On Cryptex v1, overcollateralized investors deposit funds into Cryptex and mint TCAP tokens. On Cryptex v2, traders trade leveraged perpetual contracts against liquidity pools supplied by liquidity providers.

Cryptex v1 is non-custodial asset management protocol built on Ethereum, while Cryptex v2 is an exchange for perpetual contracts on Arbitrum. Cryptex was founded by Joe Sticco, Preston Van Loon & Thomas Matzner and launched in 2021.

CTX token holders own and govern the protocol. They currently take all of the total withdrawal fees paid by investors and trading fees paid by traders (revenue).

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Cryptex

# Cryptex Active users (weekly) Methodology

## 1. Overview

Weekly Active Users (WAU) measures the number of unique user addresses that interact with the Cryptex protocol within any 7-day rolling window. This metric captures user engagement by counting distinct addresses that perform minting or burning operations across Cryptex's various vault handlers, providing insight into protocol adoption and user retention patterns. The calculation uses HyperLogLog sketches for memory-efficient unique user counting across overlapping time windows.

## 2. Data sources

- **Ethereum blockchain event logs**: Parsed smart contract events from Cryptex protocol vault handlers on Ethereum
- **Cryptex protocol events**: Specifically `TokensBurned_event` and `TokensMinted_event` from multiple vault handler contracts
- **Vault handler contracts**: Events from AAVE, LINK, DAI, ETH, USDC, and WBTC vault handlers across different contract versions (V1 and V2)
- **Chain availability metadata**: Data quality filters ensuring reliable blockchain data coverage
- **Protocol metadata**: Business segment and market sector classifications for the Cryptex protocol

## 3. Step-by-step calculation

### Component A: User identification and daily aggregation
- **Purpose**: Identify unique user addresses interacting with Cryptex protocol daily and classify their activity type
- **Logic**: Parse Ethereum event logs to extract `TokensBurned_event` and `TokensMinted_event` from various Cryptex vault handlers. Extract the `owner` parameter from these events to identify user addresses. Classify users as either "TCAP Burner" (from burn events) or "TCAP Minter" (from mint events). Aggregate to daily granularity by truncating timestamps to day level and deduplicating user addresses within each day, grouped by chain, user type, and product identifier.
- **Key fields**: `block_timestamp` (event timing), `owner` (user address), `user_type` (Burner/Minter classification), `chain` (blockchain identifier), `product_id` (protocol product classification)

### Component B: HyperLogLog sketch creation
- **Purpose**: Convert daily user sets into memory-efficient HyperLogLog sketches for scalable unique counting
- **Logic**: Transform daily user address lists into HyperLogLog sketches using precision level 15. Group by timestamp, chain, user type, and product identifier to maintain dimensional breakdowns while creating efficient data structures for unique user counting across time windows.
- **Key fields**: `timestamp` (daily granularity), `hll` (HyperLogLog sketch), `chain_id`, `user_type`, `product_id`

### Component C: Rolling window construction
- **Purpose**: Create 7-day rolling windows for each date to calculate weekly active user counts
- **Logic**: Generate a continuous date spine from the earliest available data to current date minus one day to ensure no gaps. For each date in the spine, create a 7-day lookback window including the current day plus the previous 6 days. This creates overlapping windows where each day becomes the end date of a weekly measurement period.
- **Key fields**: `timestamp` (window end date), `joinday` (individual days within each 7-day window)

### Component D: Window data alignment
- **Purpose**: Match daily HyperLogLog sketches to their corresponding 7-day windows
- **Logic**: Join the 7-day rolling windows with the daily HyperLogLog sketches, ensuring each window contains all available user data from its constituent days. Apply incremental processing logic with safety margins to efficiently update only recent data while maintaining accuracy.
- **Key fields**: `timestamp` (window end date), `hll` (daily HyperLogLog sketch), dimensional grouping fields

### Component E: Multi-dimensional WAU calculation
- **Purpose**: Calculate weekly active users across different analytical dimensions
- **Logic**: Merge HyperLogLog sketches within each 7-day window using `hll_count.merge_partial` to combine user sets while avoiding double-counting users active on multiple days. Extract the final unique count using `hll_count.extract`. Calculate this across four breakdowns: project-wide totals, by chain, by user type, and by product identifier. Each breakdown nullifies non-relevant dimensional fields while preserving the specific dimension being analyzed.
- **Key fields**: `user_wau` (weekly active user count), `dimensions` (breakdown type), relevant dimensional identifiers

### Final aggregation
- **Combining components**: Union all dimensional breakdowns (project-wide, chain-level, user-type, and product-level) into a single comprehensive dataset
- **Schema standardization**: Apply the `finalize_metric_user_wau` macro to standardize column names, data types, and add protocol metadata
- **Missing data handling**: Fill missing dates with zero values to ensure continuous time series data
- **Final calculation**: The metric represents the exact count of unique user addresses that performed any minting or burning operation within each 7-day rolling window, calculated using HyperLogLog probabilistic counting for efficiency and accuracy

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

Unique revenue-generating users over a rolling 7-day window.

CryptexCTX

Active users (weekly)

Active users (weekly)

Competitive landscape

Chain breakdown