live_analytics

DeFi Prime Rate Analysis

A comprehensive Python project for analyzing DeFi stablecoin yields and calculating a weighted “Prime Rate” for the DeFi ecosystem.

Overview

This project fetches data from DeFiLlama and other sources to:

• Calculate a weighted DeFi Prime Rate based on top stablecoin pools by TVL
• Analyze correlations between individual pools and the overall rate
• Compare DeFi yields with traditional market indicators
• Generate academic-style visualizations for research and analysis

Project Structure

Core Modules

• config.py - Centralized configuration with constants, API endpoints, and plotting styles
• utils.py - Common utility functions for data fetching, database operations, and plotting
• spr_fetcher_v1.py - Main data fetcher that calculates the DeFi Prime Rate
• spr_plotter.py - Visualization module for Prime Rate analysis
• specific_pools_fetcher.py - Fetcher for specific pool analysis
• spr_pool_identifier.py - Pool data extraction and identification utility
• yo_corr.py - yoUSD correlation analysis with the Prime Rate
• corr_analysis.py - Market correlation analysis (BTC, ETH, SPY)
• spr_db_csv.py - Database export utilities
• spr_test.py - Testing module for correlation calculations

Data Files

• data/defi_prime_rate.db - SQLite database containing historical pool data
• 512m_logo.png - Logo for plot overlays
• .env - Environment variables (not included, see setup)

Visualization Scripts

• scripts/plotly_charts.js - Main Plotly visualization script for DeFi Prime Rate analysis
• scripts/plotly_common.js - Common utilities and configurations for Plotly charts
• scripts/spr_charts.js - Specific Prime Rate visualization components
• scripts/yo_corr_charts.js - yoUSD correlation analysis visualizations
• scripts/corr_analysis_charts.js - Market correlation analysis charts
• scripts/specific_pools_charts.js - Specific pool comparison visualizations

Setup

Prerequisites

• Python 3.8+
• Polygon.io API key (for Ethereum price data)

Installation

1. Clone the repository
2. Install dependencies:

   pip install -r requirements.txt
   

3. Create a .env file with your API key:

   POLYGON_API_KEY=your_polygon_api_key_here
   

Dependencies

• requests - API calls
• pandas - Data manipulation
• matplotlib - Plotting and visualization
• numpy - Numerical computations
• python-dotenv - Environment variable management
• Pillow - Image processing for logo overlays
• seaborn - Enhanced plotting styles

Usage

1. Fetch DeFi Prime Rate Data

python spr_fetcher_v1.py

This will:

• Fetch top 100 stablecoin pools by TVL from DeFiLlama
• Calculate weighted average APY (the “DeFi Prime Rate”)
• Save data to SQLite database
• Display summary statistics

2. Create Prime Rate Visualizations

python spr_plotter.py

Generates:

• Daily vs 14-day moving average trends
• Pool contribution analysis
• Time-series contribution charts

3. Analyze Specific Pools

python specific_pools_fetcher.py

Creates visualizations comparing:

• USDC and USDT pool APY trends
• Ethereum price movements

4. yoUSD Correlation Analysis

python yo_corr.py

Performs comprehensive analysis of yoUSD vs DeFi Prime Rate:

• Time series comparison
• Rolling correlation and beta
• Scatter plot with trend analysis

5. Market Correlation Analysis

python corr_analysis.py

Analyzes correlations between:

• Bitcoin vs S&P 500
• Ethereum vs S&P 500
• Ethereum vs Bitcoin

6. Export Data to CSV

python spr_db_csv.py

Exports specific pool data to CSV format for external analysis.

7. Pool Data Identification

python spr_pool_identifier.py

Extracts and displays the latest APY and TVL data for pools 0-15 from the database with summary statistics.

8. Run Tests

python spr_test.py

Runs correlation calculation tests with both synthetic and real market data.

Key Features

Data Sources

• DeFiLlama: Pool APY and TVL data
• Polygon.io: Ethereum and traditional market price data

Analysis Capabilities

• Weighted average APY calculation based on TVL
• Rolling correlation and beta analysis
• Multi-asset correlation matrices
• Time-series contribution analysis

Visualization Features

• Academic-style plots with serif fonts
• Consistent color theming
• Logo overlays on all charts
• Multiple chart types (line, scatter, heatmap, stacked area)

Configuration

Customizing Analysis Parameters

Edit config.py to modify:

• Pool selection criteria
• Rolling window sizes
• Plotting themes and colors
• API endpoints

Adding New Pools

To analyze additional pools:

1. Add pool IDs to SPECIFIC_POOL_IDS in config.py
2. Update POOL_NAMES mapping
3. Add display names to DISPLAY_POOL_NAMES if needed

Database Schema

pool_data Table

• date (index) - Date of observation
• apy_Pool_N - APY for pool N
• tvlUsd_Pool_N - TVL in USD for pool N
• weighted_apy - Calculated weighted average APY
• ma_apy_14d - 14-day moving average APY

pool_metadata Table

• pool_id - DeFiLlama pool identifier
• name - Pool name
• current_tvl - Current TVL in USD
• current_apy - Current APY percentage
• last_updated - Timestamp of last update

Contributing

When adding new features:

1. Use the existing config.py for constants
2. Add reusable functions to utils.py
3. Follow the established type hinting patterns
4. Include comprehensive error handling
5. Add appropriate docstrings

Notes

• The project uses academic-style plotting with serif fonts and muted colors
• All timestamps are normalized to handle timezone differences
• Rate limiting is implemented for API calls
• The database is purged and refreshed on each run to ensure data freshness

License

This project is for research and educational purposes.