We extract real-time pricing, historical OHLCV, exchange metrics, and social sentiment from CryptoCompare. Delivered as clean JSON, CSV, or Parquet to S3, BigQuery, or Snowflake on your defined cadence.
Structured, schema-consistent data across all major object types — delivered clean, typed, and ready to query.
Complete list of extractable fields for Coin Profiles objects from cryptocompare.com. All fields typed and schema-versioned.
"symbol": "BTC", "name": "Bitcoin", "algorithm": "SHA-256", "proof_type": "PoW", "total_supply": 21000000, "circulating_supply": 19650000, "market_cap": 1250000000000, "launch_date": "2009-01-03"
| # | symbol | name | algorithm | proof_type | total_supply | circulating_supply |
|---|---|---|---|---|---|---|
| 1 | ||||||
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| 3 |
Complete list of extractable fields for OHLCV Historical objects from cryptocompare.com. All fields typed and schema-versioned.
"timestamp": "2023-10-25T00:00:00Z", "symbol": "ETH", "exchange": "Binance", "pair": "ETH-USDT", "open": 1780.5, "high": 1820.0, "low": 1775.2, "close": 1810.8, "volume_from": 45210.5
| # | timestamp | symbol | exchange | pair | open | high |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Real-Time Pricing objects from cryptocompare.com. All fields typed and schema-versioned.
"symbol": "SOL", "currency": "USD", "price": 142.35, "volume_24h": 2500000, "change_24h": 5.4, "change_pct_24h": 3.94, "last_market": "Coinbase", "last_update": "2023-10-25T14:32:10Z"
| # | symbol | currency | price | volume_24h | change_24h | change_pct_24h |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Exchange Metrics objects from cryptocompare.com. All fields typed and schema-versioned.
"exchange_name": "Kraken", "grade": "AA", "volume_24h": 850000000, "markets_count": 210, "pairs_count": 650, "fiat_supported": true, "country": "United States", "established_year": 2011
| # | exchange_name | grade | volume_24h | markets_count | pairs_count | fiat_supported |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Social Sentiment objects from cryptocompare.com. All fields typed and schema-versioned.
"symbol": "ADA", "twitter_followers": 1350000, "reddit_subscribers": 680000, "github_stars": 4200, "github_commits": 145, "sentiment_score": 72.5, "posts_24h": 1240
| # | symbol | twitter_followers | reddit_subscribers | github_stars | github_commits | page_views |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Our pipeline handles the complexities of high-frequency cryptocurrency data extraction: rate limits, Cloudflare protection, pagination through deep historical timeseries, and real-time polling.
Extract deep historical Open, High, Low, Close, and Volume data across multiple timeframes (minute, hourly, daily) for any supported pair.
High-frequency polling for current ticker prices, 24-hour volume, and percentage changes across aggregated markets.
Capture bid and ask depth, spread metrics, and liquidity data from individual exchanges listed on CryptoCompare.
Track Twitter followers, Reddit activity, GitHub commits, and proprietary CCData sentiment scores per coin.
Extract article titles, URLs, source publications, and publication timestamps from the CryptoCompare news aggregator.
Capture on-chain data points surfaced by CCData, including active addresses, transaction counts, and hash rates.
Extract supported trading pairs, base currencies, and quote currencies across all tracked exchanges.
Monitor exchange grades and benchmark rankings published by CCData's research division.
Run bulk historical backfills or configure continuous pipelines at minute-level cadences with change-detection diffing.
Brief in. Clean data out.
Provide target symbols, exchange pairs, or data types. We design the extraction schema together.
We configure Scrapy crawlers, proxy rotation, rate-limit management, and Cloudflare bypass for cryptocompare.com.
Schema validation, null-rate checks, timestamp alignment, and sample data review before full launch.
JSON, CSV, or Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Financial data platforms employ strict rate limiting and bot mitigation. Here is how we maintain stable extraction for high-frequency crypto data.
