Co-Pilot
Updated a month ago

claude-code-stock-deep-research-agent

Lliangdabiao
0.1k
liangdabiao/claude-code-stock-deep-research-agent
86
Agent Score

💡 Summary

A comprehensive AI-driven framework for conducting in-depth stock investment research.

🎯 Target Audience

Individual investors seeking detailed stock analysisFinancial analysts looking for efficient research toolsInvestment advisors needing structured reportsStudents studying finance and investment strategiesData scientists interested in financial modeling

🤖 AI Roast:Powerful, but the setup might scare off the impatient.

Security AnalysisMedium Risk

Risk: Medium. Review: shell/CLI command execution; outbound network access (SSRF, data egress). Run with least privilege and audit before enabling in production.

Claude Code Stock Deep Research Agent

Investment Research Edition - 专业股票投资尽调系统

⚖️ 免责声明

本研究报告不构成投资建议或推荐。所有投资存在风险,包括本金损失。

重要提示:

  1. 本报告仅供教育和信息用途
  2. 部分数据需要通过官方渠道验证
  3. 过往业绩不代表未来表现
  4. 投资决策前请自行进行尽职调查
  5. 建议咨询合格的财务顾问

🎓 研究框架

本研究基于 Claude Code Deep Research 系统:

  • 方法论: 8阶段股票投资尽调框架
  • 智能体: 28个并行研究智能体
  • 工具: WebSearch、WebFetch、综合分析
  • 质量: 多空平衡、明确风险、数据验证

Table of Contents

  1. Features
  2. Repo Structure
  3. Quick Start
  4. Stock Investment Research
  5. How It Works
  6. Customization
  7. Credits & Acknowledgements
  8. License

Features

This repository contains two specialized deep research frameworks for Claude Code:

1. 🎯 Stock Investment Research (股票投资尽调系统) ⭐ PRIMARY

An 8-phase investment due diligence framework for analyzing publicly traded companies, inspired by professional investment research methodologies.

Key Capabilities:

  • 📊 Comprehensive Analysis: Business model, industry dynamics, financial quality, governance, valuation
  • 🤖 Multi-Agent Research: ~28 parallel research agents working concurrently
  • 📈 Investment Style Adaptation: Value, growth, turnaround, dividend investing
  • 💰 Valuation Models: DCF, reverse DCF, relative valuation, scenario analysis
  • 🛡️ Risk Assessment: Bear case, black swans, monitoring checklist
  • Quality Assurance: Cross-validation (profit vs. cash flow, company vs. peers)
  • 📝 Structured Output: 20-file standardized due diligence report

Research Coverage:

  • A-shares (A股) - 中国大陆股市
  • Hong Kong stocks (港股)
  • US stocks (美股)
  • Other global markets

Output: Signal rating (🟢🟢🟢 Strong Buy / 🟡🟡🟡 Hold / 🔴🔴 Avoid) based on fundamental analysis

2. 📚 General Deep Research (通用深度研究系统)

A flexible 7-phase framework for general research topics (business, technology, academic, etc.).


Repo Structure

| File/Folder | Purpose | |-------------|---------| | CLAUDE.md | Master instructions for Claude Code | | .claude/skills/stock-question-refiner/ | Stock research question refinement skill | | .claude/skills/stock-research-executor/ | 8-phase investment due diligence executor | | .claude/commands/stock-research.md | Main stock research command | | .claude/skills/citation-validator/ | Citation verification skill | | .claude/skills/got-controller/ | Graph of Thoughts controller | | .claude/skills/synthesizer/ | Findings synthesis skill | | STOCK_RESEARCH_IMPLEMENTATION_PLAN.md | Stock research system design document | | CLAUDE2.md | Graph of Thoughts implementation details | | PROJECT_UNDERSTANDING.md | Architecture deep dive | | IMPLEMENTATION_GUIDE.md | User guide |


Quick Start

Stock Research (股票投资尽调)

# Start Claude Code claude # Set model (optional, but recommended) /model opus # Execute stock research /stock-research [股票代码或公司名称] # Examples: /stock-research 600519 # 贵州茅台 (A-share) /stock-research AAPL # 苹果公司 (US) /stock-research 腾讯 00700.HK # 腾讯控股 (HK)

The system will:

  1. Ask about your investment style (价值/成长/困境/红利), holding period, risk tolerance
  2. Deploy ~28 parallel research agents across 7 phases
  3. Generate comprehensive due diligence report in RESEARCH/STOCK_[ticker]_[company]/

