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Dataquant Connector 量化数据通道

对接 DataQuant 量化数据平台，为回测与选股提供 REST API 取数通道，覆盖 A股/港股/美股/加密货币/指数/ETF 六大市场，支持 K线、估值快照、条件筛选与宏观数据。当用户需要从 DataQuant 查询行情、K线、估值快照、条件选股或宏观数据，或消息中出现 "DataQuant" / "dataquant kline" / "dataquant screen" 等取数指令时启用本 Skill。 Skill: Dataquant Connector 量化数据通道 Owner: ai-ip Summary: 对接 DataQuant 量化数据平台，为回测与选股提供 REST API 取数通道，覆盖 A股/港股/美股/加密货币/指数/ETF 六大市场，支持 K线、估值快照、条件筛选与宏观数据。当用户需要从 DataQuant 查询行情、K线、估值快照、条件选股或宏观数据，或消息中出现 "DataQuant" / "dataquant kline" / "dataquant screen" 等取数指令时启用本 Skill。 Tags: latest:0.1.0 Version history: v0.1.0 | 2026-08-06T06:31:39.949Z | auto - Initial release of dataquant-connector skill. - Provides REST API access t

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2.4k

Updated

Oct 9, 2026

Version

0.1.0

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CLAWHUB

About

What it does, and when to use it.

Capability contract not published. No trust telemetry is available yet. 2.4K downloads reported by the source. Last updated 10/9/2026.

Avoid when

Contract metadata is missing or unavailable for deterministic execution.

Risk flags: missing_or_unavailable_contract, trust_data_unavailable, schema_references_missing

Public facts

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Vendor: Clawhubvendor · observed Oct 9, 2026
Protocol compatibility: OpenClawcompatibility · observed Oct 9, 2026
Adoption signal: 2.4K downloadsadoption · observed Oct 9, 2026
Latest release: 0.1.0release · observed Aug 6, 2026
Handshake status: UNKNOWNsecurity

Install and run

Setup complexity: low.

clawhub skill install s17425n105j31pkv7ny8pydk2s8bz3b2:dataquant-connector

Setup complexity is LOW. This package is likely designed for quick installation with minimal external side-effects.
Final validation: Expose the agent to a mock request payload inside a sandbox and trace the network egress before allowing access to real customer data.

Contract: missing

curl -s "https://www.xpersona.co/api/v1/agents/clawhub-ai-ip-dataquant-connector/snapshot"

Documentation

CLAWHUB

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Extracted files

5 files captured from the source.

SKILL.md

---
name: dataquant-connector
description: >-
  对接 DataQuant 量化数据平台，为回测与选股提供 REST API 取数通道，覆盖 A股/港股/美股/加密货币/指数/ETF 六大市场，支持 K线、估值快照、条件筛选与宏观数据。
  当用户需要从 DataQuant 查询行情、K线、估值快照、条件选股或宏观数据，或消息中出现 "DataQuant" / "dataquant kline" / "dataquant screen" 等取数指令时启用本 Skill。
---

# DataQuant Connector

## 触发条件

当用户消息包含以下任一模式时启动本 Skill：

| 类别 | 触发词（含中文/英文） |
|------|----------------------|
| 取数指令 | "用 DataQuant 取"、"DataQuant 查"、"dataquant kline"、"dataquant batch"、"dataquant detail"、"dataquant screen"、"dataquant search"、"dataquant macro"、"dataquant quota" |
| API Key 提供 | "DataQuant API Key 是"、"DQ_API_KEY="、"DATAQUANT_API_KEY" |

## 前置依赖

- **必装**：Python 3.8+，`requests`（`pip install requests`）
- **必填**：DataQuant API Key（注册地址：https://app.dataquant.trade）。优先读环境变量 `DATAQUANT_API_KEY`，缺失时向用户索取，不要自己编造。
- **Base URL**：`https://api.dataquant.trade`，认证方式 `X-API-Key` Header（CLI 已封装）。

