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

Pangolin is a non-custodial exchange protocol built on Avalanche (originally a fork of Uniswap). Pangolin was founded by an anonymous team and launched in 2020.

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

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Exchanges (DEX)

Pangolin

# Pangolin Average trading volume (per trade) Methodology

## 1. Overview

The average trading volume per trade count metric measures the average USD value of individual trades on the Pangolin DEX. This metric provides insight into trade size distribution and market activity patterns by dividing the total daily trading volume by the total number of trades executed on that day. It helps understand whether trading activity consists of many small trades or fewer large trades.

## 2. Data sources

The metric calculation relies on several key data sources:

- **Pangolin V1 swap events**: Parsed `Pair_Swap_event` logs from Pangolin V1 pair contracts on Avalanche, containing swap execution details including token amounts exchanged
- **Pangolin factory events**: Parsed `Factory_PairCreated_event` logs from the Pangolin V1 factory contract, defining trading pair relationships and token addresses
- **Avalanche transaction records**: Transaction-level data providing user addresses and execution context for swap events
- **End-of-day token price feeds**: Daily USD price data for tokens traded on Pangolin pairs
- **Token metadata**: Token symbols and decimal specifications for proper amount calculations and pair naming

## 3. Step-by-step calculation

### Component A: Raw trade data extraction
- **Purpose**: Extract and process individual swap transactions from Pangolin V1 pairs
- **Logic**: Parse `Pair_Swap_event` logs from Pangolin pair contracts on Avalanche. For each swap event, calculate the net change in token amounts by subtracting outgoing amounts from incoming amounts (`amount0In` minus `amount0Out` for token0, `amount1In` minus `amount1Out` for token1). Join with Avalanche transaction data to obtain the originating user address. Filter data to include only swaps from February 8, 2021 onwards when Pangolin launched.
- **Key fields**: `amount0In`, `amount0Out`, `amount1In`, `amount1Out` represent token flows, `transaction_hash` identifies unique trades, `user_address` identifies the trader

### Component B: Trading pair metadata enrichment  
- **Purpose**: Add trading pair context and token specifications to swap data
- **Logic**: Extract trading pair definitions from `Factory_PairCreated_event` logs, identifying token0 and token1 addresses for each pair contract. Join with token metadata to obtain decimal specifications and symbols for proper amount calculations. Set fee tier to 0.3% for all Pangolin V1 pairs. Create readable pair names by concatenating token symbols.
- **Key fields**: `token0_address`, `token1_address`, `token0_decimals`, `token1_decimals`, `fee_tier`, `account_name`

### Component C: Daily trading volume calculation
- **Purpose**: Calculate USD-denominated trading volumes for each day
- **Logic**: Group swap events by day and aggregate token amounts per transaction and log index. Correct token amounts by dividing by the appropriate decimal factor (10^decimals) for each token. Join with end-of-day price feeds to convert token amounts to USD values. Apply price manipulation protection by taking the lesser USD value between token0 and token1 amounts for each swap. Sum the resulting USD volumes by day to get total daily trading volume.
- **Key fields**: `trading_volume` represents daily USD volume, `timestamp` marks the day, `token0_amount` and `token1_amount` are decimal-corrected quantities

### Component D: Daily trade count calculation  
- **Purpose**: Count the number of unique trades executed each day
- **Logic**: Use the same processed swap data from the volume calculation. Group by day and count distinct transaction hashes to determine the number of unique trades. Each unique transaction hash represents one trade execution, regardless of how many swap events it contains.
- **Key fields**: `trade_count` represents daily trade quantity, `transaction_hash` identifies unique trades

### Final aggregation
- **Combining components**: Join the daily trading volume data with daily trade count data on matching timestamps to align volume and count for the same days
- **Currency conversion**: All USD conversions use end-of-day price feeds, ensuring consistent valuation timing across all trades within a day
- **Time aggregation**: Both volume and trade count are aggregated at daily boundaries using timestamp truncation to day level
- **Final calculation**: Calculate the average volume per trade by dividing the daily trading volume by the daily trade count using safe division to handle days with zero trades

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

PangolinPNG

Average trading volume (per trade)

Average trading volume (per trade)

Competitive landscape