Quant Trading Assistant

v1.0.1

量化交易助手 - A股技术分析+量化选股。 用于:分析A股实时行情、计算技术指标(均线/KDJ/MACD/布林带)、 量化选股策略、生成交易信号、龙虎榜/情绪周期判断。 触发场景:问股票、分析行情、选股推荐、风险提示、技术指标计算。

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Install

OpenClaw Prompt Flow

Install with OpenClaw

Best for remote or guided setup. Copy the exact prompt, then paste it into OpenClaw for dxie48892-jpg/quant-trading-assistant.

Previewing Install & Setup.
Prompt PreviewInstall & Setup
Install the skill "Quant Trading Assistant" (dxie48892-jpg/quant-trading-assistant) from ClawHub.
Skill page: https://clawhub.ai/dxie48892-jpg/quant-trading-assistant
Keep the work scoped to this skill only.
After install, inspect the skill metadata and help me finish setup.
Use only the metadata you can verify from ClawHub; do not invent missing requirements.
Ask before making any broader environment changes.

Command Line

CLI Commands

Use the direct CLI path if you want to install manually and keep every step visible.

OpenClaw CLI

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openclaw skills install quant-trading-assistant

ClawHub CLI

Package manager switcher

npx clawhub@latest install quant-trading-assistant
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Purpose & Capability
The name/description (A股技术分析、量化选股) matches the code and SKILL.md: functions fetch quotes/K‑lines from Tencent/Sina, compute MA/KDJ/MACD/BOLL, provide screening and signal functions. There are no unrelated environment variables, binaries, or installs requested. Minor note: _meta.json/homepage exist but overall source provenance is limited (no authoritative upstream repo URL).
Instruction Scope
SKILL.md explicitly documents the library functions and CLI commands and the code implements them. The runtime instructions do not direct the agent to read local files, secrets, or system config; they only call the included Python functions. The code performs outbound HTTP(S) calls to public finance APIs (expected for this purpose).
Install Mechanism
There is no install spec — this is effectively instruction + a single Python file. No external archives or obscure download URLs are used. Risk from installation is low because nothing is written/installed by an installer spec.
Credentials
The skill requires no credentials or environment variables. It makes network requests to public Tencent and Sina finance endpoints which is proportional to fetching market data. Note: those external services will see the stock symbols/queries you request — this is expected but is observable by the third parties.
Persistence & Privilege
The skill is not always:true, does not request elevated/system privileges, and does not modify other skills or global agent configuration. It runs on demand and can be invoked by the agent normally.
Assessment
This skill appears to do what it claims: fetch public A‑share market data from Tencent/Sina and compute technical indicators. Before installing, consider: (1) outbound network calls — every symbol/query you ask the skill for is sent to third‑party finance APIs (Tencent/Sina), so treat queries as observable by those services; (2) provenance — the repository/source URL is not an authoritative upstream repo in the package metadata, so if you require strong supply‑chain assurance you may want to review the full source or prefer a vetted package; (3) accuracy — quant_screen uses a small hardcoded candidate list (not a live fundamental feed), so do not treat results as authoritative trading advice; (4) financial risk — the skill includes a disclaimer but any trading decisions remain your responsibility. If you need stronger guarantees, inspect the full Python file locally and run it in an isolated environment, or adapt data sources to your trusted feeds.

Like a lobster shell, security has layers — review code before you run it.

latestvk971kmsqqyv9e65scsnpzh966n83dknq
209downloads
0stars
2versions
Updated 1mo ago
v1.0.1
MIT-0

量化交易助手

整合热门Skill源码开发的A股量化交易工具,不只是数据展示,是真正的实战决策辅助。

核心能力

模块能力数据来源
📊 实时行情腾讯/新浪API实时价格腾讯财经/新浪财经
📈 技术分析均线/KDJ/MACD/布林带/量比计算得出
💰 量化选股PE/ROE/营收增速筛选规则筛选
🔥 情绪周期冰点/启动/发酵/高潮/退潮市场情绪判断
🐉 龙头战法连板/首阴/分歧转一致实战逻辑
⚠️ 避坑指南ST/减持/商誉雷/PE异常血泪教训
📋 综合分析多维度综合研判+建议自动生成

股票代码格式

市场格式示例
上交所sh + 6位sh600519 (贵州茅台)
深交所sz + 6位sz000001 (平安银行)
创业板sz + 6位sz300750 (宁德时代)
科创板sh + 6位sh688981 (中芯国际)

常用指数

指数代码
上证指数sh000001
深证成指sz399001
创业板指sz399006
沪深300sh000300
科创50sh000688

核心功能

1. 实时行情查询

from quant_trading_assistant import get_stock_quote

# 单股
quote = get_stock_quote('sh600519')
# 返回: {name, code, price, change, change_pct, open, high, low, volume, turnover, pe, PB}

# 批量查询
quotes = get_stock_quotes(['sh600519', 'sz000858', 'sz300750'])

2. 技术指标计算

from quant_trading_assistant import get_technical_indicators

# 获取完整技术指标
indicators = get_technical_indicators('sh600519')
# 返回: {ma5, ma10, ma20, ma60, kdj{k/d/j}, macd{dif/dea/macd}, boll{upper/mid/lower}, ma_trend, volume_ratio}

3. 量化选股策略

from quant_trading_assistant import quant_screen

# 量化筛选
result = quant_screen(
    pe_max=50,       # PE上限
    roe_min=15,      # ROE下限(%)
    growth_min=20,   # 营收增速下限(%)
    debt_max=70      # 资产负债率上限(%)
)
# 返回: 符合条件股票列表

