Market makers offer trades through order book & make money on bid-ask spreads (supply-side fees).

dYdX is a non-custodial trading and derivatives protocol initially built on Ethereum and later expanded to their own blcokchain built on top of the Cosmos SDK. dYdX was founded by Antonio Juliano and launched in 2017.

DYDX tokenholders own and govern the protocol. dYdX Trading Inc. currently takes all of the total trading fees paid by traders (revenue). dYdX’s investors include a16z, Polychain, The Spartan Group, etc.

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dYdX

# dYdX Active users (weekly) Methodology

## 1. Overview

Weekly Active Users (WAU) for dYdX measures the count of unique users who interact with the dYdX protocol within any 7-day rolling window. This metric captures both general blockchain activity and specific trading activity, providing insights into user engagement across different protocol functions. The calculation uses HyperLogLog (HLL) data structures for efficient cardinality estimation and supports multiple dimensional breakdowns.

## 2. Data sources

- **dYdX blockchain events**: Message events from the dYdX blockchain that capture all transaction activity, specifically `message` event types containing sender information
- **dYdX trading events**: Match events from dYdX dYdX V4 that record trading activity, including maker and taker identities for each trade
- **Chain availability metadata**: Information about chain operational status to ensure accurate time-series calculations

## 3. Step-by-step calculation

### Component A: Daily active user identification
- **Purpose**: Identifies all unique users active on any given day across different interaction types
- **Logic**: Extracts user addresses from three distinct activity patterns: general transaction senders from message events, takers from trading matches, and makers from trading matches. Each user address is captured with its associated timestamp, chain identifier, user type classification, and product identifier. Transaction senders are classified as "Transaction sender" user type with "dYdX Chain" product, while trading participants are classified as either "taker" or "maker" user types with "dYdX V4" product identifier
- **Key fields**: `user_address` (the unique identifier), `block_timestamp` (for daily grouping), `user_type` (classification of interaction), `product_id` (protocol version), `chain` (always 'dydx')

### Component B: HyperLogLog conversion and windowing  
- **Purpose**: Converts daily active users to HyperLogLog sketches and creates rolling 7-day windows
- **Logic**: Groups daily active users by timestamp, chain, user type, and product, then converts user addresses to HLL sketches with 15-bit precision for efficient cardinality estimation. Creates a complete datespine covering all dates from the earliest data to yesterday to handle missing dates. Generates rolling 7-day windows by creating join tables that map each date to the 7 preceding days (including the date itself), enabling window calculations across potentially sparse data
- **Key fields**: `hll` (HyperLogLog sketch of users), `timestamp` (target date), `joinday` (dates within the 7-day window)

### Component C: Window-based user aggregation
- **Purpose**: Merges HLL sketches across 7-day rolling windows to count unique weekly active users
- **Logic**: Joins the rolling window structure with daily HLL sketches, matching each target date with its corresponding 7-day window of daily user data. For each timestamp and dimensional combination, merges all HLL sketches within the 7-day window and extracts the final unique count, effectively counting users who were active on any day within that 7-day period
- **Key fields**: `hll` (merged sketch), `user_wau` (extracted unique count)

### Component D: Dimensional breakdowns
- **Purpose**: Creates multiple aggregation levels for different analytical perspectives
- **Logic**: Generates four distinct dimensional views: project-level aggregation (combining all chains, products, and user types), chain-level aggregation (grouping by chain while nullifying other dimensions), user-type aggregation (grouping by interaction type), and product-level aggregation (grouping by protocol version). Each breakdown merges HLL sketches across the non-grouped dimensions to produce unique user counts at the specified granularity
- **Key fields**: `dimensions` (breakdown type), `chain_id`, `product_id`, `user_type` (dimensional attributes), `user_wau` (unique user count)

### Final aggregation
- **Combining components**: All dimensional breakdowns are combined using union operations to create a comprehensive dataset with multiple aggregation levels in a single table
- **Time aggregation**: Daily boundary logic truncates timestamps to day precision, with rolling 7-day windows calculated for each date
- **Final calculation**: The metric value represents the count of unique users active within any 7-day rolling window ending on the specified date, calculated by merging HyperLogLog sketches and extracting cardinality estimates

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

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

dYdXDYDX

Active users (weekly)

Active users (weekly)

Competitive landscape

Chain breakdown