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.

governance

github

twitter

discord

etherscan

website

Exchanges (DEX)

Balancer

# Balancer Active users (monthly) Methodology

## 1. Overview

The Balancer Monthly Active Users (MAU) metric measures the number of unique users who have interacted with Balancer V2 protocols within a 30-day rolling window. This metric answers the business question: "How many distinct users are actively using Balancer's decentralized exchange and flashloan services each month?" 

The calculation aggregates daily unique users across multiple blockchain networks using HyperLogLog data structures for efficient unique counting, then applies a 30-day rolling window to generate monthly active user counts. The metric includes both trading activity and flashloan usage to capture the full spectrum of Balancer user engagement.

## 2. Data sources

The calculation relies on several key data sources:

- **Blockchain event logs**: Parsed `Swap` events from Balancer V2 Vault contracts deployed across Ethereum, Arbitrum, Avalanche, Base, Gnosis, Optimism, and Polygon networks
- **Flashloan event logs**: Parsed `FlashLoan` events from Balancer V2 Vault contracts across the same blockchain networks  
- **Transaction records**: Transaction data from each supported blockchain to map contract interactions to actual user wallet addresses
- **Chain configuration data**: Protocol-specific configuration data that determines chain availability and data quality filters

## 3. Step-by-step calculation

### Component A: Trade activity extraction
- **Purpose**: Identifies users who performed token swaps on Balancer V2 pools
- **Logic**: Extracts `Swap` events from Balancer V2 Vault contracts across seven blockchain networks. Filters out pool join/exit transactions by ensuring both `tokenIn` and `tokenOut` are different from the pool contract address. Joins with transaction data to map each swap event to the actual user wallet address (`from_address`). Extracts the pool address by taking the first 42 characters of the `poolId` field. Truncates timestamps to daily granularity and classifies these users as "Trader" type.
- **Key fields**: `user_address` (the wallet performing the swap), `token0_address` and `token1_address` (tokens being swapped), `token0_amount` and `token1_amount` (swap quantities), `block_timestamp` (event timing), `chain` (blockchain network)

### Component B: Flashloan activity extraction  
- **Purpose**: Identifies users who utilized Balancer's flashloan functionality
- **Logic**: Extracts `FlashLoan` events from Balancer V2 Vault contracts across the same seven blockchain networks. Joins with transaction data to identify the user wallet address initiating each flashloan. Captures both the flashloan amount and associated fees. Truncates timestamps to daily granularity and classifies these users as "Flashloan Trader" type.
- **Key fields**: `user_address` (the wallet taking the flashloan), `token_address` (flashloan token), `token_amount` (flashloan size), `fee_amount` (flashloan fees), `block_timestamp` (event timing), `chain` (blockchain network)

### Component C: Daily active user aggregation
- **Purpose**: Combines trading and flashloan users into daily unique user counts
- **Logic**: Unions the trader and flashloan trader datasets, grouping by date, chain, and user type. Excludes Optimism chain data since those users are attributed to Beethoven X protocol instead of Balancer. Applies chain availability filters to ensure data quality. Creates distinct user counts per day, storing user addresses in preparation for HyperLogLog processing.
- **Key fields**: `user_dau` (unique user address), `timestamp` (daily date), `chain` (blockchain), `user_type` (Trader or Flashloan Trader)

### Component D: HyperLogLog initialization and windowing
- **Purpose**: Converts daily user sets into efficient data structures for unique counting across time periods
- **Logic**: Initializes HyperLogLog sketches from daily user address sets with precision parameter 15. Creates a complete date spine covering all historical dates to ensure no gaps in time series. Generates 30-day rolling windows by creating timestamp arrays that look back 29 days from each target date. Joins the windowed dates with HyperLogLog sketches to prepare for aggregation.
- **Key fields**: `hll` (HyperLogLog sketch), `timestamp` (target date), `joinday` (each day in the 30-day window)

### Final aggregation
- **Combining components**: Merges HyperLogLog sketches across each 30-day window using `hll_count.merge_partial()` to combine unique user sets while avoiding double-counting users who were active on multiple days within the window
- **Multi-dimensional breakdown**: Calculates MAU across four dimensions - overall project level, by individual blockchain chain, by user type (Trader vs Flashloan Trader), and by product identifier - using separate GROUP BY operations and UNION ALL to combine results
- **Time aggregation**: Uses 30-day rolling windows where each date's MAU represents unique users active from that date back through the previous 29 days, ensuring consistent monthly measurement regardless of calendar boundaries
- **Final calculation**: Extracts the final unique user count from merged HyperLogLog sketches using `hll_count.extract()`, applies the finalization macro to standardize schema and fill missing dates with zeros, and filters incremental updates to include a safety margin of additional days for data completeness

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

Unique revenue-generating users over a rolling 30-day window.

BalancerBAL

Active users (monthly)

Active users (monthly)

Competitive landscape

Chain breakdown