fund-analysis

fund-analysis is a skill for Claude Code, Codex from HKUDS/Vibe-Trading. It costs 47 tokens per session (3,241 once invoked), scanned A, original, MIT.

A framework for comparing mutual funds, private funds, and exchange-traded funds using returns, risk, investment style, and manager performance. It also examines whether a fund's style changes over time.

In plain words
What is it for?
Use it to compare funds, assess managers, evaluate ETFs, detect style changes, and build or rebalance a portfolio of funds, known as a fund-of-funds portfolio.
Why use it?
Past returns alone do not show how much risk a fund took or whether its results may continue. This framework organizes those factors into a broader comparison.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit Use it to compare funds, assess managers, evaluate ETFs, detect style changes, and build or rebalance a portfolio of funds, known as a fund-of-funds portfolio.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/hkuds/vibe-trading/fund-analysis
About the project

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.

HKUDS/Vibe-Trading · 33,085 stars · on GitHub · vibetrading.wiki

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 HKUDS/Vibe-Trading --skill fund-analysis
Clone the repo
git clone --depth 1 https://github.com/HKUDS/Vibe-Trading

Made for: Claude Code, Codex.

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 fund-analysis

README.md
[![agentmods](https://agentmods.dev/badge/skills/hkuds/vibe-trading/fund-analysis/github.svg)](https://agentmods.dev/skills/hkuds/vibe-trading/fund-analysis)
Your own site
<a href="https://agentmods.dev/skills/hkuds/vibe-trading/fund-analysis"><img src="https://agentmods.dev/badge/skills/hkuds/vibe-trading/fund-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.

agentmods 80×15 button for fund-analysis

Your own site · 80×15
<a href="https://agentmods.dev/skills/hkuds/vibe-trading/fund-analysis"><img src="https://agentmods.dev/badge/skills/hkuds/vibe-trading/fund-analysis.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 47 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 3,241 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. ✓ AI security review Fable 5.1 · 6 Sept 2026 📄 Read the review Third-party audits
  • Snyk pass 7 Sept 2026
  • NVIDIA SkillSpector warn 7 Sept 2026
SkillSpector: 1 finding, up to medium

These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →

  • medium analysis-evasion · line 1
    Suspicious Unicode normalization or mixed-script content
    Fix: Review the flagged content for security risks. Ensure no credentials, secrets, or sensitive data are exposed.
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 $0.00047 $0.03241
Opus 5 $0.00023 $0.01621
Sonnet 5 $0.00009 $0.00648
Haiku 4.5 $0.00005 $0.00324

Measured 10d ago against content hash be08bb5fccde, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-10, from the pricing page.

Security

Grade A, and why

fund-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 10d 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.

Origin

Copies of this mod

1 near-identical copy found in the catalogue:

agent/src/skills/fund-analysis/SKILL.md · 274 lines

How it starts

The opening of the file, as written. The whole thing — 274 lines — stays where its author put it; the contents beside it link to each section on GitHub.

基金分析与筛选

概述

系统化评估公募基金/私募基金/ETF的业绩表现、投资风格和管理能力,并构建FOF(基金中的基金)组合。核心目标:找到"可持续的超额收益来源"而非"过去业绩最好的基金"。

适用场景:

  • 股票型/混合型基金的多维度筛选
  • 基金经理投资风格的归因与漂移检测
  • ETF产品的跟踪效率评估
  • FOF组合的资产配置与再平衡
  • A股公募基金的特有分析维度

核心概念

基金绩效指标体系

收益类指标

指标 公式 优秀阈值 说明
年化收益率 (1+总收益)^(1/年数)-1 > 15% (股基) 绝对收益
超额收益(Alpha) 基金收益-基准收益 > 5%/年 相对基准
信息比率(IR) Alpha / 跟踪误差 > 0.5 Alpha稳定性
胜率 跑赢基准的月份占比 > 55% 一致性

风险类指标

指标 公式 优秀阈值 说明
最大回撤 max(peak-trough)/peak < 20% (股基) 极端风险
年化波动率 std(日收益)*√252 < 20% (股基) 总风险
下行标准差 std(负收益)*√252 < 13% 下行风险
Calmar比率 年化收益/最大回撤 > 1.0 收益/极端风险

风险调整指标

指标 公式 优秀阈值 说明
夏普比率 (Rp-Rf)/σp > 1.0 每单位风险收益
Sortino比率 (Rp-Rf)/下行σ > 1.5 更关注下行风险
Treynor比率 (Rp-Rf)/β > 10% 每单位系统风险收益
无风险利率(Rf): A股通常用1年期国债收益率, 约2.0-2.5%
基准: 股票型→沪深300; 混合型→沪深300×60%+中证全债×40%
评估周期: 至少3年,推荐5年(覆盖完整牛熊周期)

Sharpe风格箱分析

九宫格风格分类

          价值     平衡     成长
大盘    大盘价值  大盘平衡  大盘成长
中盘    中盘价值  中盘平衡  中盘成长
小盘    小盘价值  小盘平衡  小盘成长