4. 市场情绪判断

from quant_trading_assistant import get_market_sentiment

# 获取市场情绪周期
sentiment = get_market_sentiment()
# 返回: {phase, score, change_pct, turnover, suggestion}
# phase: 冰点/启动前期/启动后期/发酵期/高潮期/分歧期/退潮期

5. 龙头战法信号

from quant_trading_assistant import check_dragon_signals

# 检测龙头信号
signals = check_dragon_signals('sh600519')
# 返回: {signals: [{type, level, emoji, desc, action}], summary, action}
# 信号类型: 连板/首阴/分歧转一致/放量突破/回踩5日线/低位放量

6. 避坑风险检查

from quant_trading_assistant import risk_check

# 风险检查
risk = risk_check('sh600519')
# 返回: {risks, risk_score, risk_level}
# risk_level: 安全/中等/高危

7. 综合分析报告

from quant_trading_assistant import analyze_stock

# 综合分析一只股票
result = analyze_stock('sh600519')
# 返回完整分析报告,包含quote/technical/risk/dragon/sentiment/advice

命令行用法

# 分析单只股票
python quant_trading_assistant.py analyze sh600519

# 查询行情
python quant_trading_assistant.py quote sh600519

# 技术指标
python quant_trading_assistant.py tech sh600519

# 量化选股
python quant_trading_assistant.py screen

# 市场情绪
python quant_trading_assistant.py sentiment

# 龙虎信号
python quant_trading_assistant.py dragon sh600519

分析报告模板

## 📊 {股票名称}({代码}) 量化分析

### 📈 实时行情
| 指标 | 数值 | 信号 |
|------|------|------|
| 现价 | XX元 | 🟢涨/🔴跌 |
| 涨幅 | X% | - |
| 换手率 | X% | 高/低 |
| 市盈率 | X | 高/中/低 |
| 量比 | X | 放量/缩量 |

### 📉 技术面
- 均线: MA5=XX, MA10=XX, MA20=XX, MA60=XX
- KDJ: K=XX, D=XX, J=XX
- MACD: DIF=XX, DEA=XX, MACD=XX
- 布林带: 上轨=XX, 中轨=XX, 下轨=XX
- 趋势: {多头排列/空头排列/震荡整理}

### 🐉 龙头信号
- 最优信号: {信号类型}
- 动作建议: {操作建议}

### ⚠️ 风险检查
- 风险等级: {安全/中等/高危}
- 风险点: {无明显风险/具体风险}

### 🔥 市场情绪
- 当前阶段: {冰点/启动/发酵/高潮/分歧/退潮}
- 情绪评分: XX/100

### 💡 结论
**建议: {买入/持有/卖出/观望}**
- 买入逻辑: {X}
- 止损位: {X元(-8%)}
- 风险提示: {X}

技术指标说明

均线系统 (MA)

  • MA5: 5日均线,短期趋势
  • MA10: 10日均线,短中期趋势
  • MA20: 20日均线,中期趋势
  • MA60: 60日均线,长期趋势
  • 多头排列: MA5 > MA10 > MA20 > MA60 → 强势
  • 空头排列: MA5 < MA10 < MA20 < MA60 → 弱势

KDJ指标

  • K值: 快速随机指标
  • D值: 慢速随机指标
  • J值: 超买超卖预警
  • KDJ金叉(K上穿D): 买入信号
  • KDJ死叉(K下穿D): 卖出信号
  • J>80: 超买区域,谨慎追高
  • J<20: 超卖区域,关注反弹

MACD指标

  • DIF: 快线,EMA12-EMA26
  • DEA: 慢线,DIF的9日EMA
  • MACD柱: (DIF-DEA)×2
  • DIF上穿DEA(金叉): 买入信号
  • DIF下穿DEA(死叉): 卖出信号
  • MACD柱由负转正: 动能转强
  • MACD柱由正转负: 动能转弱

布林带 (BOLL)

  • 上轨: MA20 + 2×标准差
  • 中轨: MA20
  • 下轨: MA20 - 2×标准差
  • 价格触及上轨: 可能有压力
  • 价格触及下轨: 可能有支撑
  • 布林带收口: 变盘信号

量化选股策略

成长股策略

  • 连续3年营收增速 ≥ 30%
  • 净利润增速 ≥ 25%
  • ROE ≥ 15%
  • PE < 50

价值股策略

  • 市盈率(PE)< 行业均值
  • 股息率 ≥ 3%
  • 资产负债率 ≤ 50%

短线策略

  • 沿5日线上涨
  • 放量突破(量比>1.5)
  • 换手率3%-20%
  • 无ST/减持/商誉雷

龙头战法信号

信号类型说明操作
🔥🔥🔥 连板连续涨停,换手率3-20%打板介入
🔥🔥 首阴涨停后次日回调低吸博弈
🔥🔥🔥 分歧转一致高开高走追涨介入
🔥🔥 放量突破量比>1.8,涨幅>3%跟进
🔥 回踩5日线价格回踩MA5企稳低吸
🔥🔥 低位放量量比>2,低位启动关注

风险提示

⚠️ 重要声明

  • 本分析仅供参考,不构成投资建议
  • 市场有风险,投资需谨慎
  • 亏损8%请无条件止损
  • 请根据自身风险承受能力决策

更新日志

v1.0.1 (2026-03-22)

  • 修复:Windows控制台UTF-8编码输出问题
  • 修复:_meta.json作者信息修正为dxie48892-jpg

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