Building a Crypto Funding Rate Data Pipeline with Python
The article discusses the creation of a crypto funding rate data pipeline using Python. It outlines the architecture and key design decisions made to optimize data collection and presentation. The author shares insights on the importance of separating data collection from frontend presentation for better performance.
- ▪The data pipeline collects funding rate data from Bybit and Binance every 8 hours and stores it in TimescaleDB.
- ▪Annualised funding rates are calculated using a compound formula, which differs from simple multiplication methods.
- ▪The static site serves JSON snapshots generated from the database, ensuring fast page loads and no backend load.
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try { if(localStorage) { let currentUser = localStorage.getItem('current_user'); if (currentUser) { currentUser = JSON.parse(currentUser); if (currentUser.id === 3914002) { document.getElementById('article-show-container').classList.add('current-user-is-article-author'); } } } } catch (e) { console.error(e); } Kai Posted on May 17 Building a Crypto Funding Rate Data Pipeline with Python #python #crypto #webdev #data Funding Rate Data (2 Part Series) 1 What Crypto Funding Rates Actually Tell You (With Real Data) 2 Building a Crypto Funding Rate Data Pipeline with Python I needed funding rate data across multiple exchanges in a single place. Nothing free gave me what I wanted: historical rates, cross-exchange comparisons, and annualised calculations. So I built it.
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