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 (weekly) Methodology

## 1. Overview

Weekly Active Users (WAU) measures the number of unique users who interact with the Dopex protocol within any 7-day period. This metric tracks trader activity across both Dopex SSOV v1 (Single Staking Option Vaults) and Dopex CLAMM v2 (Concentrated Liquidity AMM) products on Arbitrum. The calculation uses HyperLogLog data structures to efficiently count unique users across sliding 7-day windows while providing breakdowns by chain, user type, and product version.

## 2. Data sources

- **Arbitrum blockchain event logs**: Parsed smart contract events from Dopex protocol interactions
- **Dopex SSOV v1 contract events**: `Purchase` events from various Single Staking Option Vault contracts covering ARB, DPX, RDPX, ETH, and stETH tokens with monthly and weekly expiries for both calls and puts
- **Dopex CLAMM v2 contract events**: `LogMintOption` events from V2 OptionMarket contracts for ARB, WETH, and WBTC option markets
- **Transaction metadata**: Block timestamps and transaction hashes for temporal analysis

## 3. Step-by-step calculation

### Component A: User identification
- **Purpose**: Identify unique users interacting with Dopex protocol contracts
- **Logic**: Extract user addresses from two types of Dopex protocol events on Arbitrum. For SSOV v1 contracts, capture the `sender` field from `Purchase` events across multiple option vault contracts including monthly and weekly calls/puts for various underlying tokens. For CLAMM v2 contracts, capture the `user` field from `LogMintOption` events from V2OptionMarket contracts. All identified users are classified as "Trader" type and tagged with appropriate market sector (derivatives), business segment (option), and product identifiers distinguishing between "Dopex SSOV v1" and "Dopex CLAMM v2"
- **Key fields**: `user_address` (the unique identifier), `block_timestamp` (event timing), `chain` (Arbitrum), `product_id` (SSOV v1 vs CLAMM v2), `user_type` (Trader)

### Component B: Daily user aggregation  
- **Purpose**: Create daily snapshots of unique active users
- **Logic**: Truncate event timestamps to daily boundaries and create distinct counts of user addresses per day. Preserve dimensional breakdowns including chain, user type, market sector, business segment, and product identifier. This creates a foundation dataset of daily active users that maintains all necessary dimensions for flexible analysis while ensuring each user is counted only once per day regardless of transaction volume
- **Key fields**: `timestamp` (daily boundary), `user_dau` (unique user address), dimension fields for grouping

### Component C: HyperLogLog sketch initialization
- **Purpose**: Convert daily user sets into efficient cardinality estimation structures
- **Logic**: Initialize HyperLogLog sketches for each daily user set using a precision parameter of 15. Group sketches by timestamp, chain identifier, user type, and product identifier. HyperLogLog allows efficient storage and merging of user sets while maintaining accurate cardinality estimates without storing actual user addresses
- **Key fields**: `hll` (HyperLogLog sketch), grouping dimensions for sketch creation

### Component D: Weekly window construction
- **Purpose**: Create 7-day sliding windows for weekly aggregation
- **Logic**: Generate a complete datespine from the earliest available data to the current date to ensure no missing timestamps in the final output. For each date in the datespine, create a cross-join with the 7 days ending on that date (the current day plus 6 previous days). This creates a mapping table where each date is associated with all dates in its 7-day lookback window, enabling sliding window calculations without gaps
- **Key fields**: `timestamp` (reference date), `joinday` (dates within the 7-day window)

### Component E: Window aggregation with HLL merging
- **Purpose**: Calculate weekly active user counts using sliding 7-day windows
- **Logic**: Join the 7-day window mappings with daily HLL sketches, matching each reference date with the HLL sketches from all days in its window. For each reference date and dimensional combination, merge all relevant HLL sketches using HyperLogLog merge operations to combine user sets while avoiding double-counting users who were active on multiple days. Extract the final cardinality estimate from the merged HLL sketch to get the count of unique users active in the 7-day period
- **Key fields**: Merged `hll` sketches, `user_wau` (extracted cardinality)

### Final aggregation
- **Combining components**: Create four separate dimensional breakdowns of the weekly active user data: project-level (all dimensions aggregated), chain-level (grouped by blockchain), user-type-level (grouped by user classification), and product-level (grouped by Dopex product version). Union all dimensional views into a single comprehensive dataset
- **Time aggregation**: Each row represents a specific date with the count of users active in the 7-day period ending on that date, ensuring continuous daily time series data
- **Data completeness**: Apply datespine logic to fill any missing dates with zero values, ensuring complete time series without gaps
- **Final calculation**: The final `user_wau` value represents the count of unique user addresses that interacted with Dopex protocol contracts within any 7-day sliding window, calculated using HyperLogLog cardinality estimation to handle large user sets efficiently

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

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

DopexDPX

Active users (weekly)

Active users (weekly)

Competitive landscape

Chain breakdown