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 Active users (weekly) Methodology

## 1. Overview

Weekly Active Users (WAU) for Pangolin measures the number of unique users who executed trades on the Pangolin V1 DEX on Avalanche within any 7-day period. This metric uses HyperLogLog data structures to efficiently calculate approximate distinct user counts across rolling weekly windows, providing insights into user engagement and platform activity trends. The calculation creates multiple dimensional breakdowns including project-level, chain-level, user-type-level, and product-level aggregations.

## 2. Data sources

The calculation relies on the following raw blockchain data sources:
- **Pangolin V1 swap events**: Parsed `Pair_Swap_event` logs from Pangolin V1 smart contracts on Avalanche, containing swap transaction details including token amounts, sender addresses, and contract interactions
- **Avalanche transaction records**: Raw Avalanche blockchain transaction data providing user addresses, transaction hashes, timestamps, and transaction metadata necessary to identify the actual users performing swaps

## 3. Step-by-step calculation

### Component A: Trade event extraction
- **Purpose**: Identifies and processes individual swap transactions from Pangolin V1 DEX
- **Logic**: Extracts `Pair_Swap_event` logs from Pangolin V1 smart contracts on Avalanche and joins with Avalanche transaction records to obtain user addresses. For each swap, calculates the net change in both tokens by computing the difference between `amount0In` minus `amount0Out` and `amount1In` minus `amount1Out`. Filters data starting from February 8, 2021, when Pangolin launched. Classifies all activity under the 'Exchange AMM' business line and 'dex' business segment.
- **Key fields**: `block_timestamp` for timing, `user_address` for user identification, `transaction_hash` and `log_index` for event uniqueness, `contract_user_address` from swap sender, token amount calculations, and chain classification as 'avalanche'

### Component B: Daily active user identification  
- **Purpose**: Aggregates trade data to identify unique active users per day
- **Logic**: Groups the processed trade events by day using `timestamp_trunc` to daily granularity. For each day, identifies all unique `user_address` values who executed trades, classifying them as 'Trader' user type. Maintains dimensional attributes including chain, market sector, business segment, and product identifiers. Creates the foundation for weekly rollup calculations by establishing daily user activity baselines.
- **Key fields**: `timestamp` truncated to day, `user_dau` containing unique user addresses, `user_type` set to 'Trader', and dimensional attributes for later aggregation

### Component C: HyperLogLog initialization and datespine generation
- **Purpose**: Prepares daily user data for efficient weekly aggregation and ensures continuous time series
- **Logic**: Converts daily active user lists into HyperLogLog data structures using `hll_count.init` with precision parameter 15 for memory-efficient approximate distinct counting. Simultaneously generates a continuous datespine using `generate_timestamp_array` from the earliest data date to the current date minus one day, ensuring no gaps in the time series that could affect rolling window calculations.
- **Key fields**: `hll` structures containing compressed user data, continuous `timestamp` series from datespine, and preserved dimensional attributes

### Component D: Rolling window creation
- **Purpose**: Creates 7-day rolling windows for each day to calculate weekly active users
- **Logic**: For each day in the datespine, generates a 7-day lookback period including the current day using `generate_timestamp_array` with 6-day subtraction. Creates cross-join relationships between each day and its corresponding 7-day window, then joins with the HLL daily data to collect all user activity within each rolling window. This approach ensures that each day's WAU calculation includes users active on that day plus the previous 6 days.
- **Key fields**: `timestamp` for the calculation date, `joinday` for dates within the rolling window, and matched HLL data for each day in the window

### Component E: Dimensional aggregation
- **Purpose**: Calculates WAU across different dimensional breakdowns
- **Logic**: Processes the rolling window data through four separate aggregation paths: project-level aggregation (combining all dimensions), chain-level aggregation (grouping by `chain_id`), user-type-level aggregation (grouping by `user_type`), and product-level aggregation (grouping by `product_id`). For each breakdown, merges HLL structures across the 7-day window using `hll_count.merge_partial`, then extracts the final distinct count using `hll_count.extract`. Sets unused dimensional fields to null in each breakdown to maintain schema consistency.
- **Key fields**: `dimensions` field identifying the aggregation level, preserved dimensional attributes where applicable, and `user_wau` containing the final weekly active user count

### Final aggregation
- **Combining components**: All dimensional breakdowns are combined using UNION ALL to create a comprehensive dataset with multiple aggregation levels in a single table
- **Time boundaries**: Daily boundary logic uses timestamp truncation to day level, with rolling windows calculated as 7-day periods including the current day
- **Schema standardization**: The `finalize_metric_user_wau` macro applies consistent schema formatting and implements a fill_zeros strategy to populate missing time periods with zero values, ensuring continuous time series data
- **Final calculation**: Each row represents the count of unique users who were active within a 7-day rolling window ending on the specified date, calculated using HyperLogLog approximate distinct counting for efficient processing of large user datasets

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

Unique revenue-generating users over a rolling 7-day window.

PangolinPNG

Active users (weekly)

Active users (weekly)

Competitive landscape