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/style-rotationnpx skills add FeiCoder/Skill-Factory --skill style-rotationgit 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/style-rotation)<a href="https://agentmods.dev/skills/feicoder/skill-factory/style-rotation"><img src="https://agentmods.dev/badge/skills/feicoder/skill-factory/style-rotation.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 | $0.00048 | $0.01108 |
| Opus 5 | $0.00024 | $0.00554 |
| Sonnet 5 | $0.00010 | $0.00222 |
| Haiku 4.5 | $0.00005 | $0.00111 |
Grade A, and why
style-rotation 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
风格轮动策略
基本概念
投资风格是指投资于某类具有共同收益特征或共同价格行为的股票。由于投资者的偏好会随时间变化，市场会呈现风格轮动特征。
主要风格类型
| 维度 | 类型 |
|---|---|
| 规模 | 大盘股、中盘股、小盘股 |
| 估值 | 价值型、成长型、混合型 |
| 质量 | 优质股、垃圾股 |
风格鉴别方法
1. 晨星风格箱法
3×3矩阵，从两个维度划分：
- 规模维度：大盘、中盘、小盘
- 估值维度：价值型、混合型、成长型
2. 夏普收益率法
按市值和净市比(B/P)分类：
- 高B/P为价值股，其余为成长股
- 市值前80%为中市值股，剩下为小盘股
经济逻辑
1. 经济周期解释
- 经济繁荣期：小盘股表现更好（对宏观变动更敏感）
- 经济衰退期：大盘股防御性更强
2. 行为金融解释
- 趋势追逐：投资者追涨杀跌导致风格动量
- 过度反应：风格泡沫最终破裂
- 价值回归：价格最终向价值回归
盈利预期生命周期模型
该模型将股票分为11个阶段：
反转 → 正向收益超预期 → 预期修正 → EPS动量 → 成长性 → 破灭 → 负向收益超预期 → 预期修正 → 蹩脚货 → 被忽略 → 反转
四种风格策略
| 策略 | 说明 |
|---|---|
| 成长动量 | 买入预期上升的成长股 |
| 成长反转 | 买入预期最差的成长股 |
| 价值动量 | 买入预期上升的价值股 |
| 价值反转 | 买入预期最差的价值股 |
量化预测模型
1. 相对价值法
基于均值回归理论，认为被低估/高估的股票价格最终会向均值回归。
2. Markov Switch模型
关注相对收益率的历史表现，识别风格转换状态。
3. Logistic概率模型
预测风格转换的概率：
- 输入：宏观经济、基本面、技术面指标
- 输出：风格转换概率
A股实证结论
大小盘轮动
推荐因子：
- M2同比增速（流动性）
- PPI同比增速（通胀）
- 大/小盘波动率之比（市场情绪）
预测准确率：约53.85%（历史回测）
风格动量特征
- 短期（1个月）：存在一定动量效应
- 中期（3个月）：反转效应较明显
- 长期（6-12个月）：效应不明显
实践建议
- 大小盘轮动：可频繁进行，一年多次
- 价值/成长轮动：适合年度或更长周期
- 中期反转：建议以3个月为周期操作
- 风格中性：长期投资者适合构建风格中性组合
策略示例
大小盘轮动策略
D(Rt) = α + β1·MG(t-1) + β2·PG(t-3) + β3·σ(t-3) + ε
其中：
- D(Rt) = 当月小/大盘收益率差
- MG(t-1) = 上月M2同比增速
- PG(t-3) = 3个月前PPI同比增速
- σ(t-3) = 3个月前波动率之比移动均值
轮动效果
- 2004-2010年累积收益：307.16%
- 同期上证指数收益：81.26%
- 2007年初开始累积收益：458.65%
注意事项
- A股风格效应比成熟市场更不稳定
- 预测精度相对较低
- 需关注宏观指标与股市的关联性
- 考虑交易成本对收益的影响
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 · 130 lines · 48 tokens per session scan A 8ba3f9a7c2b4
style-rotation is a skill published in the GitHub repository FeiCoder/Skill-Factory (10 stars, last pushed 6mo ago), licensed MIT. It adds 48 tokens to every session and 1,108 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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