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JRJ A-Share Market Data icon

JRJ A-Share Market Data

Professional Updated 2026.08.30

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Please refer to https://skillhub.cn/install/skillhub.md to install @user_eb96790c/jrj-quote-skill.

About this skill

Problems to Solve

In stock analysis and quantitative trading, large language models are often required to provide A-share market data, but directly generated content based on training data may be outdated or inaccurate. For example, when a user asks for the real-time price or historical trend of a stock, unreliable output can lead to analysis biases and compromise decision-making. This highlights the need for authoritative, real-time data sources to ensure AI assistant outputs are credible and usable.

Core Capabilities and Workflow

The JRJ A-Share Market Data Skill addresses this by integrating with the JRJ API. Its core capabilities include:
- Real-time Quote Queries: Fetches the latest prices, changes, trading volumes, and other data for stocks, ETFs, and indices. For instance, using commands like @user_eb96790c/jrj-quote-skill to query real-time quotes for code 600036.SH.
- Historical K-line Retrieval: Supports daily K-line data with adjustable adjustment types (qfq for forward-adjusted, hfq for backward-adjusted, none for unadjusted). Via the scripts/kline.js script, parameters such as --symbol 600036.SH --adjust qfq --limit 100 retrieve specified amounts of historical data.
- Technical Indicator Calculations: Computes various indicators locally based on K-line data, such as moving averages (ma), MACD, RSI, etc. The script automatically requests additional data for indicator preheating to ensure accuracy; e.g., for ma60, it preheats 60 K-lines.

Key Workflow: Upon user request, the skill parses parameters (e.g., --symbol, --period), calls the JRJ API for raw data, performs local indicator calculations (if --with is specified), and returns formatted results in JSON or Markdown. Error handling covers common issues like parameter errors (error code 40001) or invalid API Key (error code 40101).

Applicable Boundaries and Considerations

Although the data source is authoritative, users must note these boundaries:
- Data for Reference Only: All market data and technical indicators are for learning and research purposes only and do not constitute investment advice or trading guidance.
- Accuracy Not Guaranteed: Data may be incomplete or have limited timeliness, especially during extreme market volatility. Responses may include truncated: true, indicating incomplete data return.
- Technical Indicator Limitations: Indicators are based on historical data calculations and cannot predict future trends; they serve only as auxiliary analysis tools.
- Usage Restrictions: Commercial use or illegal activities are prohibited; the skill is limited to personal study and research. Additionally, API request limits and daily quotas apply, with error codes like 42901 (request limit exceeded) to consider.

Through this design, the skill provides a robust A-share data foundation for large language models, supporting diverse scenarios from basic queries to complex analysis. However, users should maintain caution and combine it with other information sources for decision-making.

Use Cases

  • In quantitative trading strategy development, retrieve historical K-line data for specified stocks and compute moving averages (MA) and MACD indicators to backtest and validate trend-following strategies.
  • When users inquire about individual stock real-time quotes through an AI assistant, provide current price, change percentage, and trading volume data to ensure accurate and timely information for immediate decision-making.
  • During stock analysis report writing, compute Bollinger Bands (BOLL) and Relative Strength Index (RSI) indicators based on K-line data to assess price volatility ranges and overbought/oversold conditions.
  • Integrate into automated stock screening tools to monitor real-time quotes for multiple stocks and filter out those meeting specific technical indicator criteria for investment portfolio construction.

Best For

  • Quantitative Researcher: Requires high-frequency historical data and technical indicator calculations to develop and optimize algorithmic trading models.
  • Investment Advisor: Needs real-time market data to provide clients with personalized stock analysis, trend judgments, and investment recommendations.
  • AI Financial Application Developer: When building intelligent investment assistants or chatbots, requires a reliable data source to support natural language queries and responses.
  • Individual Stock Investor: Analyzes trends of held or watched stocks, combining technical indicators to assist in judging buy/sell timing and managing risks.