## 市场代码（固定 6 个）

```
ashare     A 股
hkstock    港股
usstock    美股
crypto     加密货币
indices    全球指数
etfs       ETF
```

Coverage：A 股 ~3000 / 港股 ~1000 / 美股 ~2000 / 加密 ~100 / 指数 15 / ETF 11。

## 端点总览

| 命令 | 方法 + 路径 | CLI 子命令 |
|------|------------|-----------|
| 单标的日线 | `GET /{market}/klines/{symbol}` | `kline` |
| 批量日线 | `GET /{market}/klines`（`symbols=` 逗号分隔，必填） | `batch` |
| 单标的最新快照 | `GET /{market}/detail/{symbol}` | `detail`（单代码） |
| 批量最新快照 | `GET /{market}/detail`（`symbols=` 逗号分隔） | `detail`（多代码） |
| 条件筛选 | `GET /{market}/screen` | `screen` |
| 标的搜索 | `GET /{market}/symbols` | `search` |
| 宏观数据 | `GET /macro` | `macro` |
| 配额查询 | `GET /quota` | `quota` |

## 参数与默认值

**K 线 fields**
- 单标的 `/klines/{symbol}`：默认 `*`（全字段）。
- 批量 `/klines`：默认 `close,volume`。
- `fields` 支持短码或全名：`o,h,l,c,v,a` / `open,high,low,close,volume,amount`；非法列返回 400。
- `adj`：`bfq`（不复权，默认）/ `qfq` / `hfq`。
- `limit`：单标的默认 100；批量默认 100。服务端按套餐 `max_single_rows` 截断（free=100，pro/ent=500）。
- `offset`：默认 0。
- 批量额外支持 `date=YYYY-MM-DD`（与 `start`/`end` 互斥，取该日快照）。

**detail fields**：默认 `*` 全字段。`symbol`、`date` 始终返回，不受 `fields` 过滤。detail 接口不含 `adj_factor`。

**screen**
- `sort` 默认 `change_percent`；`order` 默认 `desc`。
- `limit` 默认 50；`offset` 默认 0。返回列固定为服务端 `_SCREEN_COLUMNS`（23 列：symbol/name/market_name/date/close + 估值/规模/动量/均线 等）。
- 过滤语法：`min_<列>` / `max_<列>`，列名必须在白名单内（见 `references/api-reference.md`）；不在白名单的列被服务端静默忽略。

**search（`/{market}/symbols`）**
- `search`：对 symbol 代码做子串匹配（例如 `600519`、`sh600519`、`BTC`）。不支持中文名称搜索——服务端仅按代码匹配，传 `贵州茅台`/`茅台` 返回空。
- `limit` 默认 50，最大 100；`offset` 默认 0。
- 返回结构：`{"market","total","count","offset","symbols":[...]}`，`symbols` 是代码字符串列表（不含名称）。

**macro**
- `indicator`：`gdp` / `cpi_ppi` / `pmi`，不传返回全部。
- `start` / `end`：年份 `YYYY`（可选）。⚠️ 服务端按字符串比较 `date`，若想包含末年数据，建议 `end` 用年末日期（如 `2025-12-31`）或省略 `end`。
- `limit` 默认 100；`offset` 默认 0。返回 `data[]` 中 `data` 字段已由服务端解析为对象，调用方无需二次 `json.loads`。

## /screen 可筛选 / 可排序字段

完整白名单（分组）见 **`references/api-reference.md` → 「/screen 字段白名单」**。筛选/排序的列名必须取自该表，否则被服务端静默忽略。

## 常用 detail 字段

完整字段（分组）见 **`references/api-reference.md` → 「常用 detail 字段」**。响应示例与完整定义以线上 api-docs 为准。

## K 线复权（adj

README.md

# DataQuant Connector

<p align="center">
  <picture>
    <source media="(prefers-color-scheme: dark)" srcset="https://img.shields.io/badge/DataQuant-API-4d8df6?style=for-the-badge&logo=data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHZpZXdCb3g9IjAgMCAxMDAgMTAwIj48dGV4dCB5PSIuOWVtIiBmb250LXNpemU9IjkwIj7wn6SbPC90ZXh0Pjwvc3ZnPg">
    <img alt="DataQuant" src="https://img.shields.io/badge/DataQuant-API-4d8df6?style=for-the-badge">
  </picture>
  <br><br>
  <b>AI Agent Skill for Quantitative Market Data &amp; Automated Backtesting</b>
  <br>
  <sub>Daily OHLCV for 6,125+ instruments across A-shares, HK, US equities, crypto, indices, and ETFs. One link installs into your AI assistant — zero config, zero code.</sub>
</p>

