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/bull-bear-linenpx skills add FeiCoder/Skill-Factory --skill bull-bear-linegit 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/bull-bear-line)<a href="https://agentmods.dev/skills/feicoder/skill-factory/bull-bear-line"><img src="https://agentmods.dev/badge/skills/feicoder/skill-factory/bull-bear-line.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 | $0.00041 | $0.00976 |
| Opus 5 | $0.00020 | $0.00488 |
| Sonnet 5 | $0.00008 | $0.00195 |
| Haiku 4.5 | $0.00004 | $0.00098 |
Grade A, and why
bull-bear-line 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 6d 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
牛熊线择时
基本概念
牛熊线是基于几何布朗运动理论，定义两根线来判断市场方向：
- 牛线（Bull Line）：上涨临界线
- 熊线（Bear Line）：下跌临界线
核心思想
- 价格突破牛线 → 牛市开始 → 买入
- 价格突破熊线 → 熊市开始 → 卖出
- 牛熊线之间 → 震荡整理
理论基础
布朗运动与股市
1900年数学家巴契里耶提出股价类似布朗运动：
- 交易行为：交易者买卖如同分子撞击粒子
- 估值与温度：市场热时估值高
- 市值与质量：小市值波动更大
- 交易量：交易活跃时波动加剧
股价布朗运动
dS = μSdt + σSdW
其中：
- μ: 期望收益率
- σ: 波动率
- dW: 布朗运动
方向性定义
将期望值上下一定距离设为临界值：
- SUPlim：强势区域上限（牛线）
- SDownlim：弱势区域下限（熊线）
策略模型
牛熊线计算
BullPrice(T) = F(S0, μ, σ, T, P)
BearPrice(T) = G(S0, μ, σ, T, P)
参数：
- S0: 初始价格
- μ: 期望收益率（需估计）
- σ: 波动率（需估计）
- T: 时间周期
- P: 置信水平
参数估计
- μ：基于历史平均收益率
- σ：基于历史波动率
- T：选择合适的回顾周期
- P：选择置信水平（如95%）
交易规则
判断标准
- 在牛线之上 → 强势状态 → 买入持有
- 在熊线之下 → 弱势状态 → 卖出观望
- 在牛熊线之间 → 盘整状态 → 不操作
实证案例（2001-2010年）
| 时间 | 操作 | 效果 |
|---|---|---|
| 2001.6.8 | 跌破牛线，卖出 | 成功规避2年半下跌 |
| 2003.12.3 | 涨过牛线，买入 | 把握4个月行情 |
| 2004.4.6 | 跌破牛线，卖出 | 规避1.5年下跌 |
| 2005.12.26 | 涨过牛线，买入 | 把握5800点大行情 |
| 2007.10.18 | 跌破牛线，卖出 | 规避历史最大下跌 |
| 2009.2.4 | 涨过牛线，买入 | 把握反弹行情 |
| 2010.10.1 | 涨过牛线，买入 | 把握脉冲行情 |
策略评价
优点
- 不过滤大机会：成功把握所有牛市
- 规避大风险：成功规避所有熊市
- 清晰信号：突破牛熊线界限明确
适合市场
- 趋势明显的市场
- 大牛大熊转换明显的A股
注意事项
- 参数敏感：μ和σ的估计影响结果
- 盘整期：牛熊线之间可能有反复
- 假突破：需要结合其他指标确认
实践建议
- 选择合适的回顾周期T
- 使用滚动估计更新参数
- 结合成交量确认信号
- 设置止损位
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.
- 6d ago First seen · 115 lines · 41 tokens per session scan A 6d01bdea02b4
bull-bear-line is a skill published in the GitHub repository FeiCoder/Skill-Factory (10 stars, last pushed 6mo ago), licensed MIT. It adds 41 tokens to every session and 976 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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