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 skills add FeiCoder/Skill-Factory --skill momentum-reversalgit 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/momentum-reversal)<a href="https://agentmods.dev/skills/feicoder/skill-factory/momentum-reversal"><img src="https://agentmods.dev/badge/skills/feicoder/skill-factory/momentum-reversal/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/feicoder/skill-factory/momentum-reversal"><img src="https://agentmods.dev/badge/skills/feicoder/skill-factory/momentum-reversal.svg" alt="Reviewed on agentmods" width="80" 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 5.1 | $0.00051 | $0.00927 |
| Opus 5 | $0.00026 | $0.00464 |
| Sonnet 5 | $0.00010 | $0.00185 |
| Haiku 4.5 | $0.00005 | $0.00093 |
Grade A, and why
momentum-reversal 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 10d 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
动量反转策略
基本概念
动量效应
过去一段时间强的股票，在未来一段时间继续保持强势。
反转效应
过去一段时间弱的股票，在未来一段时间会变强。
关键问题
- 强势/弱势持续多长时间？
- 幅度有多大？
行为金融学基础
有效市场假说(EMH)
- Fama于1965年提出
- 假设投资者理性
- 价格反映所有信息
市场异象
EMH无法解释的现象：
- 羊群效应
- 小公司效应
- 股权溢价之谜
行为金融理论
1. 羊群效应
- 投资者观察并模仿他人交易
- 信息不对称时容易产生
- 导致股价过度上涨或下跌
2. 小公司效应
- 小盘股收益率高于大盘股
- 1月份效应特别明显
- 部分由信息不对称导致
3. 反应过度与反应不足
- 过度反应：投资者过分重视近期数据
- 反应不足：投资者不能及时修正预测
4. 动量与反转效应
- 动量：短期强势持续
- 反转：长期趋势反转
阿尔法动量模型
核心理念
利用历史收益率的序贯相关性：
- 短期（1-12个月）：动量效应
- 长期（3-5年）：反转效应
选股策略
- 动量策略：买入过去强势股
- 反转策略：买入过去弱势股
A股市场特点
动量效应
- 存在但不明显
- 持续时间较短
- 短期（1个月）效果较好
反转效应
- 中期（3个月）反转较明显
- 长期效应不明显
- 热点切换频繁
策略构建
参数选择
| 参数 | 范围 | 说明 |
|---|---|---|
| 观测期 | 1-12个月 | 过去收益计算周期 |
| 持有期 | 1-12个月 | 组合持有周期 |
| 分组数 | 3-5组 | 股票分组数量 |
策略类型
-
多空策略
- 买入赢家组合
- 卖空输家组合
- 市场中性
-
多头策略
- 只买入赢家组合
- 不需要融券
收益计算
采用几何平均值：
组合收益 = (Π(1+ri))^(1/n) - 1
实证结论
风格动量
- 短期（1个月）存在动量
- 中期（3个月）反转明显
- 长期不明显
行业动量
- 1个月动量效应显著
- 3个月反转效应显著
- 建议以3个月为周期
注意事项
- 交易成本：频繁交易侵蚀收益
- 流动性：小盘股可能有流动性风险
- 时效性：动量效应可能快速消失
- 市场环境：牛熊市表现不同
实践建议
- 短期动量：1个月观测/持有
- 中期反转：3个月观测/持有
- 组合分组：5组分组
- 配合止损策略
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.
- 10d ago First seen · 144 lines · 51 tokens per session scan A ce9528e22395
momentum-reversal is a skill published in the GitHub repository FeiCoder/Skill-Factory (10 stars, last pushed 6mo ago), licensed MIT. It adds 51 tokens to every session and 927 once invoked, about $0.0003 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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