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).

governance

github

twitter

discord

etherscan

website

Exchanges (DEX)

Pangolin

# Pangolin Average revenue per user (ARPU) Methodology

## 1. Overview

Average Revenue Per User (ARPU) measures the average daily revenue generated per daily active user on the Pangolin decentralized exchange. This metric answers the business question: "How much revenue does each active user generate on average?" The calculation divides daily protocol revenue by the number of daily active users (traders). Pangolin is an automated market maker (AMM) on Avalanche that generates revenue through trading fees, with the protocol capturing 1/6 of all fees collected.

## 2. Data sources

The calculation uses several raw blockchain data sources:
- **Pangolin Factory events**: Parsed Avalanche `PairCreated` events from the Pangolin Factory contract that define trading pairs and their metadata
- **Pangolin Pair events**: Parsed Avalanche `Swap` events from individual Pangolin Pair contracts that record all trading activity
- **Transaction data**: Avalanche transaction records that provide user address information for each swap
- **External data**: End-of-day token price feeds for USD conversion and token metadata including symbols and decimal precision
- **Chain availability**: Avalanche chain availability data for filtering active periods

## 3. Step-by-step calculation

### Component A: Trading pair identification
- **Purpose**: Establishes all valid Pangolin trading pairs and their characteristics
- **Logic**: Extracts trading pair metadata from Pangolin Factory `PairCreated` events on Avalanche. Each pair is assigned a fixed fee tier of 0.3% (3000/1000000). The component joins with token metadata to get decimal precision and symbols for both tokens in each pair, creating readable pair names in the format "TOKEN0-TOKEN1"
- **Key fields**: `account_address` (pair contract), `token0_address`, `token1_address`, `token0_decimals`, `token1_decimals`, `fee_tier` (0.003), `account_name` (token symbol pair)

### Component B: Trade event processing
- **Purpose**: Captures all swap transactions and calculates token amount changes
- **Logic**: Processes `Swap` events from Pangolin Pair contracts starting from February 8, 2021. For each swap, calculates the net change in both tokens by subtracting outgoing amounts from incoming amounts (amount_in - amount_out). Links each swap to the originating user address through Avalanche transaction records using the transaction hash
- **Key fields**: `transaction_hash`, `log_index`, `user_address` (from transaction), `contract_user_address` (from event), `token0_amount`, `token1_amount`, `account_address` (pair contract)

### Component C: Trading volume calculation
- **Purpose**: Converts token amounts to USD trading volumes with manipulation protection
- **Logic**: Aggregates swap data daily and joins with trading pair metadata to correct for token decimals by dividing amounts by 10^decimals. Converts both token amounts to USD using end-of-day price feeds. Applies anti-manipulation logic by taking the minimum USD value between the two tokens in each swap, preventing inflated volumes from price manipulation. Sums absolute values of token amounts at the daily level
- **Key fields**: `timestamp` (daily), `trading_volume` (USD), `token0_amount`, `token1_amount`, `fee_tier`

### Component D: Fee and revenue calculation
- **Purpose**: Calculates total fees and protocol revenue share
- **Logic**: Multiplies daily trading volume by the 0.3% fee tier to calculate total fees collected. Applies Pangolin's revenue share model where the protocol captures 1/6 of all fees, with the remaining 5/6 going to liquidity providers. This gives the protocol its revenue for each day
- **Key fields**: `fees` (volume * 0.003), `revenue` (fees * 1/6)

### Component E: Daily active user counting
- **Purpose**: Counts distinct users who traded each day using probabilistic counting
- **Logic**: Identifies unique trader addresses from swap transactions, aggregating by day with user type labeled as "Trader". Uses HyperLogLog (HLL) probabilistic data structure for efficient distinct counting across multiple dimensional breakdowns including project-level, chain-level, user-type-level, and product-level aggregations. Applies a precision parameter of 15 for the HLL algorithm
- **Key fields**: `user_dau` (distinct count), `user_type` ("Trader"), `dimensions` (breakdown type), `hll` (HyperLogLog sketch)

### Final aggregation
- **Combining components**: The ARPU calculation divides daily revenue by daily active users using the `calculate_arpu` macro, which performs the division operation between the revenue metric and user_dau metric for matching timestamps
- **Currency conversion**: All revenue values are already in USD through the price feed conversion in the trading volume calculation step
- **Time aggregation**: Both revenue and user counts are aggregated to daily boundaries using timestamp truncation to day level
- **Final calculation**: ARPU = Daily Protocol Revenue ÷ Daily Active Users, where missing data points are filled with zeros using the fill_zeros strategy to ensure continuous time series data

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

PangolinPNG

Average revenue per user (ARPU)

Average revenue per user (ARPU)

Competitive landscape