Fluid acts as a lending, vault, and DEX platform for seamless DeFi interaction.

Fluid is a non-custodial lending, vaults, and DEX protocol built by Instadapp. Instadapp was founded by Samyak Jain & Sowmay Jain and launched in 2018.

FLUID tokenholders own and govern the protocol. They currently earn a cut of the fees generated through Fluid lending, vault and DEX services. Instadapp's investors include Coinbase Ventures, Pantera Capital, Robot Ventures, etc.

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Fluid

# Fluid Active users (monthly) Methodology

## 1. Overview

Monthly Active Users (MAU) for Instadapp measures the number of unique users who interacted with any Instadapp product within a rolling 30-day window. This metric provides insight into user engagement and retention across Instadapp's ecosystem of DeFi products including Fluid Fluid Vault (lending), Fluid Fluid DEX (trading), Fluid Fluid DEX Fluid Lite (lightweight trading), and legacy Fluid Lite products. The calculation uses HyperLogLog probabilistic data structures to efficiently count unique users across multiple dimensions and time periods.

## 2. Data sources

The metric draws from several blockchain data sources:
- **Ethereum event logs**: Parsed `FluidLiquidity_LogOperate_event`, `FluidDex_Swap_event`, `FluidDexLite_LogSwap_event`, and legacy Fluid Lite contract events (`iDAI_supplyLog_event`, `iDAI_withdrawLog_event`, `iETH_supplyLog_event`, `iETH_withdrawLog_event`, `iWBTC_supplyLog_event`, `iWBTC_withdrawLog_event`, `iUSDC_supplyLog_event`, `iUSDC_withdrawLog_event`, `iETH_Deposit_event`, `iETH_Withdraw_event`)
- **Arbitrum event logs**: Parsed `FluidLiquidity_LogOperate_event` and `FluidDex_Swap_event`  
- **Base event logs**: Parsed `FluidLiquidity_LogOperate_event` and `FluidDex_Swap_event`
- **Polygon event logs**: Parsed `FluidLiquidity_LogOperate_event` and `FluidDex_Swap_event`
- **Transaction records**: Ethereum, Arbitrum, Base, and Polygon transaction data to identify user addresses
- **Pool metadata**: Instadapp Fluid Fluid DEX Fluid Lite pool configuration data for trade categorization

## 3. Step-by-step calculation

### Component A: Daily user activity extraction
- **Purpose**: Identifies unique daily active users across all Instadapp products
- **Logic**: Extracts user addresses from four distinct activity streams. For Fluid Fluid Vault operations, captures users from lending protocol `LogOperate` events across multiple chains. For Fluid Fluid DEX trading, processes `Swap` events to identify traders. For Fluid Fluid DEX Fluid Lite, decodes compressed swap data using a JavaScript UDF to extract trading activity. For legacy Fluid Lite products, captures deposit and withdrawal events from multiple token contracts (iDAI, iETH, iWBTC, iUSDC). User addresses are obtained by joining event logs with transaction records to get the `from_address` field. Each user interaction is categorized by chain, user type (Fluid Lend user, Fluid Fluid DEX trader, Fluid Fluid DEX Fluid Lite trader, Fluid Lite user), and product ID (Fluid Vault, Fluid DEX, Fluid Fluid DEX Fluid Lite, Fluid Lite)
- **Key fields**: `user_address` from transaction `from_address`, `block_timestamp` truncated to day, `chain_id`, `user_type`, `product_id`

### Component B: HyperLogLog sketch creation  
- **Purpose**: Converts daily user data into probabilistic data structures for efficient unique counting
- **Logic**: Groups daily user data by timestamp, chain, user type, and product ID. Initializes HyperLogLog sketches with 15-bit precision for each group using the `hll_count.init` function. This creates compressed representations of unique user sets that can be efficiently merged across time periods
- **Key fields**: `hll` sketches containing probabilistic user counts, grouped by dimension combinations

### Component C: Rolling window generation
- **Purpose**: Creates 30-day sliding windows for MAU calculation
- **Logic**: Generates a complete date spine from the earliest data point to the current date. For each date in the spine, creates associations with the previous 29 days to form 30-day windows. Joins this window structure with the daily HLL data to create a comprehensive view where each date is associated with HLL sketches from its 30-day lookback period
- **Key fields**: `timestamp` for the measurement date, `joinday` for days within the 30-day window, associated HLL sketches

### Component D: MAU calculation by dimensions
- **Purpose**: Computes monthly active users across different analytical dimensions
- **Logic**: Merges HLL sketches across each 30-day window using `hll_count.merge_partial` to combine probabilistic user counts. Extracts final MAU counts using `hll_count.extract`. Creates four distinct breakdowns: project-level (total across all dimensions), chain-level (MAU per blockchain), user type-level (MAU per user category), and product-level (MAU per product). Each breakdown nulls out irrelevant dimension fields and groups appropriately
- **Key fields**: `user_mau` counts extracted from merged HLL sketches, `dimensions` indicating breakdown type, relevant dimension identifiers (`chain_id`, `user_type`, `product_id`)

### Final aggregation
- **Combining components**: Unions all dimensional breakdowns into a single result set with standardized schema
- **Currency conversion**: Not applicable for user count metrics
- **Time aggregation**: Uses daily timestamps with 30-day rolling windows to provide continuous MAU measurements
- **Final calculation**: Applies standardized schema formatting and zero-filling for periods with no activity using the `finalize_metric_user_mau` macro to ensure consistent time series data

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

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

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Active users (monthly)

Active users (monthly)

Competitive landscape

Chain breakdown