## 1. Objective
Gather and compile hourly snapshots of both supply and borrowing rates for AAVE v3 across three networks (Base, Ethereum, and Arbitrum) over the last 2 years. The goal is to provide a time-series dataset of interest rates, allowing for analysis of rate dynamics, trends, and potential opportunities or risks across different blockchains.

## 2. Data Requirements

### Essential Data Fields
- **Timestamp**: The precise hour for each data point (UTC preferred)
- **Blockchain Network**: Identify whether the data is from Base, Ethereum, or Arbitrum
- **Token**: The specific asset (e.g., USDC, DAI, ETH) that has lending and borrowing rates
- **Supply (Deposit) Rate**: The annualized rate lenders receive for depositing the token
- **Borrowing Rate**: The annualized rate borrowers pay to borrow the token

### Optional Data Fields
- **Utilization Rate**: The ratio of borrowed funds to the total supplied funds
- **Total Supply and Total Borrowed Amounts**: Context and liquidity availability indicators
- **Variable vs. Stable Rates**: Separate rates for variable and stable borrowing rates per asset

## 3. Approach to Data Collection

### Step 1: Identify Data Sources
- **Aave Subgraph API**: Official subgraph for querying historical data across networks
- **Aave Protocol Direct API**: Alternative API for rate information
- **Historical Indexing Services**: Services like Dune Analytics, Flipside Crypto, or Covalent
- **On-Chain Data Extraction**: Direct blockchain transaction logs and smart contract interactions

### Step 2: Construct and Optimize Queries
Use Aave subgraphs on each network to query data by token, network, and hourly timestamp.

### Step 3: Data Processing and Normalization
- **Data Cleaning**: Remove duplicates, handle missing data points, standardize timestamps
- **Normalization**: Ensure consistent units across all rates
- **Interpolation**: Handle missing values carefully

### Step 4: Data Storage and Access
- **Storage Format**: CSV, JSON, or SQL database
- **Versioning and Updates**: Automated scripts for dataset maintenance

## 4. Analysis Possibilities and Applications
- **Trend Analysis**: Study rate fluctuations and market demand
- **Cross-Network Comparison**: Identify rate disparities across networks
- **Risk Assessment**: Monitor rate increases and liquidity issues
- **DeFi Strategy Development**: Create optimized lending/borrowing strategies

## 5. Potential Challenges
- **Data Availability**: Incomplete historical data on newer networks
- **API/Query Limits**: Managing large dataset retrieval
- **Data Consistency**: Handling format differences across networks

## 6. Example Dataset Structure

| Timestamp (UTC) | Network | Token | Supply Rate (%) | Borrowing Rate (%) | Utilization Rate (%) |
|----------------|---------|--------|-----------------|-------------------|---------------------|
| 2022-11-01 00:00:00 | Ethereum | DAI | 1.75 | 3.25 | 65.4 |
| 2022-11-01 01:00:00 | Ethereum | DAI | 1.76 | 3.26 | 65.6 |
| 2022-11-01 00:00:00 | Arbitrum | USDC | 2.15 | 4.50 | 70.3 |
| 2022-11-01 01:00:00 | Base | ETH | 0.95 | 1.85 | 45.2 |

Hourly AAVE v3 Supply/Borrowing Rates for the last 2 years on base , Ethereum and Arbitrum

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