Liquidity providers deposit funds into IPOR Protocol and receive trading fees and asset management yield (supply-side fees).

IPOR Protocol is a non-custodial exchange for interest rate swaps built on Ethereum. IPOR Protocol was founded by Darren Camas & Dimitar Dinev, and was launched in August 2022.

IPOR tokenholders own and govern the protocol. They currently take a cut of the total trading fees paid by traders (revenue).

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IPOR Protocol

# IPOR Protocol Active users (monthly) Methodology

## 1. Overview

Monthly Active Users (MAU) for IPOR protocol measures the number of unique users who have traded derivatives (opened or closed swaps) on the platform within any 30-day rolling period. This metric provides insight into user engagement and platform adoption for IPOR's interest rate derivatives protocol. The calculation uses HyperLogLog sketches for efficient cardinality estimation across rolling time windows and provides multiple dimensional breakdowns including chain, user type, and product-level views.

## 2. Data sources

- **IPOR protocol smart contract events**: Parsed `OpenSwap` and `CloseSwap` events from Ethereum blockchain
- **IPOR v1 contract events**: Events from MiltonDai, MiltonUsdc, and MiltonUsdt contracts capturing swap opening and closing activities
- **IPOR v2 contract events**: Events from IporProtocolRouter contract for both swap opening and closing operations  
- **Ethereum transaction data**: Block timestamp information for temporal analysis
- **Blockchain event logs**: Raw Ethereum event logs containing user addresses and transaction details from IPOR protocol interactions

## 3. Step-by-step calculation

### Component A: User identification from swap events
- **Purpose**: Identifies unique trader addresses from IPOR protocol swap activities
- **Logic**: Extracts user addresses from both v1 and v2 IPOR protocol smart contract events on Ethereum. For `OpenSwap` events, captures the `buyer` address as the trader. For `CloseSwap` events, captures the `liquidator` address as the trader. All identified users are classified as 'Trader' type under the 'derivatives' market sector and 'perpetual' business segment. Processes events from MiltonDai, MiltonUsdc, MiltonUsdt contracts (v1) and IporProtocolRouter contract (v2)
- **Key fields**: `buyer` and `liquidator` addresses from swap events, `block_timestamp` for temporal analysis, contract-specific event signatures

### Component B: Daily active user calculation  
- **Purpose**: Calculates the number of unique active users per day
- **Logic**: Takes the identified trader addresses and truncates their `block_timestamp` to day granularity. Removes duplicate user addresses within each daily period to ensure each user is counted only once per day regardless of how many swaps they performed. Creates distinct combinations of timestamp, chain, user type, and product dimensions with the unique user addresses
- **Key fields**: `timestamp` (day-truncated), `chain`, `user_type`, `product_id`, `user_address` (deduplicated within each day)

### Component C: HyperLogLog sketch preparation
- **Purpose**: Converts daily user counts into probabilistic data structures for efficient MAU calculation
- **Logic**: Transforms each day's unique user addresses into HyperLogLog sketches using 15-bit precision. This probabilistic approach enables efficient cardinality estimation when merging data across the 30-day rolling windows. Groups data by timestamp, chain, user type, and product dimensions while initializing HLL sketches for each combination
- **Key fields**: `hll` sketches initialized from daily user addresses, dimensional grouping fields

### Component D: Rolling window generation
- **Purpose**: Creates 30-day rolling windows for MAU calculation
- **Logic**: Generates a complete datespine covering all days in the analysis period to ensure no timestamps are skipped. For each day, creates a rolling window spanning 30 days (29 previous days plus the current day). Uses timestamp generation functions to create comprehensive day ranges and joins these ranges with the HLL sketch data to populate each rolling window with relevant user activity data
- **Key fields**: `timestamp` for each day, `joinday` for the 30-day window periods, HLL sketches within each window

### Component E: Dimensional MAU calculation
- **Purpose**: Calculates MAU across different dimensional breakdowns
- **Logic**: Merges HLL sketches within each 30-day window to calculate unique user counts. Creates four separate dimensional views: project-level MAU (aggregated across all dimensions), chain-level MAU (broken down by blockchain), user type MAU (broken down by trader classification), and product-level MAU (broken down by specific products). Each breakdown extracts the final cardinality estimate from merged HLL sketches to produce the MAU count
- **Key fields**: `user_mau` (extracted cardinality), `dimensions` (breakdown type), `chain_id`, `user_type`, `product_id`

### Final aggregation
- **Combining components**: Unions all dimensional breakdowns (project, chain, user type, product) into a single result set with standardized schema
- **Currency conversion**: Not applicable for user count metrics
- **Time aggregation**: Uses 30-day rolling windows calculated daily, with each day representing the MAU for the trailing 30-day period ending on that date
- **Final calculation**: The final MAU value represents the estimated number of unique users who performed at least one swap transaction within any 30-day period, calculated using HyperLogLog cardinality estimation from merged probabilistic sketches across the rolling window

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

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

IPOR ProtocolIPOR

Active users (monthly)

Active users (monthly)

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