. Join with end-of-day token price feeds to convert both token amounts to USD values. Apply anti-manipulation logic by taking the lesser of the two USD values (token0 USD value vs token1 USD value) as the trade volume. This prevents inflated volumes from price manipulation attempts. Aggregate trades by day, transaction, and log index.
- **Key fields**: `trading_volume` (USD value), `timestamp` (daily), token amounts in decimal-corrected format

### Component D: Trading volume aggregation
- **Purpose**: Sum all individual trade volumes to calculate total daily trading volume
- **Logic**: Group all trades by day and sum their individual volumes to produce total daily trading volume for zkSwap. Include metadata such as chain, business line, market sector, and version information.
- **Key fields**: `trading_volume` (aggregated daily total), `timestamp`, business classification fields

### Component E: Trade count calculation
- **Purpose**: Count the number of unique trades per day
- **Logic**: Using the same underlying trade data, count distinct transaction hashes per day to determine the number of unique trading transactions. This ensures the denominator matches the same data source as the volume numerator.
- **Key fields**: `trade_count` (distinct transaction count), `timestamp`

### Final aggregation
- **Combining components**: Join daily trading volume and daily trade count data on timestamp to align the metrics
- **Currency conversion**: All volumes are already in USD from the volume calculation step using end-of-day price feeds
- **Time aggregation**: Both components use daily time boundaries (timestamp truncated to day level)
- **Final calculation**: Calculate the average trading volume per trade using safe division of total daily trading volume by daily trade count. The safe division function prevents errors when trade count is zero, returning null in such cases

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

zkSwap FinanceZF

Average trading volume (per trade)

Average trading volume (per trade)

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