Polynomial Protocol builds on top of Synthetix to create a synthetic perpetuals trading platform. Traders on Polynomial trade futures against the liquidity provided by liquidity providers on Synthetix, with liquidity providers receiving trading fees (supply-side fees).

Polynomial Protocol is a non-custodial exchange for perpetual contracts built on Optimism. Polynomial Protocol was founded by Gautham Santhosh, Salman Naseer Ahmed, & Mubaris NK, and their flagship product Polynomial Trade was launched in 2023.

The Polynomial team own and govern the protocol. Polynomial’s investors include Archetype, Genblock Capital, Caballeros Capital, etc.

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Polynomial Protocol

# Polynomial Protocol Active users (weekly) Methodology

## 1. Overview

Weekly Active Users (WAU) measures the count of distinct users who interact with the Polynomial Protocol over rolling 7-day periods. This metric answers the business question: "How many unique users are actively trading on Polynomial each week?" The calculation uses HyperLogLog approximation for efficient computation of distinct user counts across multiple dimensional breakdowns, providing weekly user engagement trends for the Polynomial derivatives trading protocol.

## 2. Data sources

The calculation relies on the following raw data sources:
- **Optimism blockchain events**: Parsed `PerpsTracking`, `PositionModified`, `ExecuteRequest`, `ExecuteLimitOrder`, and `ExecutePairOrder` events from Synthetix V2 and Polynomial V1 smart contracts
- **Transaction data**: Optimism transaction hashes, block timestamps, and transaction metadata
- **Contract events**: Event logs from ProxyPerpsV2, ProxyPerpsV21, ProxyPerpsV22, SynthetixLimitOrders, and AdvancedOrders contracts
- **Tracking codes**: Hexadecimal tracking codes embedded in trading events to identify Polynomial-specific activity

## 3. Step-by-step calculation

### Component A: User identification and filtering
- **Purpose**: Identifies valid Polynomial Protocol users from blockchain trading activity
- **Logic**: The system first filters for transactions containing the Polynomial tracking code `0x706f6c796e6f6d69616c00000000000000000000000000000000000000000000` (hexadecimal encoding of "polynomial") within Synthetix `PerpsTracking` events across ProxyPerpsV2, ProxyPerpsV21, and ProxyPerpsV22 contracts. This creates an allowlist of transaction hashes that represent genuine Polynomial trading activity. The system then extracts user addresses from two types of trading events: market order events captured through `PositionModified` events from Synthetix perpetual contracts, and limit order events captured through `ExecuteRequest`, `ExecuteLimitOrder`, and `ExecutePairOrder` events from Polynomial's order management contracts
- **Key fields**: `transaction_hash`, `trackingCode`, `account` (market orders), `user` (limit orders), `block_timestamp`, `chain`

### Component B: Daily user aggregation  
- **Purpose**: Creates daily snapshots of unique active users with dimensional attributes
- **Logic**: User events are aggregated by truncating `block_timestamp` to daily boundaries and grouping by chain, user type, market sector, business segment, and product. Each user address is classified as a "Trader" user type and associated with the "derivatives" market sector and "perpetual" business segment. The system performs distinct counting on user addresses per day to eliminate duplicate user activity within the same day, creating daily active user (DAU) counts
- **Key fields**: `timestamp` (daily truncated), `chain`, `user_type`, `user_address`, `market_sector_id`, `business_segment_id`

### Component C: HyperLogLog sketch creation
- **Purpose**: Converts daily user counts into probabilistic data structures for efficient weekly aggregation
- **Logic**: Daily active user data is transformed into HyperLogLog (HLL) sketches using BigQuery's `hll_count.init()` function with 15-bit precision. This creates approximate counting structures that can be efficiently merged across time periods. Each HLL sketch represents the set of unique users for a specific day, chain, user type, and product combination
- **Key fields**: `hll` (HyperLogLog sketch), `timestamp_grain` (set to "day")

### Component D: Rolling window construction
- **Purpose**: Creates 7-day rolling windows for weekly active user calculation
- **Logic**: A datespine is generated covering the full date range from the earliest DAU data to the current date minus one day. For each date in the datespine, the system creates a "jointables" structure that maps each target date to the seven dates that should contribute to its WAU calculation (the target date plus the previous 6 days). This ensures that every day has a complete 7-day lookback window, even when some intermediate days have no user activity
- **Key fields**: `timestamp` (target date), `joinday` (contributing dates within 7-day window)

### Component E: Window aggregation
- **Purpose**: Aggregates user activity across 7-day rolling windows using HLL merging
- **Logic**: The rolling window structure is joined with daily HLL sketches, matching each `joinday` to the corresponding daily user data. For each target date, the system collects all HLL sketches from the 7-day window and merges them using BigQuery's `hll_count.merge_partial()` function. This produces a single HLL sketch representing all unique users who were active during any day within the 7-day window
- **Key fields**: `hll` (merged sketch across 7-day window)

### Component F: Dimensional breakdowns
- **Purpose**: Calculates WAU across multiple analytical dimensions
- **Logic**: The merged weekly HLL sketches are aggregated into four distinct dimensional breakdowns. The project-level breakdown aggregates all activity regardless of chain, product, or user type. The chain-level breakdown groups by individual blockchain networks. The user type-level breakdown segments by trader categories. The product-level breakdown groups by specific product offerings. Each breakdown uses `hll_count.extract()` to convert the merged HLL sketches back into approximate distinct user counts, with unused dimension fields set to null
- **Key fields**: `dimensions` (breakdown type), `chain_id`, `product_id`, `user_type`, `user_wau` (final count)

### Final aggregation
- **Combining components**: All dimensional breakdowns are combined using UNION ALL operations to create a single result set with multiple analytical perspectives
- **Currency conversion**: No currency conversion is required as this metric counts users rather than monetary values
- **Time aggregation**: The metric uses rolling 7-day windows calculated daily, where each day's WAU represents unique users active during that day and the previous 6 days
- **Final calculation**: The final WAU value is the approximate distinct count of user addresses extracted from merged HyperLogLog sketches, representing unique users who performed any trading activity on Polynomial Protocol during the 7-day rolling window

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

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

Polynomial Protocol

Active users (weekly)

Active users (weekly)

Competitive landscape