Stakers deposit funds into StakeStone and receive a return on investment in the form of staking rewards (supply-side fees). StakeStone then deploys the staked capital to Lido.

StakeStone is an omni-chain LST (Liquid Staking Token) protocol aiming to bring native staking yields and liquidity to Layer 2s. StakeStone was founded by a anonymous team and launched in 2023.

StakeStone team controls the protocol. They do not currently take a cut of the total staking rewards earned by the protocol (revenue).

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StakeStone

# StakeStone Active users (monthly) Methodology

## 1. Overview

Monthly Active Users (MAU) measures the number of distinct users who have interacted with StakeStone's liquid staking protocol within a 30-day period. This metric tracks unique user addresses that have performed deposit or withdrawal operations on StakeStone's Ethereum contracts. The calculation uses HyperLogLog probabilistic counting to accurately measure distinct users across rolling 30-day windows, providing breakdowns by chain, user type (depositor vs withdrawer), and product dimensions.

## 2. Data sources

The calculation relies on the following blockchain data sources:

- **Ethereum event logs**: Parsed `Deposit` events from StoneVault contracts containing depositor addresses and transaction details
- **Ethereum function call traces**: Parsed withdraw function calls from AssetVault contracts containing recipient addresses
- **Transaction data**: Ethereum transaction records including success/failure status to filter only completed interactions  
- **Block data**: Ethereum block timestamps for temporal aggregation and daily/monthly boundary calculations

## 3. Step-by-step calculation

### Component A: User interaction extraction
- **Purpose**: Identifies all user addresses that have interacted with StakeStone contracts through deposits or withdrawals
- **Logic**: Extracts user addresses from two sources on Ethereum: deposit events from StoneVault contracts (capturing the `account` field as depositors) and withdraw function calls from AssetVault contracts (capturing the `to` field as withdrawers). Only successful transactions are included based on transaction status. Each interaction is tagged with the user type, chain, product identifier (stakestonev1), business segment (liquid_staking), and block timestamp.
- **Key fields**: User addresses are extracted from `account` field for deposits and `to` field for withdrawals, with `block_timestamp` providing temporal context and `transaction_hash` ensuring uniqueness

### Component B: Daily active user aggregation  
- **Purpose**: Converts raw user interactions into daily distinct user counts grouped by relevant dimensions
- **Logic**: Groups user interactions by truncated daily timestamps, chain, user type, and product identifiers. Creates distinct user counts for each day, maintaining separate counts for depositors and withdrawers. The aggregation ensures each unique user address is counted only once per day regardless of multiple transactions.
- **Key fields**: Daily `timestamp` boundaries, `chain` identifier, `user_type` classification, `product_id`, and distinct `user_address` counts

### Component C: HyperLogLog initialization
- **Purpose**: Prepares daily user counts for accurate monthly distinct counting using probabilistic data structures
- **Logic**: Converts daily user counts into HyperLogLog sketches using BigQuery's `hll_count.init` function with 15-bit precision. This enables accurate approximation of distinct users when aggregating across multiple days without double-counting users who appear on multiple days within the month.
- **Key fields**: Daily HLL sketches containing encoded user counts, organized by `timestamp`, `chain_id`, `user_type`, and `product_id`

### Component D: Rolling window construction
- **Purpose**: Creates 30-day sliding windows for each day to calculate monthly active users
- **Logic**: Generates a complete datespine covering the full time range of available data. For each day, creates a 30-day lookback window by generating timestamps for the 29 days prior plus the current day. Joins daily HLL sketches to the appropriate window days, ensuring complete coverage even for days with zero activity.
- **Key fields**: Window `timestamp` (the end date), `joinday` timestamps within each 30-day period, and associated HLL sketches for user counts

### Component E: Monthly aggregation with dimensional breakdowns  
- **Purpose**: Combines HLL sketches across 30-day windows to produce monthly active user counts with multiple analytical dimensions
- **Logic**: For each day's 30-day window, merges HLL sketches using `hll_count.merge_partial` and extracts final distinct counts using `hll_count.extract`. Creates four separate breakdowns: project-level (all users combined), chain-level (by blockchain), user type-level (depositors vs withdrawers), and product-level (by product identifier). Each breakdown maintains accurate distinct user counts across the dimension while nullifying other dimension fields.
- **Key fields**: Final `user_mau` counts with dimension identifiers (`dimensions`, `chain_id`, `user_type`, `product_id`) and temporal boundaries (`timestamp`, `timestamp_grain`)

### Final aggregation
- **Combining components**: All dimensional breakdowns are combined using UNION ALL operations to create a comprehensive dataset with multiple analytical views of the same underlying monthly active user metric
- **Currency conversion**: Not applicable - this metric counts users, not financial values
- **Time aggregation**: Uses 30-day rolling windows calculated daily, with monthly partitioning for efficient incremental processing
- **Final calculation**: The final MAU value for each dimension represents the count of distinct users who had at least one successful deposit or withdrawal interaction within the 30-day period ending on the specified date

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

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

StakeStoneSTO

Active users (monthly)

Active users (monthly)

Competitive landscape

Chain breakdown