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What does Pricecharting.com Product Collection Price History Scraper do?

Introduction

PriceCharting.com, founded by two brothers in 2007, began as a video game price guide and has since expanded to become a comprehensive price tracking platform for collectibles. The website now covers video games, sports cards, trading cards, and strategy guides, making it an invaluable resource for collectors, retailers, and market analysts.

Our PriceCharting.com scraper addresses the critical need for automated data collection from this platform. Manual data extraction from multiple product collection pages is time-consuming and prone to errors, especially when tracking price trends across hundreds or thousands of items. This scraper eliminates the manual work while providing structured, reliable access to historical pricing data that powers investment decisions and market analysis.

Comprehensive Scraper Overview

The PriceCharting.com scraper is a specialized data extraction tool designed to automatically collect price history information from product collection pages. Unlike generic web scrapers, this tool is specifically optimized for PriceCharting's unique data structure and provides clean, organized output suitable for immediate analysis.

Key advantages include robust error handling with configurable retry mechanisms, residential proxy support to avoid detection, and structured JSON output that integrates seamlessly with analytics workflows. The scraper handles various collection types, from Pokemon promotional cards to vintage video game consoles, making it versatile for different market segments.

Target users include trading card dealers tracking market trends, game store owners managing inventory pricing, investment analysts researching collectibles markets, and individual collectors monitoring their portfolio values. The tool scales from single collection analysis to bulk data extraction across multiple product categories.

Detailed Input and Output Specifications

Example url: https://www.pricecharting.com/console/pokemon-promo

Example Screenshot of product collection price history information page:

Input Format

The scraper accepts JSON configuration with three key components:

Input:

{
  "max_retries_per_url": 2, // Maximum waiting time when accessing the links you provided.
  "proxy": { // Add a proxy to ensure that during the data collection process, you are not detected as a bot.
    "useApifyProxy": true,
    "apifyProxyGroups": [
      "RESIDENTIAL" 
    ],
    "apifyProxyCountry": "SG" // You should choose an Country that coincides with the Country you want to collect data from
  },
  "urls": [ // Links to product collection price history information pages.
    "https://www.pricecharting.com/console/pokemon-promo"
  ]
}

max_retries_per_url: Controls resilience by setting maximum retry attempts for failed requests. Higher values increase success rate but extend processing time. Recommended range: 1-5 retries.

proxy configuration: Essential for avoiding bot detection. Residential proxies provide better success rates than datacenter proxies. Country selection should match your target market or data jurisdiction requirements.

urls array: Contains specific PriceCharting collection URLs. Each URL must point to a complete collection page (e.g., console-specific pages, card series pages). Individual item pages are not supported.

Output Format and Field Explanations

You get the output from the Pricecharting.com Product Collection Price History Scraper stored in a tab. The following is an example of the Information Fields collected after running the Actor.

