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stock-analysis-team

Generate multi-role stock analysis reports with charts and HTML output.

129|33|Updated Mar 14, 2026
Search Tags:#multi-agent#backtesting#charting#report-generation#yfinance#stock-analysis#financial-research
One-click install
npx skills add https://github.com/wudengyao/stock-analysis-team --skill stock-analysis-team
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: stock-analysis-team
Source: https://github.com/wudengyao/stock-analysis-team/tree/main
Command: npx skills add https://github.com/wudengyao/stock-analysis-team --skill stock-analysis-team

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires yfinance, pandas, numpy, ta, matplotlib, requests, beautifulsoup4, jinja2, plotly, and includes scripts (resource) and references (resource) components.

What problem does it solve?

Help users rapidly produce multi-dimensional stock research by coordinating analyst, researcher, trader, and risk teams to turn raw market data into structured insights, visual charts, and a shareable HTML research report.

Core Features & Use Cases

  • Automated data pipeline: fetch historical and real-time market data (A-share and US markets), compute technical indicators (MA, MACD, RSI, Bollinger), and assemble time series for analysis.
  • Multi-role analysis workflow: run fundamentals, technical, news, and sentiment analyses; perform structured bull/bear researcher debates; produce risk scores and trading recommendations.
  • Report & visualization generation: produce PNG charts and rich HTML reports (interactive via Plotly) with cover, conclusions, charts, risk radar, and trading points; supports backtesting validation for strategy evaluation.
  • Use case: run a deep-dive on 600519.SH to generate a full research report with charts, risk score, trading points, and optional backtesting of the proposed strategy.

Quick Start

Analyze AAPL (us) and generate a complete HTML research report with charts, risk score, and buy/hold/sell recommendations.

Frequently Asked Questions about stock-analysis-team

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I generate an automated stock research report with technical indicators and charts?▼

Automated stock research reports are generated by fetching market data via yfinance, computing technical indicators like MACD and RSI, rendering visual charts with matplotlib and plotly, and outputting a structured HTML document.

How do I perform multi-agent stock analysis for a single ticker?▼

Multi-agent stock analysis for a single ticker is performed by coordinating specialized AI roles to conduct fundamentals, technical, news, and sentiment analysis, culminating in structured bull and bear debates and a risk score.

Can I run stock backtesting validation and compute technical indicators using yfinance and pandas?▼

Backtesting validation and technical indicator computation are supported using yfinance to fetch historical market data and pandas with the ta library to calculate moving averages and generate strategy evaluation results.

Does this multi-role AI analysis workflow support both A-share and US market data?▼

The multi-role AI analysis workflow supports both A-share and US market data, fetching real-time and historical time series via yfinance to compute indicators and assemble daily market recaps or single-stock deep-dives.

What is the best way to visualize stock trading points and risk scores in an HTML report?▼

Visualizing stock trading points and risk scores in an HTML report is best achieved by generating interactive Plotly charts and PNG visualizations, then rendering the structured output using jinja2 templates.

Do I need Python libraries like beautifulsoup4 and jinja2 to generate financial research reports?▼

Python libraries like beautifulsoup4 for parsing news sentiment data and jinja2 for templating are required dependencies to assemble the structured JSON and HTML financial research reports.

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