Overcollateralized borrowers deposit funds into Liquity and receive LUSD stablecoin.

Liquity is a non-custodial stablecoin protocol built on Ethereum. Liquity was founded by Robert Lauko & Rick Pardoe and launched in 2020.

LQTY tokenholders own & govern the protocol. They currently take all of the total borrow & redemption fees paid by borrowers (revenue). Liquity’s investors include Lemniscap, Polychain, Alameda Research, etc.

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Liquity

# Liquity Active users (weekly) Methodology

## 1. Overview

The weekly active users (WAU) metric measures the number of unique users who interacted with the Liquity protocol within any 7-day rolling window. This metric helps understand user engagement patterns and protocol adoption over time. The calculation uses HyperLogLog probabilistic data structures to efficiently count unique users across overlapping time windows, providing dimensional breakdowns by project, chain, user type, and product.

## 2. Data sources

The calculation relies on the following raw data sources:
- **Ethereum blockchain events**: Parsed Ethereum event logs from the Liquity V1 protocol, specifically the `BorrowerOperations_LUSDBorrowingFeePaid` events
- **Transaction metadata**: Ethereum transaction records containing timestamps, transaction hashes, and log indices
- **Chain availability data**: Configuration data that determines reliable data coverage periods for different blockchain networks

## 3. Step-by-step calculation

### Component A: User interaction extraction
- **Purpose**: Identify all user addresses that have interacted with the Liquity protocol through borrowing activities
- **Logic**: Extract user addresses from Ethereum `BorrowerOperations_LUSDBorrowingFeePaid` events, which capture LUSD borrowing fee payments. Filter for distinct interactions and classify users as 'Borrower' type. Associate each interaction with chain identifier, market sector (stablecoin issuers), business segment (stablecoin issuer), and product (LUSD)
- **Key fields**: `user_address` from the `borrower` parameter, `block_timestamp` for timing, `transaction_hash` and `log_index` for deduplication, `chain` set to 'ethereum'

### Component B: Daily active user calculation  
- **Purpose**: Calculate daily unique active users (DAU) as the foundation for WAU computation
- **Logic**: Group user interactions by day using timestamp truncation to daily granularity. Count distinct user addresses within each day, creating one record per unique user per day. Include dimensional attributes like user type, product ID, and chain for downstream grouping
- **Key fields**: `timestamp` truncated to day, `user_address` as `user_dau`, `user_type`, `product_id`, `chain`

### Component C: HyperLogLog sketch creation
- **Purpose**: Convert daily user sets into probabilistic data structures for efficient weekly aggregation
- **Logic**: Transform daily active user data into HyperLogLog sketches with 15-bit precision. Group by timestamp, chain, user type, and product ID. Initialize HLL sketches for each group to enable approximate unique counting across time windows
- **Key fields**: `hll` sketch containing compressed user set representation, grouping dimensions preserved from DAU data

### Component D: Rolling window construction
- **Purpose**: Create 7-day rolling windows for each date to capture weekly activity patterns
- **Logic**: Generate a continuous datespine from the earliest activity date to the present day. For each date in the spine, create associations with that date plus the previous 6 days, resulting in 7-day windows. Join this window structure with the daily HLL sketches to align user activity data with the appropriate time windows
- **Key fields**: `timestamp` as the window end date, `joinday` for the 7 component days, matched HLL sketches for each day in the window

### Component E: Weekly unique user aggregation
- **Purpose**: Calculate unique users within each 7-day window using HLL merging
- **Logic**: For each 7-day window, merge the HLL sketches from all component days and extract the approximate unique user count. This provides the weekly active user metric while preserving dimensional breakdowns. Group by window end date and dimensional attributes
- **Key fields**: `user_wau` as the extracted unique count from merged HLL sketches

### Component F: Dimensional breakdown creation
- **Purpose**: Generate separate metric views for different analytical dimensions
- **Logic**: Create four distinct breakdowns of the WAU metric. Project-level aggregation sums across all dimensions. Chain-level breakdown preserves chain grouping. User type breakdown maintains user classification. Product-level breakdown keeps product distinctions. Each breakdown nullifies non-relevant dimensional fields and sets appropriate dimension identifiers
- **Key fields**: `dimensions` field indicating breakdown type, relevant dimensional fields preserved while others are nullified

### Final aggregation
- **Combining components**: Union all dimensional breakdowns into a single result set, creating a comprehensive view with multiple aggregation levels in one table
- **Time aggregation**: Apply daily boundary logic with 7-day rolling windows, ensuring each date represents users active in that date's 7-day lookback period
- **Schema finalization**: Apply the finalize_metric_user_wau macro to standardize schema, fill missing dates with zeros for continuous time series, and add metadata fields like data_id set to 'liquity'
- **Incremental processing**: Filter results based on incremental lookback periods to optimize processing, typically including 29 days plus configured lookback buffer for safe reprocessing of recent data

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

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

LiquityLQTY

Active users (weekly)

Active users (weekly)

Competitive landscape

Chain breakdown