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# Shadow Active users (monthly) Methodology

## 1. Overview

Monthly Active Users (MAU) measures the number of unique users who interacted with ShadowExchange protocols within any 30-day rolling period. This metric captures user engagement across flash loan and trading activities on ShadowExchange v2 and v3 protocols deployed on the Sonic blockchain. The calculation uses HyperLogLog probabilistic counting to efficiently handle large user sets and provides dimensional breakdowns by chain, product version, and user type.

## 2. Data sources

- **Sonic blockchain transaction records**: Transaction data containing user addresses (`from_address`) who initiated DeFi interactions
- **ShadowExchange v2 swap events**: Parsed `Pair_Swap_event` logs from ShadowExchange v2 contracts on Sonic, capturing trading activity with token amount changes
- **ShadowExchange v3 swap events**: Parsed `Pool_Swap_event` logs from ShadowExchange v3 contracts on Sonic, capturing trading activity with direct amount values  
- **ShadowExchange v3 flash loan events**: Parsed `Pool_Flash_event` logs from ShadowExchange v3 contracts on Sonic, capturing flash loan borrowing activity

## 3. Step-by-step calculation

### Component A: Flash loan user extraction
- **Purpose**: Identifies users who performed flash loan activities on ShadowExchange v3
- **Logic**: Extracts flash loan events from ShadowExchange v3 `Pool_Flash_event` logs on Sonic blockchain. Joins with transaction records to obtain the actual user address (`from_address`) who initiated each flash loan transaction. Captures additional context including contract addresses, token amounts (`amount0`, `amount1`), and timestamps
- **Key fields**: `user_address` from transaction `from_address`, `block_timestamp` for daily aggregation, `product_id` as 'Concentrated Liquidity', `chain` as 'sonic'

### Component B: Trading user extraction  
- **Purpose**: Identifies users who performed trading activities across ShadowExchange v2 and v3 protocols
- **Logic**: Combines swap events from both protocol versions. For v2, calculates net token changes by subtracting outbound amounts from inbound amounts (`amount0In - amount0Out`). For v3, uses direct amount values from swap events. Joins all swap events with transaction records to extract user addresses. Excludes any denylisted trading pairs through filtering logic
- **Key fields**: `user_address` from transaction `from_address`, `token0_amount` and `token1_amount` representing trade volumes, `product_id` distinguishing 'Constant Product' and 'Concentrated Liquidity'

### Component C: Daily active user aggregation
- **Purpose**: Creates daily unique user counts across all ShadowExchange activities
- **Logic**: Combines flash loan and trading users using UNION DISTINCT to eliminate duplicates within each day. Truncates timestamps to daily granularity and categorizes all users as 'Trader' type. Groups by date, chain, user type, and product to create daily active user sets
- **Key fields**: `timestamp` truncated to day, `user_dau` containing unique user addresses, `user_type` set to 'Trader'

### Component D: HyperLogLog initialization
- **Purpose**: Converts daily user sets into probabilistic data structures for efficient MAU calculation
- **Logic**: Applies HyperLogLog initialization to daily active user data using 15-bit precision. Creates HLL sketches that can be merged across time periods while maintaining approximate distinct counting capabilities. Groups by timestamp, chain, user type, and product dimensions
- **Key fields**: `hll` containing HyperLogLog sketch of daily users, maintaining all dimensional attributes

### Component E: 30-day rolling window construction
- **Purpose**: Creates overlapping 30-day periods for MAU calculation using datespine methodology
- **Logic**: Generates a complete datespine covering all dates from the earliest DAU record to current date minus one day. For each date in the datespine, creates a 30-day lookback window by generating an array of dates from 29 days prior through the current date. Joins this expanded date structure with daily HLL data to create a comprehensive window dataset
- **Key fields**: `timestamp` for the MAU calculation date, `joinday` for each day within the 30-day window, `hll` from matching daily data

### Component F: MAU window aggregation
- **Purpose**: Merges HyperLogLog sketches across 30-day windows to calculate unique monthly active users
- **Logic**: Groups the windowed data by MAU timestamp and dimensional attributes. Applies HLL partial merge operations to combine daily sketches across each 30-day period. Extracts final unique user counts from merged HLL structures, providing approximate but highly accurate MAU values
- **Key fields**: `user_mau` containing extracted unique user count from merged HLL sketches

### Component G: Dimensional breakdowns
- **Purpose**: Creates multiple analytical perspectives of MAU data for different business needs
- **Logic**: Generates four distinct dimensional views: project-level aggregation combining all chains and products, chain-level breakdown showing MAU per blockchain, user type breakdown (though currently only 'Trader' type exists), and product-level breakdown distinguishing between ShadowExchange v2 and v3 usage. Each breakdown merges HLL data appropriately and sets non-relevant dimensions to null
- **Key fields**: `dimensions` indicating breakdown type, `chain_id`, `product_id`, `user_type` with appropriate null values per dimension

### Final aggregation
- **Combining components**: Unions all dimensional breakdowns using UNION ALL to create a comprehensive MAU dataset with multiple analytical perspectives
- **Currency conversion**: Not applicable for user counting metrics
- **Time aggregation**: Uses 30-day rolling windows with daily granularity, where each day represents MAU for the preceding 30-day period
- **Final calculation**: Applies the `finalize_metric_user_mau` macro to standardize schema, fill missing days with zeros, and apply chain availability filtering. The final metric represents unique users who performed any flash loan or trading activity within each 30-day rolling period

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

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

ShadowSHADOW

Active users (monthly)

Active users (monthly)

Competitive landscape