We extract pre-owned Rolex and luxury timepiece listings, condition reports, box and papers status, and price fluctuations from Bob's Watches. Delivered as clean JSON, CSV, or Parquet to your infrastructure.
Structured, schema-consistent data across all major object types — delivered clean, typed, and ready to query.
Complete list of extractable fields for Watch Listings objects from bobswatches.com. All fields typed and schema-versioned.
"sku": "134892", "brand": "Rolex", "model": "Submariner", "reference_number": "116610LN", "price": 11495.0, "condition": "Excellent", "year": "2018", "box_papers": "Box and Papers"
| # | sku | brand | model | reference_number | price | retail_price |
|---|---|---|---|---|---|---|
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Complete list of extractable fields for Buy/Sell Pricing objects from bobswatches.com. All fields typed and schema-versioned.
"reference_number": "116610LN", "buy_price": 9500.0, "sell_price": 11495.0, "margin_spread": 1995.0, "last_updated": "2023-10-14T08:30:00Z", "historical_high": 14500.0, "demand_index": 8.7
| # | sku | reference_number | buy_price | sell_price | margin_spread | last_updated |
|---|---|---|---|---|---|---|
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Complete list of extractable fields for Technical Specs objects from bobswatches.com. All fields typed and schema-versioned.
"reference_number": "116610LN", "caliber": "3135", "power_reserve": "48 hours", "water_resistance": "300 meters", "crystal": "Sapphire", "bezel_material": "Ceramic", "case_material": "Stainless Steel"
| # | sku | reference_number | caliber | jewel_count | power_reserve | water_resistance |
|---|---|---|---|---|---|---|
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Complete list of extractable fields for Inventory & Status objects from bobswatches.com. All fields typed and schema-versioned.
"sku": "134892", "stock_status": "In Stock", "days_on_market": 12, "reserved_status": false, "warranty_status": "1 Year Bob's Warranty", "authenticity_guarantee": true, "shipping_tier": "Overnight"
| # | sku | stock_status | location | days_on_market | view_count | reserved_status |
|---|---|---|---|---|---|---|
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Complete list of extractable fields for Historical Models objects from bobswatches.com. All fields typed and schema-versioned.
"reference_number": "16610", "production_years": "1988-2010", "variant_count": 4, "average_market_price": 9200.0, "rarity_score": 3.5, "successor_reference": "116610LN"
| # | reference_number | production_years | variant_count | average_market_price | rarity_score | dial_colors |
|---|---|---|---|---|---|---|
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Extract deep technical specifications, condition reports, and pricing spreads from the industry's leading pre-owned Rolex exchange.
Map every listing to its exact reference number, allowing precise cross-market comparisons for Rolex, Omega, Patek Philippe, and Audemars Piguet.
Capture both the retail asking price and the direct buy offer price to calculate market margins and dealer spreads.
Extract detailed condition metrics, serial year estimates, and the exact status of original boxes, warranty cards, and manuals.
Normalise complex horological data including caliber numbers, power reserves, bezel materials, and bracelet types.
Collect URLs for macro photography of dials, movements, and case conditions required for algorithmic authentication models.
Track when specific references enter and leave the catalogue to calculate days on market and demand liquidity.
Handle structural differences in listings between 4-digit vintage references and modern 6-digit ceramic models.
Aggregate pricing over time to build valuation curves for specific references based on condition and completeness.
Configure high-frequency sweeps of the fresh inventory section to identify underpriced assets immediately upon listing.
Brief in. Clean data out.
Specify target brands, reference numbers, or entire catalogue sweeps. We design the extraction schema together.
We configure Scrapy crawlers, proxy rotation, and normalisation logic for horological specifications.
Schema validation, null-rate checks, and specification accuracy audits before full launch.
JSON, CSV, or Parquet pushed to your AWS S3 bucket or data warehouse on agreed cadence.
Watch specifications are notoriously unstructured. We normalise the data so your analysts can query it immediately.
Listings often use inconsistent naming for dials (e.g., 'Stick', 'Index', 'Baton') or bracelets ('Oyster', 'Jubilee', 'President'). Our pipeline applies regex-based normalisation to map these variations into standard categorical fields.
The reference number is the primary key of the watch market. We extract and clean reference numbers from titles and specification tables, ensuring you can join this data against chronos24 or other market sources.
We separate the binary 'Box and Papers' status from subjective condition ratings ('Excellent', 'Mint', 'Vintage'), allowing you to model price premiums based on completeness.
By hashing the SKU and price fields, we detect when a watch is discounted or removed from inventory, providing a clean changelog of market liquidity.
High-frequency scraping triggers rate limits. We distribute requests across US-based residential proxies to maintain continuous access to pricing updates without IP bans.
Insurers and appraisers use aggregated pricing data to determine current replacement value for specific reference numbers.
Dealers monitor buy and sell spreads across multiple platforms to identify underpriced inventory for immediate acquisition.
Alternative asset funds track the appreciation of specific vintage models and dial variations to guide acquisition strategy.
Machine learning teams scrape high-resolution macro images of authenticated watches to train counterfeit detection models.
Grey market dealers track specific serial years and conditions to fulfil client requests for rare configurations.
Authorised dealers monitor secondary market premiums to understand true demand for waitlisted models.
"Bob's Watches operates the most transparent secondary market for Rolex timepieces, defining baseline valuations for the entire luxury watch industry."
Scraping luxury watch marketplaces requires handling nuanced schema variations between vintage and modern pieces. We manage the proxy rotation, JavaScript execution, and schema normalisation so your quants can focus on pricing models rather than DOM maintenance.
Everything supported by our bobswatches.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 manages the crawl frontier and deduplication while Playwright handles dynamic content loading for image galleries and specific watch specifications.
Custom Python middleware cleans horological data, standardising references, stripping currency symbols, and mapping condition states to integers.
Airflow schedules daily or hourly sweeps of the inventory, running on AWS infrastructure with automated alerting for schema drift.
Data delivered to where your team already works — no new tooling required.
About bobswatches.com scraping, legality, and pipeline operations.
Ask us directly →Yes. Our pipeline extracts both the retail asking price and the platform's direct buy offer for specific reference numbers, allowing you to calculate the gross margin spread.
We use custom normalisation logic to map unstructured text into standard categories. For example, 'Serti', 'Diamond', and 'Factory Gem' dials are mapped to a consistent boolean or categorical field depending on your schema requirements.
We extract the direct URLs to the highest resolution images available in the gallery. We do not download the binary image files by default, but can configure the pipeline to push them to your S3 bucket if required.
For 'New Arrivals' sections, we can configure pipelines to run at hourly or sub-hourly intervals. Full catalogue sweeps are typically scheduled daily.
Yes. By monitoring the inventory status of specific SKUs, we can flag when a watch transitions to 'Sold' or is removed from the site, providing data on days-on-market.
Because we isolate and clean the reference number (e.g., '116610LN'), you can easily join this dataset with pricing data from Chrono24 or auction house results.
20-minute scoping call. Pilot dataset within the week. Production within two. Define your target reference numbers and delivery cadence. We handle the scraping infrastructure so you can focus on market analysis.
