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 statistical-arbitragegit 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/statistical-arbitrage)<a href="https://agentmods.dev/skills/feicoder/skill-factory/statistical-arbitrage"><img src="https://agentmods.dev/badge/skills/feicoder/skill-factory/statistical-arbitrage/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/statistical-arbitrage"><img src="https://agentmods.dev/badge/skills/feicoder/skill-factory/statistical-arbitrage.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 | $0.00058 | $0.03890 |
| Opus 5.5 | $0.00023 | $0.01556 |
| Sonnet 5.5 | $0.00012 | $0.00778 |
| Haiku 4.5 | $0.00006 | $0.00389 |
Grade A, and why
statistical-arbitrage 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 1mo 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.
How it starts
The opening of the file, as written. The whole thing — 429 lines — stays where its author put it; the contents beside it link to each section on GitHub.
统计套利
概述
统计套利是一种基于数学模型和历史统计规律的风险套利策略。它利用证券价格的历史统计规律进行套利，与无风险套利不同，统计套利存在历史规律失效的风险。其核心思想是通过构建多头和空头组合来对冲市场风险，获取与市场无关的稳定Alpha收益。
何时使用此技能：
- 当需要构建市场中性策略时
- 当需要寻找相关性强的资产对进行配对交易时
- 当需要在熊市或震荡市中获取稳定收益时
- 当需要利用行业或区域间的相关性进行套利时
核心概念
1. 统计套利定义
Morgan Stanley将统计套利定义为：一种基于模型的投资过程，在不依赖于经济含义的情况下，运用数量手段构建资产组合，根据证券价格与数量模型所预测的理论价值进行对比，构建证券投资组合的多头和空头，从而对市场风险进行规避，获取一个稳定的Alpha。
2. 统计套利 vs 无风险套利
| 特征 | 统计套利 | 无风险套利 |
|---|---|---|
| 理论基础 | 历史统计规律 | 价格必然收敛 |
| 风险 | 历史规律可能失效 | 理论无风险 |
| 收益 | 概率性Alpha | 确定性收益 |
| 适用范围 | 相关性资产对 | 期货到期收敛 |
3. 主要方法分类
统计套利主要有两大方法：
- β中性策略：基于日收益率建模，目标是在组合β值为零的前提下获取Alpha收益
- 协整策略：基于股价序列的协整关系建模，利用累计收益率的偏离进行套利
推荐策略：协整策略更优，因为：
- 直接利用原始变量（股价）建模
- 更容易把握累计收益率的偏离
- 错误信号较少
配对交易策略
配对交易原理
配对交易（Pairs Trading）是最经典的统计套利方法：
- 选取业务相似、股价具备均衡关系的上市公司股票
- 做空近期相对强势股，做多相对弱势股
- 等两者价差恢复均衡时平仓了结
关键特点：
- 投资标的是两只股票的价差（相对价值而非绝对价值）
- 多空同时建仓，对冲掉绝大部分市场风险
- 属于市场中性策略，与大盘走势相关性低
配对筛选流程
Step 1: 行业划分
└─ 在同一行业内筛选股票对，保证主营业务相似
Step 2: 收益率相关性检验
└─ 要求配对股票历史收益率相关系数 > 阈值（建议 > 0.85）
Step 3: 协整关系检验
└─ 检验两只股票价格序列是否存在长期均衡关系
Step 4: 价差形态过滤
└─ 筛选价差回归速度快的股票对，获得更多交易机会
协整检验方法
两只股票存在协整关系的条件：
- 两者的历史股价序列都是一阶单整（非平稳，但一阶差分后平稳）
- 这两个序列的某种线性组合是平稳的
协整方程：
Pa,t = α + β × Pb,t + εt
其中 εt（残差）必须是平稳序列。
检验步骤：
- 计算任意两只股票的相关系数
- 筛选相关系数较高的股票对（> 0.85）
- 对筛选后的股票对进行协整检验（EG两步法或Johansen检验）
- 选择残差平稳的股票对作为候选配对
交易策略设计
核心参数设置
| 参数 | 含义 | 推荐范围 |
|---|---|---|
| δ1 | 建仓阈值 | 1.0 ~ 1.3（σ倍数） |
| δ2 | 平仓阈值 | -1.0 ~ -0.7（σ倍数） |
| δ3 | 止损阈值 | 2.0 ~ 3.0（σ倍数） |
交易信号
入场条件：
- 当
spreadt > δ1 × σt：股票A相对高估，股票B相对低估- 卖空股票A，买入β倍股票B
- 当
spreadt < -δ1 × σt：股票A相对低估，股票B相对高估- 买入股票A，卖空β倍股票B
平仓条件：
- 当
spreadt > δ2 × σt或spreadt < -δ2 × σt：价差回归，平仓获利
止损条件：
- 当
spreadt > δ3 × σt或spreadt < -δ3 × σt：协整关系破坏，立即止损
阈值优化原则
- δ1 越大：潜在套利空间越大，但交易次数减少
- δ1 越小：潜在套利空间越小，但交易次数增加
- δ2 越接近0：平仓收益越高，但可能错失交易机会
最优阈值确定方法： 回测优化，在样本内寻找收益最大化的参数组合
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 1mo ago First seen · 429 lines · 58 tokens per session scan A ed5bfdfbfb4c
statistical-arbitrage is a skill published in the GitHub repository FeiCoder/Skill-Factory (11 stars, last pushed 7mo ago), licensed MIT. It adds 58 tokens to every session and 3,890 once invoked, about $0.0002 per session on Opus 5.5. 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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