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

ApeSwap is a non-custodial exchange protocol built originally on BNB Chain. ApeSwap was founded by an anonymous team and launched in 2021.

BANANA 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)

ApeSwap

# ApeSwap Active users (weekly) Methodology

## 1. Overview

ApeSwap Weekly Active Users (WAU) measures the number of unique users who executed trades on ApeSwap's decentralized exchange within any 7-day rolling window. This metric provides insight into user engagement and platform adoption by tracking distinct addresses that interact with ApeSwap's AMM trading functionality across multiple blockchain networks. The calculation uses a rolling 7-day window approach, where each day's WAU represents the count of unique users who traded on that day or any of the 6 preceding days.

## 2. Data sources

The calculation relies on the following blockchain data sources:
- **ApeSwap V1 swap events**: Parsed `Swap` events from ApePair contracts deployed on Ethereum, Polygon, Arbitrum, and BSC networks
- **Transaction records**: Blockchain transaction data from Ethereum, Polygon, Arbitrum, and BSC to map contract interactions to user addresses
- **Event parameters**: Swap event data including `amount0In`, `amount0Out`, `amount1In`, `amount1Out` fields and sender addresses
- **Temporal data**: Block timestamps from blockchain transactions for daily aggregation
- **Contract addresses**: ApeSwap V1 ApePair contract addresses across supported chains

## 3. Step-by-step calculation

### Component A: Trade event extraction
- **Purpose**: Identify all trading activity on ApeSwap across supported blockchain networks
- **Logic**: Extract `Swap` events from ApeSwap V1 ApePair contracts on Ethereum, Polygon, Arbitrum, and BSC. Join swap events with transaction data to obtain the actual user address (`from_address`) rather than just the contract sender. Filter events to include only trades occurring after February 10, 2021. Calculate token amount changes for both tokens in each swap by computing the difference between input and output amounts (`amount0In - amount0Out` and `amount1In - amount1Out`).
- **Key fields**: `block_timestamp` for temporal aggregation, `transaction_hash` for joining with transaction data, `user_address` from transaction sender, `chain` identifier, and calculated `token0_amount` and `token1_amount` deltas

### Component B: Daily active user aggregation  
- **Purpose**: Calculate daily distinct active users from trading activity
- **Logic**: Group trade events by day using `timestamp_trunc(block_timestamp, day)` and identify distinct user addresses within each day. Classify all users as 'Trader' user type since they performed swap transactions. Maintain chain-level granularity to support chain-specific analysis. Apply chain availability filtering to ensure data completeness across supported networks.
- **Key fields**: `timestamp` truncated to day granularity, `chain` for network identification, `user_type` set to 'Trader', `user_address` for distinct counting

### Component C: HyperLogLog initialization
- **Purpose**: Convert daily user sets into memory-efficient data structures for distinct counting
- **Logic**: Transform daily active user data into HyperLogLog (HLL) structures using `hll_count.init()` with precision parameter 15. This enables efficient approximate distinct counting when merging across time windows. Group by timestamp, chain, user type, and product dimensions while initializing HLL structures for each combination.
- **Key fields**: `hll` structures containing encoded user sets, dimensional grouping fields maintained from previous step

### Component D: Seven-day rolling window construction
- **Purpose**: Create overlapping 7-day periods for each calendar day
- **Logic**: Generate a complete datespine from the earliest trade date to the current date using `generate_timestamp_array()`. For each date in the spine, create a 7-day lookback window by generating an array of dates from 6 days prior through the current day. Join this temporal framework with the daily HLL data to ensure every day has access to its required 7-day historical context.
- **Key fields**: `timestamp` for the target date, `joinday` for each date within the 7-day window, joined `hll` data from corresponding days

### Component E: Multi-dimensional WAU calculation
- **Purpose**: Calculate WAU across different analytical dimensions
- **Logic**: Aggregate the 7-day window data across four dimensional breakdowns: (1) Project-level aggregation combining all chains and products, (2) Chain-level breakdown showing WAU per blockchain network, (3) User-type breakdown (currently only 'Trader'), and (4) Product-level breakdown for future product differentiation. For each dimension, merge HLL structures within the 7-day window using `hll_count.merge_partial()` and extract final distinct counts using `hll_count.extract()`.
- **Key fields**: `dimensions` indicating the breakdown type, `chain_id`, `product_id`, `user_type` as appropriate for each dimension, final `user_wau` count

### Final aggregation
- **Combining components**: Union all dimensional breakdowns (project-level, chain-level, user-type-level, product-level) into a single result set with consistent schema structure
- **Time aggregation**: Each day represents a 7-day rolling window ending on that date, providing continuous WAU measurement without gaps
- **Schema standardization**: Apply the `finalize_metric_user_wau` macro to standardize column names, data types, and add metadata fields like data source identifier ('apeswap')
- **Gap filling**: Use 'fill_zeros' strategy to populate missing days with zero values, ensuring continuous time series for analysis and visualization
- **Final calculation**: Weekly Active Users = distinct count of user addresses who executed at least one trade within the 7-day window ending on the target date, calculated using HyperLogLog approximate distinct counting for computational efficiency

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

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

ApeSwapABOND

Active users (weekly)

Active users (weekly)

Competitive landscape

Chain breakdown