Liquidity providers deposit funds into Dopex and receive premiums from options traders (supply-side fees).

Dopex is a non-custodial exchange for options contracts built on Arbitrum. Dopex was founded by the pseudonymous TzTok-Chad, and was launched in 2021.

DPX tokenholders own and govern the protocol. They currently take a cut of the total trading fees paid by traders (revenue). Dopex’s investors include Tetranode, DefiGod, Orca Traders, Pattern Research, etc.

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Dopex

# Dopex Active users (monthly) Methodology

## 1. Overview

Monthly Active Users (MAU) for Dopex measures the number of unique users who traded options on the Dopex protocol within a 30-day rolling window. This metric tracks user engagement across Dopex's two main products: SSOV v1 (Single Staking Option Vaults) and CLAMM v2 (Concentrated Liquidity Automated Market Maker) options markets. The calculation uses HyperLogLog algorithms for efficient approximate distinct counting across large datasets and provides multiple dimensional breakdowns.

## 2. Data sources

- **Arbitrum blockchain events**: Parsed event logs from Dopex v1 SSOV contracts and v2 CLAMM option market contracts
- **Option purchase events**: `Purchase` events from SSOV v3 contracts covering various underlying assets (ARB, DPX, RDPX, ETH, stETH) and option types (calls, puts, weekly, monthly)  
- **Option minting events**: `LogMintOption` events from Dopex v2 option market contracts for WETH, WBTC, and ARB
- **Transaction metadata**: Block timestamps and transaction hashes from Arbitrum blockchain
- **Contract addresses**: User addresses extracted from event parameters indicating option traders

## 3. Step-by-step calculation

### Component A: User identification and classification
- **Purpose**: Extract and classify unique users from Dopex option trading activities
- **Logic**: Parse blockchain events from Dopex v1 SSOV and v2 CLAMM contracts on Arbitrum. For v1 SSOV contracts, extract user addresses from the `sender` parameter of `Purchase` events. For v2 CLAMM contracts, extract user addresses from the `user` parameter of `LogMintOption` events. All users are classified as 'Trader' type since they represent active option purchasers or minters
- **Key fields**: `block_timestamp` for timing, `user_address` for unique identification, `chain` set to 'arbitrum', `product_id` distinguishing between 'Dopex SSOV v1' and 'Dopex CLAMM v2', `user_type` always set to 'Trader'

### Component B: Daily active user aggregation  
- **Purpose**: Calculate daily distinct active users with dimensional breakdowns
- **Logic**: Group user interactions by daily boundaries using timestamp truncation to day level. Create distinct user counts for each combination of day, chain, user type, and product. This produces a daily time series of unique active users across different dimensions
- **Key fields**: `timestamp` truncated to day boundaries, `user_dau` containing unique user addresses, dimensional attributes like `chain`, `user_type`, and `product_id`

### Component C: HyperLogLog initialization for efficient counting
- **Purpose**: Convert daily user sets into HyperLogLog sketches for memory-efficient approximate distinct counting
- **Logic**: Transform each day's set of unique users into HLL sketches using BigQuery's `hll_count.init()` function with precision parameter 15. This allows efficient storage and merging of large user sets while maintaining good accuracy for distinct counting
- **Key fields**: `hll` containing the HyperLogLog sketch representation of daily user sets, along with all dimensional attributes

### Component D: Rolling window generation
- **Purpose**: Create 30-day rolling windows for MAU calculation
- **Logic**: Generate a complete datespine covering all days from the earliest data through the current date minus one day. For each target date, create a 30-day lookback window by generating an array of the 30 days ending on that date. This ensures complete coverage without gaps that could cause undercounting
- **Key fields**: `timestamp` for the target date and `joinday` representing each day in the 30-day lookback window

### Component E: Window data collection
- **Purpose**: Gather all relevant daily HLL sketches within each 30-day window
- **Logic**: Join the rolling window structure with the daily HLL data to collect all HLL sketches that fall within each 30-day period. This creates a comprehensive dataset where each target date is associated with all user activity from the previous 30 days
- **Key fields**: HLL sketches from daily data matched to their corresponding 30-day windows, maintaining all dimensional breakdowns

### Component F: Dimensional MAU calculations
- **Purpose**: Calculate MAU across different dimensional breakdowns
- **Logic**: Create four separate dimensional views:
1. **Project-level**: Merge all HLL sketches regardless of chain, product, or user type to get total Dopex MAU
2. **Chain-level**: Group by chain and merge HLL sketches to get MAU per blockchain  
3. **User type-level**: Group by user type and merge HLL sketches (currently all 'Trader')
4. **Product-level**: Group by product ID and merge HLL sketches to get MAU for SSOV v1 vs CLAMM v2

For each breakdown, use `hll_count.merge_partial()` to combine HLL sketches and `hll_count.extract()` to get the approximate distinct count
- **Key fields**: `user_mau` containing the final distinct user count, `dimensions` indicating the breakdown type, relevant dimensional fields populated while others set to null

### Final aggregation  
- **Combining components**: Union all four dimensional breakdown results into a single dataset, creating a comprehensive view with project-level, chain-level, user-type-level, and product-level MAU metrics
- **Time aggregation**: Each row represents one day's MAU calculation (30-day rolling window ending on that date) with monthly partitioning for efficient querying
- **Final calculation**: The `user_mau` field contains the approximate distinct count of users who were active within the 30-day period ending on the given timestamp, calculated using HyperLogLog merge operations for memory efficiency and performance

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

Unique revenue-generating users over a rolling 30-day window.

DopexDPX

Active users (monthly)

Active users (monthly)

Competitive landscape

Chain breakdown