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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Pangolin

# Pangolin Trade count Methodology

## 1. Overview

The Pangolin trade count metric measures the daily number of unique trades executed on the Pangolin decentralized exchange on Avalanche. This metric counts distinct transactions containing swap operations, providing insight into daily trading activity and user engagement with the protocol. The calculation aggregates all swap events from Pangolin V1 trading pairs and counts unique transaction hashes per day.

## 2. Data sources

The calculation relies on several key data sources:
- **Swap events**: Parsed `Swap` events from Pangolin V1 trading pair contracts on Avalanche, containing trade execution details including token amounts, user addresses, and transaction identifiers
- **Factory events**: Parsed `PairCreated` events from the Pangolin V1 factory contract, establishing the relationship between trading pairs and their underlying token addresses
- **Avalanche transactions**: Transaction records from the Avalanche blockchain providing user addresses, timestamps, and transaction metadata
- **Token metadata**: Token information including symbols and decimal places for proper amount calculations and pair naming
- **End-of-day price feeds**: Daily USD price data for tokens to enable volume-based filtering, though the final metric focuses on trade count rather than volume

## 3. Step-by-step calculation

### Component A: Raw trade event extraction
- **Purpose**: Extract all swap events from Pangolin V1 trading pairs on Avalanche
- **Logic**: Query all `Swap` events from Pangolin pair contracts, joining with Avalanche transaction data to get complete trade information. Filter for trades occurring after February 8, 2021 (protocol launch). Extract key fields including `amount0In`, `amount0Out`, `amount1In`, `amount1Out` representing token flows, along with transaction hash, log index, and user addresses. Calculate net token amounts by subtracting outflows from inflows for each token in the pair.
- **Key fields**: `transaction_hash`, `log_index`, `block_timestamp`, `address` (trading pair contract), `sender` (contract user), `from_address` (transaction originator), token amount fields for both tokens in the pair

### Component B: Trading pair dimension building  
- **Purpose**: Create a comprehensive mapping of all Pangolin trading pairs with their token details
- **Logic**: Process `PairCreated` events from the Pangolin V1 factory contract to identify all valid trading pairs. Extract `token0` and `token1` addresses for each pair, applying a fixed fee tier of 0.3% (3000/1000000) for all V1 pairs. Join with token metadata to get decimal places and symbols for both tokens, creating human-readable pair names by concatenating token symbols with a hyphen.
- **Key fields**: `account_address` (pair contract), `token0_address`, `token1_address`, `token0_decimals`, `token1_decimals`, `account_name` (token0-token1 format), `fee_tier`

### Component C: Trading volume calculation and validation
- **Purpose**: Process raw trade data into properly formatted volumes with USD validation
- **Logic**: Combine trade events with trading pair dimensions to access token decimal information. Correct raw token amounts by dividing by 10 raised to the respective token's decimal places, then take absolute values to ensure positive amounts. Aggregate trades by day, transaction, and pair while preserving individual trade identifiers. Join with end-of-day price feeds for both tokens in each pair, calculating USD values for each token amount. Apply volume validation by taking the lesser of the two USD amounts to filter out potential price manipulation or low-liquidity scenarios.
- **Key fields**: `timestamp` (daily), `transaction_hash`, `log_index`, `token0_amount`, `token1_amount`, `trading_volume` (lesser USD amount), chain and business classification fields

### Final aggregation
- **Combining components**: Use the processed trading volume data as the base, focusing on transaction-level information rather than volume amounts
- **Currency conversion**: While USD conversion is performed for volume validation purposes, the final trade count metric does not require currency conversion
- **Time aggregation**: Group all trades by daily timestamp boundaries, preserving transaction hash and log index for unique identification
- **Final calculation**: Count distinct transaction hashes within each day to determine the number of unique trades executed. Apply the `finalize_metric_trade_count` macro with a "fill_zeros" strategy to ensure missing days are populated with zero values, creating a continuous time series. Filter results based on chain availability to ensure data quality and completeness.

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

Number of trades executed on a decentralized exchange.

PangolinPNG

Trade count

Trade count

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