Time: 2-4 hours for standard due diligence

Output Example

RESEARCH/STOCK_600519_Kweichow_Moutai/
├── 00_Executive_Summary.md        # 🟡🟡🟡 Hold / Fairly Valued
├── 01_Business_Foundation.md      # Products, revenue, customers
├── 02_Industry_Analysis.md        # Industry cycle, competition
├── 03_Business_Breakdown.md       # Profit drivers, economics
├── 04_Financial_Quality.md        # Cash flow, margins, red flags
├── 05_Governance_Analysis.md      # Ownership, management
├── 06_Market_Sentiment.md         # Bull/bear cases
├── 07_Valuation_Moat.md           # Moat rating, valuation
├── Financial_Data/                # Metrics, trends, peer comparison
├── Valuation/                     # DCF, scenarios
├── Risk_Monitoring/               # Bear case, monitoring checklist
└── sources/                       # Citations with quality ratings

Stock Investment Research

8-Phase Due Diligence Process

| Phase | Focus | Output | |-------|-------|--------| | 1. Business Foundation | 公司事实底座 | Products, revenue mix, customers, value chain, strategy | | 2. Industry Analysis | 行业周期分析 | Cycle stage, supply-demand, competition, policy impacts | | 3. Business Breakdown | 业务拆解 | Segments, profit engines, pricing power, economics | | 4. Financial Quality | 财务质量 | Metrics trends, cash flow vs. earnings, red flags, peers | | 5. Governance Analysis | 股权治理 | Ownership, management, capital allocation, ROIC | | 6. Market Sentiment | 市场分歧 | Bull case, bear case, key debates, verification nodes | | 7. Valuation & Moat | 估值护城河 | Moat rating (0-5), relative/absolute valuation, risks | | 8. Final Synthesis | 综合报告 | Signal rating, thesis, monitoring checklist |

Investment Style Adaptation

The system adapts research approach based on investment style:

| Style | Focus | Valuation Methods | Key Metrics | |-------|-------|-------------------|-------------| | Value (价值投资) | Intrinsic value, margin of safety | P/B, EV/EBITDA, DCF (conservative) | P/B, normalized earnings, FCF yield | | Growth (成长投资) | TAM, competitive positioning | PEG, DCF (aggressive), user models | Revenue growth, moat, TAM penetration | | Turnaround (困境反转) | Liquidity, solvency, catalysts | Liquidation value, option value | Debt ratios, cash runway, catalyst timeline | | Dividend (红利投资) | Dividend sustainability | DDM, FCF yield, yield comparison | Dividend yield, payout ratio, FCF coverage |

Quality Standards

Mandatory Cross-Validation:

  • ✅ Profit vs. cash flow (OCF/NI ratio analysis)
  • ✅ Company vs. peer comparison (key ratios, margins)
  • ✅ Bear case analysis (3-5 key risks identified)

Citation Requirements:

  • Every factual claim must include: Author, Date, Title, URL
  • Source quality rated (A-E scale)
  • A-grade sources: Annual reports, regulatory filings
  • B-grade: Industry reports, analyst research
  • C-grade: News articles, expert commentary

Signal Rating (based on fundamentals only):

  • 🟢🟢🟢 Strong Buy: Significant margin of safety, strong moat, attractive valuation
  • 🟡🟡🟡 Hold: Fairly valued or limited margin of safety
  • 🔴🔴 Avoid: Overvalued, deteriorating fundamentals, excessive risks

Example Usage

User: /stock-research 贵州茅台 600519

Claude: 我可以帮您研究贵州茅台。让我先了解您的投资需求:

       1. 投资风格:价值投资/成长投资/困境反转/红利投资?
       2. 持有周期:短期(<6月)/中期(6-18月)/长期(1-3年以上)?
       3. 关注重点:生意质量/财务质量/估值/公司治理 (选2-3个)
       4. 风险偏好:保守/平衡/激进?

User: 价值投资,长期持有3年以上,关注:生意质量、公司治理、估值,风险保守

Claude: [Generating research plan...]
       [Deploying 28 parallel research agents...]
       [Analyzing 5-10 years of financial data...]
       [Comparing with Wuliangye, Yanghe, Fenjiu...]
       [Assessing competitive moat...]
       [DCF valuation with 3 scenarios...]
       [Identifying bear case risks...]

