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 Average fees per user (AFPU) Methodology

## 1. Overview

Average Fees Per User (AFPU) measures the average daily fee revenue generated per active trader on the Pangolin decentralized exchange. This metric provides insight into user monetization and trading intensity by dividing total daily fees collected by the protocol by the number of unique daily active users. The calculation uses trading volume data from Pangolin V1 on Avalanche, applies the protocol's 0.3% fee structure, and divides by the count of distinct daily traders.

## 2. Data sources

- **Pangolin V1 Factory events**: Parsed `PairCreated` events from Pangolin's Factory contract on Avalanche that define trading pairs and their fee structures
- **Pangolin V1 Pair events**: Parsed `Swap` events from individual Pangolin trading pair contracts on Avalanche that capture all trading activity
- **Avalanche transaction records**: Transaction data providing user addresses and transaction context for swap events
- **End-of-day token price feeds**: Daily USD price data for tokens traded on Pangolin pairs
- **Token metadata**: Token information including decimal places and symbols for proper amount normalization

## 3. Step-by-step calculation

### Component A: Trading pair registry
- **Purpose**: Creates a reference of all Pangolin V1 trading pairs with their fee structures
- **Logic**: Extracts all `PairCreated` events from Pangolin's Factory contract on Avalanche. Each pair is assigned a hardcoded fee tier of 0.3% (calculated as 3000/10^6). The component joins with token metadata to retrieve decimal places and symbols for both tokens in each pair, creating pair names in the format "TOKEN0-TOKEN1"
- **Key fields**: `account_address` (pair contract address), `token0_address`, `token1_address`, `token0_decimals`, `token1_decimals`, `fee_tier` (0.3%), `account_name` (token pair name)

### Component B: Trade event processing
- **Purpose**: Processes individual swap transactions to extract trading activity and user interactions
- **Logic**: Processes `Swap` events from Pangolin pair contracts, calculating net token changes by subtracting out amounts from in amounts for both tokens in the pair. Joins with Avalanche transaction data to identify the actual user address performing the trade. Filters to include only trades from February 8, 2021 onwards when Pangolin launched
- **Key fields**: `block_timestamp`, `transaction_hash`, `user_address`, `token0_amount` (net change), `token1_amount` (net change), `account_address` (pair contract)

### Component C: Trading volume calculation
- **Purpose**: Aggregates trades into daily USD volumes with anti-manipulation safeguards
- **Logic**: Groups trades by day and joins with trading pair data to access token decimals and fee information. Normalizes token amounts by dividing by 10^decimals for proper scaling. Converts normalized amounts to USD using end-of-day price feeds for both tokens. Applies anti-manipulation logic by taking the minimum USD value between token0 and token1 amounts, preventing trades with manipulated prices from inflating volume metrics
- **Key fields**: `timestamp` (daily), `trading_volume` (USD), `fee_tier`, `user_address`, `token0_address`, `token1_address`

### Component D: Fee revenue calculation
- **Purpose**: Calculates daily fee revenue from trading volumes
- **Logic**: Aggregates daily trading volumes by summing across all trades and trading pairs. Multiplies the total daily trading volume by the fee tier (0.3%) to calculate gross fees collected by the protocol. Groups results by day, chain, and business dimensions
- **Key fields**: `timestamp`, `fees` (daily total in USD), `chain_id`, `product_id`

### Component E: Daily active user counting
- **Purpose**: Counts unique traders per day using efficient probabilistic counting
- **Logic**: Identifies distinct user addresses from daily trading activity, classifying them as "Trader" user type. Uses HyperLogLog algorithm for efficient approximate distinct counting across multiple dimensional breakdowns including project-level, chain-level, user-type-level, and product-level aggregations
- **Key fields**: `timestamp`, `user_dau` (distinct count), `user_type` ("Trader"), `chain_id`

### Final aggregation
- **Combining components**: The calculation divides daily fee revenue by daily active user count using the `calculate_arpu` macro, which performs the division operation between the fees metric and user_dau metric
- **Currency conversion**: All monetary values are converted to USD using end-of-day price feeds matched by timestamp, token address, and blockchain
- **Time aggregation**: Data is aggregated to daily boundaries using timestamp truncation to day level, with consistent timezone handling across all components
- **Final calculation**: AFPU equals total daily fees in USD divided by the count of distinct daily active users, with zero-filling applied for days with no trading activity to maintain time series continuity

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

PangolinPNG

Average fees per user (AFPU)

Average fees per user (AFPU)

Competitive landscape