Liquidity providers (LPs) deposit funds into Convex Finance and receive a return on investment (supply-side fees).

Convex Finance is an asset management and staking protocol built on Ethereum. Convex Finance launched in May 2021.

CVX tokenholders own and govern the protocol. They currently take a cut of the total yield generated (revenue).

governance

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etherscan

website

Asset management

Convex Finance

# Convex Finance Active users (weekly) Methodology

## 1. Overview

The Convex Finance Weekly Active Users (WAU) metric measures the number of unique users who interact with the Convex Finance protocol within any 7-day rolling window. This metric provides insight into user engagement and protocol adoption by tracking depositors who send tokens to Convex Finance's yield aggregator service. The calculation uses HyperLogLog data structures to efficiently handle large-scale user counting across multiple dimensions and time periods.

## 2. Data sources

- **Ethereum token transfer events**: Parsed `Transfer` events from Ethereum blockchain capturing token movements between addresses
- **Convex Finance deposit contract interactions**: Specific token transfers sent to Convex Finance's deposit account contract address `0x989aeb4d175e16225e39e87d0d97a3360524ad80`
- **Blockchain transaction metadata**: Transaction hashes, block timestamps, log indices, and user addresses from Ethereum transaction records

## 3. Step-by-step calculation

### Component A: User identification
- **Purpose**: Identify users who interact with Convex Finance by depositing tokens
- **Logic**: Filter Ethereum token transfer events to capture only transfers where tokens are sent to Convex Finance's deposit account address `0x989aeb4d175e16225e39e87d0d97a3360524ad80`. Extract the sender address as the user address and classify all users as 'Depositor' type within the 'asset_management' market sector and 'yield_aggregator' business segment. Each interaction includes transaction metadata like block timestamp, transaction hash, and log index.
- **Key fields**: `from_address` (user), `to_address` (deposit contract), `block_timestamp`, `transaction_hash`, `log_index`, `chain`

### Component B: Daily active user aggregation  
- **Purpose**: Convert user interactions into daily distinct active user counts
- **Logic**: Group user interactions by day using timestamp truncation on block timestamps to day boundaries. For each day, count distinct user addresses regardless of the number of transactions they performed. Each user is counted only once per day even if they made multiple deposits. Preserve dimension attributes like chain, user type, and product identifiers for downstream analysis.
- **Key fields**: `timestamp` (truncated to day), `user_address`, `chain`, `user_type`, `product_id`

### Component C: HyperLogLog preprocessing
- **Purpose**: Convert daily user sets into efficient probabilistic data structures for scalable counting
- **Logic**: Transform daily distinct user address sets into HyperLogLog (HLL) structures using 15-bit precision via `hll_count.init`. This enables efficient storage and merging of large user sets while maintaining accuracy for distinct counting operations. Group by timestamp, chain, user type, and product dimensions to maintain separate HLL structures for each combination.
- **Key fields**: `hll` (HyperLogLog structure), `timestamp`, `chain_id`, `user_type`, `product_id`

### Component D: Rolling window generation
- **Purpose**: Create 7-day rolling windows for each date to calculate weekly active users
- **Logic**: Generate a complete date spine covering the full time range from the earliest data to yesterday. For each date in the spine, create a 7-day window including the current date plus the 6 preceding days using timestamp array generation. This ensures consistent 7-day windows without gaps and handles cases where some days may have no user activity.
- **Key fields**: `timestamp` (window end date), `joinday` (individual days within the window)

### Component E: Weekly active user calculation
- **Purpose**: Calculate distinct users active within each 7-day rolling window
- **Logic**: For each 7-day window, collect all HLL structures from the constituent days and merge them using `hll_count.merge_partial` to combine the probabilistic sets. Extract the final distinct count using `hll_count.extract` to get the number of unique users active during that 7-day period. This approach efficiently handles overlapping user activity across multiple days within the window.
- **Key fields**: `user_wau` (weekly active user count), `timestamp`, dimension attributes

### Component F: Multi-dimensional breakdowns
- **Purpose**: Provide WAU metrics across different analytical dimensions
- **Logic**: Calculate separate WAU metrics for different grouping levels: overall project level (project_dbt_tag), by blockchain chain, by user type classification, and by product identifier. Each breakdown uses the same rolling window logic but groups by different dimension combinations. Combine all breakdowns using UNION ALL to create a comprehensive dimensional dataset.
- **Key fields**: `dimensions` (breakdown type), `chain_id`, `user_type`, `product_id`, `user_wau`

### Final aggregation
- **Combining components**: All dimensional breakdowns are combined into a single result set using UNION ALL operations, with null values used for non-applicable dimension fields in each breakdown
- **Time aggregation**: Daily boundaries are established by truncating block timestamps to day level, with 7-day rolling windows calculated for each date
- **Final calculation**: Weekly Active Users equals the distinct count of user addresses who performed at least one deposit transaction to the Convex Finance deposit contract within any 7-day rolling period, calculated using HyperLogLog merge operations for computational efficiency

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

Unique revenue-generating users over a rolling 7-day window.

Convex FinanceCVX

Active users (weekly)

Active users (weekly)

Competitive landscape

Chain breakdown