Index Cooperative offers a range of token-based index products, which could be streamed, issued, and redeemed against a fee (supply-side fees).

Index Cooperative is an asset management protocol built on Ethereum. Index Cooperative was founded by Set Protocol and launched in 2020.

INDEX tokenholders own and govern the protocol. They currently take all of the total streaming fees paid by liquidity providers (revenue).

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Index Cooperative

# Index Cooperative Active users (monthly) Methodology

## 1. Overview

Monthly Active Users (MAU) for IndexCoop measures the total number of unique users who have interacted with IndexCoop's index token products over a rolling 30-day period. This metric tracks users who mint (issue) or burn (redeem) index tokens through IndexCoop's SetProtocol v2 infrastructure. The calculation uses HyperLogLog probabilistic data structures to efficiently count distinct users across multiple dimensions including chain, user type, and product categories.

## 2. Data sources

- **IndexCoop smart contract events**: Parsed `SetTokenIssued` and `SetTokenRedeemed` events from SetProtocol v2 contracts on Ethereum and Polygon networks
- **Event parameters**: User addresses from `to` fields (for issuance events) and `redeemer` fields (for redemption events)
- **Transaction metadata**: Block timestamps and transaction hashes for temporal aggregation
- **Chain data**: Multi-chain data from Ethereum mainnet and Polygon networks

## 3. Step-by-step calculation

### Component A: User identification and classification
- **Purpose**: Identify and classify users based on their interaction type with IndexCoop products
- **Logic**: Extract user addresses from IndexCoop's SetProtocol v2 smart contract events. Parse `SetTokenIssued` events to identify users who mint index tokens (classified as 'Minter' user type) and `SetTokenRedeemed` events to identify users who redeem/burn tokens (classified as 'Burner' user type). Events are sourced from both BasicIssuanceModule and DebtIssuanceModule contracts across Ethereum and Polygon networks. Each user interaction is tagged with chain, market sector ('asset_management'), business segment ('index'), and user type metadata.
- **Key fields**: `user_address` (from `to` or `redeemer` parameters), `user_type` ('Minter' or 'Burner'), `chain`, `block_timestamp`, `product_id`

### Component B: Daily active user aggregation
- **Purpose**: Create daily unique user counts using probabilistic counting structures
- **Logic**: Group user interactions by day using truncated block timestamps. For each day, chain, user type, and product combination, initialize HyperLogLog data structures with distinct user addresses. This creates a compressed representation of daily active users that can be efficiently merged for rolling window calculations. Apply chain availability filtering to ensure data quality across supported networks.
- **Key fields**: `timestamp` (daily grain), `timestamp_grain`, `chain`, `user_type`, `product_id`, `user_dau` (HLL structure)

### Component C: Rolling window construction
- **Purpose**: Generate 30-day rolling windows for MAU calculation
- **Logic**: Create a datespine covering all days from the earliest data point to the current day. For each day in the datespine, generate an array of the current day plus the previous 29 days to create 30-day rolling windows. This ensures complete temporal coverage without gaps and allows for consistent MAU calculation even when specific days have no user activity. The datespine approach prevents missing timestamps from affecting the rolling window logic.
- **Key fields**: `timestamp` (target day), `joinday` (days within the 30-day window)

### Component D: MAU window aggregation
- **Purpose**: Combine daily HyperLogLog structures across 30-day windows
- **Logic**: Join the rolling window structure with daily HLL user data. For each target day, collect all HyperLogLog structures from the corresponding 30-day window. This creates a dataset where each target day has associated HLL data from itself and the previous 29 days, enabling efficient unique user counting across the rolling window period.
- **Key fields**: `timestamp` (target day), `hll` (daily HLL structure), dimensional attributes (`chain_id`, `user_type`, `product_id`)

### Component E: Dimensional MAU calculation
- **Purpose**: Calculate MAU across multiple business dimensions
- **Logic**: Create four separate dimensional views by grouping and merging HyperLogLog structures:
1. Project-level MAU: Merge all HLL structures across all dimensions to get total unique users
2. Chain-level MAU: Group by chain and merge HLL structures within each chain
3. User type-level MAU: Group by user type (Minter/Burner) and merge HLL structures
4. Product-level MAU: Group by product and merge HLL structures
For each grouping, extract the final distinct count from the merged HyperLogLog structures. Unused dimensions are set to null using safe casting.
- **Key fields**: `dimensions` (breakdown type), `user_mau` (distinct user count), dimensional identifiers

### Final aggregation
- **Combining components**: Union all dimensional MAU calculations (project, chain, user type, product levels) into a single result set with consistent schema
- **Standardization**: Apply the `finalize_metric_user_mau` macro with IndexCoop data identifier and zero-fill strategy to ensure consistent formatting, handle missing values, and maintain data quality standards
- **Time boundaries**: Use monthly partitioning and incremental processing with configurable lookback periods to optimize performance while maintaining data accuracy
- **Final calculation**: The final MAU value represents the distinct count of users who performed any minting or burning activity within the trailing 30-day period, calculated using probabilistic HyperLogLog structures for efficiency and accuracy

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

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

Index CooperativeINDEX

Active users (monthly)

Active users (monthly)

Competitive landscape

Chain breakdown