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 Average trading volume (per user) Methodology

## 1. Overview

The trading volume average per user metric measures the average USD trading volume per daily active user on the 0x protocol. This metric provides insight into user engagement intensity by dividing total daily trading volume by the number of unique users who executed trades on that day. The calculation combines aggregated trading volume data with distinct user counts to produce an average volume per user metric.

## 2. Data sources

The calculation relies on the following raw data sources:

- **0x protocol transaction data**: Raw transaction records from 0x protocol scraped data containing trade execution details, including timestamps, transaction hashes, log indices, chain identifiers, user addresses (takers), token symbols, and USD volume amounts
- **Chain availability configuration**: Configuration data that defines which blockchain networks are supported and have available data for the 0x protocol

## 3. Step-by-step calculation

### Component A: Trade event extraction
- **Purpose**: Extract and normalize all 0x protocol trade transactions with volume information
- **Logic**: Raw 0x protocol transactions are processed to identify trade events. Each record contains a `timestamp`, `transaction_hash`, `log_index`, `chain` identifier, `taker` address (the user executing the trade), `makerTokenSymbol` and `takerTokenSymbol` for the traded token pair, and `volumeUSD` representing the trade value in US dollars. The data is filtered to include only transactions from supported chains based on chain availability configuration.
- **Key fields**: `timestamp` for trade timing, `taker` for user identification, `volumeUSD` for trade value, `chain` for blockchain network, and `transaction_hash`/`log_index` for unique trade identification

### Component B: Trading volume aggregation  
- **Purpose**: Calculate total daily trading volume across all 0x protocol trades
- **Logic**: Trade events are grouped by day using timestamp truncation to daily granularity. For each day, chain, and protocol dimension, the `volumeUSD` values are summed to produce total daily trading volume. The aggregation includes grouping by `market_sector_id` (exchanges), `business_segment_id` (dex), and other dimensional attributes. Missing days are filled with zero values to ensure complete time series data.
- **Key fields**: Daily aggregated `trading_volume` representing total USD volume traded per day

### Component C: User identification and counting
- **Purpose**: Identify and count unique daily active users who executed trades
- **Logic**: For each trade event, the `taker` address is extracted as the user who executed the trade. Users are classified with `user_type` as 'Trader' and associated with the exchanges market sector and dex business segment. Daily distinct user counts are calculated by truncating timestamps to daily granularity and counting unique `taker` addresses per day. HyperLogLog sketches are used to accurately estimate distinct user counts even with large datasets.
- **Key fields**: `user_address` (from `taker` field) for user identification, `block_timestamp` truncated to day, and distinct count as `user_dau`

### Component D: User DAU aggregation across dimensions
- **Purpose**: Aggregate daily active user counts across different dimensional breakdowns
- **Logic**: The daily user counts are aggregated using HyperLogLog merge operations across multiple dimensions including project-level (project_dbt_tag), chain-level, user-type-level, and product-level breakdowns. This creates a comprehensive view of user activity that can be filtered and joined appropriately. The HyperLogLog approach ensures accurate distinct counting when merging user sets across dimensions.
- **Key fields**: Aggregated `user_dau` counts with associated `dimensions` identifier and relevant dimensional attributes

### Final aggregation
- **Combining components**: Daily trading volume metrics are joined with daily active user metrics on the `timestamp` field. The user DAU metric uses the 'project_dbt_tag' dimension to get project-level user counts that correspond to the trading volume scope. This ensures both metrics cover the same user population and time period.
- **Currency conversion**: No currency conversion is required as the source data already contains `volumeUSD` values in US dollars
- **Time aggregation**: Both metrics use daily time boundaries created by truncating timestamps to day granularity, ensuring consistent temporal alignment between trading volume and user counts
- **Final calculation**: The average trading volume per user is calculated using safe division of total daily trading volume by daily active user count. The `safe_divide` function handles edge cases where user DAU might be zero, preventing division by zero errors. The result represents the average USD volume traded per unique user on each day.

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

Average trading volume (per user)

Trading volume divided by daily active users.

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Average trading volume (per user)

Average trading volume (per user)

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