Liquidity providers deposit funds into SpookySwap and receive trading fees from traders (supply-side fees).

SpookySwap is a non-custodial exchange protocol built on Fantom. SpookySwap was launched in April 2021.

BOO tokenholders own and govern the protocol. They currently take a cut of the total interest paid by borrowers (revenue).

governance

github

twitter

discord

website

Exchanges (DEX)

SpookySwap

# SpookySwap Active users (weekly) Methodology

## 1. Overview

Weekly Active Users (WAU) measures the number of distinct users who executed at least one trade on SpookySwap during a rolling 7-day period. This metric provides insight into user engagement and platform activity by counting unique traders over a one-week window. The calculation uses HyperLogLog data structures for efficient distinct user counting across time windows and generates multiple dimensional breakdowns to support various analytical perspectives.

## 2. Data sources

The calculation relies on these raw data sources:
- **Blockchain events**: Parsed `Swap` events from SpookySwap v1 pair contracts on the Fantom network, containing token amounts, contract addresses, and swap details
- **Transaction data**: Fantom blockchain transaction records providing user addresses and transaction metadata for each swap
- **Token metadata**: SpookySwap pair contract information and token details for business classification

## 3. Step-by-step calculation

### Component A: Trade data extraction
- **Purpose**: Identifies all SpookySwap trading activity and extracts user addresses
- **Logic**: Processes `Swap` events from SpookySwap v1 pair contracts on Fantom blockchain starting from April 17, 2021. Joins swap events with Fantom transaction data to obtain user addresses from the `from_address` field of transactions. Calculates net token amount changes for both tokens in each swap by subtracting outgoing amounts from incoming amounts. Enriches data with business metadata including chain ('fantom'), business line ('Exchange AMM'), market sector ('exchanges'), and business segment ('dex')
- **Key fields**: `block_timestamp` for timing, `transaction_hash` for linking, `user_address` for user identification, `token0_amount` and `token1_amount` for trade volumes

### Component B: Daily active user aggregation  
- **Purpose**: Converts trade data into daily distinct user counts
- **Logic**: Groups trade data by day using `timestamp_trunc` on block timestamps. Identifies distinct user addresses for each day and classifies all users as 'Trader' user type. Creates daily snapshots of active users with chain, user type, and product dimensions
- **Key fields**: `timestamp` (day-truncated), `user_address` as unique identifier, `user_type` (always 'Trader'), `chain` and `product_id` for dimensional analysis

### Component C: HyperLogLog initialization
- **Purpose**: Converts daily user sets into efficient data structures for distinct counting
- **Logic**: Takes daily active user data and initializes HyperLogLog structures using `hll_count.init` with precision parameter 15. Groups data by timestamp, chain, user type, and product dimensions. HLL structures enable efficient approximate distinct counting when merging across time windows
- **Key fields**: `hll` data structure containing encoded user sets, dimensional grouping fields

### Component D: Rolling window creation
- **Purpose**: Creates 7-day rolling windows for each date
- **Logic**: Generates a continuous datespine from earliest trade date to current date using `generate_timestamp_array` with 1-day intervals. For each date in the spine, creates a 7-day window by generating an array from 6 days prior to the current date. Joins the window dates with daily HLL data to create a comprehensive dataset where each row represents one day within a 7-day window
- **Key fields**: `timestamp` (window end date), `joinday` (individual days within window), linked to daily HLL data

### Component E: Weekly distinct user calculation
- **Purpose**: Counts distinct users across 7-day windows
- **Logic**: Merges HLL structures across each 7-day window using `hll_count.merge_partial` to combine daily user sets. Extracts the final distinct count using `hll_count.extract`. This approach efficiently handles overlapping user activity across multiple days within the window
- **Key fields**: `user_wau` as the final distinct user count for each 7-day period

### Component F: Dimensional breakdowns
- **Purpose**: Creates multiple analytical perspectives of WAU data
- **Logic**: Generates four separate dimensional views: 1) Project-level aggregation combining all chains, user types, and products, 2) Chain-level breakdown maintaining chain separation, 3) User-type breakdown (though currently only 'Trader' exists), 4) Product-level breakdown for different SpookySwap products. Each breakdown nullifies non-relevant dimensions and aggregates HLL structures accordingly
- **Key fields**: `dimensions` field indicating breakdown type, relevant dimension fields (`chain_id`, `user_type`, `product_id`), aggregated `user_wau` counts

### Final aggregation
- **Combining components**: All dimensional breakdowns are combined using UNION ALL operations to create a comprehensive dataset with multiple analytical perspectives
- **Schema standardization**: The `finalize_metric_user_wau` macro applies consistent schema formatting, adds metadata fields (data_id as 'spookyswap'), and implements gap-filling strategy
- **Time aggregation**: Uses daily grain with rolling 7-day windows, maintaining continuous time series from earliest trade date
- **Final calculation**: Each row represents the count of distinct users who traded on SpookySwap within a 7-day period ending on the specified timestamp, broken down by various dimensional perspectives

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

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

SpookySwapBOO

Active users (weekly)

Active users (weekly)

Competitive landscape

Chain breakdown