Liquidity providers deposit NFTs and funds into Sudoswap and receive trading fees, and creators receive royalties from users (supply-side fees).

Sudoswap is a non-custodial NFT marketplace protocol built on Ethereum. Sudoswap was founded by Owen Shen and launched in 2022.

Team and investors own and govern the protocol. They currently take a cut of the total fees paid by users (revenue).

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Sudoswap

# Sudoswap Active users (weekly) Methodology

## 1. Overview

Weekly active users (WAU) measures the number of unique users who have interacted with the sudoswap NFT marketplace protocol within a 7-day rolling window. This metric answers the business question "How many unique users are actively trading NFTs on sudoswap each week?" The calculation uses a rolling 7-day window approach with HyperLogLog probabilistic counting to efficiently handle large user sets while providing dimensional breakdowns by chain, user type, and product.

## 2. Data sources

The calculation relies on the following raw data sources:
- **Ethereum blockchain function call traces**: Successful function calls to sudoswap smart contracts that represent actual NFT trading activity
- **sudoswap v1 contract interactions**: Function calls to `Pair` contracts including `swapTokenForAnyNFTs`, `swapNFTsForToken`, and `swapTokenForSpecificNFTs` functions
- **sudoswap v2 contract interactions**: Function calls to `LSSVMPairERC721ETH` and `LSSVMPairERC1155ETH` contracts for NFT-token swapping operations
- **Transaction status data**: Boolean flags indicating whether transactions completed successfully
- **Block timestamp data**: Ethereum block timestamps for temporal aggregation and time-based filtering

## 3. Step-by-step calculation

### Component A: User extraction from trading activity
- **Purpose**: Identify unique user addresses from successful NFT trading transactions
- **Logic**: Extract user addresses from successful sudoswap function calls across both v1 and v2 protocol versions. For token-to-NFT swaps, capture the `nftRecipient` parameter; for NFT-to-token swaps, capture the `tokenRecipient` parameter. Filter to include only transactions with `status` = true to ensure successful completion. Classify all captured addresses as "Trader" user type since they represent actual trading activity
- **Key fields**: `block_timestamp` for temporal analysis, `transaction_hash` for deduplication, `user_address` extracted from function parameters, `user_type` set to "Trader", `chain` set to "ethereum"

### Component B: Daily active user aggregation
- **Purpose**: Create daily unique user counts as the foundation for weekly rolling calculations
- **Logic**: Group the extracted user addresses by day using timestamp truncation to day granularity. Create distinct user records for each day, preserving dimensional attributes like chain, user type, and product information. This creates a daily active user (DAU) foundation that will be rolled up into weekly windows
- **Key fields**: `timestamp` truncated to day, `timestamp_grain` set to "day", `user_dau` containing unique user addresses, dimensional fields (`chain`, `user_type`, `product_id`)

### Component C: HyperLogLog sketch initialization
- **Purpose**: Convert daily user sets into probabilistic data structures for efficient unique counting
- **Logic**: Transform daily active user data into HyperLogLog sketches with precision 15, which enables memory-efficient storage and merging of large user sets. Group by timestamp and dimensional attributes, initializing one HLL sketch per group that contains all users for that day and dimension combination
- **Key fields**: `hll` containing HyperLogLog sketch, dimensional grouping fields maintained from previous component

### Component D: Rolling window creation
- **Purpose**: Generate 7-day rolling windows for each day to calculate weekly active users
- **Logic**: Create a complete date spine covering the full time range of available data. For each date in the spine, generate an array of the previous 6 days plus the current day (7 days total). Join this expanded date array with the HLL sketch data to create windows where each day is associated with its relevant 7-day period of user activity
- **Key fields**: `timestamp` for the window end date, `joinday` for each day within the 7-day window, HLL sketches from matching days

### Component E: Weekly aggregation with dimensional breakdowns
- **Purpose**: Calculate unique users across 7-day windows with multiple dimensional views
- **Logic**: For each rolling 7-day window, merge all HyperLogLog sketches within that window and extract the final unique user count. Create four separate dimensional breakdowns: project-level (all dimensions nullified), chain-level (group by chain), user type-level (group by user type), and product-level (group by product). Each breakdown merges HLL sketches appropriately and nullifies non-relevant dimension fields
- **Key fields**: `user_wau` extracted from merged HLL sketches, `dimensions` indicating breakdown type, relevant dimensional fields preserved while others set to null

### Final aggregation
- **Combining components**: Union all four dimensional breakdown views (project, chain, user type, product) into a single result set, with each row representing a unique combination of date, dimension type, and dimension values
- **Time aggregation**: Maintain daily granularity for the rolling windows, with each day representing the WAU for the 7-day period ending on that date
- **Schema standardization**: Apply the `finalize_metric_user_wau` macro to ensure consistent field names, data types, and metadata, while filling any missing days with zero values to maintain complete time series continuity
- **Final calculation**: The final `user_wau` value represents the estimated number of unique users who traded on sudoswap during the 7-day rolling window, calculated using HyperLogLog probabilistic counting for memory efficiency and accuracy with large user sets

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

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

SudoswapSUDO

Active users (weekly)

Active users (weekly)

Competitive landscape

Chain breakdown