Analyze stock investment opportunities using AKShare market data and scoring.
npx skills add https://github.com/Ynyqy/ZAgent --skill stock-analyzer-ynyqy
Please help me install this Agent Skill. Skill: stock-analyzer Source: https://github.com/Ynyqy/ZAgent/tree/main/zagent/workspace/skills/stock-analyzer Command: npx skills add https://github.com/Ynyqy/ZAgent --skill stock-analyzer-ynyqy
Stock analysts spend hours collecting data, calculating metrics, and synthesizing insights. Stock-analyzer automates data gathering, multi-faceted analysis, and reporting to accelerate research and decision-making.
Analyze a ticker (for example 600519) to generate a comprehensive, multi-faceted stock analysis report.
High-intent search queries and answers about installing and using this skill.
Automate stock analysis by retrieving real-time and historical market data via AKShare to evaluate technicals, capital flows, and risk assessments. This pipeline computes multi-faceted metrics to deliver reliable scoring and actionable investment recommendations.
Yes, cross-stock comparison evaluates multiple tickers to surface which has stronger momentum and favorable risk-reward. It analyzes capital flows, policy signals, and technical indicators to synthesize comparative investment opportunities.
Stock analysis with AKShare and Tushare supports northbound analysis, margin analysis, and dragon_tiger evaluations. These modules track capital flows and shareholder dynamics to compute comprehensive risk assessments and scoring.
Generate a single-stock evaluation by running an end-to-end pipeline that analyzes technicals, concept analysis, and shareholder data. The comprehensive scoring engine synthesizes these facets into actionable insights using modular scripts with robust error handling.
Yes, pandas and numpy are required dependencies to process retrieved market data and calculate technical indicators. They enable the data manipulation and numerical computation needed for the multi-faceted analysis and scoring engine.
Stock data retrieval can fail due to API rate limits or connection issues, but robust error handling maintains reliable analysis. The modular scripts implement fallback mechanisms to ensure continuous data processing and accurate risk assessment outputs.
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