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A股量化工具包

股票数据获取、分析和可视化工具包。支持A股、港股、美股数据，提供技术分析、基本面分析和投资组合管理功能。 Skill: A股量化工具包 Owner: dnaxxx-hub Summary: 股票数据获取、分析和可视化工具包。支持A股、港股、美股数据，提供技术分析、基本面分析和投资组合管理功能。 Tags: china:1.0.0, finance:1.0.0, latest:1.1.0, quant:1.0.0, stock:1.0.0 Version history: v1.1.0 | 2026-05-30T11:09:50.629Z | user 重写：全面升级为真实量化系统 — 腾讯API实时行情(零依赖) + 10策略回测框架 + 6维60分评分体系 + FFT频谱分析 + 日报生成 + 多指标融合策略引擎 v1.0.0 | 2026-05-29T09:47:55.586Z | user Initial release: A股/港股/美股行情+技术分析+基本面分析+投资组合 Archive index: Archive v1

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Downloads

1.3k

Updated

Oct 10, 2026

Version

1.1.0

Source

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About

What it does, and when to use it.

Capability contract not published. No trust telemetry is available yet. 1.3K downloads reported by the source. Last updated 10/10/2026.

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Public facts

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Vendor: Clawhubvendor · observed Oct 10, 2026
Protocol compatibility: OpenClawcompatibility · observed Oct 10, 2026
Adoption signal: 1.3K downloadsadoption · observed Oct 10, 2026
Latest release: 1.1.0release · observed May 30, 2026
Handshake status: UNKNOWNsecurity

Install and run

Setup complexity: low.

clawhub skill install s171sre9mpk2gy28m6evctkt5s83n7ss:finance-toolkit

Node.js workspace detected. Install dependencies securely: run `npm ci --ignore-scripts` to prevent post-install lifecycle triggers from running arbitrary code, then selectively audit the dependency tree.
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-dnaxxx-hub-finance-toolkit/snapshot"

Documentation

CLAWHUB

17,927 characters of source documentation, loaded on request.

Extracted files

5 files captured from the source.

SKILL.md

# 📈 A股量化工具包 (Finance Toolkit)

A股量化交易工具包，基于腾讯行情API（零依赖、免注册），提供完整的实时行情、技术指标、策略回测、监控评分、日报生成功能。

## 核心模块

| 模块 | 文件 | 功能 |
|------|------|------|
| 🚀 实时行情 | `mini_realtime.py` | 腾讯API直取实时报价/日K/分时，零依赖 |
| 📊 数据获取 | `astock_data.py` | A股全市场数据获取（基于akshare，需安装akshare） |
| ⚙️ 数据引擎 | `astock_engine.py` | 腾讯行情API封装，含缓存和curl调用 |
| 🧮 策略集 | `astock_strategies.py` | 均线金叉/RSI/布林带/KDJ等策略 |
| 🔬 回测系统 | `backtest_v3.py` | 10个策略一键回测+参数搜索+敏感性分析 |
| 📡 实时监控 | `monitor_v3.py` | 6维60分评分体系+FFT频谱分析 |
| 🎯 策略引擎 | `strategy_v4.py` | 多指标融合策略（布林带+RSI+KDJ+MACD） |
| 📋 日报系统 | `daily_report.py` | 自动生成A股市场分析日报 |
| 🔄 FFT分析 | `fourier_analyzer.py` | K线FFT频谱分析，识别主周期 |
| ⚡ 盯盘系统 | `strategy_engine.py` | 腾讯API实时盯盘，技术位判断 |

## 依赖

**最小运行时（实时行情/监控/FFT分析）：零外部依赖**，仅需 Python 3 标准库。
- `mini_realtime.py` — 仅用 `urllib`、`json`、`struct`
- `monitor_v3.py` — 仅用标准库
- `fourier_analyzer.py` — 纯Python FFT

**完整功能需要：**
- pandas、numpy（回测和多指标分析必需）
- akshare（仅 `astock_data.py` 需要，可用 `mini_realtime.py` 替代）
- matplotlib、mplfinance（仅K线图报告 `kline_report.py` 需要）

## 快速开始

### 1. 实时行情（零依赖）

```python
from finance_toolkit.mini_realtime import TencentStockAPI

api = TencentStockAPI()

# 获取实时报价
q = api.get_quote("000009")
print(f"{q['name']}: ¥{q['price']} ({q['change_pct']:+.2f}%)")

# 获取60日日K线
klines = api.get_klines("000009", 60)
closes = [k['close'] for k in klines]
print(f"最新收盘价: {closes[-1]}")
```

### 2. 60分评分体系

```python
from finance_toolkit.mini_realtime import TencentStockAPI, calc_sma, calc_rsi
from finance_toolkit.monitor_v3 import score_stock

api = TencentStockAPI()
result = score_stock("000009", api)
print(f"评分: {result['score']}/60")
print(f"信号: {result['signals']}")
```

### 3. 一键回测10个策略

```python
from finance_toolkit.backtest_v3 import DataFetcher, BacktestEngine, BacktestReport

fetcher = DataFetcher()
df = fetcher.get("sz000009", 500)  # 获取500日K线
dp = BacktestEngine._prepare(df)

# 10个策略对比排名
results = BacktestReport.brief(dp, "中国宝安")
```

