Liquidity providers deposit funds into Bancor and receive trading fees from traders (supply-side fees).

Bancor is a non-custodial exchange protocol built on Ethereum. Bancor was founded by Galia Benartzi, Guy Benartzi, Eyal Hertzog, & Yudi Levi and launched in 2016.

BNT tokenholders own and govern the protocol. They currently take a cut of the total trading fees paid by traders (revenue). Bancor's investors include DeFiance Capital, ParaFi Capital, Mechanism Capital, etc.

governance

github

twitter

discord

etherscan

website

Exchanges (DEX)

Bancor

# Bancor Active users (weekly) Methodology

## 1. Overview

The Bancor Weekly Active Users (WAU) metric measures the number of unique users who performed at least one trade on the Bancor protocol within a rolling 7-day window. This metric provides insights into user engagement and adoption trends for the Bancor decentralized exchange aggregator platform. The calculation uses HyperLogLog data structures to efficiently count unique users across multiple dimensional breakdowns while maintaining accuracy for large datasets.

## 2. Data sources

The calculation relies on the following raw data sources:
- **Bancor protocol events**: Parsed `BancorNetwork_TokensTraded_event` logs from Bancor v2.1 and `Converter_Conversion_event` logs from Bancor v3 on Ethereum
- **Ethereum transaction records**: Transaction data containing user addresses, timestamps, and transaction metadata
- **Chain availability data**: Configuration data that ensures metrics are only calculated for supported blockchain networks with complete data coverage

## 3. Step-by-step calculation

### Component A: Trade event extraction
- **Purpose**: Extract and standardize trading activity from Bancor protocol versions
- **Logic**: The system processes trading events from two Bancor protocol versions on Ethereum. For v2.1, it extracts data from `BancorNetwork_TokensTraded_event` logs containing source token, target token, amounts, and fees. For v3, it processes `Converter_Conversion_event` logs with similar trade information. Token addresses are normalized by converting the native ETH identifier to the WETH contract address for consistency. Token amounts include both the traded amount and any associated fees to capture the total economic activity.
- **Key fields**: `block_timestamp`, `transaction_hash`, `user_address`, `token0_address`, `token1_address`, `token0_amount`, `token1_amount`

### Component B: Daily active user identification  
- **Purpose**: Identify unique users active each day from trading activity
- **Logic**: The system joins trading events with Ethereum transaction records to extract user addresses from the `from_address` field. Timestamps are truncated to day granularity to group activity by calendar days. All users are classified as 'Trader' type since the metric focuses on trading activity. The system applies chain availability filters to ensure only complete, high-quality data is included in the calculation.
- **Key fields**: `timestamp` (truncated to day), `chain`, `user_type`, `user_address`

### Component C: HyperLogLog initialization
- **Purpose**: Convert daily user data into efficient probabilistic data structures for unique counting
- **Logic**: Daily active user data is converted into HyperLogLog format using a precision parameter of 15, which provides high accuracy for cardinality estimation while maintaining computational efficiency. This allows the system to perform set operations across large numbers of users without storing individual user identifiers.
- **Key fields**: `hll` (HyperLogLog data structure), `timestamp`, `chain_id`, `user_type`, `product_id`

### Component D: Rolling window construction
- **Purpose**: Create 7-day rolling windows for weekly active user calculation
- **Logic**: The system generates a complete datespine covering all days from the earliest trade date to the current date minus one day. For each date in the datespine, it creates a 7-day lookback window by generating timestamps for the current day and the previous 6 days. This ensures that weekly calculations don't skip days with no trading activity and maintains consistent 7-day windows.
- **Key fields**: `timestamp` (window end date), `joinday` (individual days within each window)

### Component E: Weekly aggregation by dimensions
- **Purpose**: Calculate unique weekly active users across different dimensional breakdowns
- **Logic**: The system merges HyperLogLog data within each 7-day window and creates four separate dimensional breakdowns. The project-level breakdown provides overall protocol metrics with all dimension fields set to null. The chain-level breakdown segments users by blockchain network. The user-type breakdown groups by user classification (all 'Trader' for Bancor). The product-level breakdown segments by specific product offerings within the protocol. Each breakdown uses HLL merge operations to combine daily unique user sets into weekly unique counts.
- **Key fields**: `dimensions`, `chain_id`, `product_id`, `user_type`, `user_wau`

### Final aggregation
- **Combining components**: The four dimensional breakdowns are combined using union operations to create a comprehensive dataset with multiple analytical perspectives in a single table
- **Currency conversion**: No currency conversion is required as this metric counts users rather than monetary values
- **Time aggregation**: Daily user data is aggregated into 7-day rolling windows, with each day representing the count of unique users active in the trailing 7-day period
- **Final calculation**: The final WAU value for each dimension and date represents the exact count of unique users who performed at least one trade within the 7-day window ending on that date, extracted from merged HyperLogLog data structures

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

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

BancorBNT

Active users (weekly)

Active users (weekly)

Competitive landscape

Chain breakdown