Users deposit funds into pools, called Hypervisors, and receive a return on investment (supply-side fees) after rebalancing.

Gamma Strategies is an asset management protocol built on Ethereum and other blockchains. Gamma Strategies was founded by an anonymous team and launched in 2020.

GAMMA tokenholders own and govern the protocol. They currently take a cut of the total return on investment & total assets under management paid by liquidity providers (revenue).

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Gamma Strategies

# Gamma Strategies Active users (weekly) Methodology

## 1. Overview

Weekly Active Users (WAU) measures the number of unique users who interact with Gamma Strategies' yield farming protocol within any 7-day rolling window. This metric captures user engagement across Gamma's automated liquidity management positions (Hypervisors) on multiple blockchains. The calculation uses a rolling 7-day window approach, meaning each day's WAU includes all unique users who were active on that day or any of the 6 preceding days. This provides a smooth, continuous measure of weekly user activity without the artificial boundaries of calendar weeks.

## 2. Data sources

The calculation relies on these key data sources:

- **Parsed smart contract events**: `Deposit` and `Withdraw` events from Gamma Strategies' Hypervisor contracts across multiple blockchain networks
- **Multi-chain blockchain data**: Event logs from Ethereum, Arbitrum, Optimism, and Polygon networks
- **Contract event types**: Events from multiple Hypervisor versions (V1 and V2) and different DEX integrations including Uniswap, QuickSwap, and Zyberswap
- **Event metadata**: Block timestamps, transaction hashes, log indexes, and user addresses from blockchain event logs

## 3. Step-by-step calculation

### Component A: User interaction extraction
- **Purpose**: Identifies all user addresses that interact with Gamma Strategies' Hypervisor contracts through deposits and withdrawals
- **Logic**: Extracts user addresses from parsed `Deposit` and `Withdraw` events across multiple chains and contract versions. For deposit events, captures the `sender` address as the depositor. For withdrawal events, captures the `to` address as the withdrawer. Each interaction is classified by user type (Depositor or Withdrawer), chain, and timestamp. The process handles multiple Hypervisor contract versions (V1 and V2) and different DEX integrations (Uniswap, QuickSwap, Zyberswap) across Ethereum, Arbitrum, Optimism, and Polygon networks
- **Key fields**: `block_timestamp` for timing, `user_address` for user identification, `user_type` for interaction classification (Depositor/Withdrawer), `chain` for network identification

### Component B: Daily active user calculation  
- **Purpose**: Aggregates unique user addresses by day to establish the foundation for weekly calculations
- **Logic**: Groups user interactions by day using timestamp truncation and counts distinct user addresses within each day. Each user is counted only once per day regardless of how many transactions they perform. The aggregation maintains segmentation by chain, user type, and product ID to enable multi-dimensional analysis. This creates the base daily active user dataset that feeds into the weekly rolling window calculation
- **Key fields**: `timestamp` (truncated to day), `user_dau` (distinct user address), `chain`, `user_type`, `product_id`

### Component C: HyperLogLog sketch creation
- **Purpose**: Converts daily user data into probabilistic data structures that enable efficient distinct counting across time windows
- **Logic**: Transforms the daily active user data into HyperLogLog sketches using BigQuery's `hll_count.init` function with 15-bit precision. These sketches allow for approximate distinct counting that can be efficiently merged across multiple time periods without storing or processing the actual user address lists. This approach significantly improves performance when calculating rolling windows across large datasets
- **Key fields**: `hll` (HyperLogLog sketch), `timestamp`, `chain_id`, `user_type`, `product_id`

### Component D: Rolling window construction
- **Purpose**: Creates 7-day rolling windows for each date to capture weekly active user patterns
- **Logic**: Generates a complete date spine from the first available DAU date to present, then creates cross-join relationships where each date is associated with itself and the 6 preceding days. This creates overlapping 7-day windows that slide forward one day at a time. The window construction ensures no gaps in the timeline and provides consistent 7-day lookback periods for every date in the analysis period
- **Key fields**: `timestamp` (target date), `joinday` (dates within the 7-day window)

### Final aggregation
- **Combining components**: The rolling windows are joined with the HyperLogLog sketches to bring together all user activity data within each 7-day period. HyperLogLog sketches are merged across the 7-day windows using `hll_count.merge_partial` to combine the probabilistic user counts
- **Multi-dimensional breakdown**: Separate calculations are performed for four different aggregation levels: project-level (all users combined), chain-level (grouped by blockchain), user_type-level (grouped by Depositor/Withdrawer), and product_id-level (grouped by specific products). Each dimension nullifies non-relevant grouping fields
- **Final calculation**: The distinct user count is extracted from the merged HyperLogLog sketches using `hll_count.extract`, providing the final WAU value. All dimensional breakdowns are combined using union operations to create a single result set with different granularity levels indicated by the `dimensions` field
- **Time aggregation**: The calculation uses daily boundaries with rolling 7-day windows, meaning each day's WAU represents unique users active in the current day plus the previous 6 days. This creates a smooth trending metric without calendar week boundaries

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

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

Gamma StrategiesGAMMA

Active users (weekly)

Active users (weekly)

Competitive landscape

Chain breakdown