The text provides an in-depth overview of methods for collecting financial data from NASDAQ, highlighting the opportunities and challenges involved. It outlines three primary techniques: direct API access, using enterprise proxy infrastructure to scale operations, and employing AI-powered web scraping with the Model Context Protocol (MCP). NASDAQ offers a wealth of market data, including price, historical performance, company information, and additional features like interactive charts and earnings calendars, which traders and businesses use for research, backtesting, and competitive intelligence. The document further explains the technical process of identifying and accessing JSON API endpoints, utilizing Python and requests to extract data efficiently. For large-scale data collection, it recommends using residential proxies to overcome anti-bot systems and discusses the setup for integrating AI with web scraping infrastructure via MCP, which simplifies data extraction from dynamic websites. The conclusion emphasizes choosing the appropriate method based on specific needs and suggests evaluating custom scrapers versus purchasing datasets for enterprise-level solutions, with references to Bright Data's products and services for broader data applications.