[ // List of product collection price history information
  {
    "url": "https://www.pricecharting.com/console/pokemon-promo",
    "title": "Prices for Pokemon Promo Pokemon Cards",
    "chart_data": {
      "median": [
        [
          "2021-02-03 00:00:00+00:00",
          508
        ],
        [
          "2021-03-03 00:00:00+00:00",
          487
        ],
        [
          "2021-04-03 00:00:00+00:00",
          493
        ],
        [
          "2021-05-03 00:00:00+00:00",
          463
        ],
        [
          "2021-06-03 00:00:00+00:00",
          441
        ],
        [
          "2021-07-03 00:00:00+00:00",
          429
        ],
        [
          "2021-08-24 18:05:58+00:00",
          417
        ],
        [
          "2021-09-03 19:55:27+00:00",
          420
        ],
        [
          "2021-10-02 02:05:02+00:00",
          400
        ],
        [
          "2021-11-02 08:04:54+00:00",
          435
        ],
        [
          "2021-12-02 09:05:02+00:00",
          470
        ],
        [
          "2022-01-03 00:00:00+00:00",
          480
        ],
        [
          "2022-02-02 09:05:06+00:00",
          482
        ],
        [
          "2022-03-10 17:26:40+00:00",
          496
        ],
        [
          "2022-04-02 08:02:59+00:00",
          481
        ],
        [
          "2022-05-02 08:02:55+00:00",
          482
        ],
        [
          "2022-06-02 08:02:50+00:00",
          497
        ],
        [
          "2022-07-02 08:03:00+00:00",
          475
        ],
        [
          "2022-08-02 08:03:03+00:00",
          461
        ],
        [
          "2022-09-02 08:02:59+00:00",
          463
        ],
        [
          "2022-10-02 08:02:48+00:00",
          467
        ],
        [
          "2022-11-02 08:03:04+00:00",
          455
        ],
        [
          "2022-12-02 09:03:11+00:00",
          463
        ],
        [
          "2023-01-02 09:03:20+00:00",
          457
        ],
        [
          "2023-02-02 09:03:21+00:00",
          496
        ],
        [
          "2023-03-02 09:03:43+00:00",
          475
        ],
        [
          "2023-04-02 08:03:44+00:00",
          461
        ],
        [
          "2023-05-02 08:03:32+00:00",
          455
        ],
        [
          "2023-06-02 08:03:46+00:00",
          449
        ],
        [
          "2023-07-02 08:03:38+00:00",
          438
        ],
        [
          "2023-08-02 08:04:07+00:00",
          428
        ],
        [
          "2023-09-02 08:03:45+00:00",
          415
        ],
        [
          "2023-10-02 08:03:35+00:00",
          412
        ],
        [
          "2023-11-02 08:03:46+00:00",
          400
        ],
        [
          "2023-12-02 09:03:53+00:00",
          423
        ],
        [
          "2024-01-02 09:03:25+00:00",
          418
        ],
        [
          "2024-02-02 09:03:36+00:00",
          464
        ],
        [
          "2024-03-02 09:03:55+00:00",
          424
        ],
        [
          "2024-04-02 08:03:34+00:00",
          419
        ],
        [
          "2024-05-02 08:03:38+00:00",
          422
        ],
        [
          "2024-06-02 08:03:35+00:00",
          425
        ],
        [
          "2024-07-02 08:03:55+00:00",
          435
        ],
        [
          "2024-08-02 08:04:12+00:00",
          400
        ],
        [
          "2024-09-02 08:03:58+00:00",
          399
        ],
        [
          "2024-10-02 08:03:48+00:00",
          401
        ],
        [
          "2024-11-02 08:04:02+00:00",
          400
        ],
        [
          "2024-12-02 09:04:00+00:00",
          399
        ],
        [
          "2025-01-02 09:03:50+00:00",
          407
        ]
      ],
      "value": [
        [
          "2021-02-03 00:00:00+00:00",
          1567
        ],
        [
          "2021-03-03 00:00:00+00:00",
          1739
        ],
        [
          "2021-04-03 00:00:00+00:00",
          1716
        ],
        [
          "2021-05-03 00:00:00+00:00",
          1635
        ],
        [
          "2021-06-03 00:00:00+00:00",
          1411
        ],
        [
          "2021-07-03 00:00:00+00:00",
          1437
        ],
        [
          "2021-08-24 18:05:58+00:00",
          1376
        ],
        [
          "2021-09-03 19:55:27+00:00",
          1403
        ],
        [
          "2021-10-02 02:05:02+00:00",
          1397
        ],
        [
          "2021-11-02 08:04:54+00:00",
          1335
        ],
        [
          "2021-12-02 09:05:02+00:00",
          1352
        ],
        [
          "2022-01-03 00:00:00+00:00",
          1435
        ],
        [
          "2022-02-02 09:05:06+00:00",
          1403
        ],
        [
          "2022-03-10 17:26:40+00:00",
          1539
        ],
        [
          "2022-04-02 08:02:59+00:00",
          1505
        ],
        [
          "2022-05-02 08:02:55+00:00",
          1505
        ],
        [
          "2022-06-02 08:02:50+00:00",
          1556
        ],
        [
          "2022-07-02 08:03:00+00:00",
          1543
        ],
        [
          "2022-08-02 08:03:03+00:00",
          1557
        ],
        [
          "2022-09-02 08:02:59+00:00",
          1561
        ],
        [
          "2022-10-02 08:02:48+00:00",
          1499
        ],
        [
          "2022-11-02 08:03:04+00:00",
          1467
        ],
        [
          "2022-12-02 09:03:11+00:00",
          1749
        ],
        [
          "2023-01-02 09:03:20+00:00",
          1520
        ],
        [
          "2023-02-02 09:03:21+00:00",
          1524
        ],
        [
          "2023-03-02 09:03:43+00:00",
          1454
        ],
        [
          "2023-04-02 08:03:44+00:00",
          1453
        ],
        [
          "2023-05-02 08:03:32+00:00",
          1456
        ],
        [
          "2023-06-02 08:03:46+00:00",
          1457
        ],
        [
          "2023-07-02 08:03:38+00:00",
          1469
        ],
        [
          "2023-08-02 08:04:07+00:00",
          1462
        ],
        [
          "2023-09-02 08:03:45+00:00",
          1471
        ],
        [
          "2023-10-02 08:03:35+00:00",
          1448
        ],
        [
          "2023-11-02 08:03:46+00:00",
          1413
        ],
        [
          "2023-12-02 09:03:53+00:00",
          1403
        ],
        [
          "2024-01-02 09:03:25+00:00",
          1415
        ],
        [
          "2024-02-02 09:03:36+00:00",
          1486
        ],
        [
          "2024-03-02 09:03:55+00:00",
          1433
        ],
        [
          "2024-04-02 08:03:34+00:00",
          1602
        ],
        [
          "2024-05-02 08:03:38+00:00",
          1587
        ],
        [
          "2024-06-02 08:03:35+00:00",
          1600
        ],
        [
          "2024-07-02 08:03:55+00:00",
          1604
        ],
        [
          "2024-08-02 08:04:12+00:00",
          1709
        ],
        [
          "2024-09-02 08:03:58+00:00",
          1685
        ],
        [
          "2024-10-02 08:03:48+00:00",
          1659
        ],
        [
          "2024-11-02 08:04:02+00:00",
          1656
        ],
        [
          "2024-12-02 09:04:00+00:00",
          1659
        ],
        [
          "2025-01-02 09:03:50+00:00",
          1658
        ]
      ]
    },
    "from_url": "https://www.pricecharting.com/console/pokemon-promo"
  }, // ... Many other product collection price history details
]

