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 market-sentiment-timinggit 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/market-sentiment-timing)<a href="https://agentmods.dev/skills/feicoder/skill-factory/market-sentiment-timing"><img src="https://agentmods.dev/badge/skills/feicoder/skill-factory/market-sentiment-timing.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 5.1 | $0.00048 | $0.01149 |
| Opus 5 | $0.00024 | $0.00575 |
| Sonnet 5 | $0.00010 | $0.00230 |
| Haiku 4.5 | $0.00005 | $0.00115 |
Grade A, and why
market-sentiment-timing 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 8d 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
市场情绪择时
基本概念
市场情绪是指投资者的心理和情绪状态，反映对市场的乐观或悲观程度。
核心观点
心理情绪造就90%的行情：T(趋势) = G(资金) + P(心理)
A股市场特点
- 个人投资者众多
- 羊群效应显著
- 情绪波动大
情绪指标类型
1. 直接调查类
- 投资者信心指数
- 投资意愿指数
2. 折溢价率类
- 封闭式基金折价率
- 可转债转股溢价率
- 权证的溢价率
3. 新股指标
- IPO首日涨跌幅
- IPO发行PE
- 新股发行数量
4. 市场指标
- 上涨家数百分比
- 创新高/低股票数
- 涨停家数
5. 投资者行为
- 新增开户数
- 基金仓位
- 保证金交易
- 资金出入
情绪指数构建
主成分分析法
选取7个核心指标：
- 封闭式基金折价率
- 转股溢价率
- IPO首日涨跌幅
- IPO发行PE
- 上涨家数百分比
- 混合型基金平均仓位
- 股票型基金平均仓位
情绪指数公式
情绪指数 = 0.111×封闭式基金折价率
- 0.242×转股溢价率
+ 0.489×IPO首日涨跌幅
+ 0.437×IPO发行PE
+ 0.207×上涨家数百分比
+ 0.470×混合型基金仓位
+ 0.483×股票型基金仓位
情绪变化指数
反映情绪的月度变化：
- 高情绪区域 → 继续上涨概率高
- 低情绪区域 → 继续下跌概率高
- 极端低情绪 → 可能反转
择时策略
1. 长期看区域
核心理念：均值回归
- 情绪低迷 → 股市被低估 → 买入
- 情绪高涨 → 股市被高估 → 卖出
操作方法：
- 以1倍标准差(σ)为分界线
- σ以上：高风险区，谨慎
- -σ以下：安全区，大胆介入
2. 短期看变化
核心理念：情绪变化
- 情绪变化指数区域2：收益率最差
- 剔除最差月份，选择其他区域
操作方法：
- 观察情绪变化指数
- 避开情绪变化不利的月份
- 顺势操作
实证数据
当期收益统计
情绪指数区域：
| 区域 | 月均收益 | 正收益占比 |
|---|---|---|
| 高区(1) | 最高 | 最高 |
| 次高区(2) | 较高 | 较高 |
| 次低区(3) | 较低 | 较低 |
| 低区(4) | 最低 | 28.57% |
下期收益统计
情绪指数：
- 高区 → 继续表现好
- 低区 → 极端时可能反转
情绪变化指数：
- 区域4 → 下期收益最好
- 区域2 → 下期收益最差
策略效果
2005-2010年：
- 长期区域策略：显著超额收益
- 短期变化策略：显著超额收益
- 沪深300基准：129.45%
实践要点
数据获取
- 封闭式基金折价率：天天基金网
- 可转债溢价率：东方财富
- 基金仓位：基金公司公开数据
- 上涨家数：交易所行情数据
策略参数
- 区域划分：1倍标准差
- 再平衡周期：月度
- 调仓：月初或月末
注意事项
- 数据质量：部分指标可能存在估计误差
- 时效性：情绪指标受当期影响大
- 极端情况：极端情绪可能预示反转
- 综合判断：结合多种指标
适用场景
- 大盘择时
- 仓位管理
- 风险预警
- 市场情绪监控
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.
- 8d ago First seen · 167 lines · 48 tokens per session scan A 6bf8fda59ce5
market-sentiment-timing 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 1,149 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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