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Coinshift

# Coinshift Active users (weekly) Methodology

## 1. Overview

Coinshift weekly active users (WAU) measures the number of unique users who interacted with Coinshift vault contracts within any 7-day period. This metric helps assess user engagement and platform activity by tracking both depositors (users adding funds to vaults) and withdrawers (users removing funds from vaults). The calculation uses HyperLogLog algorithms for efficient distinct user counting across multiple dimensional breakdowns.

## 2. Data sources

- **Ethereum token transfer events**: Parsed `Transfer` events from Ethereum blockchain involving specific Coinshift vault contracts
- **Token transfer data by sender**: Transfer events organized by the `from_address` to identify vault withdrawals
- **Token transfer data by recipient**: Transfer events organized by the `to_address` to identify vault deposits  
- **Blockchain transaction data**: Block timestamps and transaction metadata from Ethereum
- **Coinshift vault configuration**: Hardcoded vault contract addresses, associated token addresses, and product identifiers for specific Coinshift products (csusdl, csusdc)
- **Token metadata**: Information about tokens involved in vault operations

## 3. Step-by-step calculation

### Component A: User interaction extraction
- **Purpose**: Identify and classify user interactions with Coinshift vaults based on token transfer direction
- **Logic**: Extract token transfer events involving predefined Coinshift vault contracts. For each configured vault (identified by contract address and associated token), classify transfers as either deposits or withdrawals. Deposits occur when users send tokens to vault contracts, making the user a "Depositor". Withdrawals occur when vault contracts send tokens to users, making the receiving user a "Withdrawer". Apply a cutoff date filter to only include activity from 2025-01-01 onwards for idle asset AUM fee calculations.
- **Key fields**: Block timestamp (`block_timestamp`), chain identifier, user address (`from_address` for deposits, `to_address` for withdrawals), token address, product identifier, and user type classification

### Component B: Daily active user aggregation  
- **Purpose**: Calculate daily unique users using HyperLogLog sketches for efficient distinct counting
- **Logic**: Group user interactions by day, truncating block timestamps to daily granularity. For each combination of date, chain, user type, and product, create HyperLogLog sketches of unique user addresses. This enables scalable distinct user counting across multiple dimensions without storing individual user addresses.
- **Key fields**: Daily timestamp (`timestamp`), chain identifier (`chain`), user type, product identifier (`product_id`), and HLL sketch of user addresses (`user_dau`)

### Component C: Weekly rolling window creation
- **Purpose**: Generate 7-day rolling windows for calculating weekly active users
- **Logic**: Create a datespine covering all days from the minimum available date to yesterday. For each day in the datespine, generate the 7 days leading up to and including that day (a 7-day window). This creates a comprehensive mapping of each target date to its corresponding 7-day lookback period, ensuring no missing timestamps affect the window calculations.
- **Key fields**: Target date (`timestamp`) and corresponding window days (`joinday`)

### Component D: Weekly user aggregation with HLL merging
- **Purpose**: Merge daily HLL sketches across 7-day windows to calculate weekly unique users
- **Logic**: Join the 7-day rolling windows with daily HLL sketches to associate each target date with all relevant daily user data from its 7-day window. Merge HLL sketches across the 7-day periods using HLL merge operations, then extract the final distinct count. This approach efficiently calculates weekly active users while handling overlapping time periods and maintaining dimensional breakdowns.
- **Key fields**: Target date, dimensional attributes (chain, user type, product), and merged HLL sketches

### Component E: Dimensional breakdown creation
- **Purpose**: Generate multiple views of weekly active users across different dimensional cuts
- **Logic**: Create separate aggregations for project-level (overall WAU), chain-level (WAU by blockchain), user-type-level (WAU by depositors vs withdrawers), and product-level (WAU by specific Coinshift products like csusdl, csusdc). Each breakdown nullifies irrelevant dimensions while preserving the relevant grouping dimension. Extract final counts from merged HLL sketches for each dimensional view.
- **Key fields**: Timestamp, dimension type (`dimensions`), relevant dimensional values, and weekly active user count (`user_wau`)

### Final aggregation
- **Combining components**: Union all dimensional breakdowns into a single result set with consistent schema, combining project-level, chain-level, user-type-level, and product-level views
- **Schema standardization**: Apply the `finalize_metric_user_wau` macro to standardize the output schema with proper data identifiers, metric naming, and missing value handling
- **Time boundaries**: Fill missing days with zero values using the "fill_zeros" strategy to ensure continuous time series data
- **Final calculation**: The result provides weekly active user counts across multiple dimensional cuts, where each row represents the number of unique users who interacted with Coinshift vaults within a 7-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 7-day window.

Coinshift

Active users (weekly)

Active users (weekly)

Competitive landscape