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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NFT marketplaces

Sudoswap

# Sudoswap Active users (monthly) Methodology

## 1. Overview

Monthly Active Users (MAU) measures the number of unique users who interacted with Sudoswap NFT marketplace within a 30-day rolling window period. This metric uses a trailing 30-day calculation approach, where each day's MAU represents the count of distinct users who performed trading activities in the current day plus the previous 29 days. The calculation uses HyperLogLog probabilistic data structures to efficiently count unique users across large datasets while maintaining accuracy.

## 2. Data sources

The methodology relies on the following raw data sources:

- **Ethereum blockchain function call traces**: Transaction traces containing successful function call executions to Sudoswap smart contracts
- **Sudoswap V1 contract interactions**: Function calls to Pair contracts including `swapTokenForAnyNFTs`, `swapNFTsForToken`, and `swapTokenForSpecificNFTs` functions
- **Sudoswap V2 contract interactions**: Function calls to LSSVMPair contracts for both ERC721 and ERC1155 tokens, including `swapTokenForSpecificNFTs`, `swapNFTsForToken2`, and `swapNFTsForToken` functions
- **Transaction metadata**: Block timestamps, transaction hashes, and execution status data
- **Smart contract execution data**: Function call parameters containing user addresses and transaction outcomes

## 3. Step-by-step calculation

### Component A: User identification and daily aggregation
- **Purpose**: Identifies unique users from Sudoswap contract interactions and aggregates them by day
- **Logic**: The system extracts user addresses from successful function calls to Sudoswap smart contracts on Ethereum. For swap functions, it captures the recipient addresses from function parameters such as `nftRecipient` and `tokenRecipient`, which represent the users receiving NFTs or tokens from trades. Each user interaction is timestamped using the block timestamp and truncated to day granularity. Users are classified by type as "Trader" based on their interaction with swap functions. Only successful transactions (where status equals true) are included in the calculation.
- **Key fields**: `user_address` extracted from function parameters, `block_timestamp` truncated to day, `user_type` set to "Trader", `chain` identifier, and `product_id` for dimensional breakdown

### Component B: HyperLogLog sketch creation
- **Purpose**: Converts daily user lists into probabilistic data structures for efficient unique counting
- **Logic**: Daily unique user addresses are converted into HyperLogLog sketches using BigQuery's built-in HLL functions. This probabilistic approach allows efficient storage and merging of large user sets while maintaining high accuracy for cardinality estimation. Each day's user set is initialized as an HLL sketch with precision parameter 15, providing a good balance between accuracy and storage efficiency.
- **Key fields**: `hll` containing the HyperLogLog sketch of daily unique users, `timestamp` for the day, and dimensional attributes like `chain_id`, `user_type`, and `product_id`

### Component C: Rolling window construction
- **Purpose**: Creates 30-day sliding windows for calculating monthly active users
- **Logic**: A datespine is generated covering the full date range from the first recorded user activity to the current date minus one day. For each date in the datespine, the system creates a 30-day window by generating an array of dates from 29 days prior through the current date. This approach ensures that each day's MAU calculation includes exactly 30 days of user activity data, creating a true rolling monthly metric.
- **Key fields**: `timestamp` for each day, `joinday` representing each day within the 30-day window, and corresponding HLL sketches for user counting

### Component D: Dimensional breakdown calculation
- **Purpose**: Calculates MAU across different analytical dimensions
- **Logic**: The system processes MAU calculations across four different dimensional breakdowns. The project-level breakdown aggregates all users regardless of other attributes. The chain-level breakdown maintains chain separation for multi-chain analysis. The user type breakdown segments users by their interaction patterns. The product breakdown allows analysis by specific product offerings. For each breakdown, HLL sketches within the 30-day windows are merged using `hll_count.merge_partial`, and the final unique user count is extracted using `hll_count.extract`.
- **Key fields**: `dimensions` indicating the breakdown type, relevant dimensional attributes (`chain_id`, `user_type`, `product_id`), and `user_mau` containing the final count

### Final aggregation
- **Combining components**: All dimensional breakdowns are combined using union operations, creating a comprehensive dataset with MAU values across different analytical perspectives while maintaining consistent schema structure
- **Time aggregation**: Each day represents a trailing 30-day calculation, so the timestamp indicates the end date of the 30-day window being measured
- **Data standardization**: The `finalize_metric_user_mau` macro applies consistent formatting, fills missing dates with zeros where appropriate, and ensures schema compliance across all dimensional breakdowns
- **Final calculation**: The final MAU value represents the count of distinct users who interacted with Sudoswap within any 30-day trailing window, calculated using probabilistic HyperLogLog structures for scalability and performance

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

Unique revenue-generating users over a rolling 30-day window.

SudoswapSUDO

Active users (monthly)

Active users (monthly)

Competitive landscape

Chain breakdown