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grad-emh梯度有效值

Agent Skill

grad-emh 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

总安装

360

周安装

15

GitHub Stars

125

下载量

120
CodexClaudeCursorGemini CLI

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

unknown

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:grad-emh(梯度有效值)
来源仓库:https://github.com/asgard-ai-platform/skills
仓库路径:skills/grad-emh
安装命令:
npx skills add https://github.com/asgard-ai-platform/skills --skill grad-emh
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

skills.shnpx skills
npx skills add https://github.com/asgard-ai-platform/skills --skill grad-emh

简介

grad-emh 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中快速定位候选结果。

  • 适用于信号处理、电气工程或数值计算研究场景。
  • 通过关键词和来源线索匹配,返回相关算法与技术参数。
  • 安装方式:npx skills add https://github.com/asgard-ai-platform/skills --skill grad-emh
  • 建议确认权限范围、维护状态,以及是否涉及联网或命令执行。

SKILL.md

Efficient Market Hypothesis (EMH)

Overview

The Efficient Market Hypothesis (Fama, 1970) posits that asset prices fully reflect available information, making it impossible to consistently earn abnormal returns. EMH is organized into three forms — weak, semi-strong, and strong — each defined by the information set reflected in prices.

When to Use

  • Evaluating whether a trading strategy exploits genuine inefficiency
  • Designing event studies (semi-strong form test)
  • Assessing if active management adds value over passive indexing
  • Debating the validity of technical or fundamental analysis

When NOT to Use

  • As justification to ignore all market anomalies without investigation
  • When markets are clearly illiquid or informationally segmented
  • For normative claims — EMH describes price behavior, not what prices "should" be

Assumptions

IRON LAW: In an efficient market, prices reflect available information —
beating the market consistently requires either superior information
or accepting more risk. No free lunch.

Key assumptions:

  1. Large number of rational, profit-maximizing participants
  2. Information is costless and available simultaneously to all participants
  3. Transaction costs do not prevent trading on information
  4. Investors react quickly and unbiasedly to new information

Methodology

Step 1 — Identify the Information Set

  • Weak form: past prices and trading volume only
  • Semi-strong form: all publicly available information
  • Strong form: all information including private/insider information

Step 2 — Determine the Testable Implication

FormInformation ReflectedImplication
WeakHistorical pricesTechnical analysis cannot earn excess returns
Semi-strongAll public infoFundamental analysis cannot earn excess returns
StrongAll info (public + private)Even insiders cannot earn excess returns

Step 3 — Select Appropriate Test

  • Weak: autocorrelation tests, runs tests, filter rules
  • Semi-strong: event studies (abnormal returns around announcements)
  • Strong: insider trading profitability studies

Step 4 — Interpret Results with Joint-Hypothesis Awareness

Any test of efficiency is simultaneously a test of the asset pricing model used to define "abnormal" return.

Output Format

## EMH Assessment: [Market / Strategy]

### Efficiency Form Tested
- Form: [weak / semi-strong / strong]
- Information set: [description]

### Evidence
| Test | Result | Supports Efficiency? |
|------|--------|---------------------|
| [test name] | [finding] | [Yes/No/Ambiguous] |

### Known Anomalies in This Context
- [List relevant anomalies and their current status]

### Conclusion
- [Efficiency assessment with caveats]
- [Joint-hypothesis caveat]

Gotchas

  • Joint-hypothesis problem: you cannot test efficiency without assuming an equilibrium model
  • Grossman-Stiglitz paradox (1980): if markets are perfectly efficient, no one has incentive to gather information
  • Anomalies (momentum, value, size) persist but may reflect risk or data mining
  • EMH does not claim prices are always "correct" — only that mispricings are not systematically exploitable
  • Market efficiency varies by market segment; large-cap equities are more efficient than micro-caps
  • Behavioral finance provides systematic counterexamples but does not necessarily invalidate EMH

References

  • Fama, E. (1970). Efficient capital markets: a review of theory and empirical work. *Journal of Finance*, 25(2), 383-417.
  • Grossman, S. & Stiglitz, J. (1980). On the impossibility of informationally efficient markets. *American Economic Review*, 70(3), 393-408.
  • Malkiel, B. (2003). The efficient market hypothesis and its critics. *Journal of Economic Perspectives*, 17(1), 59-82.

适合场景

01

用户想查找某类 Agent Skill 时

02

需要根据任务场景推荐可安装能力包时

03

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

Codex

32.71%
按下载量换算39

Claude

28.37%
按下载量换算34

Cursor

21.06%
按下载量换算25

Gemini CLI

8.71%
按下载量换算10

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

需要联网

该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

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