CryptoCompare uses Cloudflare to block automated traffic. Our crawlers use residential ISP proxies with realistic browser fingerprints and automated challenge solving to maintain uninterrupted access to public endpoints.
Extracting minute-level OHLCV or real-time ticker data requires aggressive polling. We distribute requests across large IP pools and implement adaptive backoff algorithms to prevent HTTP 429 Too Many Requests errors.
Retrieving years of historical minute-level data requires complex pagination logic. We handle timestamp boundaries, missing periods, and timezone normalisation to ensure continuous, gap-free timeseries datasets.
For static coin metadata and exchange profiles, we maintain a hash index of last-seen values. Subsequent runs only push diffs, reducing compute cost and downstream processing load.
Every run emits structured logs to our observability stack. We alert on null-rate spikes, stale timestamps, and coverage drops, responding before you notice missing data.
Quant funds use deep historical OHLCV data to backtest trading strategies across multiple exchanges and asset pairs.
Wealth management platforms integrate real-time pricing and historical data to track digital asset portfolio performance.
Data scientists correlate CCData social metrics and GitHub activity with price movements to build predictive models.
Traders monitor cross-exchange pricing and order book depth to identify short-term arbitrage opportunities.
Universities and research institutes analyse long-term crypto market trends, volatility, and adoption metrics.
Tax software providers require accurate historical pricing at specific timestamps to calculate capital gains and losses.
"CryptoCompare provides the foundational market data for the digital asset ecosystem, but extracting clean, continuous timeseries data requires dedicated infrastructure."
Most teams underestimate the complexity of financial data extraction: handling strict rate limits, Cloudflare blocks, timestamp normalisation, and gap-free historical pagination requires significant engineering effort. DataFlirt absorbs that complexity so your quants can focus on alpha generation, not pipeline maintenance.
Everything supported by our cryptocompare.com scraper — rendered SPA elements, auth walls, rate-limit evasion and beyond.
Open-source tooling on proven cloud infra — no vendor lock-in, full observability.
Scrapy handles crawl orchestration and rate limiting. Playwright manages JavaScript execution and Cloudflare challenge solving. Combined via scrapy-playwright middleware.
We maintain pools of residential ISP proxies globally. Rotation happens per-request with sticky sessions where required to prevent IP bans and rate limiting.
Pipelines run on AWS Lambda and ECS. Airflow handles scheduling, dependency management, and SLA alerting. Time-series data is staged in ClickHouse before delivery.
Data delivered to where your team already works — no new tooling required.
About cryptocompare.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available market data from CryptoCompare is generally permissible under applicable law. DataFlirt targets only public, non-authenticated pricing, volume, and metadata. We do not extract personal data or circumvent authentication walls for premium enterprise feeds. Clients should review CCData terms of service and consult legal counsel for specific use cases.
We use residential ISP proxies, automated challenge solvers, and adaptive request timing. Our infrastructure distributes requests across large IP pools to stay within acceptable thresholds while maintaining high-frequency extraction.
We extract data for any exchange and trading pair listed on the public CryptoCompare platform, including Binance, Coinbase, Kraken, Bitfinex, and hundreds of smaller regional exchanges.
Real-time polling pipelines achieve sub-minute latency for ticker prices and 24-hour volume metrics on a defined symbol set. Historical OHLCV backfills are processed in parallel for rapid delivery.
We extract the most granular historical data publicly exposed by CryptoCompare, which is typically minute-level OHLCV. True unaggregated tick-by-tick order book data usually requires a direct exchange connection or a premium enterprise CCData API key.
Our smallest packages start at a defined list of target symbols or exchanges with daily delivery. For high-frequency polling or massive historical backfills, we price based on compute volume and delivery frequency. Contact us with your specific requirements.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a massive historical OHLCV backfill or a continuous real-time pricing feed across thousands of pairs, we scope, build, and operate the pipeline. Tell us what you need.
