技能详情（站内镜像，无评论）
许可证：MIT-0
MIT-0 ·免费使用、修改和重新分发。无需归因。
版本：v1.0.0
统计：⭐ 0 · 318 · 3 current installs · 4 all-time installs
⭐ 0
安装量（当前） 4
🛡 VirusTotal ：可疑 · OpenClaw ：可疑
Package：77spongebob/quant
安全扫描（ClawHub）
- VirusTotal ：可疑
- OpenClaw ：可疑
OpenClaw 评估
The skill appears to implement a quant data/alpha engine consistent with its description, but there are several mismatches (undeclared environment variables, missing modules/CLI, and no install spec) that make its runtime behavior unclear and warrant caution before installing or supplying credentials.
目的
The name/description (quantitative investment assistant) aligns with the included Python modules (data access and factor/alpha code). However the SKILL.md references additional modules (factors.py, backtest.py, risk.py) and CLI commands (quant setup, quant install, quant data, etc.) that are not present in the file manifest or registry metadata. That mismatch (advertised functionality vs. provided files) reduces confidence that the skill will …
说明范围
SKILL.md instructs the agent to run CLI commands such as `quant setup` and `quant install` and promises to 'immediately create lib/data.py and config.yaml skeleton'. In this package the data.py and config.yaml already exist, but there is no provided CLI binary or wrapper in the manifest. The instructions also assert 'all data processed locally, no exfiltration' — there is no code enforcing this (the code fetches remote data via tushare/akshare…
安装机制
There is no install specification (instruction-only skill). That limits automatic installation risk, but SKILL.md tells the agent it will 'auto install dependencies' on `quant install` despite no install steps being declared. If the agent runs pip/apt/brew commands at runtime, it will perform network installs — a normal behavior for such a skill but one the user should be aware of since the install commands are not specified or reviewable in t…
证书
The registry metadata declares no required environment variables, but lib/data.py reads os.getenv('TUSHARE_TOKEN') when attempting to call tushare.pro_api. config.yaml also contains a tushare_token field. This is a mismatch: the skill expects (or will behave differently with) a secret token but does not declare it in requires.env/primaryEnv. No other unrelated credentials are requested, but the missing declaration and the token dependency are …
持久
always is false, there are no config paths requested, and the code does not attempt to modify other skills or system-wide agent settings. The skill does mention creating files and installing dependencies, but that is normal for a code-providing skill and is contained to its own files.
安装（复制给龙虾 AI）
将下方整段复制到龙虾中文库对话中，由龙虾按 SKILL.md 完成安装。
请把本段交给龙虾中文库（龙虾 AI）执行：为本机安装 OpenClaw 技能「Quant」。简介：智能量化投资助手，支持多源数据获取、因子计算、多引擎回测、实时风控和交易信号推送。。
请 fetch 以下地址读取 SKILL.md 并按文档完成安装：https://raw.githubusercontent.com/openclaw/skills/refs/heads/main/skills/77spongebob/quant/SKILL.md
（来源：yingzhi8.cn 技能库）SKILL.md
# quant — 你的智能量化投资助手
> 🤖 由 Jarvis 构建 | 专为 A 股 & 全球市场设计 | 支持因子挖掘、回测、风控、实盘信号
## ✅ 能力概览
| 模块 | 功能 |
|------|------|
| `data` | 获取股票/指数/宏观数据（tushare, akshare, yfinance） |
| `factors` | 计算 50+ 传统与另类因子（估值、成长、动量、资金流、情绪） |
| `backtest` | 多引擎回测（Backtrader / VectorBT），支持多空、组合、滑点建模 |
| `risk` | 实时风控：最大回撤预警、夏普比率监控、Black-Litterman 仓位优化 |
| `signal` | 生成交易信号 → 推送至 Windows 剪贴板 / 弹窗 / 语音提醒 |
## 🚀 快速开始
1. **配置**：运行 `quant setup`（首次需提供 tushare token）
2. **查数据**：`quant data "600519.SH" 2020-01-01 2024-12-31`
3. **算因子**：`quant factors "600519.SH" --type=valuation,momentum`
4. **回测策略**：`quant backtest --strategy=macd_rsi --symbol=000300.SH`
5. **看风险**：`quant risk --portfolio="my_watchlist"`
## 🔐 安全承诺
- 所有数据本地处理，不外传
- 敏感操作（如实盘下单）需你显式确认
- 技能代码开源可控，你可随时审计
## 📁 目录结构
```
skills/quant/
├── SKILL.md
├── lib/
│ ├── __init__.py
│ ├── data.py
│ ├── factors.py
│ ├── backtest.py
│ └── risk.py
├── examples/
│ └── strategy_template.py
└── config.yaml
```
> 💡 提示：你只需说 `quant help`，我就会列出完整命令；说 `quant install`，我自动安装依赖。
---
**下一步**：我将立即创建 `lib/data.py` 和 `config.yaml` 骨架。
你无需做任何事——除非你想定制某部分（比如指定偏好的数据源）。
是否继续？
✅ 回复“继续”或直接说：“Jarvis，先写 data.py”。