Universal Financial Scraper with Enhanced Crawling and Voice Interaction

In financial data analysis, analysts and developers often need to gather real-time information from scattered media, announcements, and market interfaces. However, they face challenges like inconsistent data sources, strict anti-crawling mechanisms (e.g., CAPTCHAs, IP bans), and complex dynamic page rendering. Manual handling of these tasks is time-consuming and prone to errors due to crawler failures. The Universal Financial Scraper addresses these issues through optimized crawling logic and new voice interaction capabilities.

Core Capabilities and Working Principles

Built with a zero-dependency core, this tool offers multi-module coverage. Key capabilities include:
- Full-Network Sentiment Monitoring: Uses crawl_global_sentiment to scrape 60+ business and financial media sources and RSS feeds, combining sentiment analysis and deduplication to generate structured snapshots.
- Intelligent Multi-Level Crawling: Implements an API→HTTP→browser degradation chain (e.g., batch_smart_crawl) to handle anti-crawling automatically, including manual CAPTCHA takeover and proxy rotation.
- Content Recognition and Extraction: Uses smart_extract to identify tables, key-value blocks, and financial figures in web pages without extra dependencies.
- Voice Interaction: Supports voice-triggered scraping via run_voice.py for quick queries.

Operationally, users can trigger functions through natural language dialogue—for example, inputting "scrape sentiment for XX company over the last 7 days" calls crawl_global_sentiment. For dynamic pages, browser_human_fetch simulates human-like actions (e.g., reading pauses, scrolling). Risk metric analysis is performed via analyze_risk_metrics to calculate annualized volatility, Sharpe ratios, and more.

Applicable Scenarios and Considerations

This tool is suitable for public financial data scraping, supporting institutional queries, market data retrieval, and research report searches. However, note its capability boundaries:
- Fully Supported: Institutional lists, sentiment monitoring, batch crawling, document parsing (PDF/Word), and risk analysis.
- Not Supported: Pages requiring login (e.g., WeChat Official Account backends), paywalled content (e.g., Wind terminal data), and real-time WebSocket pushes.
- Partially Supported: Dynamically rendered pages (requires Playwright), and sites with strict anti-crawling (e.g., bank websites, success rates 30-50%).

Expected success rates vary by target—e.g., Tiantian Fund reaches 95%+, while bank websites only 30-50%. Dependencies are tiered: core functions are zero-dependency, while browser and akshare require additional installation.