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.
DEV.to (Top) files mainly under programming. We currently carry 4,924 of its stories.
Story provenance
Source · retrieval · rights · ranking — open for full record
inspect →
Story provenance
Attribution is not the same as permission. This drawer separates discovery metadata, excerpts, WeSearch-generated summaries, reuse status, and whether the publisher receives the visit. Nothing here claims a legal grant the publisher has not made.
Record
| Original publisher | DEV.to (Top) |
| Canonical URL | https://dev.to/fundingkai/building-a-crypto-funding-rate-data-pipeline-with-python-2go4 |
| Publication time | Sun, 17 May 2026 21:08:53 +0000 |
| Retrieval time | 2026-05-17T21:33:21.000Z |
| Last seen | 2026-05-17T21:33:21.000Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | RjdcGTRND8mt |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
Rights status (four layers)
WeSearch handling by dimension
| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
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.
…
Excerpt limited to ~120 words for fair-use compliance. The full article is at DEV.to (Top).
Discussion
0 commentsMore from DEV.to (Top)
