We extract order books, trade histories, candlestick charts, P2P merchant rates, and token metadata from Binance. 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 Spot Markets objects from binance.com. All fields typed and schema-versioned.
"symbol": "BTCUSDT", "base_asset": "BTC", "quote_asset": "USDT", "last_price": 64321.5, "price_change_pct": 2.45, "volume_24h": 41256.32, "high_24h": 65100.0, "low_24h": 62850.0
| # | symbol | base_asset | quote_asset | last_price | price_change | price_change_pct |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Order Book objects from binance.com. All fields typed and schema-versioned.
"symbol": "ETHUSDT", "timestamp": "2026-05-12T09:14:33Z", "bids_price": 3450.25, "bids_qty": 12.5, "asks_price": 3450.26, "asks_qty": 4.2, "depth_level": 1, "market_type": "spot"
| # | symbol | timestamp | bids_price | bids_qty | asks_price | asks_qty |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for P2P Listings objects from binance.com. All fields typed and schema-versioned.
"advertiser_name": "CryptoKingIN", "asset": "USDT", "fiat": "INR", "price": 88.45, "available_qty": 5000.0, "min_limit": 1000.0, "month_finish_rate": 99.5, "trade_methods": "['IMPS', 'UPI']"
| # | advertiser_name | advertiser_id | asset | fiat | price | available_qty |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Futures Data objects from binance.com. All fields typed and schema-versioned.
"symbol": "SOLUSDT", "mark_price": 145.62, "index_price": 145.65, "last_funding_rate": 0.0001, "next_funding_time": "2026-05-12T16:00:00Z", "open_interest": 1254300.5, "time": "2026-05-12T09:14:33Z"
| # | symbol | mark_price | index_price | estimated_settle_price | last_funding_rate | next_funding_time |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Candlestick Data objects from binance.com. All fields typed and schema-versioned.
"symbol": "BNBUSDT", "interval": "1h", "open_time": "2026-05-12T08:00:00Z", "open_price": 590.2, "high_price": 595.5, "low_price": 588.1, "close_price": 594.3, "volume": 12450.5
| # | symbol | interval | open_time | open_price | high_price | low_price |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our Binance scraper handles every layer of the exchange: spot markets, derivatives, P2P boards, and order book depth — with distributed rate-limit management and geographic proxy routing built in.
Extract real-time ticker pricing, 24h rolling window statistics, and asset metadata across all listed trading pairs.
Capture L2/L3 order book snapshots with bid/ask arrays, quantities, and price levels at millisecond precision.
Track P2P exchange rates, merchant limits, completion rates, and payment methods across fiat currencies.
Extract mark prices, index prices, funding rates, and open interest for USD-M and COIN-M perpetual contracts.
Extract candlestick data across multiple timeframes for backtesting and technical analysis model training.
Monitor new token offerings, staking yields, total locked value, and historical participation metrics.
Capture aggregated public trade logs including price, quantity, timestamp, and buyer/seller maker flags.
Track borrow rates, margin tiers, and cross-margin collateral ratios for listed assets.
Run bulk historical exports or configure continuous polling pipelines at sub-second cadences.
Distributed extraction architecture prevents IP bans and 429 errors during high-frequency data collection.
Brief in. Clean data out.
Provide trading pairs, fiat currencies, or P2P parameters. We design the extraction schema together.
We configure Scrapy / Playwright crawlers, proxy rotation, and session management for binance.com.
Schema validation, null-rate checks, price-outlier detection, and volume verification before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Binance employs strict rate limits and geo-blocking. Here's how we stay resilient — and why quantitative teams choose managed infrastructure over DIY.
Binance employs strict rate limiting and geo-blocking. Our crawlers use residential ISP proxies with geographic targeting, randomised request timing, and IP rotation to maintain uninterrupted access.
For high-frequency order book data, we maintain persistent WebSocket connections. For historical K-lines and P2P listings, we utilise distributed REST polling with dynamic backoff algorithms.
Binance P2P and Launchpad pages are heavily JavaScript-rendered single-page applications. We run full browser sessions to capture dynamic merchant data that headless HTTP clients miss entirely.
Exchange frontends update frequently. Our selector strategy uses multiple fallback chains per field so a layout change does not break your quantitative data pipeline overnight.
Every run emits structured logs to our observability stack. We alert on null-rate spikes, stale prices, and coverage drops. SLA uptime is contractual, not aspirational.
Hedge funds and prop desks use historical order book and trade data to backtest high-frequency trading algorithms.
Market makers monitor spatial arbitrage opportunities between Binance spot, futures, and competing exchanges.
OTC desks and remittance startups track fiat-to-crypto merchant rates, liquidity limits, and payment methods.
Analysts correlate token funding rates, open interest, and liquidation volumes to gauge market positioning.
ML teams use years of historical K-line data to train predictive price models and volatility classifiers.
Research firms track Launchpool staking yields, circulating supply changes, and token unlock events.
"Binance processes billions in daily volume, generating the most critical pricing signals in crypto — but historical depth is inaccessible unless you build the infrastructure."
Most teams underestimate the investment required: reliable Binance extraction requires distributed IP management, WebSocket connection resilience, heavy JavaScript rendering for P2P markets, and anomaly monitoring. DataFlirt absorbs that complexity so your quants can focus on alpha — not the infrastructure.
Everything supported by our binance.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, deduplication, and retry logic. Playwright handles JavaScript rendering for P2P and Launchpad views.
We maintain pools of residential ISP proxies. Rotation happens per-request with sticky sessions where required to bypass geo-restrictions.
Pipelines run on AWS Lambda and ECS. Airflow handles scheduling, dependency management, and SLA alerting. All state stored in managed Postgres.
Data delivered to where your team already works — no new tooling required.
About binance.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available market data from Binance is generally permissible. DataFlirt targets only public, non-authenticated pricing, P2P, and order book data. We do not extract personal data or circumvent authentication walls. Clients should review Binance's ToS and consult legal counsel.
We use distributed residential ISP proxies, randomised request timing, and geographic rotation modelled on human behaviour. For high-frequency extraction, we distribute requests across thousands of IPs to remain well below Binance's WAF thresholds.
Yes. We extract fiat-to-crypto exchange rates, merchant limits, completion rates, and supported payment methods across all supported fiat currencies on the Binance P2P platform.
For REST polling, pipelines achieve sub-minute latency. For order books and ticker pricing, we configure persistent WebSocket connections for sub-second updates. Historical bulk exports are processed daily.
Yes. We extract years of historical K-line data across multiple intervals — from 1-minute to 1-month — for backtesting and quantitative analysis.
Our smallest packages start at a defined set of trading pairs or fiat currencies with weekly delivery. For full-exchange coverage or sub-second streaming, we price based on volume and compute requirements.
Absolutely. We provide a sample run of up to 100 trading pairs or P2P merchant listings as part of the pre-engagement scoping process to validate schema fit and data quality.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off historical K-line dump or a continuous P2P rate monitoring feed — we scope, build, and operate the pipeline. Tell us what you need.