---

<p align="center">
  <a href="#what-is-dataquant-connector"><b>About</b></a> ·
  <a href="#who-is-this-for"><b>Who It's For</b></a> ·
  <a href="#use-cases"><b>Use Cases</b></a> ·
  <a href="#financial-data-coverage"><b>Coverage</b></a> ·
  <a href="#installation-guide"><b>Install</b></a> ·
  <a href="#automated-backtesting-pipeline"><b>Pipeline</b></a> ·
  <a href="#skill-structure"><b>Structure</b></a> ·
  <a href="#cli-usage"><b>CLI</b></a> ·
  <a href="#rest-api-reference"><b>API</b></a> ·
  <a href="#中文版">中文版</a>
</p>

---

## What Is DataQuant Connector?

**DataQuant Connector** is an AI Agent Skill that equips your assistant with direct access to structured financial market data — daily OHLCV (open, high, low, close, volume, amount) for over 6,125 instruments across six global markets. Built on the [DataQuant](https://app.dataquant.trade/) quantitative data platform, accessible via a simple REST interface.

Designed for quantitative backtesting workflows: describe a strategy in plain language, and your AI handles data retrieval, script generation, execution, and reporting — end to end.

## Who Is This For?

- **AI-assisted quantitative researchers** who design and validate trading strategies through natural-language interaction with an AI agent
- **Developers building AI agents** that need a structured, low-latency financial data source with a simple REST interface
- **Individual traders** who want to backtest ideas without writing data pipelines, web scrapers, or ETL jobs

## Use Cases

All powered by daily OHLCV plus a per-instrument latest snapshot (valuation, size, momentum, 52-week position) — enough for screening without pulling financial statements.

| Use Case | Example Prompt |
|----------|---------------|
| **Rule-based Strategy Backtest** | "Backtest Kweichow Moutai 2020–2025, buy on MA20/MA60 golden cross, sell on death cross" |
| **Event-driven Analysis** | "Buy CSI 300 ETF the day after a PBoC RRR cut and hold for 30 days — run this for all RRR cuts since 2015" [^1] |
| **Multi-asset Portfolio Backtest** | "Equal-weight the top 10 CSI 300 constituents by volume, rebalance monthly, 2020–2025" |

[^1]: Event dates (e.g. RRR cut announcements) are resolved by the AI agent through s

_meta.json

{
  "ownerId": "kn7b3zgpkdg237k84q5gk786858bzfhj",
  "slug": "dataquant-connector",
  "version": "0.1.0",
  "publishedAt": 1785997899949
}

references/api-reference.md

# DataQuant Connector — 详细参考

> 本文件是 `SKILL.md` 的外置参考，承载篇幅较大的字段表与参数表，避免占满调用上下文（progressive disclosure）。
> 所有接口、参数、默认值、字段均核对自线上 api-docs（https://app.dataquant.trade/api-docs）及平台后端源码，确保与线上服务一致。

## 市场代码

| 代码 | 市场 |
|------|------|
| ashare | A 股 |
| hkstock | 港股 |
| usstock | 美股 |
| crypto | 加密货币 |
| indices | 全球指数 |
| etfs | ETF |

Coverage：A 股 ~3000 / 港股 ~1000 / 美股 ~2000 / 加密 ~100 / 指数 15 / ETF 11。

## /screen 字段白名单（后端 `DETAIL_FILTERABLE`，完整列表）

任意 `min_<列>` / `max_<列>` 或 `sort=<列>` 都必须是下列字段之一；不在白名单的列被服务端静默忽略。

| 分组 | 字段 |
|------|------|
| 估值 | `pe_ratio` `pe_lyr` `pb_ratio` `dividend_ratio_ttm` `eps_ttm` |
| 规模 | `total_market_cap` `circulating_market_cap` `total_shares` `float_shares` |
| 活跃度 | `turnover_rate` `volume_ratio` `range_pct` |
| 动量 | `change_percent` `chg_5d` `chg_10d` `chg_20d` `chg_60d` `chg_ytd` |
| 位置/均线 | `close_vs_ma20` `close_vs_52w_high` `ma5` `ma10` `ma20` `ma60` `high_52week` `low_52week` |
| 行情 | `volume` `amount` `open` `high` `low` `close` |

screen 返回列固定为服务端 `_SCREEN_COLUMNS`（23 列：symbol / name / market_name / date / close + 估值 / 规模 / 动量 / 均线 等）。

