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Etherex

# Etherex Active users (monthly) Methodology

## 1. Overview

Monthly Active Users (MAU) measures the count of unique wallet addresses that interacted with the Etherex protocol within any 30-day rolling window. This metric combines users who performed trading activities (swaps) and flash loan operations on the Etherex decentralized exchange. The calculation uses HyperLogLog data structures to efficiently aggregate unique users across time windows while avoiding double-counting users who perform multiple activity types within the same period.

## 2. Data sources

- **Ethereum event logs**: Parsed Etherex V2 `Pair_Swap_event` logs from the Linea blockchain capturing trading activities on the V2 protocol
- **Ethereum event logs**: Parsed Etherex V3 `Pool_Swap_event` logs from the Linea blockchain capturing trading activities on the V3 protocol  
- **Ethereum event logs**: Parsed Etherex V3 `Pool_Flash_event` logs from the Linea blockchain capturing flash loan activities
- **Transaction data**: Linea blockchain transaction records providing user wallet addresses for each transaction
- **Chain configuration**: Chain availability filters ensuring only supported blockchain networks are included in calculations

## 3. Step-by-step calculation

### Component A: Trading activity extraction
- **Purpose**: Identifies unique users who performed swap transactions on Etherex V2 and V3 protocols
- **Logic**: Extracts swap events from both Etherex V2 pair contracts and V3 pool contracts on Linea blockchain. For V2 swaps, calculates token amount changes by subtracting `amount0Out` and `amount1Out` from `amount0In` and `amount1In` respectively. For V3 swaps, uses direct `amount0` and `amount1` values. Joins event logs with transaction data to map contract interaction addresses to actual user wallet addresses via the transaction `from_address` field. Groups results by day, chain, user type (classified as 'Trader'), and product ID
- **Key fields**: `block_timestamp` for daily grouping, `user_address` from transaction data for unique user identification, `product_id` distinguishing between Constant Product and Concentrated Liquidity, `chain` identifying Linea network

### Component B: Flash loan activity extraction  
- **Purpose**: Identifies unique users who executed flash loan operations on Etherex V3 protocol
- **Logic**: Extracts flash loan events from Etherex V3 pool contracts on Linea blockchain by parsing `Pool_Flash_event` logs. Captures flash loan amounts for both tokens (`amount0` and `amount1`) and the contract user address from the `sender` field. Joins with transaction data to obtain the actual user wallet address performing the flash loan. Groups results by day, chain, user type (classified as 'Trader'), and product ID
- **Key fields**: `block_timestamp` for daily aggregation, `user_address` from joined transaction data, `amount0` and `amount1` for flash loan token quantities, `product_id` set to Concentrated Liquidity

### Component C: Daily active user consolidation
- **Purpose**: Combines trading and flash loan users into daily unique active user counts
- **Logic**: Merges users from trading activities and flash loan activities using UNION DISTINCT to eliminate duplicate counting of users who perform both activity types on the same day. All users are classified under the 'Trader' user type regardless of activity. Applies chain availability filters to ensure only supported blockchain networks are included. Results are grouped by timestamp (day), chain, user type, and product ID
- **Key fields**: `timestamp` truncated to daily granularity, `user_address` for unique user identification, `user_type` set to 'Trader', `chain` and `product_id` for dimensional grouping

### Component D: HyperLogLog sketch preparation
- **Purpose**: Converts daily user lists into HyperLogLog sketches for efficient aggregation
- **Logic**: Takes daily active user data and initializes HyperLogLog sketches using the `hll_count.init` function with precision parameter 15. Groups sketches by timestamp, timestamp grain (day), chain ID, user type, and product ID. This creates compressed representations of daily user sets that can be efficiently merged across time windows
- **Key fields**: `hll` containing the HyperLogLog sketch of daily users, dimensional fields for grouping

### Component E: Rolling window creation
- **Purpose**: Establishes 30-day rolling windows for MAU calculation
- **Logic**: Creates a datespine covering all days from the earliest available data to yesterday. For each date in the spine, generates a 30-day lookback window including the current day plus the previous 29 days using `generate_timestamp_array`. Cross-joins the datespine with the window ranges to create all possible day-window combinations. This ensures complete coverage without gaps in the time series even when no activity occurred on specific days
- **Key fields**: `timestamp` for the MAU calculation date, `joinday` for each day within the 30-day window

### Component F: Window aggregation
- **Purpose**: Aggregates HyperLogLog sketches across each 30-day window
- **Logic**: Joins the rolling window structure with daily HLL sketches on matching days within each window. For each 30-day period, collects all HLL sketches from constituent days. Groups by the window end timestamp and dimensional attributes (chain, user type, product ID) while preserving individual HLL sketches for merging
- **Key fields**: `timestamp` for the MAU period, `hll` sketches from each day in the window, dimensional grouping fields

### Component G: Dimensional breakdown calculation
- **Purpose**: Calculates MAU across different dimensional breakdowns
- **Logic**: Processes four separate dimensional views of the data. Project-level MAU aggregates across all chains and products. Chain-level MAU breaks down by individual blockchain networks. User-type-level MAU segments by user classification. Product-ID-level MAU separates V2 and V3 protocol usage. For each breakdown, merges HLL sketches using `hll_count.merge_partial` and extracts final unique user counts with `hll_count.extract`. Sets unused dimensional fields to null for each breakdown type
- **Key fields**: `user_mau` containing the final monthly active user count, `dimensions` indicating the breakdown type, relevant dimensional fields populated while others set to null

### Final aggregation
- **Combining components**: Combines all dimensional breakdowns using UNION ALL to create a comprehensive MAU dataset with project-level, chain-level, user-type-level, and product-ID-level views
- **Currency conversion**: No currency conversion required as this metric counts users rather than monetary values  
- **Time aggregation**: Uses 30-day rolling windows where each day represents the MAU for that day and the preceding 29 days, providing daily MAU values that smooth out short-term fluctuations
- **Final calculation**: The final MAU value represents the count of unique wallet addresses that performed any trading or flash loan activity on Etherex protocol within the specified 30-day rolling window, calculated using HyperLogLog sketch merging to ensure accurate distinct counts across large datasets

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

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

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