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.

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Derivative exchanges

SynFutures

# SynFutures Active users (monthly) Methodology

## 1. Overview

Monthly active users (MAU) measures the number of distinct users who performed any trading activity on SynFutures across a rolling 30-day period. This metric tracks user engagement across SynFutures' derivatives trading platform spanning multiple blockchain networks and protocol versions. The calculation uses HyperLogLog probabilistic data structures to efficiently estimate distinct users across large time windows and multiple dimensions, providing insights into trading activity at the project, chain, and user type levels.

## 2. Data sources

The calculation relies on the following raw blockchain data sources:

- **SynFutures v1 trading events**: Parsed `Trade` events from `FuturesChainlinkPair`, `FuturesUniswapPair`, and `BTCFutures` contracts deployed on Arbitrum, BSC, Ethereum, and Polygon networks
- **SynFutures v3 trading events**: Parsed `Trade` and `Sweep` events from `Instrument` contracts deployed on Base and Blast networks  
- **Blockchain transaction data**: Transaction timestamps and block data from all supported chains (Ethereum, Arbitrum, Base, Blast, BSC, Polygon) to establish temporal context
- **Event parameters**: Trader addresses extracted from event logs (`trader` parameter in v1 events, `trader` parameter in v3 Trade events, `trader` parameter in v3 Sweep events)

## 3. Step-by-step calculation

### Component A: User activity extraction
- **Purpose**: Extract all trader addresses and their activity timestamps from SynFutures trading events across all supported chains and protocol versions
- **Logic**: Query parsed blockchain events from SynFutures contracts to identify trading activity. For SynFutures v1, collect trader addresses from `Trade` events on Chainlink and Uniswap-based futures pairs, plus Bitcoin futures on Polygon. For SynFutures v3, collect trader addresses from both `Trade` and `Sweep` events on instrument contracts. Each event record includes the `trader` address, transaction timestamp, blockchain network, and standardized metadata categorizing the activity as derivatives trading in the perpetual segment
- **Key fields**: `trader` address (the user performing the trade), `block_timestamp` (when the transaction occurred), `chain` (blockchain network), `user_type` (classified as "Trader"), `transaction_hash` (unique transaction identifier)

### Component B: Daily active user aggregation  
- **Purpose**: Calculate daily distinct active users by aggregating trader addresses to the day level
- **Logic**: Group all trading activity by truncated day timestamp and chain, then count distinct trader addresses for each day-chain combination. This creates daily snapshots of unique users who performed any trading activity on each blockchain network. Apply chain availability filtering to ensure data quality and completeness across all supported networks
- **Key fields**: `timestamp` (day-level timestamp), `chain` (blockchain network), `user_type` (user classification), `product_id` (null for SynFutures), `user_dau` (distinct daily active user address)

### Component C: HyperLogLog sketch preparation
- **Purpose**: Convert daily user lists into probabilistic data structures for efficient distinct counting across time windows
- **Logic**: Transform daily active user data into HyperLogLog sketches using BigQuery's `hll_count.init` function with 15-bit precision. This creates compact probabilistic representations of daily user sets that can be efficiently merged across multiple days to estimate distinct counts over longer time periods
- **Key fields**: `hll` (HyperLogLog sketch of daily users), `timestamp` (day-level timestamp), `chain_id` (blockchain identifier), `user_type` (user classification)

### Component D: Rolling window generation
- **Purpose**: Create 30-day rolling windows for each day to capture all users active within monthly periods
- **Logic**: Generate a datespine covering all days from the first available data through yesterday. For each day in the spine, create a 30-day lookback window by generating an array of dates from 29 days prior through the current day. Join this expanded date range with the daily HyperLogLog sketches to collect all user activity within each 30-day window
- **Key fields**: `timestamp` (target day for MAU calculation), `joinday` (individual days within the 30-day window), `hll` (daily user sketches within the window)

### Component E: Monthly active user calculation
- **Purpose**: Calculate distinct user counts across 30-day windows using HyperLogLog merging
- **Logic**: For each day's 30-day window, merge all daily HyperLogLog sketches using `hll_count.merge_partial` to combine user sets across the time period. Extract the final distinct count using `hll_count.extract` to get the approximate number of unique users active within the 30-day period. This provides an efficient estimation of monthly active users without exact distinct counting across large datasets
- **Key fields**: `user_mau` (estimated distinct users in 30-day period), `timestamp` (day for which MAU is calculated), dimensional attributes for breakdowns

### Component F: Dimensional breakdowns
- **Purpose**: Calculate MAU across multiple analytical dimensions for comprehensive reporting
- **Logic**: Generate four separate dimensional views of the MAU data. Create project-level totals by aggregating all users regardless of chain or other attributes. Generate chain-level breakdowns by grouping users by blockchain network. Provide user type analysis (currently only "Trader" type exists). Create product-level views (currently null for SynFutures as it's treated as a single product). Each breakdown maintains the same HyperLogLog merging logic but groups data differently
- **Key fields**: `dimensions` (breakdown type), `chain_id` (for chain breakdowns), `user_type` (for user type breakdowns), `product_id` (for product breakdowns), `user_mau` (distinct users in each dimension)

### Final aggregation
- **Combining components**: Union all dimensional breakdowns (project-level, chain-level, user type-level, and product-level) into a single result set with standardized schema
- **Time aggregation**: MAU values represent rolling 30-day windows calculated daily, providing smooth trend analysis without monthly boundary effects
- **Schema standardization**: Apply the `finalize_metric_user_mau` macro to ensure consistent output format, fill missing days with zero values, and add required metadata fields
- **Final calculation**: The final MAU value for each day represents the estimated number of distinct trader addresses that performed any trading activity on SynFutures within the 30-day period ending on that day, calculated using probabilistic HyperLogLog sketching for computational efficiency

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

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

SynFuturesF

Active users (monthly)

Active users (monthly)

Competitive landscape

Chain breakdown