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XSwap

XSwap | Data Breakdowns

# XSwap Active users (monthly) Methodology

## 1. Overview

Monthly Active Users (MAU) for xSwap measures the number of unique users who have interacted with the xSwap decentralized exchange protocol within any 30-day rolling window. This metric captures user engagement across both trading activities and flash loan utilizations on the xSwap DEX, which operates on the XDC blockchain network. The calculation uses HyperLogLog probabilistic counting for efficient computation across large datasets and provides dimensional breakdowns by project, chain, user type, and product.

## 2. Data sources

- **XDC blockchain events**: Parsed `Swap` events from xSwap v2 `Pair` contracts and xSwap v3 `Pool` contracts on the XDC network
- **XDC flash loan events**: Parsed `Flash` events from xSwap v3 `Pool` contracts on the XDC network  
- **Transaction data**: XDC blockchain transaction records containing user addresses and transaction metadata
- **Denylist data**: Hardcoded list of problematic trading pair contract addresses across multiple chains that have limited price feed support

## 3. Step-by-step calculation

### Component A: Flash loan user extraction
- **Purpose**: Identifies users who executed flash loans through xSwap v3 pools
- **Logic**: Extracts `Flash` events from xSwap v3 `Pool` contracts on XDC blockchain, filters out denylisted trading pairs, and joins with transaction data to resolve the actual user addresses from the `from_address` field. The events contain `sender`, `amount0`, and `amount1` fields representing the flash loan parameters.
- **Key fields**: `block_timestamp` for daily aggregation, `transaction_hash` for joining with transaction data, `address` as the pool contract address, `sender` as the contract-level user, and resolved `user_address` from transaction records

### Component B: Trading user extraction  
- **Purpose**: Identifies users who executed trades through xSwap v2 and v3 protocols
- **Logic**: Combines `Swap` events from both xSwap v2 `Pair` contracts and v3 `Pool` contracts on XDC blockchain. For v2, calculates net token amounts using `amount0In`, `amount0Out`, `amount1In`, `amount1Out` fields. For v3, uses direct `amount0` and `amount1` fields. Joins with transaction data to get user addresses and filters out denylisted pairs.
- **Key fields**: `block_timestamp` for time boundaries, `sender` from events, `token0_amount` and `token1_amount` representing trade volumes, and resolved `user_address` from transactions

### Component C: Daily active user aggregation
- **Purpose**: Combines all user activities into daily distinct user counts
- **Logic**: Unions flash loan users and trading users, truncates timestamps to daily boundaries, and creates distinct user counts per day by dimensions including chain, user type (always "Trader"), and product identifiers. Uses `union distinct` to eliminate duplicate users who performed multiple activities on the same day.
- **Key fields**: `timestamp` truncated to day granularity, `chain` identifier, `user_type` classification, `product_id`, and `user_dau` representing the unique user address

### Component D: HyperLogLog initialization
- **Purpose**: Converts daily user sets into probabilistic data structures for efficient MAU calculation
- **Logic**: Initializes HyperLogLog sketches from daily active user data with precision parameter 15, grouping by timestamp, chain, user type, and product dimensions. This enables efficient distinct counting across 30-day windows without storing individual user addresses.
- **Key fields**: `timestamp` for the daily boundary, dimensional grouping fields, and `hll` sketch containing the probabilistic user count data

### Component E: Rolling window construction
- **Purpose**: Creates 30-day rolling windows for MAU calculation using datespine approach
- **Logic**: Generates a complete datespine from the earliest data date to current date, then creates a cross-join structure where each target date is associated with all dates in its preceding 30-day window. This ensures no missing days affect the MAU calculation and provides consistent 30-day windows.
- **Key fields**: `timestamp` as the target date for MAU calculation, `joinday` representing each day within the 30-day lookback window

### Component F: Window-based HLL aggregation
- **Purpose**: Merges HyperLogLog sketches within each 30-day window to calculate MAU
- **Logic**: Joins the rolling window structure with daily HLL data, then groups by target timestamp and dimensions to merge all HLL sketches within each 30-day period. Uses `hll_count.merge_partial()` to combine sketches and `hll_count.extract()` to get the final distinct user count.
- **Key fields**: `timestamp` for the MAU date, dimensional breakdowns, and `user_mau` as the final monthly active user count

### Final aggregation
- **Combining components**: Creates separate dimensional views (project-level, chain-level, user type-level, product-level) by grouping HLL merges differently and setting non-relevant dimensions to null, then unions all breakdowns together
- **Time aggregation**: Uses 30-day rolling windows where each day's MAU represents unique users active in that day and the preceding 29 days, providing smooth trend analysis
- **Final calculation**: Applies the `finalize_metric_user_mau` macro to standardize the schema, fill missing days with zeros using the specified fill strategy, and ensure consistent dimensional structure across all xSwap metrics

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

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

XSwapXSP

Active users (monthly)

Active users (monthly)

Competitive landscape