The scraper returns four critical data fields for each processed collection:

URL: The original source URL from your input list. This field enables data traceability and allows you to map results back to specific collections when processing multiple URLs simultaneously.

Title: The collection name as displayed on PriceCharting (e.g., "Pokemon Promo Cards", "Super Nintendo Games"). This human-readable identifier helps categorize and organize your extracted data, especially when combining results from multiple collections.

Chart Data: The core value of this scraper - comprehensive price history information including current market prices, historical trends, condition-based pricing (loose, complete, new), and sale data points. This structured data enables trend analysis, price forecasting, and market timing decisions.

From URL: Source verification field that confirms the extraction origin. This redundancy ensures data integrity and helps identify any processing errors or URL redirections during scraping.

Usage Guidelines and Best Practices

Step 1: Identify target collections by browsing PriceCharting's category pages. Focus on collection-level URLs rather than individual item pages for optimal data coverage.

Step 2: Configure proxy settings based on your location and data requirements. Use residential proxies for better success rates, and select proxy countries that align with your market focus.

Step 3: Set appropriate retry values considering your time constraints and data criticality. Higher retry counts improve success rates but increase processing time.

Step 4: Monitor output for data completeness and quality. Implement validation checks to ensure all expected collections returned data.

Common troubleshooting: If scraping fails, verify URL formats match PriceCharting's collection structure. Check proxy connectivity and consider adjusting retry parameters. Some collections may have restricted access or require additional handling.

Best practices: Space out large collection requests to avoid overwhelming the target server. Implement local caching to avoid re-scraping unchanged data. Validate output data formats before feeding into analysis pipelines.

Business Benefits and Applications

Time efficiency: Manual data collection from PriceCharting typically requires hours per collection. This scraper reduces that time to minutes while providing more comprehensive data coverage.

Real-world applications: Trading card shops use scraped data for dynamic pricing strategies. Game collectors track portfolio values and identify investment opportunities. Market researchers analyze pricing trends across different collectibles categories to predict market movements.

Business value: Automated price tracking enables data-driven inventory decisions, competitive pricing strategies, and market timing optimization. For businesses dealing with multiple collectibles categories, this represents significant operational efficiency gains and improved profit margins through better market insight.

Conclusion

The PriceCharting.com scraper transforms manual data collection into an automated, reliable process that delivers structured market intelligence. By providing comprehensive price history data in a standardized format, it enables collectors, retailers, and analysts to make informed decisions based on current market trends and historical patterns.

Ready to streamline your collectibles market analysis? Start extracting valuable price history data today and gain the competitive edge that comes from automated market intelligence.

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Your feedback

We are always working to improve Actors' performance. So, if you have any technical feedback about Pricecharting.com Product Collection Price History Scraper or simply found a bug, please create an issue on the Actor's Issues tab in Apify Console.