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 trade) Methodology

## 1. Overview

The trading volume average per trade count metric measures the average USD value per trade on the Bancor v1 decentralized exchange. This metric is calculated by dividing the total daily trading volume by the total number of trades, providing insight into the typical trade size on the platform. It helps assess user trading behavior and market activity patterns by showing whether trading activity consists of many small trades or fewer large trades.

## 2. Data sources

The calculation relies on several key data sources:
- **Bancor smart contract events**: Parsed `BancorNetwork_TokensTraded_event` and `Converter_Conversion_event` logs from Bancor v1 contracts on Ethereum, containing trade execution details including token addresses, amounts, and fees
- **Ethereum transaction data**: Transaction records providing user addresses and execution context for each trade
- **Token metadata**: Token information including symbols, decimal places, and contract addresses for proper amount normalization
- **End-of-day price feeds**: Daily token price data in USD for currency conversion and valuation

## 3. Step-by-step calculation

### Component A: Trade data extraction
- **Purpose**: Extract and normalize raw trade events from Bancor v1 smart contracts
- **Logic**: The system identifies trade events from two Bancor contract versions - `BancorNetwork_TokensTraded_event` (v2.1) and `Converter_Conversion_event` (v3). For each event, it extracts source and target token addresses, traded amounts, and fee amounts. Native ETH addresses represented as `0xeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeee` are normalized to the WETH contract address `0xc02aaa39b223fe8d0a0e5c4f27ead9083c756cc2`. The system joins trade events with Ethereum transaction data to identify the user address initiating each trade.
- **Key fields**: `sourceToken`/`targetToken` addresses, `sourceAmount`/`targetAmount` values, `targetFeeAmount`, `transaction_hash`, `block_timestamp`, and user `from_address`

### Component B: Volume calculation with price protection
- **Purpose**: Calculate USD trading volume for each trade with price manipulation protection
- **Logic**: Daily trades are aggregated and joined with token metadata to correct decimal places by dividing raw amounts by 10^decimals. Each corrected token amount is then converted to USD using end-of-day price feeds matched by timestamp, token address, and chain. To protect against price manipulation, the system calculates USD values for both tokens in each trade pair and takes the minimum value using the `least()` function. This ensures that artificially inflated token prices cannot inflate the trading volume metric.
- **Key fields**: Decimal-corrected `token0_amount` and `token1_amount`, corresponding `price_usd` values, and the resulting `trading_volume` as the lesser of the two USD amounts

### Component C: Trade count calculation
- **Purpose**: Count the number of unique trades per day
- **Logic**: The system counts distinct transactions rather than individual token swaps to avoid double-counting complex trades that might involve multiple token pairs within a single transaction. It uses `approx_count_distinct` on `transaction_hash` grouped by day, chain, and business dimensions. This approach ensures each unique trading transaction is counted exactly once regardless of its internal complexity.
- **Key fields**: `transaction_hash` for uniqueness identification, `timestamp` for daily grouping, and resulting `trade_count`

### Final aggregation
- **Combining components**: The trading volume and trade count are calculated separately using the same underlying trade data, then joined by timestamp for the final calculation
- **Currency conversion**: USD conversion occurs at the individual trade level using end-of-day price feeds, with price manipulation protection applied before aggregation
- **Time aggregation**: All data is aggregated to daily boundaries using timestamp truncation, with both metrics calculated for the same time periods
- **Final calculation**: The average trading volume per trade is computed using safe division of total daily trading volume by total daily trade count, with zero-filling applied when no trades occur on a given day to maintain data completeness

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

BancorBNT

Average trading volume (per trade)

Average trading volume (per trade)

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