We extract bid/ask spreads, last sale prices, trade volume, price history, size-level market depth, and product metadata from StockX. 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 Market Pricing objects from stockx.com. All fields typed and schema-versioned.
"style_id": "DD1391-100", "title": "Nike Air Jordan 1 High OG 'Chicago Reimagined'", "size": "US 10", "lowest_ask": 420.00, "highest_bid": 395.00, "last_sale_price": 408.00, "retail_price": 180.00, "price_premium_pct": 127, "currency": "USD"
| # | product_id | style_id | title | brand | colourway | size |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Price History objects from stockx.com. All fields typed and schema-versioned.
"style_id": "DD1391-100", "size": "US 10", "trade_count_30d": 284, "avg_price_30d": 412.50, "price_high_52w": 490.00, "price_low_52w": 340.00, "price_volatility_30d": 8.4, "volume_weighted_avg_price": 409.80
| # | product_id | style_id | size | sale_price | sale_date | trade_count_7d |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Product Metadata objects from stockx.com. All fields typed and schema-versioned.
"style_id": "DD1391-100", "brand": "Nike", "model": "Air Jordan 1 High OG", "colourway": "White/Varsity Red/Black", "release_date": "2022-10-29", "retail_price": 180.00, "category": "Sneakers", "silhouette": "Air Jordan 1"
| # | product_id | style_id | title | brand | model | colourway |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Size-Level Market Depth objects from stockx.com. All fields typed and schema-versioned.
"style_id": "DD1391-100", "size": "US 9.5", "lowest_ask": 435.00, "highest_bid": 400.00, "ask_count": 14, "bid_count": 9, "spread_pct": 8.7, "trade_count_30d": 41
| # | style_id | size | size_type | lowest_ask | highest_bid | last_sale_price |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Our StockX scraper captures the full market signal stack: bid/ask spreads, last sale prices, size-level market depth, price premiums over retail, 52-week ranges, trade volume, and product metadata — structured for quantitative analysis, not just reference.
Lowest ask, highest bid, spread in absolute and percentage terms, and order count on each side — captured per size per run, giving you a live order book snapshot for any StockX listing.
Last sale price, 7/30/90-day trade counts, 30-day average price, 52-week high/low, and volume-weighted average price — the full time-series market data stack per product per size.
Capture the premium over retail as both an absolute figure and a percentage — the core metric for resale market valuation, brand heat scoring, and investment thesis construction.
Full size run coverage per product — bid/ask, spread, last sale, trade volume, and liquidity score per individual size — not just the aggregate product view.
30-day price volatility, bid/ask spread compression, and trade count trends — structured quantitative signals for resale market timing, arbitrage detection, and portfolio risk assessment.
Style ID, brand, silhouette, colourway, release date, and retail price — the master product reference layer that anchors all market pricing records to a clean product identity.
Monitor upcoming release dates and retail prices for products added to StockX pre-release — enabling forward-looking premium projections before a product hits the secondary market.
Run one-off bulk snapshots or configure continuous pipelines at hourly or daily cadences — with change-detection diffing to capture bid/ask movements efficiently.
StockX pricing can be queried in USD, GBP, EUR, AUD, and other supported currencies — normalised to your base currency per run with exchange rate metadata attached.
Brief in. Clean data out.
Provide style ID lists, brand filters, category selections, or release date ranges. We design the market data schema and size coverage together.
We configure Scrapy / Playwright crawlers with residential proxies, anti-fingerprinting measures, and size-level market depth querying for stockx.com.
Schema validation, bid/ask completeness checks, premium calculation audits, and size coverage verification before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence — structured for direct ingestion into quantitative models.
StockX has some of the most aggressive bot detection of any consumer marketplace — protecting its market data from systematic extraction. Here's how we stay resilient.
StockX operates some of the most sophisticated bot detection in consumer eCommerce — with TLS fingerprint analysis, browser behaviour scoring, and IP reputation checks layered together. Our crawlers use US residential ISP proxies with advanced Playwright fingerprint spoofing, randomised mouse movement patterns, and exponential-with-jitter retry timing to maintain durable access.
StockX's bid/ask spreads, price history charts, and size-level market depth panels are fully JavaScript-rendered via React. We run complete Playwright browser sessions with dynamic panel hydration and scroll-triggered data loading — capturing market microstructure that headless HTTP clients cannot access.
