Liquidity providers deposit funds into Balancer and receive trading fees from traders (supply-side fees). In addition, Balancer provides liquidity providers with additional yield using boosted pools.

Balancer is a non-custodial exchange protocol built on Ethereum. Balancer was founded by Fernando Martinelli and launched in 2018.

BAL tokenholders own and govern the protocol. They currently take a cut of the total trading and yield fees (revenue). Balancer's investors include DeFiance Capital, Three Arrows Capital, Alameda Research, etc.

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Balancer

# Balancer Average trading volume (per trade) Methodology

## 1. Overview

This metric measures the average USD value per trade on the Balancer protocol by dividing total daily trading volume by the number of unique trades. It provides insight into typical trade sizes and trading patterns across Balancer's automated market maker (AMM) pools. The calculation uses the conservative approach of taking the minimum USD value between traded token pairs to mitigate the impact of price manipulation or stale price data.

## 2. Data sources

- **Balancer V2 swap events**: Parsed `Vault_Swap_event` logs from Ethereum, Arbitrum, Avalanche, Base, Gnosis, Optimism, and Polygon blockchains containing token swap details
- **Transaction records**: Blockchain transaction data from all supported chains providing user addresses and transaction metadata
- **Token metadata**: Token contract information including decimal places and symbols for amount normalization
- **End-of-day price feeds**: Daily USD price data for all traded tokens to enable volume calculations
- **Fee tier events**: Balancer pool and protocol fee update events (collected but not used in the final calculation)

## 3. Step-by-step calculation

### Component A: Raw trade extraction
- **Purpose**: Extract and filter valid swap transactions from Balancer V2 pools
- **Logic**: Query parsed `Vault_Swap_event` logs across all supported blockchains. Filter out pool liquidity operations (joins/exits) by excluding trades where either `tokenIn` or `tokenOut` addresses equal the pool address extracted from `poolId`. Join with transaction records to obtain the initiating user address for each swap. Create unique trade identifiers using block number and log index concatenation.
- **Key fields**: `tokenIn`, `tokenOut`, `amountIn`, `amountOut`, `poolId`, transaction hash, block timestamp

### Component B: USD volume calculation  
- **Purpose**: Convert raw token amounts to USD trading volume with price manipulation protection
- **Logic**: Unpivot trade data to process each token separately, reducing redundant joins. Join with token metadata to apply decimal normalization, converting raw token amounts to human-readable values. Join with end-of-day price feeds to calculate USD value for each token amount. Pivot data back to trade level and calculate trading volume by taking the minimum USD value between the two tokens in each trade (using `least` function). This conservative approach prevents inflated volumes from price manipulation or stale price data.
- **Key fields**: Raw token amounts converted via decimals, daily USD prices, calculated `usd_amount` per token

### Component C: Trading volume aggregation
- **Purpose**: Sum all individual trade volumes to calculate total daily trading volume  
- **Logic**: Aggregate the calculated trading volumes from all valid trades by day. Exclude Optimism chain data as it is attributed to Beethoven X protocol instead of Balancer. Apply time zone normalization and timestamp truncation to ensure consistent daily boundaries.
- **Key fields**: `trading_volume` summed by timestamp and chain

### Component D: Trade count calculation
- **Purpose**: Count the number of unique trades per day
- **Logic**: Count distinct transaction hashes to determine the number of unique trading transactions. This approach counts each transaction once regardless of how many individual swaps it contains. Exclude Optimism chain data consistent with volume calculations. Group by day to align with volume aggregation periods.
- **Key fields**: Distinct `transaction_hash` counts by timestamp

### Final aggregation  
- **Combining components**: Join daily trading volume totals with daily trade counts using timestamp as the key, ensuring both metrics cover the same time periods and exclude the same chains
- **Currency conversion**: All volume calculations already converted to USD using end-of-day price feeds during the intermediate processing step
- **Time aggregation**: Both volume and count metrics aggregated at daily grain with consistent timestamp truncation
- **Final calculation**: Divide total daily trading volume by total daily trade count using safe division (`safe_divide`) to handle cases where trade count might be zero, returning null for those scenarios rather than causing division errors

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

BalancerBAL

Average trading volume (per trade)

Average trading volume (per trade)

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