Liquidity providers deposit funds into SynFutures and receive trading fees from traders (supply-side fees).

SynFutures is a non-custodial decentralized exchange for perpetual contracts built on Base and multiple EVM-based chains. SynFutures was founded by Rachel Lin and was launched in 2021. 

SynFutures’ team & investors own and govern the protocol. They currently take a cut of the total trading fees paid by traders (revenue). SynFutures’ investors include Polychain Capital, Framework Ventures, Pantera Capital, etc.

website

twitter

governance

github

discord

Derivative exchanges

SynFutures

# SynFutures Active users (weekly) Methodology

## 1. Overview

Weekly Active Users (WAU) for SynFutures measures the approximate number of unique users who performed trading activities within rolling 7-day periods across the protocol's multi-chain derivatives platform. This metric answers the business question: "How many distinct traders used SynFutures products in the past week?" The calculation uses HyperLogLog sketches for memory-efficient approximate distinct counting across the time window, enabling accurate deduplication of users who trade on multiple days within the 7-day period.

## 2. Data sources

The metric is built from the following raw data sources:
- **Blockchain event logs**: Parsed `Trade` and `Sweep` events from SynFutures smart contracts across multiple blockchain networks (Arbitrum, Base, Blast, BSC, Ethereum, and Polygon)
- **SynFutures V1 contracts**: `Trade` events from `FuturesChainlinkPair`, `FuturesUniswapPair`, and `BTCFutures` contract types
- **SynFutures V2 contracts**: `Trade` events from `WMATIC-USDC-DEXV2` contract types on Polygon
- **SynFutures V3 contracts**: `Trade` and `Sweep` events from `Instrument` contract types on Base and Blast
- **Transaction metadata**: Block timestamps and transaction hashes for temporal ordering and deduplication

## 3. Step-by-step calculation

### Component A: User interaction capture
- **Purpose**: Identify all user addresses that performed trading activities across SynFutures contracts
- **Logic**: Extract trader addresses from parsed blockchain event logs across all supported chains and protocol versions. Filter for `Trade` events from various contract types including `FuturesChainlinkPair`, `FuturesUniswapPair`, `Instrument`, `BTCFutures`, and `WMATIC-USDC-DEXV2` contracts, as well as `Sweep` events from `Instrument` contracts. Extract the trader address from event parameters (either `trader` or `taker` field depending on contract version). Classify all interactions with market sector as derivatives and business segment as perpetual, with user type as Trader.
- **Key fields**: `block_timestamp`, `transaction_hash`, `chain`, `user_address`, `user_type`, `market_sector_id`, `business_segment_id`, `product_id`

### Component B: Daily user aggregation  
- **Purpose**: Create daily distinct user counts per dimensional breakdown
- **Logic**: Group user interactions by day using timestamp truncation to day level. Create distinct user counts grouped by chain, user type, market sector, business segment, and product. Apply chain availability filtering to ensure only data from chains with sufficient coverage is included. This produces daily active user lists that serve as input for the weekly aggregation.
- **Key fields**: `timestamp` (daily), `timestamp_grain`, `chain`, `user_type`, `product_id`, `user_dau` (distinct user address)

### Component C: HyperLogLog sketch initialization
- **Purpose**: Convert daily user lists into memory-efficient approximate distinct counting structures
- **Logic**: Transform daily user address lists into HyperLogLog sketches with precision 15, which enables efficient approximate distinct counting when merging across multiple days. Group sketches by timestamp, chain, user type, and product dimensions to maintain the same dimensional structure as the daily aggregation.
- **Key fields**: `timestamp`, `chain_id`, `user_type`, `product_id`, `hll` (HyperLogLog sketch)

### Component D: Date spine and window construction
- **Purpose**: Create complete date coverage and 7-day rolling windows for WAU calculation
- **Logic**: Generate a complete date series from the earliest data point to the current day using daily intervals to ensure no gaps in the time series. For each date in the spine, create joins to the 7 preceding days (including the current date) to form rolling 7-day windows. This ensures every date has a complete 7-day lookback period for WAU calculation, even if some days had no trading activity.
- **Key fields**: `timestamp` (target date), `joinday` (dates within the 7-day window)

### Component E: Weekly window HLL aggregation
- **Purpose**: Merge HyperLogLog sketches across 7-day windows to calculate approximate weekly distinct users
- **Logic**: Join the 7-day windows with the daily HLL sketches, matching on the join dates. Group by target timestamp and dimensional attributes (chain, user type, product), then merge the HLL sketches across all days in the window. Extract the final approximate distinct count from the merged HLL sketch to get the weekly active user count.
- **Key fields**: `timestamp`, `chain_id`, `user_type`, `product_id`, `user_wau` (approximate distinct count)

### Component F: Dimensional breakdown
- **Purpose**: Create WAU metrics across different analytical dimensions
- **Logic**: Calculate WAU at multiple levels of granularity by grouping the merged HLL sketches differently. Create separate breakdowns for project level (all dimensions null), chain level (group by chain), user type level (group by user type), and product level (group by product). Set non-applicable dimensions to null for each breakdown level. Combine all breakdowns using union operations to create a comprehensive dimensional view.
- **Key fields**: `dimensions` (breakdown type), `chain_id`, `user_type`, `product_id`, `user_wau`

### Final aggregation
- **Combining components**: Union all dimensional breakdowns (project, chain, user type, product level) into a single result set with consistent schema structure
- **Time aggregation**: No currency conversion needed as this is a user count metric. Daily boundary logic uses timestamp truncation to day level with rolling 7-day windows
- **Final calculation**: The final WAU value represents the approximate distinct count of users who performed any trading activity within the rolling 7-day period, calculated using merged HyperLogLog sketches for memory efficiency and deduplication accuracy across the multi-day window

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

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

SynFuturesF

Active users (weekly)

Active users (weekly)

Competitive landscape

Chain breakdown