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.

governance

github

twitter

discord

etherscan

website

Derivative exchanges

dYdX

# dYdX Active addresses (daily) Methodology

## 1. Overview

Daily active addresses for dydx measures the number of unique wallet addresses that performed any activity on the dydx blockchain within a 24-hour period. This metric captures user engagement by counting distinct addresses that either sent general transactions or participated in trading activities. The calculation uses HyperLogLog probabilistic counting to efficiently handle large datasets while maintaining accuracy for unique address counting.

## 2. Data sources

- **dydx blockchain event logs**: Message events from the dydx blockchain containing user transaction and trading activity
- **General transaction events**: Message events with `event_type = 'message'` that capture all transaction senders via the `sender` field in event attributes
- **Trading match events**: Message events with `event_type = 'match'` that capture decentralized exchange activity including both maker and taker subaccount addresses

## 3. Step-by-step calculation

### Component A: General transaction users
- **Purpose**: Identifies users who sent general transactions on dydx blockchain
- **Logic**: Extracts sender addresses from message events where `event_type` equals 'message'. Parses the `sender` field from the JSON `event_attributes` to identify transaction originators. Truncates block timestamps to daily boundaries and classifies these users as 'Transaction sender' type under the 'dYdX Chain' product
- **Key fields**: `block_timestamp` for daily grouping, `sender` address from event attributes, `user_type` set to 'Transaction sender'

### Component B: Trading users (takers)
- **Purpose**: Identifies users who acted as takers in dydx dYdX V4 trading matches
- **Logic**: Extracts taker subaccount addresses from message events where `event_type` equals 'match'. Parses the `taker_subaccount` field from the JSON `event_attributes` to identify users taking liquidity. Truncates block timestamps to daily boundaries and classifies these users as 'taker' type under the 'dYdX V4' product
- **Key fields**: `block_timestamp` for daily grouping, `taker_subaccount` address from event attributes, `user_type` set to 'taker'

### Component C: Trading users (makers)
- **Purpose**: Identifies users who acted as makers in dydx dYdX V4 trading matches
- **Logic**: Extracts maker subaccount addresses from message events where `event_type` equals 'match'. Parses the `maker_subaccount` field from the JSON `event_attributes` to identify users providing liquidity. Truncates block timestamps to daily boundaries and classifies these users as 'maker' type under the 'dYdX V4' product
- **Key fields**: `block_timestamp` for daily grouping, `maker_subaccount` address from event attributes, `user_type` set to 'maker'

### Component D: HyperLogLog aggregation
- **Purpose**: Efficiently calculates unique user counts across different dimensional breakdowns
- **Logic**: Combines all user addresses from the three sources using UNION DISTINCT to eliminate duplicates within each day. Creates HyperLogLog sketches for each combination of date, chain, user type, and product ID to enable memory-efficient distinct counting. Generates separate aggregations for project-level totals, chain-level breakdowns, user type breakdowns, and product ID breakdowns
- **Key fields**: Daily HLL sketches grouped by `timestamp`, `chain`, `user_type`, and `product_id`

### Final aggregation
- **Combining components**: Extracts unique user counts from HLL sketches using `hll_count.extract()` function applied to merged sketches for each dimensional breakdown
- **Currency conversion**: Not applicable - this metric counts addresses, not monetary values
- **Time aggregation**: Daily boundaries established by truncating all timestamps to day level using `timestamp_trunc(block_timestamp, day)`
- **Final calculation**: Filters the aggregated results to include only 'chain' and 'project_dbt_tag' dimensions, renames the `user_dau` field to `active_addresses_daily`, and applies schema finalization with zero-filling for days without activity to ensure continuous time series data

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

Unique active addresses over a fixed 24-hour window.

dYdXDYDX

Active addresses (daily)

Active addresses (daily)

Competitive landscape

Chain breakdown