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.

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Bancor

# Bancor Average trading volume (per user) Methodology

## 1. Overview

Trading volume average per user measures the average USD trading volume per daily active user on the Bancor protocol. This metric provides insight into user engagement depth by dividing total daily trading volume by the number of unique traders, showing how much volume each active user generates on average. The calculation combines trading volume data from Bancor v1 swaps with daily active user counts derived from the same trading activity.

## 2. Data sources

The metric relies on several key data sources:

- **Blockchain events**: Parsed Bancor v1 protocol events from Ethereum, specifically `TokensTraded` events from BancorNetwork contracts and `Conversion` events from Converter contracts
- **Transaction data**: Ethereum transaction records containing user addresses and timestamps for trade attribution
- **Token metadata**: Token information including symbols and decimal places for amount normalization
- **External data**: End-of-day token price feeds for USD conversion of trading volumes

## 3. Step-by-step calculation

### Component A: Trade event extraction and processing
- **Purpose**: Extract and standardize raw trading data from Bancor v1 protocol events
- **Logic**: Parse `TokensTraded` and `Conversion` events from Bancor contracts on Ethereum, capturing source tokens, target tokens, trade amounts, and fee amounts. Join with Ethereum transaction data to identify the user address (`from_address`) for each trade. Filter trades to include only those from June 16, 2020 onwards. Standardize native ETH token addresses by converting the placeholder address `0xeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeee` to the WETH contract address for consistency across price lookups
- **Key fields**: `sourceToken`/`targetToken` addresses, `sourceAmount`/`targetAmount` values, `conversionFee` amounts, transaction `from_address` as user identifier, and `block_timestamp` for temporal grouping

### Component B: Volume calculation and USD conversion  
- **Purpose**: Calculate daily USD trading volumes with decimal correction and price manipulation protection
- **Logic**: Group trades by day and aggregate token amounts while correcting for token decimals using metadata. Join with token metadata to get decimal places and symbols for proper amount calculation. Convert raw token amounts to decimal-adjusted amounts by dividing by 10 raised to the token's decimal places. Join with end-of-day price feeds to get USD prices for both tokens in each trade. Calculate USD value for both token sides of each trade and take the lesser of the two values to prevent price manipulation from affecting volume calculations. Sum the resulting USD volumes by day
- **Key fields**: Decimal-adjusted `token0_amount` and `token1_amount`, `price_usd` from price feeds, and calculated `trading_volume` in USD

### Component C: Daily active user counting
- **Purpose**: Count distinct daily active users who performed trades using efficient aggregation
- **Logic**: Extract distinct user addresses from daily trading activity and classify them as 'Trader' user type. Group by day and use HyperLogLog functions to efficiently count unique users across different dimensional breakdowns. Generate project-level aggregation where all users are counted together regardless of chain, product, or user type. The HyperLogLog approach allows for accurate distinct counting while maintaining computational efficiency
- **Key fields**: `user_address` for distinct counting, `timestamp` for daily grouping, and aggregated `user_dau` count using HyperLogLog functions

### Final aggregation
- **Combining components**: Join the daily trading volume totals with daily active user counts on the same timestamp. Sum all trading volume across different dimensional breakdowns and take the maximum DAU count (which represents the project-level total)
- **Currency conversion**: All volume calculations use end-of-day USD prices, with the lesser value between token pairs used to prevent manipulation
- **Time aggregation**: Both volume and user metrics are aggregated to daily boundaries using timestamp truncation to day level
- **Final calculation**: Divide the total daily trading volume by the daily active user count using safe division to handle cases where DAU might be zero, resulting in the average trading volume per daily active user in USD

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

Average trading volume (per user)

Trading volume divided by daily active users.

BancorBNT

Average trading volume (per user)

Average trading volume (per user)

Competitive landscape