Curation Signal Optimizer

A Python application that optimizes curation signals using TheGraph API and Supabase for data storage.

Prerequisites

• Python 3.x
• pip (Python package installer)
• Access to TheGraph API
• Access to Supabase database

Setup

1. Clone the repository:

git clone https://github.com/yourusername/curation_signal_optimizer.git
cd curation_signal_optimizer

2. Install Python dependencies:

cd python_app
pip install -r requirements.txt

3. Create a .env file in the root directory using the provided .env.example as a template:

cp .env.example .env

4. Update the .env file with your credentials:

• THEGRAPH_API_KEY: Your TheGraph API key
• SUPABASE_USERNAME: Your Supabase username
• SUPABASE_PASSWORD: Your Supabase password

Running the Application

1. Start the Streamlit application:

cd python_app
streamlit run streamlit_curation.py

2. The application will open in your default web browser.

Running Tests

cd python_app/tests
pip install -r requirements.txt
pytest

Database Schema

The application requires the following Supabase table structure:

Table: qos_hourly_query_volume

Tracks query volume and fees for subgraphs on an hourly basis.

Column	Type	Description
subgraph_deployment_ipfs_hash	text	IPFS hash of the subgraph deployment
total_query_fees	numeric	Total fees collected for queries
query_count	integer	Number of queries in the time period
end_epoch	timestamp	End time of the hourly period

Indexes

• Primary Key: Composite key of (subgraph_deployment_ipfs_hash, end_epoch)
• Index on end_epoch for time-based queries

Application Structure

• api/: API integrations for TheGraph and Supabase
• models/: Core business logic and optimization algorithms
• ui/tabs/: Streamlit UI components
• utils/: Utility functions and configuration
• tests/: Unit tests

Features

• Query volume analysis
• Curation signal optimization
• Subgraph list management
• Summary statistics and reporting