lean-capm-alpha-rank

lean-capm-alpha-rank is a skill for Claude Code from Travisun/Opptrix. It costs 83 tokens per session (1,363 once invoked), scanned A, original, Apache-2.0.

An educational CAPM workflow that estimates how a group of assets performed relative to a chosen market benchmark. CAPM is a model that separates market-linked return from an estimated residual return, often called alpha.

In plain words
What is it for?
Use it to rank Chinese stocks or ETFs by estimated CAPM alpha against a benchmark such as the CSI 300 or CSI 500.
Why use it?
It provides a consistent ranking based on historical regression results rather than raw returns alone. The output is only an illustrative historical comparison, not a complete multi-factor attribution.

Skill for Claude Code

Written for Claude Code: allowed-tools in frontmatter.

Good fit Use it to rank Chinese stocks or ETFs by estimated CAPM alpha against a benchmark such as the CSI 300 or CSI 500.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/travisun/opptrix/lean-capm-alpha-rank
Install

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.

Any agent
npx skills add Travisun/Opptrix --skill lean-capm-alpha-rank
Clone the repo
git clone --depth 1 https://github.com/Travisun/Opptrix

Made for: Claude Code.

Wrote 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.

agentmods badge for lean-capm-alpha-rank

README.md
[![agentmods](https://agentmods.dev/badge/skills/travisun/opptrix/lean-capm-alpha-rank.svg)](https://agentmods.dev/skills/travisun/opptrix/lean-capm-alpha-rank)
Your own site
<a href="https://agentmods.dev/skills/travisun/opptrix/lean-capm-alpha-rank"><img src="https://agentmods.dev/badge/skills/travisun/opptrix/lean-capm-alpha-rank.svg" alt="Measured on agentmods" height="20"></a>
Per session 83 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,363 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00083 $0.01363
Opus 5 $0.00042 $0.00681
Sonnet 5 $0.00017 $0.00273
Haiku 4.5 $0.00008 $0.00136

Measured 4d ago against content hash 97dabddd83e5, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-07, from the pricing page.

Security

Grade A, and why

lean-capm-alpha-rank 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.

packages/agent-skills/builtin/lean-capm-alpha-rank/SKILL.md · 82 lines

What it actually says

LEAN CAPM Alpha

方法溯源 QuantConnect LEAN 社区算法/示例思路;本技能是平台工具编排的投研工作流禁止假装跑完整 LEAN 引擎或输出 LEAN 回测曲线、订单日志冒充引擎结果。实际取数与计算仅限 Opptrix 工具(行情/财务/opptrix_run 沙盒等)。

何时使用

用户要在 A股/场内 ETF 样本期内对一篮子标的做 相对基准(如沪深300)的 CAPM/单因子回归 Alpha(残差)排序示意(LEAN 方法溯源,非美股原版照搬)。默认交付可预览网页。

边界:因子研究回测用 @skill:factor-research;组合暴露用 @skill:factor-exposure。本技能是教育/示意排序,非完整多因子归因。

A股适配(默认)

  • 默认市场 CN(A股 / 场内 ETF)。用户点名美股/港股再切换,并声明数据口径与微观结构差异。
  • 默认基准与宇宙:沪深300/中证500 等 CN 指数成分;Beta/残差相对 CN 基准估计。
  • 融券受限 → Alpha 多空改为「多头高 Alpha 排序」或「多头+空仓」。
  • 涨跌停日收益截断会影响 Beta 估计,样本须注明。
  • 不可硬适配或数据缺口时:首页横幅写清完整度(partial 或更严)+ 必要时 ask_user

分析架构(投研方法)

  • 问题/假设:相对选定基准,谁在样本期呈现更高回归截距/残差均值为正?
  • 证据清单:标的与基准收益序列、回归表、排序
  • 多维交叉验证:全样本 vs 分段;Beta 稳定否
  • 结论与不确定:历史 Alpha≠未来;无成本/无停牌处理须披露
  • 微观/制度风险:涨跌停钝化、T+1、ST/停牌、融券受限(及相关会计口径差异);不得按美股连续可成交或自由做空假设叙事
  • 事实 | 假设 | 推断 分栏强制

数据维度

维度 取数方向 缺失时
标的与基准 ask_user / 搜索 先确认
收益序列 get_instrument_quotes 样本不足则降级
回归/排序 opptrix_run 简化 OLS 并标假设
交付 list_web_vendorcreate_web 可跳过口头要点
A股基准/宇宙 CN 指数成分 + 行情估计 Beta 无基准序列则 ask_user / partial

步骤

  1. 确认默认 CN:标的/宇宙为 A股或场内 ETF(用户点名其他市场再切换并声明差异)。应用涨跌停/T+1/融券受限等微观约束(见 A股适配)。
  2. 确认宇宙、基准、样本期:无风险利率假设显式
  3. LEAN 溯源边界:灵感来自 Alpha 排序示例;不跑 LEAN
  4. 回归并排序:输出 Alpha/Beta/R² 表
  5. 过拟合警示:分段对照若可
  6. 交付网页(默认)create_web

网页报告建议目录

  1. 范围:默认 A股/场内 ETF + LEAN 溯源
  2. LEAN 溯源与「非引擎」声明
  3. Alpha/Beta 排序表
  4. 分段稳健性(若有)
  5. 事实 / 假设 / 推断分栏
  6. A股适配与限制(默认 CN;微观结构/代理/完整度)
  7. 免责声明(非荐股;历史≠未来)

禁止

  • 荐股、目标价、仓位建议;编造未返回数字
  • 把历史 Alpha 写成「持续跑赢保证」
  • 禁止假装跑完整 LEAN 引擎或伪造 LEAN 日志/回测净值
  • 禁止无交付就结束(默认须有 web 产物,除非用户明确只要口头要点)
  • 禁止把美股成分/ETF 清单不经映射直接当 A股结果
  • 禁止假设可自由融券做空
Changes

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.

  1. 4d ago First seen · 82 lines · 83 tokens per session scan A 97dabddd83e5

Subscribe to this mod's changes

lean-capm-alpha-rank is a skill published in the GitHub repository Travisun/Opptrix (230 stars, last pushed yesterday), licensed Apache-2.0. It adds 83 tokens to every session and 1,363 once invoked, about $0.0004 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 2026-09-03.

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