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 skloxo/TideTrading --skill fund-analysisgit clone --depth 1 https://github.com/skloxo/TideTradingWrote 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/skloxo/tidetrading/fund-analysis)<a href="https://agentmods.dev/skills/skloxo/tidetrading/fund-analysis"><img src="https://agentmods.dev/badge/skills/skloxo/tidetrading/fund-analysis.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 | $0.00047 | $0.03241 |
| Opus 5 | $0.00023 | $0.01621 |
| Sonnet 5 | $0.00009 | $0.00648 |
| Haiku 4.5 | $0.00005 | $0.00324 |
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 8d 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.
This is a copy
100% identical to fund-analysis — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
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%
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.
- 8d ago First seen · 274 lines · 47 tokens per session scan A be08bb5fccde
fund-analysis is a skill published in the GitHub repository skloxo/TideTrading (10 stars, last pushed yesterday), 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. It is 100% identical to fund-analysis, differing in 0 lines, and is treated as a copy.
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