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BytesAgain β€Ί Skills β€Ί AI Stock Analyst
πŸ¦€ ClawHub

AI Stock Analyst

by @chienchandler

AI-powered Chinese A-share stock analyst. Fetches real-time market data, technical indicators, valuations, and news via AkShare, then generates scored invest...

Versionv1.0.0
Downloads873
#translation#finance#legal
TERMINAL
clawhub install ai-stock-analyst

πŸ“– About This Skill

name: ai-stock-analyst description: "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." version: 1.0.0 metadata: openclaw: requires: env: [] bins: ["python3"] anyBins: ["python3", "python"] emoji: "πŸ“ˆ" homepage: "https://github.com/chienchandler/ai-stock-analyst" os: ["win32", "macos", "linux"] install: [{"cmd": "pip install akshare", "description": "Install AkShare for market data"}] tags: ["finance", "stocks", "chinese-a-shares", "investment", "analysis"] author: chienchandler

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  [--days 30]

3. Fetch news using the provided script: 

   python ./scripts/stock_news.py

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
β”œβ”€β”€ Single stock analysis (e.g., "analyze 600519")
β”‚   β†’ Run stock_data.py β†’ Run stock_news.py β†’ Analyze β†’ Report
β”œβ”€β”€ Multiple stocks comparison
β”‚   β†’ Run stock_data.py for each β†’ Compare β†’ Summary table
β”œβ”€β”€ Market overview
β”‚   β†’ Run stock_data.py --market-overview β†’ Summarize trends
└── Sector analysis
    β†’ Run stock_data.py --sectors β†’ 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 θ΄΅ε·žθŒ…ε°
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)

| Range | Signal | Typical Triggers | |-------|--------|-----------------| | +/-4.0 to +/-5.0 | Strong | Major breakout, significant policy change, critical news | | +/-2.0 to +/-3.9 | Moderate | Policy tailwind, sector rotation, fundamental shift | | +/-0.5 to +/-1.9 | Weak | Sentiment shift, valuation deviation, volume change | | 0.0 to +/-0.4 | Neutral | Insufficient info or no clear direction |

Multi-dimensional Analysis

Always consider ALL dimensions β€” 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

    • πŸ’‘ Examples

    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  [--days 30]

    3. Fetch news using the provided script: 

       python ./scripts/stock_news.py

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

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