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Calyx

# Calyx Active users (weekly) Methodology

## 1. Overview

Weekly Active Users (WAU) measures the number of unique users who interact with Calyx launchpads within any 7-day period. This metric provides insight into user engagement patterns for Calyx, a token launchpad protocol on the NEAR blockchain. The calculation uses HyperLogLog sketches to efficiently count unique users across rolling 7-day windows, ensuring accurate deduplication while maintaining computational efficiency.

## 2. Data sources

- **NEAR blockchain transactions**: Raw transaction records containing user interactions, signer addresses, and function call parameters
- **NEAR blockchain receipts**: Detailed execution logs including method calls, function arguments, and execution status for contract interactions
- **Calyx contract creation events**: NEAR receipt logs for launchpad creation transactions that establish new Calyx launchpad contracts
- **NEAR function call logs**: Parsed function call arguments and method signatures for token transfers and intent-based transactions
- **Token transfer events**: NEAR native token transfer records showing movement of assets to and from launchpad addresses

## 3. Step-by-step calculation

### Component A: Launchpad identification
- **Purpose**: Identify all valid Calyx launchpad contracts deployed on NEAR
- **Logic**: Parse NEAR receipt logs to find transactions with `new` method calls that create launchpad contracts. Extract the `sale_token_address` and `launchpad_address` from base64-decoded function call arguments in the receipt data. Filter for successful executions only and transactions occurring after 2025-10-05 (one day before Calyx protocol launch).
- **Key fields**: `launchpad_address` (contract address), `sale_token_address` (associated token), `block_timestamp` (creation time)

### Component B: Classic transfer detection  
- **Purpose**: Identify direct token transfers from users to Calyx launchpads
- **Logic**: Parse NEAR transaction records to find transfers where the receiver is a known Calyx launchpad address. Decode base64-encoded function arguments to extract the `receiver_id`. Match these transfers with receipt logs showing successful `ft_on_transfer` method calls. Exclude transactions where the signer equals the sale token address to filter out self-transactions.
- **Key fields**: `signer_account_id` (user address), `receiver_id` (launchpad address), `block_timestamp` (transaction time)

### Component C: Intent-based transfer detection
- **Purpose**: Identify complex intent-based transactions that result in transfers to launchpads
- **Logic**: Parse NEAR transactions containing signed intent messages. Decode base64-encoded arguments and extract JSON structures containing signed intent arrays. For each intent, identify those with `intent_type` of `ft_withdraw` targeting Calyx launchpad addresses. Cross-reference with receipt logs to confirm successful `ft_on_transfer` execution. Exclude transactions from sale token addresses.
- **Key fields**: `signer_account_id` (user address), `receiver_id` (target launchpad), `intent_type` (must be ft_withdraw)

### Component D: Daily active user aggregation
- **Purpose**: Convert transaction-level data to daily unique user counts
- **Logic**: Combine classic transfers and intent-based transfers into a unified dataset. Truncate all transaction timestamps to day boundaries using `timestamp_trunc`. Deduplicate user addresses within each day to count unique daily active users. Classify all users as type 'Investor' and assign chain as 'near'. Apply chain availability filtering to ensure data quality.
- **Key fields**: `timestamp` (day-level), `user_address` (unique identifier), `user_type` (always 'Investor'), `chain` (always 'near')

### Component E: HyperLogLog sketch preparation
- **Purpose**: Prepare efficient data structure for unique user counting across time windows
- **Logic**: Initialize HyperLogLog sketches for each day's user set using `hll_count.init` with precision parameter 15. Group by timestamp, chain, user type, and product dimensions to create separate sketches for different breakdowns.
- **Key fields**: `hll` (HyperLogLog sketch), `timestamp_grain` (day), dimensional grouping fields

### Component F: Rolling window construction
- **Purpose**: Create 7-day rolling windows for weekly active user calculation
- **Logic**: Generate a complete datespine covering all days from protocol launch to present. For each date, create a 7-day window by generating timestamps from 6 days prior to the current date. Join this window structure with daily HLL sketches to ensure complete coverage without missing dates.
- **Key fields**: `timestamp` (window end date), `joinday` (individual days within window)

### Final aggregation
- **Combining components**: Merge HLL sketches across each 7-day window using `hll_count.merge_partial` to combine unique user sets from multiple days. Extract final counts using `hll_count.extract` to get the weekly active user number.
- **Currency conversion**: Not applicable - this is a user count metric
- **Time aggregation**: Each timestamp represents the end date of a 7-day rolling window. Users are counted as active in a week if they had any transaction within that 7-day period.
- **Final calculation**: Group merged HLL sketches by different dimensions (project-level, chain-level, user_type-level, product_id-level) and extract unique counts. Union all dimensional breakdowns and apply final metric standardization using the `finalize_metric_user_wau` macro with zero-filling for missing days.

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

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

Calyx

Active users (weekly)

Active users (weekly)

Competitive landscape