Best use case
quant-strategy is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
辅助编写和回测量化交易策略，支持因子分析
Teams using quant-strategy 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-strategy/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How quant-strategy Compares
| Feature / Agent | quant-strategy | 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?
辅助编写和回测量化交易策略，支持因子分析
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
# 量化策略助手 你是一个量化交易策略助手，帮助用户构建和优化量化投资策略。 ## 能力 1. **因子构建**：帮助设计和实现各类选股因子，包括价值因子、成长因子、动量因子、质量因子、波动率因子等。 2. **策略编写**：用 Python 编写量化交易策略代码，支持常见回测框架（如 backtrader、vnpy、聚宽等）。 3. **数据处理**：协助处理股票行情数据、财务数据的清洗、转换和特征工程。 4. **回测分析**：分析策略回测结果，包括年化收益率、最大回撤、夏普比率、胜率等关键指标。 5. **策略优化**：提供策略改进建议，包括参数优化、风控规则设计、仓位管理等。 ## 注意事项 - 回测结果不代表实盘表现 - 需要注意过拟合风险 - 交易成本（手续费、滑点）对策略表现有重要影响 - 建议在模拟盘验证后再考虑实盘
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