Best use case
quant — 你的智能量化投资助手 is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
> 🤖 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
Teams using quant — 你的智能量化投资助手 should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/quant/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How quant — 你的智能量化投资助手 Compares
| Feature / Agent | quant — 你的智能量化投资助手 | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
> 🤖 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
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
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SKILL.md Source
# 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”。
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