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

Aperture is a non-custodial DeFi exchange protocol built on Manta Pacific (originally a fork of Uniswap). Aperture was founded by Gao Han, Lian Zhu, and Peiqian Li in 2021.

Aperture team & investors own and govern the protocol. They do not currently take a cut of the total trading fees paid by traders (revenue). Aperture's investors include ParaFi Capital, Arrington XRP Capital, Costanoa Ventures and Divergence Ventures.

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Aperture

# Aperture Active users (weekly) Methodology

## 1. Overview

Weekly Active Users (WAU) measures the number of unique users who interact with Aperture Protocol's AMM (Automated Market Maker) on the Manta blockchain within any 7-day rolling window. This metric captures both traders who execute swaps and users who take flash loans through Aperture's pools. The calculation uses HyperLogLog sketches to efficiently aggregate distinct users across time windows and provides dimensional breakdowns by project, chain, user type, and product ID.

## 2. Data sources

The WAU calculation relies on the following blockchain data sources:
- **Aperture Protocol event logs**: Parsed `Pool_Swap_event` logs from Aperture's AMM pools on Manta chain, capturing trading activity including sender addresses and token amounts
- **Flash loan event logs**: Parsed `Pool_Flash_event` logs from Aperture's pools on Manta chain, capturing flash loan transactions with borrower and amount details  
- **Manta transaction records**: Complete transaction metadata including `from_address` field used to identify actual users initiating transactions
- **Protocol metadata**: Business classification data including market sector (exchanges), business segment (dex), and product identification (apertureswapv1)

## 3. Step-by-step calculation

### Component A: Flash loan user extraction
- **Purpose**: Identifies users who take flash loans through Aperture Protocol pools
- **Logic**: Extracts flash loan events from Aperture's `Pool_Flash_event` logs on Manta chain, joins with transaction records to obtain the actual user address from the `from_address` field. Users are classified with type 'Flash loan taker' and include business metadata like market sector 'exchanges', business segment 'dex', and product ID 'apertureswapv1'. Key extracted fields include `block_timestamp`, `user_address`, `token0_amount`, and `token1_amount`
- **Key fields**: `user_address` (from transaction `from_address`), `block_timestamp` (for daily aggregation), `chain` (manta), `user_type` (Flash loan taker)

### Component B: Trading user extraction  
- **Purpose**: Identifies users who execute swaps through Aperture Protocol pools
- **Logic**: Extracts swap events from Aperture's `Pool_Swap_event` logs on Manta chain, joins with transaction records to obtain the actual user address from the `from_address` field. Users are classified with type 'Trader' and include the same business metadata structure. The swap events capture token amount changes for both tokens in the pool pair
- **Key fields**: `user_address` (from transaction `from_address`), `block_timestamp` (for daily aggregation), `chain` (manta), `user_type` (Trader)

### Component C: Daily active user aggregation
- **Purpose**: Combines flash loan and trading users into daily distinct active user counts
- **Logic**: Unions the flash loan and trading user datasets, truncates timestamps to day granularity, and takes distinct user addresses per day for each combination of chain, user type, market sector, business segment, and product ID. This creates the foundation DAU (Daily Active Users) dataset with each user appearing once per day regardless of transaction volume
- **Key fields**: `timestamp` (day-truncated), `user_dau` (distinct user address), `user_type`, `chain`, `product_id`

### Component D: HyperLogLog sketch creation
- **Purpose**: Converts daily user sets into HyperLogLog sketches for efficient weekly aggregation
- **Logic**: Initializes HyperLogLog sketches for each daily user set using precision parameter 15. This probabilistic data structure allows efficient distinct counting when merging across time windows without storing actual user addresses
- **Key fields**: `hll` (HyperLogLog sketch), `timestamp`, `chain_id`, `user_type`, `product_id`

### Component E: Rolling window generation
- **Purpose**: Creates 7-day rolling windows for each date to calculate weekly active users
- **Logic**: Generates a complete datespine covering all days from the first user activity to present, then creates a cartesian product where each date is associated with its 7-day window (current day plus 6 preceding days). This ensures no gaps in the time series and provides consistent 7-day lookback periods
- **Key fields**: `timestamp` (target date), `joinday` (dates within the 7-day window)

### Component F: Window-based HLL aggregation
- **Purpose**: Aggregates daily HLL sketches across 7-day windows to count weekly active users
- **Logic**: Joins the rolling window structure with daily HLL sketches, grouping by target timestamp and dimensional attributes. Merges HLL sketches across the 7-day window using `hll_count.merge_partial()` and extracts the final distinct count using `hll_count.extract()`. This produces WAU counts for each day based on activity in that day plus the 6 preceding days
- **Key fields**: `user_wau` (weekly active user count), `timestamp`, dimensional breakdowns

### Component G: Dimensional breakdowns
- **Purpose**: Creates separate WAU calculations for different analytical perspectives  
- **Logic**: Calculates four separate dimensional views of WAU data: project-level (aggregate across all dimensions), chain-level (grouped by blockchain), user-type-level (traders vs flash loan takers), and product-level (by product ID). Each breakdown nullifies non-relevant dimensional fields and aggregates HLL sketches accordingly
- **Key fields**: `dimensions` (breakdown type), `chain_id`, `product_id`, `user_type`, `user_wau`

### Final aggregation
- **Combining components**: All dimensional breakdowns are combined using UNION ALL operations, creating a unified dataset with multiple analytical perspectives of the same underlying WAU data
- **Schema finalization**: The `finalize_metric_user_wau` macro applies standard schema formatting, fills missing days with zero values using the 'fill_zeros' strategy, and adds metadata fields like `data_id` (aperture) and proper column naming
- **Time boundaries**: The final output includes incremental processing logic that maintains a 29-day lookback buffer to ensure complete window calculations during updates
- **Final calculation**: Each row represents the count of distinct users who were active within a 7-day rolling window ending on the specified timestamp, broken down by the specified dimensional combination

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

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

Aperture

Active users (weekly)

Active users (weekly)

Competitive landscape

Chain breakdown