Liquidity providers deposit funds into Pika Protocol and receive trading fees from traders (supply-side fees).

Pika Protocol is a non-custodial exchange for perpetual contracts built on Optimism. Pika Protocol was founded by an anonymous team and was launched in 2021.

PIKA tokenholders own and govern the protocol. Pika Protocol has no private investors.

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Pika Protocol

# Pika Protocol Active users (weekly) Methodology

## 1. Overview

The Weekly Active Users (WAU) metric measures the number of unique users who traded on Pika Protocol within any 7-day period. This metric provides insight into user engagement and platform adoption by tracking users who open or close perpetual trading positions across all supported protocol versions. The calculation uses HyperLogLog aggregation for efficient approximate distinct counting across rolling 7-day windows, with breakdowns by project, chain, user type, and product dimensions.

## 2. Data sources

The calculation relies on the following data sources:
- **Optimism blockchain event logs**: Parsed smart contract events from Pika Protocol's perpetual trading contracts
- **Pika Protocol contract events**: `NewPosition` and `ClosePosition` events from PikaPerp contracts across multiple protocol versions (v2, v3, v4)
- **Smart contract addresses**: Multiple deployed versions of Pika Protocol contracts on Optimism network
- **Event parameters**: User addresses extracted from trading event logs

## 3. Step-by-step calculation

### Component A: User identification from trading events
- **Purpose**: Identifies unique users who performed trading actions on Pika Protocol
- **Logic**: Extracts user addresses from parsed `NewPosition` and `ClosePosition` events across three protocol versions (v2, v3, v4) deployed on Optimism. Each event contains a `user` parameter identifying the trader's wallet address. All identified users are classified as 'Trader' type since they're interacting with derivatives trading functionality. The system captures both position opening and closing events to ensure comprehensive user activity tracking.
- **Key fields**: `block_timestamp` for timing, `user` address from events, `transaction_hash` for deduplication, chain identifier set to 'optimism'

### Component B: Daily active user aggregation
- **Purpose**: Converts raw trading events into daily distinct user counts
- **Logic**: Groups the identified trading events by day using timestamp truncation to daily granularity. Creates distinct counts of user addresses per day, preserving dimensional attributes including chain, user type (always 'Trader'), and product identifiers. This creates the foundational Daily Active User (DAU) dataset that feeds into the weekly calculations.
- **Key fields**: `timestamp` truncated to day, `user_address` for distinct counting, `chain`, `user_type`, `product_id`

### Component C: HyperLogLog conversion and window preparation
- **Purpose**: Prepares DAU data for efficient weekly aggregation using approximate distinct counting
- **Logic**: Converts daily user lists into HyperLogLog sketches with 15-bit precision for memory-efficient set operations. Creates a comprehensive date spine covering the full analysis period, then generates join tables that map each day to its corresponding 7-day window (current day plus previous 6 days). This window approach ensures every day gets a complete 7-day lookback period for WAU calculation.
- **Key fields**: `hll` sketches containing user sets, `timestamp` for daily boundaries, `joinday` for window mapping

### Component D: Rolling window aggregation
- **Purpose**: Calculates 7-day rolling WAU using HyperLogLog merging
- **Logic**: For each day, identifies all daily HLL sketches within the 7-day window (current day plus previous 6 days). Merges these sketches using HyperLogLog merge operations to create approximate distinct counts across the full window period. This approach efficiently handles overlapping user sets without double-counting users who traded on multiple days within the week.
- **Key fields**: Merged `hll` sketches representing weekly user sets, dimensional attributes preserved from daily data

### Component E: Dimensional breakdowns
- **Purpose**: Creates multiple analytical perspectives on WAU data
- **Logic**: Generates four separate dimensional views of the weekly active users: project-level aggregation (all users regardless of chain/product), chain-level breakdown (users by blockchain), user-type breakdown (users by classification), and product-level breakdown (users by specific product). Each breakdown aggregates the appropriate HLL sketches and extracts final distinct counts while setting non-relevant dimension fields to null.
- **Key fields**: `user_wau` extracted from merged HLL sketches, `dimensions` indicating breakdown type, relevant dimensional identifiers

### Final aggregation
- **Combining components**: Unions all dimensional breakdowns (project, chain, user type, product) into a single comprehensive dataset with consistent schema structure
- **Time aggregation**: Maintains daily granularity with each day representing users active in the trailing 7-day period ending on that date
- **Final calculation**: Applies finalization macro to standardize schema, fill missing periods with zeros, and ensure consistent output format across all Pika Protocol user metrics

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

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

Pika ProtocolPIKA

Active users (weekly)

Active users (weekly)

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