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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Honeypop

# Honeypop Active users (weekly) Methodology

## 1. Overview

Weekly Active Users (WAU) measures the number of unique users who interacted with the Honeypot protocol within any rolling 7-day period. This metric answers the business question of user engagement and protocol adoption by tracking users who performed trading or flash loan activities on the platform. The calculation uses HyperLogLog data structures to efficiently count distinct users across overlapping 7-day windows, ensuring accurate deduplication when users are active across multiple days within the same week.

## 2. Data sources

The WAU metric relies on several raw data sources:
- **Blockchain events**: Parsed Honeypot v1 smart contract events from Scroll blockchain, specifically `Pool_Swap_event` logs for trading activity and `Pool_Flash_event` logs for flash loan usage
- **Transaction data**: Scroll blockchain transaction records containing `from_address` fields that identify the actual users initiating protocol interactions
- **Smart contract data**: Honeypot v1 protocol contract addresses deployed on Scroll blockchain
- **Configuration data**: Denylisted trading pairs configuration that excludes low-liquidity token pairs with limited price feed support
- **Protocol metadata**: Business line classifications, market sector identifications, and version information for the Honeypot protocol

## 3. Step-by-step calculation

### Component A: Daily active users extraction
- **Purpose**: Identifies unique users who performed any activity on Honeypot protocol each day
- **Logic**: Extracts user addresses from two types of activities. First, captures traders by parsing `Pool_Swap_event` logs from Honeypot v1 contracts on Scroll, joining with transaction records to get the actual user address from the `from_address` field. Second, captures flash loan users by parsing `Pool_Flash_event` logs using the same joining pattern. Both activity types are combined using union distinct to ensure users performing multiple activity types on the same day are counted only once. Filters out denylisted trading pairs based on configuration to exclude low-liquidity tokens with limited price support.
- **Key fields**: `user_address` from transaction `from_address`, `block_timestamp` for daily grouping, `chain` identifier, `user_type` as 'Trader', and `product_id` for product classification

### Component B: HyperLogLog daily aggregation  
- **Purpose**: Creates efficient data structures for counting distinct users per day with multiple dimensional breakdowns
- **Logic**: Groups the daily active users by day, chain, user type, and product ID. Creates HyperLogLog data structures for each group using `hll_count.init` with precision 15 to enable efficient distinct counting. This approach allows for accurate user deduplication when calculating weekly aggregations later.
- **Key fields**: `timestamp` truncated to day, `timestamp_grain` as 'day', `chain_id`, `user_type`, `product_id`, and `hll` containing the HyperLogLog structure

### Component C: Datespine and rolling window creation
- **Purpose**: Establishes the framework for calculating WAU across all dates with proper 7-day lookback periods
- **Logic**: Creates a complete datespine covering all dates from the earliest user activity to yesterday using `generate_timestamp_array`. For each date in the datespine, generates a 7-day rolling window by creating an array of the current date plus the 6 preceding days. This ensures every possible 7-day period is captured, including periods that span days with no activity.
- **Key fields**: `timestamp` for each date, `joinday` representing each day within the 7-day lookback window

### Component D: Weekly user aggregation
- **Purpose**: Combines daily HyperLogLog structures across 7-day windows to count distinct weekly active users
- **Logic**: Joins the 7-day rolling windows with the daily HyperLogLog data to capture all users active within each 7-day period. Groups by the end date of each 7-day window along with dimensional attributes. Merges HyperLogLog structures using `hll_count.merge_partial` to combine user sets from multiple days, then extracts the final distinct count using `hll_count.extract` to get the WAU value.
- **Key fields**: `timestamp` as the 7-day window end date, dimensional groupings, and `user_wau` as the distinct user count

### Final aggregation
- **Combining components**: Creates separate dimensional breakdowns for project-level, chain-level, user type, and product ID perspectives. Each breakdown aggregates the weekly user data differently by nulling out non-relevant dimensions while preserving the specific dimension of interest
- **Time aggregation**: Uses daily boundaries with 7-day rolling windows, where each day represents the end date of a week-long period that includes the current day and the 6 previous days
- **Final calculation**: Applies the `finalize_metric_user_wau` macro to standardize the schema, add metadata fields, and implement a fill-zeros strategy to ensure missing days are represented with zero values rather than gaps in the data

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

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

Honeypop

Active users (weekly)

Active users (weekly)

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