Thales deposits liquidity into marketplaces and receive fees from traders (revenue). The funds collected are used to buyback and burn the THALES governance token.

Thales is a non-custodial exchange for parimutuel contracts built on Optimism, Arbitrum and Polygon. Thales was founded by Danijel Gornjakovic and was launched in December 2021.

THALES tokenholders own and govern the protocol. Thales’ investors include Framework Ventures, Apollo Capital, etc.

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Derivative exchanges

Thales

# Thales Active users (weekly) Methodology

## 1. Overview

Weekly Active Users (WAU) for Thales measures the number of unique addresses that interact with any Thales product within a 7-day rolling window. This metric provides insight into weekly user engagement across Thales' suite of decentralized derivatives products including binary options, ranged markets, sports betting, parlay markets, and speed markets. The calculation uses HyperLogLog sketches to efficiently count unique users across multiple chains and product categories while maintaining accuracy.

## 2. Data sources

The WAU calculation is built from the following raw blockchain data sources:

- **Thales AMM events**: Parsed `BoughtFromAmm` and `SoldToAMM` events from ThalesAMM contracts capturing binary options trading activity
- **Ranged Markets AMM events**: Parsed `BoughtFromAmm` and `SoldToAMM` events from RangedMarketsAMM contracts for ranged options trading
- **Sports AMM events**: Parsed `BoughtFromAmm` and `SoldToAMM` events from SportsAMM contracts for sports betting activity  
- **Parlay AMM events**: Parsed `ParlayMarketCreated` events from ParlayAMM contracts capturing parlay bet creation
- **Speed Markets AMM events**: Parsed `MarketCreated` events from SpeedMarketsAMM contracts for speed trading activity
- **Multi-chain coverage**: Events are collected from Arbitrum, Base, Optimism, and Polygon blockchains
- **Transaction metadata**: Block timestamps and transaction hashes for temporal and uniqueness context
- **User addresses**: Extracted from various event parameters including `buyer`, `seller`, `account`, and `user` fields depending on the specific contract event

## 3. Step-by-step calculation

### Component A: User event extraction
- **Purpose**: Identifies all user interactions with Thales products across multiple chains and products
- **Logic**: Extracts user addresses from parsed smart contract events across five different Thales product categories. Events are filtered to specific contract addresses on Arbitrum, Base, Optimism, and Polygon. Each event type maps to either a "Buyer" or "Seller" user classification based on the nature of the interaction. Trading events (`BoughtFromAmm`, `SoldToAMM`) identify both buyers and sellers, while market creation events (`ParlayMarketCreated`, `MarketCreated`) identify buyers/creators only.
- **Key fields**: `block_timestamp` for temporal analysis, `user_address` for unique user identification, `chain` for multi-chain tracking, `user_type` for buyer/seller classification, and `transaction_hash` for deduplication

### Component B: Daily active user aggregation  
- **Purpose**: Creates daily unique user counts as the foundation for weekly calculations
- **Logic**: Groups user events by day using timestamp truncation to daily granularity. Deduplicates user addresses within each day to count unique daily active users. Maintains dimensional breakdowns by chain, user type (buyer/seller), and product categories. Each unique combination of timestamp, chain, user type, and product gets its own daily user count.
- **Key fields**: `timestamp` truncated to day, `user_dau` representing unique user addresses, `chain` for blockchain identification, `user_type` for interaction classification

### Component C: HyperLogLog sketch creation
- **Purpose**: Converts daily user sets into HyperLogLog sketches for efficient storage and merging
- **Logic**: Transforms the daily unique user counts into HyperLogLog (HLL) sketches with precision parameter 15. This probabilistic data structure enables efficient counting of unique users across time windows while using minimal storage. Each daily user set becomes an HLL sketch that can be merged with other sketches to calculate union cardinality.
- **Key fields**: `hll` sketches containing compressed user address information, maintaining all dimensional attributes for downstream aggregation

### Component D: Rolling window construction
- **Purpose**: Creates 7-day rolling windows for each date to enable WAU calculation
- **Logic**: Generates a complete datespine covering all dates from the earliest user activity to yesterday. For each date in the spine, creates joins to the previous 6 days plus the current day (7 total days). This ensures every possible 7-day window is represented even when some days have no user activity. The window construction uses timestamp arithmetic to identify the exact 7-day period ending on each date.
- **Key fields**: `timestamp` for the end date of each 7-day window, `joinday` representing each day within the rolling window period

### Component E: Weekly unique user calculation
- **Purpose**: Computes unique user counts across each 7-day rolling window
- **Logic**: For each 7-day window, merges the HLL sketches from all days in that window using HLL merge operations. The merged sketch represents the union of all users active during those 7 days. Extracts the final unique count from the merged sketch, which provides the weekly active user count for that window. This process maintains all dimensional breakdowns throughout the aggregation.
- **Key fields**: `user_wau` representing the final weekly active user count, with dimensional attributes preserved

### Final aggregation
- **Combining components**: Creates multiple dimensional views of the WAU data by aggregating the weekly user counts at different levels. Produces breakdowns by project (overall Thales), by individual blockchain, by user type (buyers vs sellers), and by product category. Uses null values to indicate unused dimensional attributes in each breakdown.
- **Time aggregation**: Maintains daily granularity for the WAU calculation, meaning each day represents the end of a 7-day rolling window. No additional time period aggregation is applied.
- **Final calculation**: The weekly active users metric represents the exact count of unique addresses that interacted with any Thales product during the 7-day period ending on each date. The HyperLogLog approach provides approximate but highly accurate counts (typically >99% accuracy) while enabling efficient computation across large datasets and multiple dimensions.

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

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

ThalesTHALES

Active users (weekly)

Active users (weekly)

Competitive landscape

Chain breakdown