We extract L2 order books, historical trades, OHLCV candles, and asset metadata from Kraken. Delivered as clean JSON, CSV, or Parquet to S3, BigQuery, or Snowflake on your cadence.
Structured, schema-consistent data across all major object types — delivered clean, typed, and ready to query.
Complete list of extractable fields for Ticker & Pricing objects from kraken.com. All fields typed and schema-versioned.
"pair_name": "XXBTZUSD", "ask_price": 64231.1, "bid_price": 64231.0, "last_trade_price": 64231.1, "volume_24h": 4192.4819, "vwap_24h": 63912.4, "low_24h": 62100.0, "high_24h": 64890.5, "timestamp": "2026-05-12T09:14:00Z"
| # | pair_name | ask_price | ask_whole_lot_volume | ask_lot_volume | bid_price | bid_whole_lot_volume |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for L2 Order Book objects from kraken.com. All fields typed and schema-versioned.
"pair_name": "XETHZUSD", "snapshot_timestamp": "2026-05-12T09:14:00Z", "side": "ask", "price_level": 3450.25, "volume": 12.45, "order_timestamp": 1715505231, "depth_rank": 1, "is_update": false
| # | pair_name | snapshot_timestamp | side | price_level | volume | order_timestamp |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for OHLCV Candles objects from kraken.com. All fields typed and schema-versioned.
"pair_name": "XXBTZUSD", "interval_minutes": 60, "timestamp": 1715504400, "open_price": 64100.5, "high_price": 64350.0, "low_price": 63980.2, "close_price": 64231.1, "vwap": 64185.3, "volume": 342.15, "trade_count": 1842
| # | pair_name | interval_minutes | timestamp | open_price | high_price | low_price |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Historical Trades objects from kraken.com. All fields typed and schema-versioned.
"pair_name": "XXRPZUSD", "price": 0.5124, "volume": 1500.0, "timestamp": 1715505231.412, "buy_sell": "buy", "market_limit": "market", "miscellaneous": "", "trade_id": 14928192
| # | pair_name | price | volume | timestamp | buy_sell | market_limit |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Asset Metadata objects from kraken.com. All fields typed and schema-versioned.
"asset_id": "XXBT", "asset_class": "currency", "altname": "XBT", "decimals": 10, "display_decimals": 5, "collateral_value": 1.0, "status": "enabled", "minimum_order": 0.0001
| # | asset_id | asset_class | altname | decimals | display_decimals | collateral_value |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our Kraken scraper handles high-frequency exchange feeds, WebSocket streams, REST rate limits, and precision decimal formatting to deliver clean quantitative data.
Extract deep order book state across hundreds of pairs. Bid and ask levels, volumes, and timestamps captured at specified polling intervals.
Retrieve historical pricing data at 1m, 5m, 15m, 1h, 4h, and 1d intervals. Open, high, low, close, volume, and VWAP metrics included.
Monitor 24-hour volume, VWAP, last trade price, and current spread across the entire Kraken asset catalogue.
Extract every public trade execution for a given pair. Includes price, volume, side, order type, and millisecond timestamps.
Track margin fee rates, collateral weights, and futures contract specifications across Kraken platforms.
Monitor advertised APY and reward rates for proof-of-stake assets listed on the exchange.
Extract decimal precision parameters, minimum order sizes, and trading status flags for all listed instruments.
Optimised request scheduling to capture high-frequency market movements without triggering Cloudflare blocks.
Export tick data as lightweight JSON, tabular CSV, or compressed Parquet files for time-series databases.
Brief in. Clean data out.
Specify trading pairs, required depth, time intervals, and historical lookback periods. We map the schema.
We configure extraction logic, handle rate limits, and implement precision decimal parsing for crypto assets.
Schema validation, gap detection in time-series data, and outlier checks before production deployment.
JSON, CSV, or Parquet pushed to your S3 bucket, BigQuery dataset, or delivered via Webhook on schedule.
Crypto exchanges deploy aggressive rate limiting and complex data structures. Here is how we maintain data integrity.
Kraken enforces strict API call limits based on a decay model. We distribute requests across our proxy network to maintain high-frequency polling for order books without hitting HTTP 429 errors or temporary bans.
Floating-point math destroys cryptocurrency data integrity. We extract and deliver all pricing and volume metrics as high-precision strings or exact numeric types to prevent rounding errors in downstream systems.
Extracting historical trades requires precise timestamp pagination. Our state management tracks the exact millisecond of the last extracted trade, ensuring zero duplicate records and zero missing ticks during high-volume periods.
While public APIs exist, certain metadata and staking details require scraping Kraken web properties. We use Playwright with residential proxies to bypass Cloudflare turnstiles and extract undocumented frontend state.
Exchanges frequently update asset tickers or halt trading pairs. Our pipeline monitors for structural changes, delistings, and anomalous volume drops, alerting you immediately if market conditions affect data availability.
Quantitative trading desks ingest historical OHLCV and trade tick data to backtest strategies and tune execution algorithms.
Market participants track L2 order book depth across multiple exchanges to identify and execute cross-exchange arbitrage.
Liquidity providers analyse historical spread dynamics and order book density to optimise quoting strategies.
Fintech applications pull daily close prices and historical exchange rates to calculate portfolio valuations and tax liabilities.
Universities and research institutions analyse market microstructure, volatility patterns, and liquidity events in crypto markets.
Analysts correlate trade volume spikes and order book imbalances with external news events to model market sentiment.
"Kraken provides deep liquidity across hundreds of pairs, but reconstructing historical order books requires infrastructure built for high-throughput extraction."
Most teams underestimate the compute required to process raw crypto exchange feeds. Extracting L2 depth and historical ticks requires persistent connection management, rate-limit circumvention, and precision decimal handling. DataFlirt absorbs that complexity so your quants can focus on alpha generation.
Everything supported by our kraken.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.
Distributed request architecture allows sub-second polling of public endpoints without triggering rate limit bans.
Redis-backed state management ensures exact millisecond continuation for historical trade extraction, preventing data gaps.
High-volume tick data is batched, compressed, and written directly to object storage via AWS Lambda and ECS workers.
Data delivered to where your team already works — no new tooling required.
About kraken.com scraping, legality, and pipeline operations.
Ask us directly →Scraping public market data, such as order books and trade histories, is generally permissible. DataFlirt extracts only publicly accessible, non-authenticated market information. We do not handle private keys or execute trades. Clients should review exchange terms of service and consult legal counsel.
Kraken uses a complex call rate decay system. We model this decay precisely and distribute requests across a large pool of residential and datacenter IPs, ensuring we remain well below the threshold for any single connection.
We can extract trade history back to the inception of the trading pair on Kraken. For high-volume pairs like BTC/USD, this requires processing millions of records via stateful pagination.
Cryptocurrency volumes and prices require high decimal precision. We extract and deliver all numerical data as exact strings by default, ensuring your downstream systems can parse them into appropriate Decimal types without data loss.
Our pipelines monitor asset metadata continuously. If an asset is halted, delisted, or undergoes a ticker change, the pipeline logs the event and continues extraction based on your configured fallback rules.
For REST-based polling pipelines, we can deliver snapshots at sub-minute intervals depending on the number of pairs tracked. Data is pushed immediately via Webhook or batched to S3.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a historical dump of OHLCV candles or continuous L2 order book snapshots across 50 pairs, we build and operate the infrastructure. Tell us your requirements.
