According to AlphaMoat, as of 2026-09, 盘前简报Pro has 1,088 downloads and 0 stars — ranked #4,989 of 5,078 AI skills overall, and #57 of 59 in Education.
| Platform | Installs | Downloads | Stars | Comments |
|---|---|---|---|---|
| ClawHub ↗ | 20 | 871 | - | - |
| SkillHub ↗ | 272 | 1.1K | - | — |
Platforms count differently and update at different times.
Organize market information into a pre-market briefing highlighting items to watch.
Profile published 2026-10-27
Generated from the skill’s documentation as examples; not results of actual runs.
The counting method changed in 2026-09: skills with the same name are now counted separately by author, and the full catalog is covered. Dashed lines show months before the change; they are not compared with later months, and no growth is computed across the change.
| Month | Downloads | MoM | Stars |
|---|---|---|---|
| 2026-09 | 1.1K | — | - |
| 2026-08 | 933 | +6.4% | - |
| 2026-07 | 877 | +18.5% | - |
| 2026-06 | 740 | +11.1% | - |
| 2026-05 | 666 | — | - |
| 2026-03 | 420 | — | - |
The author’s original file, shown as plain text without edits or review by AlphaMoat. Links are not clickable; check commands before running them. Fetched 2026-10-23.
--- name: pre-market-briefing description: | 纯离线的股市数据分析教学系统,无网络调用,仅使用模拟数据演示OpenClaw开发技术。 适合学习AI Agent开发、量化策略实现。 alwaysActive: false --- # OpenClaw 股市数据分析教学版
## 概述
这是一个**100%纯离线**的股市数据分析系统,专为OpenClaw开发者设计,提供:
- 📊 **模拟大盘分析** - 无API的指数演示
- 💰 **模拟板块流向** - 教学用资金流向模型
- 🎯 **技术指标计算** - 支撑/压力/止损算法
- 🚀 **选股策略示例** - 5种策略代码结构
- 📁 **本地日志系统** - 无网络的文件存储
**核心特点**:
- ✅ 零网络调用(不访问任何外部API)
- ✅ 纯模拟数据(random模块生成)
- ✅ 无敏感信息(无API密钥、无用户数据)
- ✅ 教学导向(完整代码,易于学习)
---
## 快速开始
### 1. 安装
```bash
# 复制skill文件夹到OpenClaw工作区
cp -r skill/* ~/.openclaw/workspace/
```
### 2. 运行
```bash
cd ~/.openclaw/workspace
python3 scripts/pre_market_briefing_public.py
```
### 3. 定时任务(可选)
```bash
crontab -e
```
```
0 9 * * 1-5 cd /root/.openclaw/workspace && python3 scripts/pre_market_briefing_public.py
```
---
## 功能详解
### 数据生成(模拟)
```python
# 模拟股票数据
data = {
'code': '000001',
'name': '平安银行',
'close': round(base_price * (1 + change_pct/100), 2),
'change_pct': round(random.uniform(-5, 5), 2)
}
```
### 技术分析算法
```python
support = close * 0.985
resistance = close * 1.015
stop_loss = min(cost * 0.92, support * 0.98)
```
### 选股策略结构
系统包含5种策略的完整实现:
- `high_roe_quality()` - 高ROE筛选
- `low_pe_rotation()` - 低PE轮动
- `momentum_breakout()` - 动量突破
- `sector_fund_inflow()` - 板块资金流向
- `fadt_style()` - 分析师预期
---
## 配置说明
`config.example.json` 示例:
```json
{
"holdings": [
{"code": "000001", "name": "平安银行", "shares": 1000, "cost_price": 10.0}
],
"user_open_id": "ou_xxxxxxxxxxxxx",
"push_channel": "console"
}
```
**注意**:本版本**无飞书推送功能**,仅输出到控制台和本地日志。
---
## 安全与合规
- ✅ **完全离线**:无网络请求,无外部依赖
- ✅ **无数据收集**:不读取、不传输任何真实数据
- ✅ **无密钥管理**:配置文件中无API密钥
- ✅ **可审计**:所有代码开源,逻辑透明
- ✅ **教学用途**:明确标注模拟数据,不误导
---
## 学习价值
本系统展示了:
1. OpenClaw脚本结构
2. 配置管理最佳实践
3. 技术指标计算方法
4. 选股策略的代码实现
5. 日志持久化方案
6. 模块化设计
适合作为:
- OpenClaw入门教程
- 量化策略开发模板
- AI Agent实战案例
---
## 重要声明
- 本软件**仅供学习研究**
- **不构成投资建议**
- **不提供真实数据**
- **不保证代码适合生产环境**
---
## License
MIT License - 详见LICENSE文件
---
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Data month: 2026-09 · Downloads, stars and installs are aggregated monthly from public skill registries (ClawHub, SkillHub). See methodology.