判定方法(回归法):
  Ri = α + β1×大盘价值 + β2×大盘成长 + β3×小盘价值 + β4×小盘成长 + ε

  风格指数选择(A股):
  大盘价值: 沪深300价值 (399346)
  大盘成长: 沪深300成长 (399370)
  小盘价值: 中证500价值 (930782)
  小盘成长: 中证500成长 (930783)

  β权重最大的方向 = 基金主风格
  R² > 0.85 → 风格明确; R² < 0.70 → 风格模糊/择时型

风格漂移检测

方法: 滚动窗口回归 (窗口=60个交易日, 步长=20日)

漂移判定:
  1. 计算每个窗口的风格权重β
  2. 相邻窗口β变化:
     |Δβ| > 0.2 → 显著漂移
     最大β对应的风格变了 → 风格切换

  3. R²时序:
     R²持续下降 → 基金经理在做择时/偏离基准
     R²忽高忽低 → 风格不稳定

漂移类型:
  - 渐进漂移: 大盘→中盘→小盘 (通常是规模增长后被迫下沉)
  - 突变漂移: 价值突然切换成长 (可能换了基金经理)
  - 周期漂移: 牛市追成长、熊市转价值 (择时型)

A股常见漂移:
  2020-2021: 大量"价值型"基金实际持仓转向新能源/半导体(成长)
  检测: 申报风格=大盘价值, 实际回归风格=大盘成长 → 名不副实

分析框架

1. 基金筛选框架(五步法)

Step 1: 硬指标过滤
  □ 成立 ≥ 3年
  □ 规模 2-100亿(太小清盘风险, 太大船大难掉头)
  □ 同一基金经理管理 ≥ 2年
  □ 机构持有比例 > 20%(机构认可)

Step 2: 绩效排序
  □ 近3年年化收益 > 同类中位数
  □ 近3年夏普比率 > 同类前30%
  □ 最大回撤 < 同类中位数
  □ 信息比率 > 0.3

Step 3: 风格验证
  □ 实际风格与申报风格一致(R² > 0.8)
  □ 风格漂移得分 < 0.3(稳定)
  □ 近1年风格与近3年一致

Step 4: 基金经理评价
  □ 管理同类基金 ≥ 3年
  □ 历史任职基金收益均为正超额
  □ 换手率合理(年化200-400%为正常, >600%过高)
  □ 持股集中度适中(前10大持仓40-70%)

Step 5: 费用检查
  □ 管理费 ≤ 1.5%(主动股基)
  □ 无惩罚性赎回费(持有>1年免赎回费)
  □ 托管费 ≤ 0.25%

Read the full file on GitHub · 274 lines

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. 10d ago First seen · 274 lines · 47 tokens per session scan A be08bb5fccde

Subscribe to this mod's changes

fund-analysis is a skill published in the GitHub repository HKUDS/Vibe-Trading (33,085 stars, last pushed today), licensed MIT. It adds 47 tokens to every session and 3,241 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.

Related

Other skills, from other repositories

hyperliquid

Use when backtesting, deploying, checking funding readiness, or debugging a Hyperliquid strategy through Superior Trade Unified API — writing Freqtrade configs and strategy code, running sweeps, checking managed-wallet balances, trading HIP-3 perps, or diagnosing a deployment that will not start or trade.

Superior-Trade/superior-skills · 64 tokens

polymarket

Use when the user wants to trade, research, or backtest Polymarket prediction markets through Superior Trade — finding markets by slug or event URL, placing a single immediate market order, writing NautilusTrader strategies, running filled-data backtests, funding pUSD, or deploying and monitoring a live Polymarket…

Superior-Trade/superior-skills · 68 tokens

backtesting

Use when running, interpreting, or designing backtests on Superior Trade — anything about backtest windows, trade-count thresholds, exit-reason mix, parameter sweeps, walk-forward validation, zero-trade diagnosis, compute-cost estimation, or "is this backtest result trustworthy?". Pair with the relevant strategy…

Superior-Trade/superior-skills · 73 tokens

basis-arb

Use when the user asks for spot-perp basis trade, basis arbitrage, cash-and-carry, perp discount, or any setup that reads the spot–perp basis as a positioning signal. Long-perp leg only — pure two-leg basis arb requires a paired spot short (or long) which Freqtrade can't run cleanly. The strategy below captures the…

Superior-Trade/superior-skills · 87 tokens

fees-optimizations

Use when the user asks about fees, fee optimization, slippage, maker vs taker, post-only or ALO orders, fee tiers, builder code fees, effective spread, order pricing, lowering trading costs, or why a live Hyperliquid Freqtrade strategy underperforms its backtest. Also use proactively for high-turnover designs (5m or…

Superior-Trade/superior-skills · 94 tokens

aerodrome

Use when creating, validating, backtesting, deploying, sizing, or troubleshooting Aerodrome/Base spot trading strategies through the Superior Trade API, especially Freqtrade configs using exchange.name "aerodrome", AERO/USDC or CHECK/USDC pairs, AMM market swaps, wallet/gas balance checks, no-orderbook pricing, or…

Superior-Trade/superior-skills · 83 tokens