### 4. 实时监控+FFT分析

```python
from finance_toolkit.monitor_v3 import monitor_all
from finance_toolkit.mini_realtime import TencentStockAPI
from finance_toolkit.fourier_analyzer import analyze_spectrum, get_strategy_hints

# 监控所有股票
results = monitor_all()

# FFT频谱分析
api = TencentStockAPI()
klines = api.get_klines("000009", 200)
closes = [k['close'] for k in klines]
spectrum = analyze_spectrum(closes)
hints = get_strategy_hints(spectrum)
print(f"主周期: {hints['dominant_cycle']}天")
print(f"建议: {hints['advice']}")
```

### 5. 策略引擎（多指标融合）

```python
from finance_toolkit.strategy_v4 import DataSource, Backtest

ds = DataSource()
df = ds.get_kline("sz000009", 500)
bt = Backtest(df)
result = bt.run("bollinger_rsi_fusion")
print(result['metrics'])
```

### 6. 日报生成

```python
from finance_toolkit.daily_report import generate_daily_report

report = generate_daily_report()
print(report['market_overview'])
```

## 命令行用法

```bash
# 快速回测
python -m finance_toolkit.backtest_v3 brief

# 完整回测+分段分析
python -m finance_toolkit.backtest_v3 full

# 参数扫描优化
python -m finance_toolkit

README.md

# 📈 A股量化工具包 (Finance Toolkit)

A股量化交易分析工具包，基于腾讯行情API直取数据，零外部依赖即可运行。

## 功能一览

- **实时行情** — 腾讯API直取，无需注册/API密钥
- **技术指标** — MA、RSI、布林带、KDJ、MACD
- **10策略回测** — 一键对比、参数搜索、敏感性分析
- **60分评分** — 6维度实时监控评分体系
- **FFT频谱分析** — K线傅里叶分析，识别主周期
- **多指标融合策略** — 布林带+RSI+KDJ+MACD融合
- **日报生成** — 自动生成A股市场分析日报

## 快速示例

```python
from finance_toolkit.mini_realtime import TencentStockAPI

api = TencentStockAPI()
q = api.get_quote("000009")
print(f"{q['name']}: ¥{q['price']}")
```

更多示例见 [SKILL.md](SKILL.md)。

## 目录

```
finance_toolkit/
├── mini_realtime.py     # 零依赖实时行情（核心）
├── monitor_v3.py        # 实时监控+60分评分
├── backtest_v3.py       # 统一回测框架
├── strategy_v4.py       # 多指标融合策略引擎
├── astock_data.py       # A股数据获取（需akshare）
├── astock_engine.py     # 腾讯行情引擎
├── astock_strategies.py # 策略集（备选）
├── fourier_analyzer.py  # FFT频谱分析
├── strategy_engine.py   # 盯盘策略系统
├── daily_report.py      # 日报生成
└── kline_report.py      # K线图报告
```

_meta.json

{
  "ownerId": "kn71mebbt22z0hvy3y0s8j50s183m8ba",
  "slug": "finance-toolkit",
  "version": "1.1.0",
  "publishedAt": 1780139390629
}

skill-card.md

## Description:

A China A-share quantitative analysis toolkit that helps agents fetch market data, run technical indicators and backtests, monitor scores, perform FFT analysis, and generate daily reports.

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

## Publisher:

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

### License/Terms of Use:

MIT-0

## Use Case:

Developers, finance analysts, and agent builders use this skill to guide Python-based A-share market analysis workflows, including market-data retrieval, technical analysis, strategy backtesting, monitoring, and report generation. Outputs should be reviewed as analytical support rather than financial advice.

### Deployment Geography for Use:

Global

## Known Risks and Mitigations:

Risk: The skill contacts third-party market-data providers and queried stock symbols may be disclosed.

Mitigation: Use only when this disclosure is acceptable for the stocks being analyzed, and avoid querying sensitive watchlists in trusted or confidential environments.

Risk: The security review flagged under-disclosed automatic interpreter handoff behavior.

Mitigation: Review or remove the MSYS64 interpreter handoff before using the skill in a trusted agent environment.

Risk: The security review flagged optional external bridge behavior for alert synchronization.

Mitigation: Review or disable the optional system_bridge alert synchronization before deployment.

## Reference(s):

- [ClawHub skill page](https://clawhub.ai/dnaxxx-hub/skills/finance-toolkit)
- [Publisher profile](https://clawhub.ai/user/dnaxxx-hub)
- [Artifact README](artifact/README.md)
- [Artifact skill file](artifact/SKILL.md)

## Skill Output:

**Output Type(s):** [text, markdown, code, shell commands, guidance]

**Output Format:** [Markdown guidance with Python examples and shell command snippets]

**Output Parameters:** [1D]

**Other Properties Related to Output:** [May include finance-analysis outputs, strategy signals, backtest summaries, monitoring scores, FFT insights, and daily-report content for reviewer validation.]

## Skill Version(s):

1.1.0 (source: server release evidence)

## 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.

package.json

{
  "name": "finance-toolkit",
  "version": "1.0.0",
  "description": "A股量化工具包 — 实时行情/技术指标/回测/监控/策略引擎。腾讯API直取，零外部依赖",
  "keywords": [
    "a-stock",
    "china-stock",
    "quantitative-trading",
    "backtesting",
    "technical-analysis",
    "bollinger-bands",
    "rsi",
    "macd",
    "kdj",
    "stock-monitor",
    "trading-strategy",
    "tencent-api",
    "finance",
    "openclaw",
    "python",
    "量化交易",
    "A股",
    "股票分析",
    "回测"
  ],
  "author": "Yu (姜翔)",
  "license": "MIT",
  "files": [
    "SKILL.md",
    "README.md",
    "finance_toolkit/"
  ]
}

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