StockX market data differs materially by size — a US 9.5 may trade at a 30% premium to a US 12 for the same colourway. We query every available size for each product on every run, building a granular size-level market depth picture that aggregate product views obscure.
Retail price is captured from product metadata and stored alongside market pricing on every run. Premium over retail — in both absolute and percentage terms — is computed at extraction time and delivered as a structured field, not a post-processing burden on your side.
Every run emits structured logs to our observability stack. We alert on bid/ask field null-rates, premium outliers, size coverage drops, and schema drift — and respond before you notice. Hourly cadence pipelines receive enhanced monitoring given the pace of market data movement.
Resellers and resale investment funds use bid/ask spreads, trade volume, and price premium signals to identify arbitrage opportunities — buying at ask in underpriced sizes and selling into demand concentrations.
Sneaker brands, retailers, and trend agencies use StockX price premium data as a real-time brand heat metric — tracking which silhouettes, colourways, and collabs command the highest secondary premiums.
Brands and retailers use secondary market premiums to inform retail price setting, limited release allocation, and SNKRS / raffle strategy — understanding where consumer demand exceeds primary supply.
Academics and financial analysts model sneaker and streetwear markets as alternative asset classes — using StockX price history, volatility, and liquidity data as the primary dataset.
ML teams use StockX price history, trade volume, and product metadata to train secondary market price prediction models — forecasting premium trajectories for new releases.
PE firms and analysts evaluate sneaker market dynamics and brand positioning using StockX premium trends — informing investment theses in footwear brands, resale platforms, and fashion retail.
"StockX is the world's leading marketplace for authenticated sneakers and streetwear — and its bid/ask order book, price premium data, and trade volume signals are the closest thing to a Bloomberg terminal for the resale economy."
StockX has some of the most aggressive bot detection of any consumer marketplace, with TLS fingerprinting, behaviour scoring, and multi-layer IP reputation checks. Reliable access requires advanced Playwright fingerprint spoofing, US residential proxies, and tuned retry logic — all absorbed by DataFlirt so your quant and research teams get clean, structured market data.
Everything supported by our stockx.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, size-level record fanout, and retry logic. Playwright handles React rendering, fingerprint spoofing, and dynamic market data panel interactions. Combined via scrapy-playwright middleware.
We maintain pools of US residential ISP proxies tuned specifically for StockX's traffic profile. Rotation happens per-request with exponential-with-jitter timing to avoid detection pattern matching.
Pipelines run on AWS Lambda (burst) and ECS (sustained). Airflow handles scheduling, dependency management, and SLA alerting. Hourly pipelines receive enhanced monitoring given market data velocity.
Data delivered to where your team already works — no new tooling required.
About stockx.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available market pricing data from StockX is generally permissible under applicable law — reinforced by the hiQ v. LinkedIn ruling and similar precedents establishing that public data on open websites is generally accessible. DataFlirt targets only public, non-authenticated market data and does not extract personal account data, transaction histories, or any information behind authentication walls. We recommend clients review StockX's ToS independently and consult legal counsel for specific use cases.
StockX operates multi-layer bot detection including TLS fingerprint analysis, browser behaviour scoring, and IP reputation checking. Our pipeline uses US residential ISP proxies, advanced Playwright fingerprint spoofing, randomised mouse-movement patterns, and exponential-with-jitter retry timing. We monitor block rates in real time and trigger rotation automatically — maintaining durable access without triggering detection thresholds.
Yes. We query every available size for each product on every run — extracting lowest ask, highest bid, spread, order count per side, and last sale price per size. Size-level market depth is one of the most analytically valuable and hardest-to-extract signals on StockX, and it's a core part of our extraction schema.
For defined style ID sets, we can run pipelines at hourly cadence — capturing bid/ask movements, new last sale records, and spread compression as they happen. For broader catalogues, daily cadence with change-detection diffing is the standard configuration.
Yes. Every run produces timestamped snapshots of bid/ask, last sale, and volume per size per product. We maintain a time-series table from the day your pipeline starts — enabling price trend analysis, volatility modelling, and VWAP calculations over your engagement period.
Absolutely. We provide a sample run of up to 200 style IDs — including full size-run market depth, price premiums, and 30-day volume — as part of the pre-engagement scoping process so you can validate schema fit before signing any contract.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a snapshot of current bid/ask spreads across a style catalogue or a continuous hourly market data feed — we scope, build, and operate the pipeline.
