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Can Yılmaz
Can Yılmaz

Posted on • Originally published at apify.com

Scraping CoinGecko Derivatives for quant traders: what data is available and how to use it

#webscraping #apify #crypto #productivity

If you are working in the quant traders space and you have ever needed CoinGecko Derivatives as a structured feed, you know the gap between "the data exists on a website" and "the data is in my notebook" can swallow a whole sprint. Here is what the dataset actually contains and the workflow I would build around it.

Why this data matters for quant traders

The short version: back-testing strategies, monitoring liquidity, building risk dashboards and feeding price-discovery models. CoinGecko Derivatives Scraper Scrape Crypto Futures & Perpetual Tickers Scrape 22,000+ crypto derivative tickers from CoinGecko in a single run every perpetual and futures contract across all derivatives exchanges and export them to JSON, CSV or Excel. For quant traders, DeFi analysts and on-chain data engineers, the value is having a normalised, queryable representation of a source that ordinarily fights structured access.

Fields available

The dataset comes back with these fields per record:

  • market -- market
  • symbol -- symbol
  • indexId -- index id
  • contractType -- contract type
  • price -- price
  • priceChangePercent24h -- price change percent24h
  • index -- index
  • basis -- basis
  • spread -- spread
  • fundingRate -- funding rate
  • openInterest -- open interest
  • volume24h -- volume24h
  • lastTradedAt -- last traded at
  • expiredAt -- expired at
  • scrapedAt -- scraped at

The mix is decent. You get enough identifying information to deduplicate across runs, enough content to actually answer questions, and enough timestamps to do time-series work.

Two example records

Trimmed for readability:

{
  "market": "OrangeX Futures",
  "symbol": "SOL-USDT-PERPETUAL",
  "indexId": "SOL",
  "contractType": "perpetual",
  "price": 91.27,
  "priceChangePercent24h": 0.5063848524878908,
  "index": 91.35,
  "basis": 0.054764512595837894,
  "spread": null,
  "fundingRate": 0.0137
}
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{
  "market": "AscendEX  (BitMax) (Futures)",
  "symbol": "BTC-PERP",
  "indexId": "BTC",
  "contractType": "perpetual",
  "price": 80597.63,
  "priceChangePercent24h": 1.4252645086617937,
  "index": 80662.516666667,
  "basis": 0.05286122491717306,
  "spread": null,
  "fundingRate": 0.047
}
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A quant trader could start asking real questions on day one with this shape: aggregate counts across categorical fields, distributions on numeric fields, simple text analysis on the long-form content.

A workflow that works

If I were dropping this into an existing quant traders stack:

  1. Schedule a recurring scrape. Daily or every few hours depending on how fast the source updates.
  2. Land it raw. Object storage, partitioned by date. Cheap, replayable, future-proof against schema changes.
  3. Curate. Dedup on the natural key, type-cast the columns, surface the curated view to your dashboard or notebook layer.
  4. Layer enrichment. Most quant traders workflows need a second source -- reference data, internal CRM, third-party signal -- to extract real value. Build that join early.

Honest trade-offs

This is not a magic dataset. Things to know up-front:

  • The source can rate-limit you. Plan for retries and back-off.
  • Free-text fields are noisy. Budget for cleaning.
  • Schema can drift if the source redesigns. Wire up assertions on record counts and key presence.

Concrete questions you could answer day one

A quant trader working with this dataset could, on the first day:

  • Rank entities by any numeric field, broken down by a categorical field, to find leaders and laggards.
  • Build a time-series of new entries per day from the timestamp columns to see growth or decline.
  • Pull the long-form text into a quick TF-IDF or topic-model to surface what the dataset is actually about under the hood.
  • Spot duplicates and near-duplicates as a data-quality exercise, which often surfaces interesting structural anomalies in the source.

None of those questions require a finished pipeline. A notebook, the JSON file, and an afternoon are enough.

Verdict

For quant traders, this is a useful input -- not a finished answer, but a strong starting point that saves you from writing a brittle HTML parser of your own. The marginal cost of trying it on a real project is a few hours; the marginal value if the dataset clicks with your workflow is open-ended.

For live, customizable extractions of this data, the actor that produced the dataset shown above is published on the Apify Store: logiover/coingecko-derivatives-scraper. It supports JSON, CSV and Excel exports and runs on a schedule.

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