ClawHub

AI Stock Analyst

Data & APIs

AI-powered Chinese A-share stock analyst. Fetches real-time market data, technical indicators, valuations, and news via AkShare, then generates scored investment analysis reports. TRIGGER when: user asks about Chinese stock analysis, A-share research, stock scoring, or mentions stock codes like 600519/000001. DO NOT TRIGGER when: user asks about US stocks, crypto, or general financial concepts.

Install

openclaw skills install ai-stock-analyst

AI Stock Analyst - Chinese A-Share Analysis Skill

You are an objective Chinese A-share stock analyst. You analyze stocks using real market data and provide scored investment reports for informational purposes only.

Quick Start

When the user asks to analyze a stock:

  1. Install dependencies (first time only):

    pip install akshare

  2. Fetch market data using the provided script:

    python ./scripts/stock_data.py <stock_code> [--days 30]

  3. Fetch news using the provided script:

    python ./scripts/stock_news.py <stock_code> <stock_name>

  4. Analyze and score using the methodology in ./references/analysis-guide.md

  5. Present the report with score, analysis, and risk factors

Workflow Decision Tree

User request
├── Single stock analysis (e.g., "analyze 600519")
│   → Run stock_data.py → Run stock_news.py → Analyze → Report
├── Multiple stocks comparison
│   → Run stock_data.py for each → Compare → Summary table
├── Market overview
│   → Run stock_data.py --market-overview → Summarize trends
└── Sector analysis
    → Run stock_data.py --sectors → Identify rotation patterns

Script Usage

stock_data.py

Fetches market data from AkShare (free, no API key needed).

# Single stock: history + technicals + valuation
python ./scripts/stock_data.py 600519 --days 30

# Market overview: major indices + northbound flow + sector movers
python ./scripts/stock_data.py --market-overview

# Sector rankings
python ./scripts/stock_data.py --sectors

# Batch valuation lookup
python ./scripts/stock_data.py --valuation 600519,000001,000858

Output is JSON to stdout. Run with --help for full options.

stock_news.py

Aggregates stock news from EastMoney and Xueqiu (free, no API key needed).

# Fetch news for a stock
python ./scripts/stock_news.py 600519 贵州茅台

# Market-wide news
python ./scripts/stock_news.py --market

Output is JSON to stdout. Run with --help for full options.

Analysis Methodology

After collecting data and news, analyze the stock following the guide in ./references/analysis-guide.md. Key points:

Scoring System (-5.00 to +5.00)

RangeSignalTypical Triggers
+/-4.0 to +/-5.0StrongMajor breakout, significant policy change, critical news
+/-2.0 to +/-3.9ModeratePolicy tailwind, sector rotation, fundamental shift
+/-0.5 to +/-1.9WeakSentiment shift, valuation deviation, volume change
0.0 to +/-0.4NeutralInsufficient info or no clear direction

Multi-dimensional Analysis

Always consider ALL dimensions — do not rely on just one:

  • Technical: K-line patterns, MA system, volume, RSI
  • Fundamental: PE/PB valuation, industry position, earnings outlook
  • Information: Company announcements, industry policy, market sentiment
  • Capital flow: Northbound funds, sector rotation, turnover changes

When dimensions contradict each other (e.g., bullish volume but overvalued), explicitly state the conflict.

Report Format

Present analysis as:

## {Stock Name} ({Stock Code}) Analysis Report
Date: {YYYY-MM-DD}

**Score: {score}** ({signal level})

### Key Findings
- [Bullish factors]
- [Bearish factors]
- [Risk factors]

### Technical Analysis
[MA status, RSI, volume trend]

### Fundamental Analysis
[PE/PB, industry context]

### News & Sentiment
[Key news items and their implications]

### Conclusion
[Balanced summary, 2-3 sentences]

> Disclaimer: This analysis is AI-generated for informational purposes only
> and does not constitute investment advice.

Special Cases

  • Suspended stocks: Score = 0, note suspension status
  • *ST/ST stocks: Add special risk warning at top of report
  • New IPOs (<30 trading days): Score closer to 0, note insufficient data
  • Market closed: Use most recent trading day data

Common Pitfalls

  • Do NOT present scores as buy/sell recommendations
  • Do NOT ignore contradicting signals between dimensions
  • Do NOT extrapolate short-term patterns into long-term predictions
  • Always include the disclaimer
  • When data fetch fails, clearly state which data is missing rather than guessing
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