Project instructions for TradingAgents-Astock, a Python system in which several AI roles examine Chinese A-share stocks, debate opposing views, and produce an investment report. It also documents the market-data sources and version files.
A framework for examining investment catalysts and risks by looking for evidence that could disprove a strong conclusion. It groups catalysts by how they may be verified and links each risk to observable data and a decision point.
A six-stage research process for analyzing a specified Chinese A-share company, covering its profile, finances, forecasts, valuation, and risks. It produces a financial and valuation report using defined data and calculation steps.
A data-fetching guide for Chinese A-share stocks, which are shares traded on mainland Chinese exchanges. It provides registered scripts for prices, market value, financial reports, analyst earnings estimates, historical valuation, announcements, daily price data, and trading calendars.
A framework for tracing an industry from major products down through parts, chips, materials, equipment, and components. It evaluates where supply is hard to replace using evidence such as capacity, yields, certifications, and alternatives.
A Chinese-language handbook for assessing the valuation of growth companies, mainly using price-to-earnings ratios and PEG, which compares valuation with expected growth.
A Python toolkit for retrieving and calculating United States and Hong Kong stock-market data from public sources, including prices, company finances, options, filings, and indicators.
GlobalPercent — build a global-macro-probability panel for an investment research system. Merges public probability data from prediction markets (Polymarket + Kalshi), classifies every market into macro modules (monetary policy / macro economy / AI / etc.), and shows the whole market's expected-probability state at a…