Liquidity providers deposit funds into SyncSwap and receive trading fees from traders (supply-side fees).

SyncSwap is a non-custodial exchange protocol built on zkSync Era (originally a fork of Uniswap). SyncSwap was founded by an anonymous team in 2023.

The SyncSwap team owns and governs the protocol. They currently take a cut of the trading fees paid by the users (revenue).

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SyncSwap

# SyncSwap Average trading volume (per trade) Methodology

## 1. Overview

The SyncSwap average trading volume per trade metric measures the mean USD value of individual trades on the SyncSwap decentralized exchange. This metric is calculated by dividing the total daily trading volume by the total number of trades, providing insights into trade size patterns and user behavior. The calculation combines comprehensive trade data processing with robust price manipulation protection to ensure accurate volume measurements across both Classic and Stable pool types on zkSync Era.

## 2. Data sources

The methodology relies on several categories of blockchain and external data:

- **Swap events**: Parsed `Swap` events from SyncSwap's Classic and Stable pool contracts on zkSync Era, containing token inflow/outflow amounts for each trade
- **Pool creation events**: `PoolCreated` events from both `StablePoolFactory` and `ClassicPoolFactory` contracts that establish trading pair relationships and initial fee structures
- **Fee management events**: `SetPoolSwapFee` and `SetPoolProtocolFee` events that track dynamic changes to trading and protocol fees over time
- **Transaction records**: zkSync Era transaction data that links swap events to user addresses and provides transaction context
- **Token metadata**: Token registry data containing decimal precision, symbols, and contract addresses for proper amount normalization
- **End-of-day price feeds**: Daily USD price data for all traded tokens to enable accurate volume conversion

## 3. Step-by-step calculation

### Component A: Raw trade data extraction
- **Purpose**: Extract and process individual swap transactions from SyncSwap pools
- **Logic**: Parse `Swap` events from both Classic and Stable pool contracts on zkSync Era. For each swap, calculate the net change in token amounts by subtracting outflow from inflow (`amount_in` - `amount_out`) for both tokens in the pair. Join with transaction records to identify the originating user address. Create unique trade identifiers using block number and log index concatenation.
- **Key fields**: `token0_amount`, `token1_amount`, `account_address` (pool), `user_address`, `transaction_hash`, `block_timestamp`

### Component B: Trading pair metadata integration
- **Purpose**: Enrich trade data with pool characteristics and token information
- **Logic**: Join trades with pool creation events to establish token pair relationships, fee tiers, and pool types (Classic vs Stable). Link with token metadata to obtain decimal precision for each token. Handle ETH/WETH address standardization by converting ETH addresses to WETH equivalents. Extract initial fee tiers and protocol fee configurations from pool creation events.
- **Key fields**: `token0_address`, `token1_address`, `token0_decimals`, `token1_decimals`, `fee_tier`, `protocol_fee_tier`, `product_id`

### Component C: Dynamic fee updates
- **Purpose**: Apply time-based fee changes to ensure accurate fee calculations
- **Logic**: Track fee updates using `SetPoolSwapFee` and `SetPoolProtocolFee` events, creating time ranges for each fee tier. Use lead window functions to establish validity periods for each fee setting. Join trades with applicable fee updates based on trade timing, preferring updated fees over initial pool settings when available.
- **Key fields**: Updated `fee_tier` and `protocol_fee_tier` based on event timestamps

### Component D: Amount normalization and USD conversion
- **Purpose**: Convert raw token amounts to standardized USD values
- **Logic**: Normalize token amounts by dividing by the appropriate power of 10 based on token decimals (`amount / 10^decimals`). Join with end-of-day price feeds to get USD prices for both tokens. Calculate USD values by multiplying normalized amounts with respective prices. Apply price manipulation protection by taking the minimum USD value between the two tokens in each pair, ensuring volume calculations aren't inflated by potential price inconsistencies.
- **Key fields**: `trading_volume` (USD value using minimum of both token calculations)

### Component E: Volume aggregation
- **Purpose**: Calculate total daily trading volume
- **Logic**: Group trades by timestamp (daily), chain, market sector, business segment, and product. Sum the USD trading volumes across all trades within each group. Apply the finalize metric macro to ensure consistent schema, validate identifiers, and fill missing periods with zeros.
- **Key fields**: Aggregated `trading_volume` by day and classification dimensions

### Component F: Trade count calculation
- **Purpose**: Count unique trades per day
- **Logic**: Using the same base trade data and grouping dimensions as volume calculation, count distinct transaction hashes to determine the number of unique trades. This ensures each transaction is counted only once regardless of how many events it generated. Apply the finalize metric macro with zero-filling for consistency with volume data.
- **Key fields**: `trade_count` (distinct transaction hash count)

### Final aggregation
- **Combining components**: Join the daily trading volume totals with daily trade counts using timestamp matching. Both metrics share the same temporal and dimensional grouping structure.
- **Currency conversion**: USD conversion occurs at the individual trade level using end-of-day price feeds, ensuring consistent pricing across all volume calculations.
- **Time aggregation**: Daily boundary aggregation using timestamp truncation to day level, with consistent handling across both volume and count metrics.
- **Final calculation**: Apply safe division to handle edge cases where trade count might be zero: `safe_divide(total_trading_volume, trade_count)`. The result represents the average USD value per trade. Apply the finalize metric macro to ensure proper schema structure and gap-filling with zeros for periods with no trading activity.

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

SyncSwap

Average trading volume (per trade)

Average trading volume (per trade)

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