$ show simonlin1212/global-stock-data
global-stock-data is a comprehensive financial data pipeline designed for AI coding assistants to access US and Hong Kong market data. It implements a 13-layer architecture that fetches data from 11 diverse sources including exchanges (CBOE), regulatory bodies (SEC, FINRA), financial data providers (Eastmoney, Yahoo Finance), and macroeconomic sources (Treasury, CFTC). The system normalizes data from various formats (JSON, CSV, text) into consistent schemas for real-time quotes, historical prices, technical indicators, fundamentals, options data, SEC filings, short volume, and macroeconomic indicators. Unlike a traditional application, it functions as a reusable data toolkit where each layer can be independently accessed to solve specific financial data acquisition problems.
The LLM identified 5 files worth highlighting for reuse:
Implements a sophisticated HTTP client that handles source-specific rate limits, SEC User-Agent requirements, and distinguishes between transient 'data not available' conditions versus configuration/network errors that must bubble up. Demonstrates advanced API integration patterns for financial data sources with nuanced error conditions.
Automatically manages the complex cookie+crumb authentication required by Yahoo Finance APIs, including session caching and transparent refresh. Solves a common pain point in financial data integration with Yahoo's non-standard auth mechanism.
Extracts structured financial data from SEC XBRL filings, handling the complex nested structure, automatic unit selection (USD vs shares), and filtering to relevant forms (10-K/10-Q). Demonstrates expertise in working with the notoriously complex XBRL data format.
Leverages SEC's free frames API to perform market-wide fundamental screening on any XBRL tag. Handles the subtle differences between instant and duration concepts in XBRL reporting, implementing smart retry logic for instant concepts that require the 'I' suffix in period notation.
Parses OCC option symbols correctly (including adjusted roots with numbers from corporate actions), calculates unusual activity using volume/open interest ratios, and provides chain-level analytics like put/call ratios and net delta exposure. Demonstrates deep understanding of options market mechanics and flow detection techniques.