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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Exchanges (DEX)

Bancor

# Bancor Trade count Methodology

## 1. Overview

The Bancor trade count metric measures the daily number of unique trades executed through the Bancor DEX aggregator protocol on Ethereum. This metric counts distinct transactions that contain Bancor trading activity, providing insight into user adoption and platform utilization. The calculation identifies unique trades by counting distinct transaction hashes per day, ensuring each transaction is counted only once regardless of how many trading events it contains.

## 2. Data sources

The calculation relies on several key data sources:
- **Bancor protocol events**: Parsed `TokensTraded` events from Bancor v2.1 `BancorNetwork` contracts and `Conversion` events from Bancor v3 `Converter` contracts on Ethereum
- **Ethereum transaction data**: Transaction records containing user addresses, transaction hashes, and block timestamps
- **Token metadata**: Token contract information including symbols, decimal places, and standardized addresses
- **End-of-day price feeds**: Daily USD price data for tokens to enable price manipulation filtering and validation

## 3. Step-by-step calculation

### Component A: Raw trade event extraction
- **Purpose**: Identifies and standardizes all Bancor trading events across protocol versions
- **Logic**: Extracts trading events from two Bancor protocol versions on Ethereum. For v2.1, uses `TokensTraded` events from `BancorNetwork` contracts with fields for source token (`sourceToken`), target token (`targetToken`), source amount (`sourceAmount`), target amount (`targetAmount`), and target fee amount (`targetFeeAmount`). For v3, uses `Conversion` events from `Converter` contracts with from token (`_fromToken`), to token (`_toToken`), amount (`_amount`), return (`_return`), and conversion fee (`_conversionFee`). Joins with Ethereum transaction data to get user addresses and applies date filtering starting from 2020-06-16. Normalizes native ETH token addresses from `0xeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeee` to WETH address `0xc02aaa39b223fe8d0a0e5c4f27ead9083c756cc2` for consistent token handling
- **Key fields**: `transaction_hash`, `log_index`, `block_timestamp`, `token0_address`, `token1_address`, `token0_amount`, `token1_amount`, `account_address`, `user_address`

### Component B: Trading volume calculation and validation
- **Purpose**: Processes trade amounts, corrects decimals, converts to USD, and filters out potential price manipulation
- **Logic**: Aggregates trade data on a daily level using timestamp truncation to day boundaries. Joins with token metadata to get decimal places and symbols for both tokens in each trade. Corrects token amounts by dividing raw amounts by 10 raised to the power of token decimals. Joins with end-of-day price feeds to get USD prices for both tokens. Converts token amounts to USD values by multiplying corrected amounts by respective token prices. Applies price manipulation filtering by taking the lesser of the two USD values (token0 USD amount vs token1 USD amount) using the `least()` function, ensuring trades with suspicious price discrepancies are valued conservatively
- **Key fields**: `timestamp`, `trading_volume`, `token0_amount`, `token1_amount`, account and token identifiers

### Component C: Trade count aggregation
- **Purpose**: Counts unique trades per day by counting distinct transaction hashes
- **Logic**: Groups processed trading data by day, chain, and business classification dimensions. Counts distinct transaction hashes using approximate count distinct function to determine unique trades per day. Each transaction hash represents one unique trade regardless of how many individual token swaps or events occurred within that transaction. Groups by `timestamp`, `timestamp_grain`, `chain_id`, `market_sector_id`, `business_segment_id`, and `product_id` to maintain proper dimensional structure
- **Key fields**: `trade_count`, `timestamp`, classification dimensions

### Final aggregation
- **Combining components**: The final metric directly uses the trade count from the aggregation step without additional combining
- **Currency conversion**: Not applicable for count metrics - no currency conversion needed
- **Time aggregation**: Daily boundaries defined by timestamp truncation to day level, with data processed incrementally using configurable lookback days
- **Final calculation**: The final trade count value represents the approximate count of distinct transaction hashes per day for Bancor trading activity, standardized through the `finalize_metric_trade_count` macro with fill zeros strategy to handle periods with no trading activity

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

Number of trades executed on a decentralized exchange.

BancorBNT

Trade count

Trade count

Competitive landscape

Chain breakdown