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

Holdstation DeFutures is a non-custodial perpetual exchange protocol built on zkSync Era. Holdstation DeFutures was founded by Hoai Nam and Trung Banh, and launched in 2023.

HOLD tokenholders own and govern the protocol. They currently take a cut of the total trading fees paid by traders (revenue). Holdstation's investors include ViaBTC Capital, Kryber, K300 Ventures, etc.

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Holdstation

# Holdstation Active users (monthly) Methodology

## 1. Overview

This metric measures the monthly active users (MAU) for Holdstation DeFutures, a derivatives trading platform on zkSync Era. The calculation tracks unique trader addresses who execute trades over rolling 30-day windows. MAU is calculated using probabilistic HyperLogLog counting to efficiently estimate unique users across multiple dimensional breakdowns including overall project, chain, user type, and product levels.

## 2. Data sources

- **zkSync Era blockchain events**: Parsed event logs from the HoldStation DeFutures smart contracts
- **Trading execution events**: Specifically `LimitExecuted` and `MarketExecuted` events from the `HSTradingCallbacks` contract that capture all trading activity
- **Transaction metadata**: Block timestamps and transaction hashes for temporal tracking
- **User identification data**: Trader wallet addresses extracted from event parameters
- **Smart contract addresses**: HoldStation DeFutures contract addresses for event filtering

## 3. Step-by-step calculation

### Component A: User event extraction
- **Purpose**: Identify unique trading events and extract trader addresses from blockchain data
- **Logic**: Parse `LimitExecuted` and `MarketExecuted` events from the `HSTradingCallbacks` contract on zkSync Era. Extract the `trader` parameter from each event, which contains the wallet address of the user executing the trade. Each event represents a completed limit order or market order execution on the Holdstation DeFutures platform. Classify all users as 'Trader' type and assign them to the 'holdstationdefuturesv1' product category with 'derivatives' market sector and 'perpetual' business segment.
- **Key fields**: `block_timestamp` for temporal analysis, `transaction_hash` for deduplication, `trader` address for user identification, event type classification

### Component B: Daily active user aggregation
- **Purpose**: Calculate daily unique active users using probabilistic counting
- **Logic**: Group trading events by day using timestamp truncation to daily boundaries. For each day, collect all unique trader addresses and initialize HyperLogLog (HLL) data structures for efficient unique counting. Group results by timestamp, chain, user type, and product to maintain dimensional breakdown capability. Apply chain availability filtering to ensure only valid chain data is included. This creates the foundation dataset of daily unique users needed for monthly calculations.
- **Key fields**: `timestamp` (daily boundaries), `chain` identifier, `user_type` classification, `product_id`, `user_dau` (unique user addresses), `timestamp_grain` set to 'day'

### Component C: Rolling window construction  
- **Purpose**: Create 30-day sliding windows for each target date to calculate monthly metrics
- **Logic**: Generate a complete datespine covering all days from the earliest available data to yesterday. For each target date in the spine, create associations with that date and the preceding 29 days using timestamp array generation. This ensures every day has a complete 30-day lookback window even if some intermediate days have no trading activity. The cross-join operation between the datespine and the 29-day lookback arrays creates all necessary date combinations for the sliding window calculation.
- **Key fields**: `timestamp` (target date), `joinday` (dates within the 30-day window for each target date)

### Component D: Monthly active user calculation with HLL
- **Purpose**: Calculate 30-day unique user counts using probabilistic data structures
- **Logic**: Join the rolling window date combinations with the daily HLL user data. For each target date, collect the HLL sketches from that date and the previous 29 days. Merge these HLL sketches using HLL merge operations to combine unique user estimates across the 30-day period. This probabilistic approach efficiently estimates unique users without requiring exact deduplication of large address sets. Group by timestamp and dimensional attributes to maintain breakdown structure.
- **Key fields**: `timestamp`, dimensional grouping fields (`chain_id`, `user_type`, `product_id`), `hll` (merged HyperLogLog sketches)

### Component E: Dimensional breakdowns
- **Purpose**: Create multiple analytical views of the MAU data across different business dimensions
- **Logic**: Generate four separate dimensional breakdowns from the merged HLL data. Create project-level totals by merging all HLL sketches regardless of other dimensions. Create chain-level breakdowns by grouping and merging HLL sketches by chain while setting other dimensions to null. Similarly create user-type-level and product-level breakdowns by grouping on the respective dimension. Each breakdown uses HLL extract operations to convert the probabilistic sketches into final user count estimates. Combine all breakdowns using union operations to create a unified output structure.
- **Key fields**: `dimensions` (breakdown type), `user_mau` (extracted unique user count), dimensional identifier fields

### Final aggregation
- **Combining components**: Union all dimensional breakdowns into a single result set with consistent schema structure including timestamp, dimension type, dimension values, and user count
- **Probabilistic counting**: Use HyperLogLog merge and extract operations to efficiently estimate unique users across 30-day windows without exact deduplication
- **Time aggregation**: Apply daily timestamp boundaries with 30-day rolling windows, ensuring continuous coverage through datespine methodology  
- **Final calculation**: Extract unique user counts from merged HLL sketches, apply schema standardization through the finalize_metric_user_mau macro, and fill missing periods with zeros to ensure complete time series coverage

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

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

HoldstationHOLD

Active users (monthly)

Active users (monthly)

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