Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/feicoder/skill-factory/quant-timing-intronpx skills add FeiCoder/Skill-Factory --skill quant-timing-introgit clone --depth 1 https://github.com/FeiCoder/Skill-FactoryWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/feicoder/skill-factory/quant-timing-intro)<a href="https://agentmods.dev/skills/feicoder/skill-factory/quant-timing-intro"><img src="https://agentmods.dev/badge/skills/feicoder/skill-factory/quant-timing-intro.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00043 | $0.00652 |
| Opus 5 | $0.00022 | $0.00326 |
| Sonnet 5 | $0.00009 | $0.00130 |
| Haiku 4.5 | $0.00004 | $0.00065 |
Grade A, and why
quant-timing-intro scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 4d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
What it actually says
量化择时入门
什么是量化择时
量化择时是利用数量化方法，通过对各种宏观微观指标的量化分析，试图找到影响大盘走势的关键信息，并对未来走势进行预测。
择时目标
- 上涨：买入持有
- 下跌：卖出清仓
- 震荡：高抛低吸
挑战
- 大盘走势与宏观经济、微观企业、国家政策、国际形势密切相关
- 准确判断走势具有相当难度
量化择时主要方法
| 方法 | 说明 |
|---|---|
| 趋势择时 | 基于技术指标判断趋势 |
| 市场情绪择时 | 基于投资者情绪指标 |
| 有效资金模型 | 基于资金流向 |
| 牛熊线 | 基于布朗运动理论 |
| Hurst指数 | 基于分形理论 |
| SVM分类 | 基于机器学习 |
| SWARCH模型 | 基于宏观指标 |
| 异常指标 | 基于特殊市场状态 |
择时指标分类
1. 趋势型指标
- MA（移动平均）
- MACD
- DMA
- TRIX
2. 情绪型指标
- 投资者信心指数
- 封闭式基金折溢价率
- 新股指标
- 基金仓位
3. 资金型指标
- 资金净流入
- 有效资金
- M2货币供应
4. 预测型指标
- SVM分类
- 神经网络
- 回归模型
择时策略评价
核心指标
- 累计收益率：总收益水平
- 胜率：交易成功概率
- 夏普比率：风险调整收益
- 最大回撤：最大亏损幅度
- 交易次数：策略活跃程度
择时效果判断
- 不错过大的系统性机会
- 能回避较大的系统性风险
- 能良好地辨别盘整状态
A股择时特点
- 个人投资者众多，情绪影响大
- 政策市特征明显
- 牛短熊长特征
- 波动性较大
使用场景
- 大盘择时判断
- 仓位管理决策
- 风险管理
- 资产配置调整
注意事项
- 没有100%准确的择时
- 需要结合多种方法
- 考虑交易成本
- 定期优化参数
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 4d ago First seen · 100 lines · 43 tokens per session scan A cc00e73d05a4
quant-timing-intro is a skill published in the GitHub repository FeiCoder/Skill-Factory (10 stars, last pushed 6mo ago), licensed MIT. It adds 43 tokens to every session and 652 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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