| title | Computed data |
|---|---|
| description | All the computed data that Pond3r uses |
Pond3r uses a combination of on-chain data, social data, and price data to compute the data that is used to create computed datasets.
Our computed data pipeline works by:
- Collecting raw data: We aggregate data from multiple sources including on-chain transactions, price feeds, and social platforms
- Processing and transforming: Raw data is cleaned, normalized, and structured for efficient querying
- Calculating metrics: We compute complex metrics like APYs, correlations, and trends that would be inefficient to calculate in real-time
- Storing in optimized format: Processed data is stored in a format optimized for quick retrieval based on natural language queries
- Regular updates: Computed datasets are updated at regular intervals to ensure data freshness
Pond3r provides the following computed datasets:
| Dataset | Description | Update Frequency |
|---|---|---|
| Apys | Apys for most of the yield protocols | Daily |
| Social Sentiment | Aggregated sentiment analysis from Farcaster and other social platforms | Daily |
| Most trade tokens on Farcaster | Most traded tokens on Farcaster | Daily |
Our AI will automatically determine when to use computed data versus fetching real-time data directly from on-chain sources.
- Faster Response Times: Pre-computed metrics avoid complex calculations during query time
- Historical Trend Analysis: Access patterns and changes over time that wouldn't be visible from current state alone
- Cross-Protocol Insights: Compare metrics across different protocols and chains standardized to the same methodology
- Complex Calculations: Access metrics that require significant computation (e.g., correlation analysis, risk metrics)
- Reduced On-Chain Load: Minimize the need for extensive on-chain queries for commonly requested data