Market makers offer trades through order book & make money on bid-ask spreads paid by takers.

0x is a non-custodial exchange protocol built on Ethereum. 0x was founded by Amir Bandeali & Will Warren and launched in 2016.

ZRX tokenholders own and govern the protocol. They currently do not take any of the total trading fees paid by traders (revenue). 0x’s investors include Coinbase Ventures, Jump Capital, Pantera Capital, etc.

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Exchanges (DEX)

# 0x Active users (weekly) Methodology

## 1. Overview

Weekly Active Users (WAU) measures the number of unique users who traded on the 0x protocol within any 7-day rolling period. This metric provides insight into user engagement and trading activity trends on 0x's decentralized exchange infrastructure. The calculation uses HyperLogLog data structures to efficiently count unique users across rolling time windows and provides breakdowns by blockchain network, user type, and product dimensions.

## 2. Data sources

- **0x protocol transaction data**: Raw trading transaction records from the 0x protocol across supported blockchain networks, containing trader addresses, transaction metadata, timestamps, and chain information
- **Blockchain event logs**: Parsed transaction events and logs from 0x protocol smart contracts that capture DEX trading activity
- **Multi-chain data**: Transaction records from all blockchain networks where 0x protocol operates, enabling cross-chain user activity tracking

## 3. Step-by-step calculation

### Component A: User event normalization

- **Purpose**: Extract and standardize user addresses from 0x protocol trading transactions
- **Logic**: Process raw 0x protocol transaction data to identify unique user interactions. Each transaction record contains a `taker` address representing the user executing the trade. Extract distinct combinations of transaction metadata including `block_timestamp`, `transaction_hash`, `log_index`, and `chain`. Classify all users as 'Trader' type within the 'dex' business segment and 'exchanges' market sector. Standardize the `taker` address as the primary `user_address` identifier.
- **Key fields**: `block_timestamp` for timing, `transaction_hash` and `log_index` for transaction identification, `chain` for blockchain network, `taker` address converted to `user_address`, `user_type` set to 'Trader'

### Component B: Daily user aggregation with HLL initialization

- **Purpose**: Aggregate users to daily granularity and initialize HyperLogLog sketches for efficient unique counting
- **Logic**: Group the normalized user events by truncating `block_timestamp` to daily boundaries. Create distinct user records for each day, chain, user type, and product combination. Initialize HyperLogLog (HLL) sketches with precision 15 for each unique `user_address` to enable efficient unique user counting in subsequent rolling window calculations. Apply chain availability filters to ensure data quality and restrict analysis to dates up to the current processing boundary.
- **Key fields**: `timestamp` truncated to day, `timestamp_grain` set to 'day', `chain`, `user_type`, `product_id`, `user_dau` containing the user address for HLL processing

### Component C: Rolling window datespine generation

- **Purpose**: Create a complete time series backbone and define 7-day rolling windows for WAU calculation
- **Logic**: Generate a continuous daily datespine covering the full time range from the earliest user activity to the current date minus one day. For each date in the datespine, create a 7-day rolling window by generating timestamps for the current day plus the previous 6 days. This ensures every day has a complete 7-day window for WAU calculation, even when some days within the window have no user activity. Handle incremental processing by limiting the datespine to recent periods when updating existing data.
- **Key fields**: `timestamp` for each day, `joinday` for each day within the 7-day rolling window

### Component D: WAU window aggregation

- **Purpose**: Combine daily user data across 7-day rolling windows using HLL merge operations
- **Logic**: Join the rolling window datespine with the daily HLL user data on matching timestamps. For each day's 7-day window, collect all HLL sketches from the constituent days, grouped by `chain_id`, `user_type`, and `product_id`. This creates the foundation for calculating unique users across the rolling 7-day period while maintaining the ability to break down results by different dimensions.
- **Key fields**: `timestamp` for the window end date, `chain_id`, `user_type`, `product_id`, `hll` sketches for unique user counting

### Component E: Multi-dimensional WAU breakdowns

- **Purpose**: Calculate WAU across different analytical dimensions while preserving unique user counts
- **Logic**: Create separate WAU calculations for four different breakdown levels. For project-level WAU, merge all HLL sketches across all chains, user types, and products. For chain-level breakdown, group by `chain_id` and merge HLL sketches within each chain. For user type breakdown, group by `user_type` and merge HLL sketches across chains and products. For product-level breakdown, group by `product_id` and merge HLL sketches across chains and user types. Extract the final unique user count from the merged HLL sketches using HLL count extraction functions.
- **Key fields**: `dimensions` indicating breakdown type, dimension-specific fields (`chain_id`, `user_type`, `product_id`), `user_wau` containing the unique user count

### Final aggregation

- **Combining components**: Union all dimensional breakdown results into a single dataset, creating a comprehensive view of WAU across project, chain, user type, and product dimensions
- **Time aggregation**: Maintain daily granularity with each record representing the WAU for a specific date's 7-day rolling window
- **Data standardization**: Apply the finalize_metric macro to standardize the schema, add metadata fields, and implement a 'fill_zeros' strategy to populate missing days with zero values for consistent time series analysis
- **Final calculation**: The `user_wau` field contains the count of unique users who traded on 0x protocol within the 7-day rolling window ending on each date, calculated using HyperLogLog merge operations to ensure mathematical accuracy across the distributed calculation

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

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

0xZRX

Archived

Active users (weekly)

Active users (weekly)

Competitive landscape

Chain breakdown