Auctioneers create auctions on Bounce Finance which users can participate in.

Bounce Finance is a non-custodial auction protocol built on Ethereum and other adjacent blockchains. Bounce Finance was founded by Ryan Fang & Jack Lu and launched in 2020.

AUCTION tokenholders own and govern the protocol. They currently take a cut of the total auction fees paid by auction creators (revenue). Bounce Finance's investors include Coinbase Ventures, Binance Labs, Pantera Capital, etc.

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Infrastructure

Bounce Finance

# Bounce Finance Active users (weekly) Methodology

## 1. Overview

Weekly Active Users (WAU) for Bounce Finance measures the number of unique users who interact with Bounce Finance's auction platform within any 7-day rolling window. This metric answers the business question "How many distinct users are actively engaging with Bounce Finance auctions each week?" The calculation uses HyperLogLog data structures to efficiently aggregate unique users across multiple blockchain networks and user interaction types over rolling weekly periods.

## 2. Data sources

The metric is calculated from the following raw data sources:

- **Blockchain events**: Parsed `BounceFixedSwap` smart contract events from Ethereum, Arbitrum, Avalanche, BSC, Fantom, Gnosis, Optimism, and Polygon blockchains
- **Event types**: Five specific event types capturing different user interactions:
  - `Created` events when users create new auctions
  - `Swapped` events when users purchase tokens in auctions  
  - `CreatorClaimed` events when auction creators claim proceeds
  - `UserClaimed` events when buyers claim purchased tokens
  - `Reversed` events when users request refunds
- **Event data**: Each event contains user addresses (`sender` field), block timestamps, transaction hashes, and chain identifiers
- **Chain availability data**: Metadata about when Bounce Finance contracts were deployed on each blockchain network

## 3. Step-by-step calculation

### Component A: User interaction extraction
- **Purpose**: Extract and classify all user interactions with Bounce Finance contracts across multiple blockchains
- **Logic**: Parse `BounceFixedSwap` contract events from eight different blockchain networks. Extract the `sender` address from each event as the user identifier, along with the `block_timestamp` for timing. Classify users into five interaction types based on the event type: "Auction creator" for `Created` events, "Buyer" for `Swapped` events, "Claimer - Creator" for `CreatorClaimed` events, "Claimer - User" for `UserClaimed` events, and "Refunder" for `Reversed` events. Add metadata including chain identifier, business line classification as "Auction", market sector as "infrastructure", and business segment as "other"
- **Key fields**: `user_address` (the sender from events), `block_timestamp` (event timing), `chain` (blockchain network), `user_type` (interaction classification), `transaction_hash` (unique transaction identifier)

### Component B: Daily active user aggregation  
- **Purpose**: Calculate daily distinct active users by aggregating interactions within each calendar day
- **Logic**: Group user interactions by truncated daily timestamps using `timestamp_trunc` to ensure consistent day boundaries. Create distinct counts of `user_address` values within each day, preserving dimensions like chain, user type, and product identifiers. Set timestamp grain as "day" and maintain all dimensional attributes for later aggregation
- **Key fields**: `timestamp` (truncated to day), `timestamp_grain` (set to "day"), `user_dau` (distinct user address), `chain`, `user_type`, `product_id`

### Component C: HyperLogLog initialization
- **Purpose**: Convert daily user sets into HyperLogLog data structures for efficient storage and merging across time windows  
- **Logic**: Initialize HyperLogLog sketches for each day's user set using `hll_count.init` with precision parameter 15. Group by timestamp, chain, user type, and product dimensions. This creates compressed representations of daily user sets that can be efficiently merged to calculate weekly unique users without double-counting users who appear on multiple days
- **Key fields**: `hll` (HyperLogLog sketch of daily users), maintaining all dimensional groupings from daily aggregation

### Component D: Rolling window datespine creation
- **Purpose**: Generate a complete time series datespine that creates 7-day rolling windows without skipping missing dates
- **Logic**: Create a datespine using `generate_timestamp_array` from the minimum date in the daily user data to the current date minus one day, with daily intervals. For each date in the datespine, generate a cross-join with the 7 dates that form its lookback window (current date plus 6 preceding days). This ensures every date has a complete 7-day window even if some days have no user activity
- **Key fields**: `timestamp` (the target date for WAU calculation), `joinday` (each of the 7 days in the rolling window)

### Component E: Weekly active user calculation
- **Purpose**: Calculate weekly active users by merging HyperLogLog sketches across 7-day rolling windows
- **Logic**: Join the rolling window datespine with daily HLL data, matching each `joinday` in the window to the corresponding daily `timestamp` in the HLL data. For each target date, collect HLL sketches from all 7 days in its window. Use `hll_count.merge_partial` to combine the HLL sketches from the 7-day period, then `hll_count.extract` to get the estimated count of unique users across the full window. This accounts for users who appear on multiple days within the week without double-counting them
- **Key fields**: `user_wau` (weekly active user count), maintaining dimensional breakdowns for chain, user type, and product

### Component F: Dimensional breakdowns
- **Purpose**: Create multiple views of WAU data across different business dimensions
- **Logic**: Generate four separate dimensional aggregations by grouping HLL merges differently. Project-level breakdown merges all dimensions to get overall platform WAU. Chain-level breakdown groups by chain identifier to show per-blockchain activity. User type breakdown groups by interaction classification to show activity by user behavior type. Product breakdown groups by product identifier for future product-specific analysis. Each breakdown nullifies non-relevant dimensions and sets a `dimensions` field to identify the aggregation level
- **Key fields**: `dimensions` (identifies breakdown type), `chain_id`, `user_type`, `product_id` (dimension-specific identifiers), `user_wau` (weekly active user count for that dimension)

### Final aggregation
- **Combining components**: Union all dimensional breakdowns using `union all` to create a single result set containing project-level, chain-level, user-type-level, and product-level WAU metrics
- **Time aggregation**: Apply daily boundary logic where each date's WAU represents unique users active in the 7-day window ending on that date
- **Chain filtering**: Apply chain availability filtering to ensure WAU calculations only include dates when Bounce Finance contracts were actually deployed and active on each blockchain
- **Final calculation**: The final `user_wau` value for each date and dimension represents the estimated count of unique users who had at least one interaction with Bounce Finance contracts during the 7-day rolling window ending on that date, calculated using HyperLogLog merge operations for memory efficiency and accuracy

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

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

Bounce FinanceAUCTION

Active users (weekly)

Active users (weekly)

Competitive landscape

Chain breakdown