Overcollateralized borrowers deposit funds into Abracadabra and receive MIM stablecoin.

Abracadabra is a non-custodial stablecoin protocol built on Ethereum and other adjacent blockchains. Abracadabra was founded by Daniele Sestagalli and launched in 2021.

SPELL tokenholders own and govern the protocol. Currently, the interest and borrowing fees paid by borrowers (revenue) are shared between tokenholders and the project treasury.

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Abracadabra.money

# Abracadabra.money Active users (weekly) Methodology

## 1. Overview

Weekly Active Users (WAU) for Abracadabra measures the count of unique users who interact with the Abracadabra protocol within any 7-day rolling window. This metric captures user engagement across the protocol's core lending functions (borrowing and repaying) as well as cross-chain bridging activities via LayerZero integration. The calculation uses HyperLogLog data structures for efficient cardinality estimation and provides breakdowns by chain, user type, and product dimensions.

## 2. Data sources

- **Abracadabra protocol events**: Parsed `LogBorrow` and `LogRepay` events from Cauldron lending contracts deployed across Ethereum, Arbitrum, Avalanche, BSC, and Fantom chains
- **LayerZero bridging events**: Parsed `SendToChain` events from LayerZero OFT (Omnichain Fungible Token) contracts and proxy contracts for cross-chain operations
- **Token transfer events**: Blockchain transaction logs containing `Transfer` events used to extract actual user addresses for bridging operations through wrapper contracts
- **Transaction metadata**: Blockchain transaction records providing timestamps, transaction hashes, and block information for temporal aggregation

## 3. Step-by-step calculation

### Component A: User activity extraction
- **Purpose**: Identifies all user addresses that interact with Abracadabra protocol across supported chains
- **Logic**: Extracts user addresses from multiple event types across different chains. For standard lending operations, directly captures user addresses from `LogBorrow` and `LogRepay` events on Ethereum, Arbitrum, Avalanche, BSC, and Fantom. For cross-chain bridging via LayerZero, identifies `SendToChain` events and filters for transactions involving specific wrapper contract addresses (`0x287176dfbec7e8cee0f876fc7b52960ee1784adc` and `0xe1261e47b08a22df93af46889ee504c2aa6dfd4c`). For bridging operations, joins with token transfer events to extract the actual user address rather than the wrapper contract address
- **Key fields**: `user_address` (the actual user wallet address), `user_type` (classified as Borrower, Repayer, or Bridger), `chain` (blockchain where activity occurred), `block_timestamp` (when the activity happened), `transaction_hash` (unique transaction identifier)

### Component B: Daily user aggregation  
- **Purpose**: Converts raw user activity into daily active user counts with dimensional breakdowns
- **Logic**: Groups user activity data by truncating timestamps to daily boundaries. Creates distinct user counts for each combination of day, chain, user type, and product. All users are classified under the stablecoin issuers market sector since Abracadabra is a stablecoin lending protocol. Applies chain availability filtering to ensure only supported chains are included
- **Key fields**: `timestamp` (truncated to day level), `timestamp_grain` (set to 'day'), `chain`, `user_type`, `product_id`, `user_dau` (distinct user address for that day)

### Component C: HyperLogLog initialization
- **Purpose**: Converts daily user data into HyperLogLog format for efficient unique user counting across time windows
- **Logic**: Takes the daily active user data and initializes HyperLogLog sketches with precision parameter 15 for each combination of day, chain, user type, and product. Groups by these dimensions to prepare for window-based aggregation
- **Key fields**: `hll` (HyperLogLog sketch of users), `timestamp`, `chain_id`, `user_type`, `product_id`

### Component D: Rolling window creation
- **Purpose**: Creates a 7-day rolling window framework for calculating weekly active users
- **Logic**: Generates a complete datespine covering all dates from the earliest user activity to yesterday. For each date in the datespine, creates join relationships with the 6 preceding days to form 7-day windows. Joins this window structure with the daily HyperLogLog data to ensure all days within each window have the appropriate user data
- **Key fields**: `timestamp` (the end date of each 7-day window), `joinday` (each of the 7 days within the window), `hll` (daily user sketches within the window)

### Component E: Dimensional breakdowns
- **Purpose**: Aggregates weekly active users across different dimensional cuts
- **Logic**: Creates four separate breakdowns by merging HyperLogLog sketches within each 7-day window. Project-level breakdown aggregates all users regardless of chain, user type, or product. Chain-level breakdown maintains chain separation but aggregates across user types and products. User type breakdown maintains user type separation but aggregates across chains and products. Product-level breakdown maintains product separation but aggregates across chains and user types. Uses `hll_count.merge_partial` to combine daily sketches and `hll_count.extract` to get final unique user counts
- **Key fields**: `user_wau` (unique weekly active users), `dimensions` (breakdown type: 'project_dbt_tag', 'chain', 'user_type', 'product_id'), dimension-specific fields (`chain_id`, `user_type`, `product_id`)

### Final aggregation
- **Combining components**: Unions all four dimensional breakdowns into a single result set, with null values for non-relevant dimensions in each breakdown
- **Time aggregation**: Uses 7-day rolling windows where each day represents the count of unique users who were active within that day and the 6 preceding days
- **Final calculation**: Applies the `finalize_metric_user_wau` macro to standardize the schema, add metadata fields like `data_id` set to 'abracadabra', and fills missing days with zero values to ensure continuous time series data

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

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

Abracadabra.moneySPELL

Active users (weekly)

Active users (weekly)

Competitive landscape

Chain breakdown