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

PancakeSwap is a non-custodial exchange protocol built on Binance Smart Chain and other blockchains (originally a fork of Uniswap). PancakeSwap was founded by an anonymous team and launched in 2020.

CAKE tokenholders own and govern the protocol. They currently take a cut of the total trading fees paid by traders (revenue).

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PancakeSwap

# PancakeSwap Average trading volume (per trade) Methodology

## 1. Overview

The typical USD value of individual trades on PancakeSwap is measured by the average trading volume (per trade) metric. This metric is calculated by dividing the total daily trading volume by the total number of trades for the same day. Insights into trade size patterns and market behavior are provided, helping to understand whether trading activity is composed of many small trades or fewer large trades. Both PancakeSwap V2 and V3 deployments across multiple chains are covered by the calculation, and anti-manipulation measures are included to ensure data quality.

## 2. Data sources

- **Blockchain events**: `Swap` and `PairCreated` events from `Pair` and `Factory` contracts (V2), `Swap` and `PoolCreated` events from `Pool` and `Factory` contracts (V3), and transaction records, covering Arbitrum, Base, BSC, Ethereum, and 2 other chains
- **External data**: End-of-day asset price feeds and asset metadata (decimals and symbols)
- **Configuration data**: Hardcoded denylist of trading pairs with limited price feed support for low liquidity assets

## 3. Step-by-step calculation

### Component A: Raw trade data extraction

- **Purpose**: Swap events from both PancakeSwap V2 and V3 are extracted and standardized across all supported chains.
- **Logic**: `Swap` events are collected from V2 pairs (using input and output asset calculations) and V3 pools (using direct asset amounts). For V2 events, asset amounts are calculated as the difference between input and output amounts to determine the net change for each asset. For V3 events, the amounts are taken directly from the event parameters. Protocol fees are captured separately for V3 trades. The transaction hash, log index, participating addresses, and timestamp are included in each trade record. Swap events are joined with transaction data to identify the actual user initiating the trade (as opposed to intermediate contracts).
- **Key fields**: `transaction_hash`, `log_index`, `block_timestamp`, `account_address`, `contract_user_address`, `user_address`, `amount_0`, `amount_1`, `revenue_0`, `revenue_1`

### Component B: Trading pair validation and metadata enrichment

- **Purpose**: Valid trading pairs are identified and enriched with asset metadata while problematic pairs are excluded.
- **Logic**: `PairCreated` events from V2 factories and `PoolCreated` events from V3 factories are collected to build a comprehensive list of legitimate trading pairs. Each pair is enriched with asset metadata including decimal places and symbols from the asset reference data. A denylist of specific trading pair addresses with limited price feed support due to low liquidity is maintained, and these pairs are excluded from all calculations. Fee tier information is preserved for V3 pools while a standard 0.25% fee assumption is used for V2 pairs.
- **Key fields**: `account_address`, `chain`, `version`, `address_0`, `address_1`, `decimals_0`, `decimals_1`, `fee_tier`, `account_name`

### Component C: Volume calculation with anti-manipulation measures

- **Purpose**: USD trading volume is calculated for each trade while price manipulation is prevented.
- **Logic**: Raw asset amounts are processed and normalized by decimal precision. Absolute values of all asset amounts are taken to handle directional differences between V2 (which uses signed amounts) and V3 (which uses absolute amounts). To filter out wash trading and price manipulation, a per-trade oracle price band is applied to identify and exclude trades where the USD values of the two swap legs are divergent by more than 100-fold. For the remaining trades, asset amounts are converted to USD using end-of-day price feeds matched by timestamp, asset address, and chain. To further prevent price manipulation, USD values are calculated for both assets in each swap, and the minimum value is used as the final trading volume. Skewing of the volume calculation by artificially inflated prices for one asset is thereby prevented. All individual trade volumes are summed by daily aggregation.
- **Key fields**: `trading_volume`, `amount_0`, `amount_1`

### Component D: Trade count calculation

- **Purpose**: The number of unique trades per day is counted to enable average calculation.
- **Logic**: The same processed trade data as volume calculation is used, but distinct transaction hashes are counted instead of summing amounts. Each unique trade is thereby counted exactly once regardless of the number of assets involved or internal contract interactions. Trades are grouped by timestamp and unique transaction hashes are counted by daily aggregation.
- **Key fields**: `trade_count`

### Component E: Final aggregation

- **Purpose**: Volume and trade count metrics are combined to calculate the final average trading volume per trade.
- **Logic**: Trading volume totals and trade count totals are joined by timestamp to ensure matching time periods. End-of-day price feeds matched to the specific trade date, asset, and chain are used for all USD conversions. Data is aggregated at daily boundaries using timestamp truncation to the day level. Average trading volume per trade is calculated by dividing the daily trading volume sum by the daily trade count sum, using safe division to handle cases where the trade count might be zero (returning null in such cases).
- **Key fields**: `trading_volume`, `trade_count`, `trading_volume_avg_per_trade_count`

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

PancakeSwapCAKE

Average trading volume (per trade)

Average trading volume (per trade)

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