Honeypop is an AMM and concentrated liquidity layer on Scroll, built for efficient swaps and real yield. It will also feature a Morpho-powered money market, enabling seamless lending and borrowing.

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Exchanges (DEX)

Honeypop

# Honeypop Active users (monthly) Methodology

## 1. Overview

Monthly Active Users (MAU) for Honeypop measures the approximate number of unique users who performed trading or flash loan activities on the Honeypop decentralized exchange within a 30-day rolling window. This metric answers the business question: "How many unique users are actively engaging with Honeypop's DEX functionality over the past 30 days?" The calculation uses HyperLogLog sketches for efficient approximate counting of unique addresses across the rolling time window.

## 2. Data sources

The calculation relies on these underlying data sources:
- **Blockchain events**: Parsed `Pool_Swap_event` and `Pool_Flash_event` logs from Honeypop v1 smart contracts deployed on the Scroll blockchain
- **Transaction data**: Scroll blockchain transaction records containing user addresses and transaction metadata
- **Smart contract events**: Event logs from Honeypop's automated market maker (AMM) contracts capturing trading and flash loan activities
- **Chain availability metadata**: Information about which blockchain networks are available and active for the protocol
- **Protocol metadata**: Business classification data marking Honeypop as an "Exchange AMM" in the "dex" business segment

## 3. Step-by-step calculation

### Component A: Trading activity extraction
- **Purpose**: Identifies users who performed token swaps on Honeypop
- **Logic**: Extracts `Pool_Swap_event` logs from Honeypop v1 contracts on Scroll blockchain. Filters out any denylisted trading pairs (though currently none are configured). Joins event logs with transaction records to resolve the actual user address from the `from_address` field of the transaction. Captures token amounts, timestamps, and contract addresses for each swap event.
- **Key fields**: `user_address` (the actual trader), `block_timestamp` (when the swap occurred), `account_address` (the pool contract), `token0_amount` and `token1_amount` (swap quantities)

### Component B: Flash loan activity extraction  
- **Purpose**: Identifies users who utilized flash loan functionality on Honeypop
- **Logic**: Extracts `Pool_Flash_event` logs from Honeypop v1 contracts on Scroll blockchain. Similar to trading events, joins with transaction data to get actual user addresses and filters out denylisted pairs. Captures flash loan amounts, sender addresses, and timing information for each flash loan event.
- **Key fields**: `user_address` (the flash loan user), `block_timestamp` (when the flash loan occurred), `contract_user_address` (the immediate contract caller), `token0_amount` and `token1_amount` (flash loan quantities)

### Component C: Daily active user aggregation
- **Purpose**: Combines trading and flash loan users into daily unique active user counts
- **Logic**: Truncates timestamps to daily granularity and combines users from both trading and flash loan activities using union distinct operation. This ensures users active in both activities on the same day are counted only once. All users are classified as 'Trader' user type. Applies chain availability filtering to ensure only valid blockchain data is included.
- **Key fields**: `timestamp` (day-level), `chain` (blockchain identifier), `user_type` (always 'Trader'), `user_dau` (the unique user address)

### Component D: HyperLogLog initialization
- **Purpose**: Converts daily user data into efficient data structures for rolling window calculations
- **Logic**: Groups daily active user data by timestamp, chain, user type, and product dimensions. Initializes HyperLogLog sketches for each group using `hll_count.init()` function with precision parameter 15. This creates compact probabilistic data structures that can efficiently estimate unique counts when merged across time periods.
- **Key fields**: `hll` (the HyperLogLog sketch containing user count estimates), dimensional grouping fields for later aggregation

### Component E: Rolling window construction
- **Purpose**: Creates 30-day rolling windows for each day to calculate MAU
- **Logic**: Generates a continuous datespine from the minimum DAU date to current date. For each date in the datespine, creates a 30-day lookback window (including 29 prior days plus the current day) using timestamp array generation. Joins this window structure with the daily HLL data to capture all user activity within each 30-day period.
- **Key fields**: `timestamp` (the end date of each 30-day window), `joinday` (each day within the 30-day lookback period)

### Component F: Dimensional aggregations
- **Purpose**: Creates MAU calculations at different analytical levels
- **Logic**: Performs separate aggregations for four dimensional views: project-level (all data combined), chain-level (grouped by blockchain), user-type-level (grouped by user classification), and product-level (grouped by product identifier). For each dimension, merges HyperLogLog sketches across the 30-day windows using `hll_count.merge_partial()` and extracts the final unique user count using `hll_count.extract()`.
- **Key fields**: `dimensions` (the aggregation level), `user_mau` (the estimated unique user count), relevant dimensional identifiers

### Final aggregation
- **Combining components**: Unions all dimensional aggregation results into a single dataset containing MAU calculations at project, chain, user-type, and product levels
- **Currency conversion**: Not applicable for user count metrics
- **Time aggregation**: Uses 30-day rolling windows ending on each calendar day, with each window containing the current day plus the previous 29 days
- **Final calculation**: The final `user_mau` value represents the approximate number of unique user addresses that performed either trading or flash loan activities within the 30-day rolling window, calculated using HyperLogLog approximation algorithms for efficient computation across large datasets

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

Unique revenue-generating users over a rolling 30-day window.

Honeypop

Active users (monthly)

Active users (monthly)

Competitive landscape