Vibe-Trading is a personal trading agent that gives an AI system tools for market analysis, algorithmic trading, backtesting, and related workflows. It is for users who want an agent to research and evaluate trading strategies or manage simulated and other trading activities. The catalogue contains skills that expose these trading capabilities to compatible agents.
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 HKUDS/Vibe-Trading --skill etf-analysisgit clone --depth 1 https://github.com/HKUDS/Vibe-TradingWrote 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/hkuds/vibe-trading/etf-analysis)<a href="https://agentmods.dev/skills/hkuds/vibe-trading/etf-analysis"><img src="https://agentmods.dev/badge/skills/hkuds/vibe-trading/etf-analysis/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/hkuds/vibe-trading/etf-analysis"><img src="https://agentmods.dev/badge/skills/hkuds/vibe-trading/etf-analysis.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Snyk pass
- NVIDIA SkillSpector pass
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 | $0.00039 | $0.08871 |
| Opus 5 | $0.00019 | $0.04436 |
| Sonnet 5 | $0.00008 | $0.01774 |
| Haiku 4.5 | $0.00004 | $0.00887 |
Grade A, and why
etf-analysis 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 today.
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.
Copies of this mod
4 near-identical copies found in the catalogue:
- etf-analysis — 100% identical, 0 lines differ
- 问小达选ETF — 97% identical, 1,117 lines differ
- cash-flow-etf-analysis — 91% identical, 1,233 lines differ
- broad-index-etf-analysis — 89% identical, 1,239 lines differ
How it starts
The opening of the file, as written. The whole thing — 871 lines — stays where its author put it; the contents beside it link to each section on GitHub.
ETF 分析 Skill
定位
ETF（交易所交易基金）是被动投资与资产配置的核心工具。本 skill 覆盖 ETF 产品分析、选择方法论、策略应用、中国市场特色以及数据驱动的量化分析方法，为构建基于 ETF 的量化策略与组合提供完整框架。
1. ETF 产品分类
1.1 按标的资产分类
| 类型 | 代表产品 | 特点 |
|---|---|---|
| 宽基 ETF | 沪深300ETF (510300)、中证500ETF (510500)、创业板ETF (159915)、科创50ETF (588000) | 流动性最好，交易成本最低，适合核心仓位 |
| 行业 ETF | 消费ETF (159928)、医疗ETF (512170)、半导体ETF (512480)、银行ETF (512800) | 行业轮动工具，持仓集中度高 |
| 主题 ETF | 新能源ETF (516160)、碳中和ETF、元宇宙ETF | 主题炒作属性强，生命周期短 |
| 策略ETF / Smart Beta | 红利ETF (510880)、低波ETF、质量ETF、动量ETF | 因子暴露明确，费率通常略高于宽基 |
| 商品 ETF | 黄金ETF (518880)、豆粕ETF (159985)、原油ETF (162411) | 实物/期货支撑，注意展期损耗 |
| 债券 ETF | 国债ETF (511010)、信用债ETF、可转债ETF (511380) | 利率敏感，久期管理关键 |
| 跨境 ETF (QDII) | 纳指ETF (159632)、标普500ETF (513500)、日经225ETF (513880) | 汇率风险+溢价风险双重叠加 |
| 货币 ETF | 华宝添益 (511990)、银华日利 (511880) | T+0 申赎，流动性管理工具 |
1.2 结构类型
- 普通 ETF：场内交易，实物申赎（一篮子股票换购），折溢价有套利机制自动收敛
- LOF（上市开放式基金）：场内外均可交易，折溢价套利路径相同但效率略低
- ETF 联接基金：场外渠道购买的 ETF 替代品，T+1 申赎，无折溢价，适合定投
- 杠杆/反向 ETF：日内恒定杠杆，长期持有有衰减效应（见第 3.4 节）
2. ETF 核心指标
2.1 跟踪误差（Tracking Error）
衡量 ETF 复制指数能力的最核心指标。
日跟踪误差 = std(ETF日收益率 - 指数日收益率)
年化跟踪误差 = 日跟踪误差 × √252
评级标准（A股宽基ETF）：
- 优秀：年化跟踪误差 < 0.2%
- 合格：0.2% ~ 0.5%
- 较差：> 0.5%
跟踪误差来源：
- 管理费和托管费（持续拖累，每日计提）
- 分红处理时机（分红再投资延迟）
- 成分股纳入/剔除时的买卖冲击
- 现金仓位（申赎带来的暂时性现金拖累）
- 停牌股处理（用替代品或现金替代）
- 指数编制方法（全复制 vs 抽样复制）
2.2 信息比率（Information Ratio）
IR = (ETF年化收益率 - 指数年化收益率) / 年化跟踪误差
对 ETF 来说 IR 通常为负（因费率拖累），IR 越接近 0 越好。
2.3 折溢价率
折溢价率 = (ETF市价 - ETF净值IOPV) / ETF净值IOPV × 100%
- 正溢价：市价 > 净值，套利者卖出 ETF / 申购一篮子股票换购，溢价收敛
- 负折价：市价 < 净值，套利者买入 ETF / 赎回一篮子股票，折价收敛
- 异常溢价场景：跨境 QDII ETF（额度限制导致持续溢价）、停牌股比例高的行业 ETF
2.4 流动性指标
| 指标 | 含义 | 参考阈值 |
|---|---|---|
| 日均成交额 | 买卖方便程度 | 宽基 > 1亿，行业 > 2000万 |
| 买卖价差（Spread） | 即时交易成本 | < 0.05% 为优质 |
| 盘口深度 | 单笔大额交易冲击 | 买卖各5档累计 > 500万为佳 |
| 换手率 | 活跃程度 | 过低则流动性风险高 |
2.5 费率体系
综合费率 = 管理费 + 托管费 + 指数使用费
（不含交易佣金、印花税、申赎费）
长期费率影响公式：
N年费率复利损耗 = (1 - 年费率)^N
例：年费率0.5% vs 0.15%，10年差距 ≈ 3.5%，20年差距 ≈ 6.8%
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.
- today Changed 35754891e5ff
- 10d ago First seen · 871 lines · 39 tokens per session scan A d18b107bad10
etf-analysis is a skill published in the GitHub repository HKUDS/Vibe-Trading (33,085 stars, last pushed today), licensed MIT. It adds 39 tokens to every session and 8,871 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 2026-08-30.
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