## 常用 detail 字段（后端 `DETAIL_COLUMNS`）

| 分组 | 字段 |
|------|------|
| 标识 | `symbol` `date` `name` `market_name` |
| 行情 | `open` `high` `low` `close` `pre_close` `avg_price` `volume` `amount` `change` `change_percent` |
| 估值 | `pe_ratio` `pe_fwd` `pe_lyr` `pb_ratio` `dividend_ratio_ttm` `dividend_ttm` `eps_ttm` `wb_ratio`(港股特有) |
| 规模 | `total_market_cap`(亿元·本币) `circulating_market_cap` `total_shares` `float_shares` |
| 动量 | `chg_5d` `chg_10d` `chg_20d` `chg_60d` `chg_ytd` |
| 52 周 | `high_52week` `low_52week` `close_vs_52w_high` |
| 均线 | `ma5` `ma10` `ma20` `ma60` `close_vs_ma20` |

`detail` 接口默认返回全部字段；`symbol`、`date` 始终返回，不受 `fields` 过滤；detail 不含 `adj_factor`。

## 套餐与配额（后端 `PLANS_DEFINITION`）

| | 免费版 | 专业版 | 企业版 |
|---|---|---|---|
| 日配额（行） | 5,000 | 200,000 | 2,000,000 |
| 速率（rpm，文档值） | 30 | 120 | 600 |
| 批量标的 | 5 | 50 | 50 |
| 单次行数 | 100 | 500 | 500 |

- 配额按「返回行数」计：kline 按行数、detail 按标的数、screen 按 `limit`。
- 速率：api-docs 文档值为上表；**服务端另设全局 `200/min` 硬上限**，超限返回 429。
- 建议：批量请求之间留 ≥ 0.5s 间隔；先用 `/quota` 看剩余再决定分批或缩减时间跨度。

## 错误码（HTTP 状态）

| HTTP | 含义 | 处理 |
|------|------|------|
| 400 | 参数错误 | `fields` 非法 / `symbols` 超套餐上限 / `market` 不存在 / `indicator` 未知 |
| 401 | 认证失败 | `X-API-Key` 缺失、无效或已禁用 → 让用户检查 Key |
| 403 | 禁止访问 | 仅 dashboard 写操作的 CSRF 校验；本 Skill 只做 GET 查询，正常不会触发 |
| 404 | 资源不存在 | 标的代码不存在 / `macro` 库未就绪 |
| 429 | 速率或配额耗尽 | 退避后重试；仍失败则告知用户配额用尽 |
| 503 | 服务暂不可用 | 优雅关闭中；稍后重试 |

skill-card.md

## Description:

DataQuant Connector gives agents REST API access to DataQuant market data for OHLCV and K-line retrieval, valuation snapshots, conditional screening, macro data, and quota checks across A-shares, Hong Kong stocks, U.S. stocks, crypto, indices, and ETFs.

This skill is ready for commercial/non-commercial use.

## Publisher:

[ai-ip](https://clawhub.ai/user/ai-ip)

### License/Terms of Use:

MIT

## Use Case:

External developers, AI-assisted quantitative researchers, and individual traders use this skill to retrieve structured DataQuant market data and guide agent-assisted backtesting, screening, and analysis workflows.

### Deployment Geography for Use:

Global

## Known Risks and Mitigations:

Risk: API keys can leak if pasted into chat or passed on the command line.

Mitigation: Set DATAQUANT_API_KEY through a protected environment variable or secret store, and avoid sharing real keys in chat or shell history.

Risk: Generated backtest code or trading analysis may be incorrect or misleading if run without review.

Mitigation: Review generated code and outputs before execution or investment decisions, and consider using a virtual environment with pinned dependencies.

## Reference(s):

- [ClawHub Skill Page](https://clawhub.ai/ai-ip/skills/dataquant-connector)
- [Server-Resolved GitHub Repository](https://github.com/ai-ip/dataquant-connector)
- [DataQuant API Reference](references/api-reference.md)
- [Interactive DataQuant API Documentation](https://app.dataquant.trade/api-docs)
- [DataQuant Homepage](https://app.dataquant.trade/)

## Skill Output:

**Output Type(s):** [Text, Markdown, Code, Shell commands, Configuration, Guidance]

**Output Format:** [Markdown guidance with shell commands and JSON API output]

**Output Parameters:** [1D]

**Other Properties Related to Output:** [Requires a valid DataQuant API key; CLI calls return JSON from DataQuant REST endpoints.]

## Skill Version(s):

0.1.0 (source: server release metadata)

## Ethical Considerations:

Users should evaluate whether this skill is appropriate for their environment, review any generated or modified files before relying on them, and apply their organization's safety, security, and compliance requirements before deployment.

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