How to perform a scraping for ticks? - briefly
Use a Python scraper (e.g., Scrapy or BeautifulSoup) to request the target pages, parse the HTML for tick symbols and associated data, handle pagination or dynamic loading as needed, and write the extracted information to a CSV or database.
How to perform a scraping for ticks? - in detail
Extracting high‑frequency market data requires a systematic approach that addresses source identification, request formulation, data handling, and compliance. Begin by selecting a reliable provider that publishes tick‑by‑tick information, such as a public exchange API, a broker’s data feed, or a web portal that lists transaction timestamps and prices. Verify that the service permits automated access; review the terms of service and any rate‑limit policies before proceeding.
Next, design the request logic. For RESTful endpoints, construct URLs that include parameters for the instrument, date range, and desired fields (price, volume, timestamp). Use HTTP GET with appropriate headers, including an authentication token if required. When dealing with web pages, employ a headless browser or an HTTP client to fetch the HTML, then locate the JavaScript variables or JSON blobs that contain the tick records. Ensure that the client respects cookies and session identifiers to avoid being blocked.
After receiving the raw response, parse the content into a structured format. For JSON, decode directly into dictionaries or data frames. For HTML tables, apply a parser such as BeautifulSoup to extract rows and columns, then convert them to numeric types. Store the cleaned data in a time‑series database or a CSV file, preserving the original timestamp precision (often to the millisecond).
Implement error handling and throttling. Detect HTTP status codes indicating temporary bans (e.g., 429) and back off exponentially before retrying. Log failed requests with timestamps and error messages to facilitate debugging. Periodically verify data integrity by comparing a sample of retrieved ticks against a known reference source.
Finally, schedule the scraping routine. Use a task scheduler (cron, Windows Task Scheduler, or a workflow manager like Airflow) to run the script at the required frequency—continuous for live feeds or periodic for historical batches. Include monitoring alerts that trigger when the process stops, when data volume falls below expectations, or when unexpected schema changes occur.
By following these steps—source validation, request construction, parsing, storage, resilience measures, and automation—you can reliably acquire tick‑level data for analysis or trading applications.