Output: RESEARCH/STOCK_600519_Kweichow_Moutai/

🟡🟡🟡 Hold / Fairly Valued

Investment Thesis:
茅台是中国领先的白酒品牌,拥有强大的品牌护城河和定价能力。然而当前估值(P/E 32x)安全边际有限。建议继续持有现有仓位,但新投资应等待10-15%回调至¥1,750-1,850区间。

Key Metrics:
- Market Cap: ¥2.8T
- P/E (TTM): 32x (above 5-year average)
- Gross Margin: 91.2%
- ROE: 31%
- Moat Rating: 5/5 (Very Strong)

Top 3 Reasons to Consider:
1. Unassailable brand moat with 800-year heritage
2. Exceptional margins (91% gross, 53% net)
3. Strong cash generation (OCF/NI > 1.0)

Top 3 Reasons to Avoid:
1. Full valuation (P/E 32x, limited margin of safety)
2. Regulatory risk (government scrutiny of luxury pricing)
3. Competitive intensification (Wuliangwa narrowing gap)

Monitoring Checklist:
✅ Strengthen: Price pulls back 10-15% to ¥1,750-1,850
❌ Exit: Price drops below ¥1,300 (-35%), net margin < 45%

[Full report: 20 files, 127 sources, 50+ pages]

How It Works

Stock Research Workflow

User: /stock-research [ticker]
  ↓
stock-question-refiner skill
  - Asks: Investment style? Holding period? Focus areas? Risk tolerance?
  ↓
Structured Research Prompt (investment parameters, priorities, constraints)
  ↓
stock-research-executor skill
  ├─ Phase 1: Business Foundation (4 parallel agents)
  ├─ Phase 2: Industry Analysis (4 parallel agents)
  ├─ Phase 3: Business Breakdown (4 parallel agents)
  ├─ Phase 4: Financial Quality (4 parallel agents)
  ├─ Phase 5: Governance Analysis (4 parallel agents)
  ├─ Phase 6: Market Sentiment (4 parallel agents)
  └─ Phase 7: Valuation & Moat (4 parallel agents)
  ↓
citation-validator skill
  - Verifies all claims have citations
  - Rates source quality (A-E)
  ↓
Comprehensive Investment Due Diligence Report
  - Signal rating
  - 8 phase reports
  - Financial data tables
  - Valuation analysis
  - Risk monitoring checklist

Key Innovations

  1. Investment Style Adaptation: Research approach tailored to value, growth, turnaround, or dividend investing
  2. Parallel Multi-Agent Execution: ~28 agents working concurrently for efficiency
  3. Mandatory Cross-Validation: Profit vs. cash flow, company vs. peers, bear case analysis
  4. Structured Output: Standardized 20-file report format
  5. Quality Assurance: A-E source quality rating, citation verification

General Research Workflow (Secondary)

[ Question ] → [ stock-question-refiner ]
      ↓
[ Structured Prompt ]
      ↓
[ research-executor ]
      ├─ Planning (break into subtopics)
      ├─ Multi-Agent Research (parallel)
      ├─ Source Triangulation (A-E rating)
      └─ Synthesis (combine findings)
      ↓
[ Citation Validation ]
      ↓
[ Research Report ]

Customization

Adapting Stock Research Parameters

The system automatical

5-Dim Analysis
Clarity8/10
Novelty9/10
Utility9/10
Completeness9/10
Maintainability8/10
Pros & Cons

Pros

  • Comprehensive multi-agent research capabilities
  • Structured output with standardized reports
  • Adaptable to various investment styles
  • Mandatory cross-validation for quality assurance

Cons

  • Complex setup may deter casual users
  • Requires understanding of investment concepts
  • Time-consuming for comprehensive reports
  • Dependent on data quality from external sources

Related Skills

pytorch

S
toolCode Lib
92/ 100

“It's the Swiss Army knife of deep learning, but good luck figuring out which of the 47 installation methods is the one that won't break your system.”

agno

S
toolCode Lib
90/ 100

“It promises to be the Kubernetes for agents, but let's see if developers have the patience to learn yet another orchestration layer.”

nuxt-skills

S
toolCo-Pilot
90/ 100

“It's essentially a well-organized cheat sheet that turns your AI assistant into a Nuxt framework parrot.”

Disclaimer: This content is sourced from GitHub open source projects for display and rating purposes only.

Copyright belongs to the original author liangdabiao.