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

Katana is a non-custodial exchange protocol built on Ronin. Katana was founded by Sky Mavis and launched in 2021.

The Ronin team own and govern the protocol. They currently take a cut of the total trading fees paid by traders (revenue).

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Exchanges (DEX)

Katana (Ronin)

# Katana Active users (weekly) Methodology

## 1. Overview

The Katana Weekly Active Users (WAU) metric measures the number of unique users who executed at least one trade on the Katana decentralized exchange within a rolling 7-day window. This metric provides insights into user engagement and platform activity over time. The calculation uses HyperLogLog sketches for efficient computation of unique user counts across overlapping time windows, and provides breakdowns across multiple dimensions including chain, user type, and product level.

## 2. Data sources

The calculation relies on the following raw data sources:

- **Katana DEX events**: Parsed `Pair_Swap` events from the Katana v1 smart contracts deployed on the Ronin blockchain, capturing all trading activity including token amounts and contract addresses
- **Ronin transaction data**: Ronin blockchain transaction records providing user addresses (`from_address`) and transaction metadata for each swap
- **Incremental processing metadata**: Table modification timestamps used for efficient incremental data processing and determining processing windows

## 3. Step-by-step calculation

### Component A: Trade fact extraction
- **Purpose**: Extract and standardize all Katana trading activity from raw blockchain events
- **Logic**: Parse `Pair_Swap` events from Katana v1 smart contracts on Ronin blockchain. Join swap events with Ronin transaction data to obtain user addresses. Calculate token amount changes by computing the difference between `amount0In` minus `amount0Out` for the first token, and `amount1In` minus `amount1Out` for the second token. Classify all users as 'Trader' type and categorize under 'Exchange AMM' business line within the 'exchanges' market sector and 'dex' business segment.
- **Key fields**: `block_timestamp`, `transaction_hash`, `log_index`, `user_address`, `token0_amount`, `token1_amount`, contract `address`

### Component B: Daily active user aggregation  
- **Purpose**: Aggregate trading activity to identify unique users per day
- **Logic**: Group all trades by day using timestamp truncation to day granularity. Extract unique user addresses for each day to create daily active user lists. Assign 'day' as the timestamp grain and maintain chain, user type, and product metadata for dimensional analysis.
- **Key fields**: `timestamp` (day-truncated), `user_address` as `user_dau`, `chain`, `user_type`, `product_id`

### Component C: HLL initialization and windowing
- **Purpose**: Prepare daily user data for efficient weekly aggregation using probabilistic data structures
- **Logic**: Convert daily user address lists into HyperLogLog sketches using precision 15 for efficient set operations. Create a comprehensive datespine covering all days from the earliest trade date to yesterday. Generate rolling 7-day windows by joining each target date with the previous 6 days, creating overlapping windows that capture all possible 7-day periods.
- **Key fields**: `hll` (HyperLogLog sketch), `timestamp` (target date), `joinday` (constituent days in window)

### Component D: Multi-dimensional WAU calculation
- **Purpose**: Calculate weekly active users across different analytical dimensions
- **Logic**: For each 7-day window, merge HyperLogLog sketches across the constituent days to compute unique weekly active users. Create separate aggregations for four dimensional breakdowns: project level (no dimensional filters), chain level (grouped by `chain_id`), user type level (grouped by `user_type`), and product level (grouped by `product_id`). For each breakdown, set irrelevant dimension fields to null while preserving the relevant grouping dimension.
- **Key fields**: `user_wau` (extracted from merged HLL), `dimensions` (breakdown type), dimension-specific fields (`chain_id`, `user_type`, `product_id`)

### Final aggregation
- **Combining components**: Union all dimensional breakdowns into a single result set, with each row representing WAU for a specific date, dimension type, and dimension value combination
- **Time aggregation**: Use rolling 7-day windows where each date represents users active in that date plus the previous 6 days, providing continuous daily WAU measurements
- **Final calculation**: Extract the final unique user count from the merged HyperLogLog sketches using `hll_count.extract()` function, which provides the estimated distinct count of users across the 7-day window

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

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

Katana (Ronin)

Active users (weekly)

Active users (weekly)

Competitive landscape