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Coinshift

# Coinshift Active users (monthly) Methodology

## 1. Overview

Monthly Active Users (MAU) for Coinshift measures the number of unique users who have interacted with Coinshift vaults over a rolling 30-day period. This metric tracks user engagement across Coinshift's yield-generating products by identifying users who deposit tokens into or withdraw tokens from Coinshift vaults. The calculation uses HyperLogLog data structures to efficiently count distinct users across multiple dimensional breakdowns including project-level totals, individual chains, user types (depositors vs withdrawers), and specific products.

## 2. Data sources

The calculation relies on the following raw data sources:
- **Ethereum token transfer events**: Parsed `Transfer` events from Ethereum blockchain logs, specifically tracking transfers involving Coinshift vault addresses
- **Indexed transfer data**: Token transfer events indexed by both sender and receiver addresses to efficiently identify vault interactions
- **Chain availability metadata**: Data coverage information to ensure consistent reporting across supported blockchain networks
- **Vault configuration data**: Hardcoded mappings of vault contract addresses to their associated token addresses and product identifiers

## 3. Step-by-step calculation

### Component A: User interaction identification
- **Purpose**: Identifies all user interactions with Coinshift vaults by tracking token deposits and withdrawals
- **Logic**: The system monitors token transfers involving specific Coinshift vault addresses on Ethereum. For deposits, it identifies transfers where the vault address is the recipient (`to_address`) and classifies the sender as a "Depositor". For withdrawals, it identifies transfers where the vault address is the sender (`from_address`) and classifies the recipient as a "Withdrawer". The model is configured with three specific vault configurations covering USDC and USDL tokens across different vault addresses, mapped to product identifiers `csusdl` and `csusdc`. Only interactions occurring after January 1, 2025 are included to align with AUM fee calculation periods.
- **Key fields**: `block_timestamp` for timing, `user_address` for user identification, `user_type` for interaction classification, `product_id` for product mapping, `chain_id` for blockchain identification, `token_address` for asset tracking

### Component B: Daily active user aggregation  
- **Purpose**: Calculates daily distinct user counts grouped by relevant dimensions
- **Logic**: User interactions are aggregated to daily granularity by truncating blockchain timestamps to day boundaries. The system counts distinct user addresses for each combination of date, chain, user type (depositor/withdrawer), and product ID. This creates the foundation Daily Active User (DAU) dataset that feeds into the MAU calculation. Chain availability filters are applied to ensure consistent data coverage.
- **Key fields**: `timestamp` (daily granularity), `user_address` for distinct counting, `chain`, `user_type`, `product_id` for dimensional grouping

### Component C: HyperLogLog conversion and windowing
- **Purpose**: Converts DAU data into HyperLogLog structures and creates 30-day rolling windows for MAU calculation
- **Logic**: Daily user counts are converted into HyperLogLog (HLL) data structures with 15-bit precision to enable efficient distinct user counting across time periods. A complete datespine is generated covering all days from the first recorded interaction to the current date to ensure no gaps in the time series. For each day, a 30-day rolling window is created by joining with the previous 29 days of data. This windowing approach captures all users who were active within any 30-day period ending on each date.
- **Key fields**: `hll` structures containing user data, `timestamp` for window boundaries, dimensional grouping fields preserved from DAU calculation

### Component D: Dimensional breakdowns
- **Purpose**: Creates MAU calculations across different analytical dimensions
- **Logic**: The windowed HLL data is aggregated into four distinct dimensional views. Project-level MAU combines all users across all dimensions to provide total platform activity. Chain-level MAU breaks down activity by blockchain network. User-type-level MAU separates depositors from withdrawers to understand different engagement patterns. Product-level MAU tracks activity for individual Coinshift products. Each breakdown merges the relevant HLL structures and extracts the final distinct user count.
- **Key fields**: `dimensions` field indicating breakdown type, `user_mau` containing final distinct user counts, dimension-specific fields (`chain_id`, `user_type`, `product_id`) populated only for relevant breakdowns

### Final aggregation
- **Combining components**: All dimensional breakdowns are combined using UNION ALL operations to create a single comprehensive dataset with different dimensional views of MAU data
- **Currency conversion**: Not applicable for user count metrics
- **Time aggregation**: Daily timestamps represent the end date of each 30-day rolling window, with MAU calculated as distinct users active within the preceding 30 days
- **Final calculation**: The final MAU value for each dimension and date represents the count of unique user addresses that had at least one interaction with Coinshift vaults during the 30-day period ending on that date, calculated by merging HyperLogLog structures across the rolling window and extracting the distinct count

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

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

Coinshift

Active users (monthly)

Active users (monthly)

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