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XSwap | Data Breakdowns

# XSwap Active users (weekly) Methodology

## 1. Overview

Weekly Active Users (WAU) measures the number of distinct users who performed trading or flash loan activities on XSwap DEX within rolling 7-day periods. This metric provides insights into user engagement and platform adoption by tracking unique addresses that interact with XSwap's automated market maker functionality across different time windows. The calculation uses HyperLogLog probabilistic data structures to efficiently aggregate large numbers of unique users across multiple dimensional breakdowns.

## 2. Data sources

The metric relies on several XDC blockchain data sources and supporting datasets:

- **XDC blockchain event logs**: Parsed `Swap` events from XDC v2 Pair contracts and v3 Pool contracts, plus `Flash` events from v3 Pool contracts
- **XDC transaction records**: Transaction metadata including sender addresses (`from_address`) and timestamps
- **Trading pair denylist**: Hardcoded list of trading pair contract addresses excluded due to limited price feed support for low-liquidity tokens
- **Protocol metadata**: Business classification data including market sectors, business segments, and product identifiers

## 3. Step-by-step calculation

### Component A: Daily active user identification
- **Purpose**: Identifies unique user addresses performing XSwap activities each day
- **Logic**: Extracts user addresses from XDC blockchain events for both trading and flash loan activities. For trading, processes `Swap` events from v2 Pair contracts and v3 Pool contracts on XDC. For flash loans, processes `Flash` events from v3 Pool contracts. Joins event data with XDC transaction records to obtain the actual user addresses (`from_address` field) who initiated the transactions. Filters out trading pairs from the denylist to exclude low-liquidity pairs with limited price support. Groups unique user addresses by day, chain, user type (all classified as 'Trader'), and product metadata.
- **Key fields**: `user_address` from transaction records, `block_timestamp` truncated to day boundaries, `chain` identifier, `product_id`, and business classification fields

### Component B: HyperLogLog initialization  
- **Purpose**: Converts daily user sets into probabilistic data structures for efficient aggregation
- **Logic**: Takes the daily active user data and initializes HyperLogLog sketches with 15-bit precision using the `hll_count.init` function. This creates compact representations of user sets that can be efficiently merged across time periods without losing accuracy in cardinality estimation.
- **Key fields**: `hll` sketches containing probabilistic representations of daily user sets, grouped by `timestamp`, `chain_id`, `user_type`, and `product_id`

### Component C: Rolling window creation
- **Purpose**: Generates 7-day rolling windows for each timestamp to calculate weekly active users
- **Logic**: Creates a datespine covering all days from the earliest available data to the current date minus one day. For each date in the datespine, generates an array of the 7 preceding days (including the current day) using `generate_timestamp_array` with 6-day lookback. Joins this window structure with the daily HLL data to create combinations of target dates with their corresponding 7-day historical periods.
- **Key fields**: `timestamp` representing the end date of each 7-day window, `joinday` representing each day within the window period

### Component D: Dimensional aggregations
- **Purpose**: Calculates WAU across different dimensional breakdowns for flexible reporting
- **Logic**: Creates four separate aggregation views: project-level (no dimensional breakdown), chain-level (grouped by `chain_id`), user type-level (grouped by `user_type`), and product-level (grouped by `product_id`). For each dimension, merges HLL sketches across the 7-day windows using `hll_count.merge_partial` to combine probabilistic user sets, then extracts final cardinality estimates using `hll_count.extract`. Sets unused dimension fields to null for each breakdown type.
- **Key fields**: `user_wau` containing the estimated count of unique weekly active users, `dimensions` indicating the aggregation level, and relevant dimensional identifiers

### Final aggregation
- **Combining components**: Unions all dimensional breakdowns (project-level, chain, user type, product) into a single result set using `union all` operations
- **Standardization**: Applies the `finalize_metric_user_wau` macro with 'fill_zeros' strategy to standardize the output schema and fill missing dates with zero values
- **Time boundaries**: Uses incremental processing with monthly partitioning and safety margins to handle updates efficiently while maintaining data completeness
- **Final calculation**: The final WAU value represents the probabilistic estimate of unique user addresses that performed trading or flash loan activities within each 7-day rolling window, aggregated across the specified dimensional breakdown

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

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

XSwapXSP

Active users (weekly)

Active users (weekly)

Competitive landscape