Overcollateralized borrowers deposit funds into Prisma Finance and receive mkUSD stablecoin.

Prisma Finance is a non-custodial stablecoin protocol built on Ethereum. Prisma Finance was founded by an anonymous team and launched in the fall of 2023.

PRISMA tokenholders own & govern the protocol. They currently take all of the total fees paid by borrowers (revenue). Prisma Finance’s investors include Michael Egorov, OKX Ventures, and Frax Finance.

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Prisma Finance

# Prisma Finance Active users (monthly) Methodology

## 1. Overview

Monthly Active Users (MAU) for Prisma Finance measures the number of unique users who interacted with the Prisma Finance V1 protocol within any 30-day rolling window. This metric tracks user engagement across the protocol's core functionality of trove management (debt position creation and updates) on Ethereum. The calculation uses HyperLogLog data structures to efficiently deduplicate users across time periods and provides breakdowns by chain, user type, and product.

## 2. Data sources

- **Ethereum blockchain events**: Parsed `TroveCreated` and `TroveUpdated` events from the Prisma Finance V1 BorrowerOperations smart contract on Ethereum
- **Transaction data**: Ethereum transaction timestamps and hashes associated with the borrower operations events
- **Event parameters**: User addresses extracted from the `borrower` parameter in the contract events

## 3. Step-by-step calculation

### Component A: User identification and classification
- **Purpose**: Identify unique users interacting with Prisma Finance protocol and classify their activity type
- **Logic**: Extract user addresses from Ethereum BorrowerOperations contract events. `TroveCreated` events identify users who created new debt positions (classified as 'Trove Creator'), while `TroveUpdated` events capture users modifying existing positions (classified as 'Trove Updater'). Each event includes the user address from the `borrower` parameter, block timestamp, transaction hash, and chain identifier
- **Key fields**: `borrower` address as `user_address`, `block_timestamp` for timing, `user_type` classification, and protocol metadata including `product_id` as 'mkusd' and business segment classifications

### Component B: Daily active users aggregation  
- **Purpose**: Calculate Daily Active Users (DAU) by aggregating unique user addresses per day
- **Logic**: Group user events by day using timestamp truncation to day boundaries. Deduplicate user addresses within each day to count distinct active users. Group results by timestamp, chain, user type, and product to create daily user activity snapshots
- **Key fields**: Daily `timestamp`, `user_address` as `user_dau`, dimensional groupings for `chain`, `user_type`, and `product_id`

### Component C: HyperLogLog encoding for efficient deduplication
- **Purpose**: Convert daily user lists into HyperLogLog sketches to enable memory-efficient deduplication across longer time windows
- **Logic**: Initialize HyperLogLog sketches from the daily unique user addresses using a precision parameter of 15. This allows efficient merging and counting of unique users across multiple days without storing individual addresses
- **Key fields**: `hll` sketches containing encoded user address data, maintained with timestamp and dimensional groupings

### Component D: Rolling 30-day window creation
- **Purpose**: Create 30-day sliding windows for MAU calculation
- **Logic**: Generate a date spine covering the full time range of available data. For each date, create a 30-day lookback period (29 days prior plus the current day) using timestamp array generation. Join this window structure with the daily HyperLogLog data to capture all user activity within each 30-day period
- **Key fields**: Window `timestamp` representing the end date of each 30-day period, `joinday` timestamps for the lookback period

### Component E: Multi-dimensional MAU calculation
- **Purpose**: Calculate Monthly Active Users across different business dimensions
- **Logic**: For each 30-day window, merge the HyperLogLog sketches across all days in the window and extract the unique count. Create separate aggregations for four dimensions: overall project level (nulling out dimensional fields), by chain (grouping by `chain_id`), by user type (grouping by `user_type`), and by product (grouping by `product_id`). Each dimension creates a separate result set with appropriate null values for non-relevant dimensions
- **Key fields**: `user_mau` containing the final unique user count, `dimensions` indicating the aggregation level, dimensional identifiers (`chain_id`, `user_type`, `product_id`)

### Final aggregation
- **Combining components**: Union all dimensional breakdowns into a single result set, with each row representing MAU for a specific dimension and time period
- **Schema standardization**: Apply the `finalize_metric_user_mau` macro to standardize the output schema and fill missing time periods with zero values
- **Time boundaries**: Results filtered to include only complete 30-day windows, with incremental processing managing updates efficiently
- **Final calculation**: The `user_mau` value represents the count of unique user addresses that had at least one interaction with the Prisma Finance protocol within the 30-day rolling window ending on the specified date

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

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

Prisma FinancePRISMA

Active users (monthly)

Active users (monthly)

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