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Openclaw Data China Stock

Community
shaoxing-xie
openclaw-data-china-stock

A-share/ETF/Option market data collection plugin for OpenClaw.

Overview

Publishershaoxing-xie
Repositoryopenclaw-data-china-stock
Skill nameopenclaw-data-china-stock
Stars
53
Forks
21
Bundled files
263
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 263 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by shaoxing-xie on GitHub. Read the source before you install it.

Installation

Install the Openclaw Data China Stock AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/shaoxing-xie/openclaw-data-china-stock/tree/main
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/shaoxing-xie/openclaw-data-china-stock.git \
  .claude/skills/openclaw-data-china-stock
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Openclaw Data China Stock in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Openclaw Data China Stock on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Openclaw Data China Stock is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

OpenClaw Data China Stock

This plugin provides a ClawHub/OpenClaw compatible toolset for collecting A-share, ETF, and listed option data.

Key tools

  • tool_fetch_market_data: Cross-asset unified entry (recommended).
  • tool_fetch_index_data / tool_fetch_etf_data / tool_fetch_option_data: Compatibility/alias unified entries.
  • tool_get_option_contracts: Fetch option contracts by underlying.
  • tool_read_market_data / tool_read_*: Read previously cached Parquet data (when enabled).

Paired skill suite

  • skills/china-macro-analyst/SKILL.md
  • skills/technical-analyst/SKILL.md
  • skills/market-scanner/SKILL.md
  • skills/market-sentinel/SKILL.md
  • skills/fund-flow-analyst/SKILL.md
  • skills/strategy-backtester/SKILL.md (MVP mode)
  • skills/fundamental-analyst/SKILL.md

China macro analyst tools

  • Primary: tool_fetch_macro_data / tool_fetch_macro_snapshot
  • Compatibility wrappers: tool_fetch_macro_* (21 tools; kept for backward compatibility)
  • Skill: skills/china-macro-analyst/SKILL.md (institutional 4-section narrative template; rules in macro_config.yaml)

Multi-factor equity screening (A-share)

  • tool_screen_equity_factors: Single entry for oscillation/trend templates (e.g. reversal_5d, fund_flow_3d, sector_momentum_5d) over hs300|zz500|zz1000|a_share|custom; returns quality_score, degraded, config_hash, optional sw_mapping stats. Implemented in plugins/analysis/equity_factor_screening.py; registered in tool_runner.py / config/tools_manifest.yaml (+ JSON). JSON Schema: docs/schemas/tool_screen_equity_factors.schema.json.
  • tool_batch_fetch: Batch runner includes tool_screen_equity_factors (see plugins/merged/tool_batch_fetch.py whitelist).
  • 申万一级映射:config/sw_industry_level1_mapping.json,由 scripts/update_sw_industry_level1_mapping.py 生成(默认乐咕乐股 sw_index_first_info + sw_index_third_cons;可选 SW_MAP_USE_EM_SPOT=1 走东财快照)。消费侧:plugins/analysis/sw_industry_mapping.py。

助手侧夜盘落盘、质量门禁与熔断工具(tool_finalize_screening_nightly / tool_set_screening_emergency_pause)在 etf-options-ai-assistant 仓库注册;规程 Skill ota_equity_factor_screening_brief 仅在该助手仓维护并同步到 Gateway。

Fund-flow tools (pick one job, do not duplicate)

  • tool_capital_flow: Single-stock summary for workflows that need flow_judgement / risk_flags style outputs (e.g. limit-up strategies).
  • tool_fetch_a_share_fund_flow: Tabular / ranking / history for onshore A-share money flow (query_kind selects market/sector/stock tables, big deals, main-force ranks, sector drill-down). Uses Eastmoney/Tonghuashun-style AkShare routes with an explicit attempt chain; raw data is not investment advice.
  • tool_fetch_northbound_flow: Stock Connect northbound flows (cross-border), separate from onshore A-share flow tools—keep narratives and citations distinct.
  • tool_fetch_a_share_technical_screener: Tonghuashun-style technical stock screeners (new highs, consecutive up days, volume patterns, MA breakouts, etc.) via AkShare stock_rank_*_ths—not the same as locally computed MACD/RSI from OHLC (use tool_stock_data_fetcher / tool_calculate_technical_indicators for those).

Safety and independence

The plugin is designed to run independently (no dependency on any other repository):

  • It does not inject ~/.openclaw/.env.
  • It supports plugin-specific cache/data paths (configured inside the plugin).

Why it helps retail users

  • Unified cross-asset entry (tool_fetch_market_data) to reduce “tool switching” friction.
  • Multi-source provider priority + automatic fallback to avoid single-provider outages breaking your workflow.
  • Default read-only disk cache semantics (data_cache.enabled=false) to minimize local data pollution risk.

Typical usage

Example: fetch A-share index daily historical data:

yaml
tools:
  - name: tool_fetch_market_data
    params:
      asset_type: index
      view: historical
      asset_code: "000001"
      period: daily
      start_date: "20260201"
      end_date: "20260228"

Example: fetch ETF 5-minute bars:

yaml
tools:
  - name: tool_fetch_market_data
    params:
      asset_type: etf
      view: minute
      asset_code: "510300"
      period: "5"
      start_date: "20260201"
      end_date: "20260228"

Example: fetch latest CPI and use macro snapshot:

yaml
tools:
  - name: tool_fetch_macro_data
    params:
      dataset: cpi
      latest_only: true
  - name: tool_fetch_macro_snapshot
    params:
      scope: monthly
      include_quadrant: true

Cache policy

  • Default: data_cache.enabled=false
    • Disk parquet reads are allowed (if cache exists).
    • Disk parquet writes are skipped.
    • Corrupted parquet files are not deleted in this mode.
  • data_cache.enabled=true
    • Disk parquet reads and writes are both enabled.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

and 140 more files.

Frequently asked questions

What does the Openclaw Data China Stock AI skill do?

A-share/ETF/Option market data collection plugin for OpenClaw.

Why use Openclaw Data China Stock on TypingMind?

Because you install it once and use it with any model. Openclaw Data China Stock is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Openclaw Data China Stock in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/shaoxing-xie/openclaw-data-china-stock/tree/main. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Openclaw Data China Stock?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Openclaw Data China Stock?

As many as you like. As long as a model supports skills, you can use Openclaw Data China Stock with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Openclaw Data China Stock AI skill free?

Yes. It is published on GitHub by shaoxing-xie under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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