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einstein-research-breadth-dv爱因斯坦研究广度 dv

Agent Skill

einstein-research-breadth-dv 用于查找、检索和筛选相关信息,适合在 OpenClaw 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

3,133

周安装

128

GitHub Stars

公开资料未说明

下载量

1,014
OpenClaw

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

MIT-0

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:einstein-research-breadth-dv(爱因斯坦研究广度 dv)
来源仓库:https://github.com/clawdiri-ai/einstein-research-breadth-dv
安装命令:
openclaw skills install einstein-research-breadth-dv
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 OpenClaw 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。

ClawHubOpenClaw
openclaw skills install einstein-research-breadth-dv

简介

einstein-research-breadth-dv 使用 TraderMonty 公共数据量化市场广度健康状况。

  • 专为 OpenClaw 设计,适用于评估整体市场情绪与资金流向。
  • 通过 ClawHub 安装,无需 API 密钥即可生成 0-100 综合健康评分。
  • 使用前需确认权限范围、维护状态,以及是否会触发公开数据抓取操作。
  • 建议结合其他指标交叉验证以提高判断准确性。

SKILL.md

id
einstein-research-breadth
name
einstein-research-breadth
description
Quantifies market breadth health using TraderMonty's public CSV data.
version
1.0.0
author
DaVinci
last_amended_at
null
trigger_patterns
[]
pre_conditions
git_repo_required
false
tools_available
[]
expected_output_format
natural_language

Market Breadth Analyzer

Overview

This skill quantifies the health of market breadth using public data from TraderMonty's GitHub repository. It generates a composite score from 0-100 (100 = healthy) across six key components, providing a quick, data-driven assessment of market participation.

Key Features:

  • Composite Score (0-100): Single, easy-to-understand metric for breadth health.
  • 6-Component Analysis:

1. % Stocks > 50-day MA 2. % Stocks > 200-day MA 3. 1-Month New Highs - New Lows 4. Advance-Decline Line (ADL) Momentum 5. % Bullish (AAII Sentiment) 6. S&P 500 distance from 200-day MA

  • No API Key Required: Uses a publicly available CSV, making it free and reliable.
  • Historical Context: Compares the current score to its 3-month and 6-month moving averages.

When to Use This Skill

Explicit Triggers:

  • "What's the current market breadth?"
  • "Is this rally broad-based?"
  • "Analyze market participation."
  • "Show me the advance-decline health."
  • User asks about "market breadth," "A-D line," "% stocks above moving average."

Implicit Triggers:

  • User is concerned about a narrow, top-heavy market rally (e.g., led by only a few mega-cap stocks).
  • User is assessing the risk of a market downturn, as poor breadth is often a leading indicator.

When NOT to Use:

  • For real-time, intraday breadth data (this is end-of-day).
  • For individual stock analysis.
  • For deep technical analysis of a single indicator (this skill provides a composite view).

Workflow

Step 1: Execute the Analysis Script

The entire process is handled by a single Python script.

# Run the breadth analysis
python3 skills/market-breadth/scripts/breadth_analyzer.py

The script performs the following actions:

  1. Downloads Data: Fetches the latest Market-Breadth-Data.csv from TraderMonty's public GitHub repo.
  2. Calculates Components: For each of the 6 components, it calculates a normalized score (0-100) based on its current value relative to its 1-year range.
  3. Computes Composite Score: A weighted average of the 6 component scores.

- % > 50d MA: 25% - % > 200d MA: 25% - NH-NL: 20% - ADL Momentum: 15% - AAII Bullish: 10% (inverse scoring) - SPX distance from 200d MA: 5%

  1. Generates Report: Outputs a JSON file and a human-readable Markdown summary.

Step 2: Analyze the Output

The script produces two files:

  • breadth_report_YYYY-MM-DD.json
  • breadth_report_YYYY-MM-DD.md

JSON Output:

{
  "composite_score": 78.5,
  "assessment": "Healthy",
  "trend": "Improving",
  "components": {
    "stocks_above_50d_ma": 85,
    "stocks_above_200d_ma": 90,
    "new_highs_lows": 75,
    "ad_line_momentum": 60,
    "aaii_bullish_inverse": 70,
    "spx_distance_from_200d_ma": 95
  },
  "moving_averages": {
    "3_month": 65.2,
    "6_month": 58.9
  }
}

Markdown Report:

  • Overall Score: 78.5 / 100 (Healthy)
  • Trend: Improving (Current > 3-Month MA)
  • Component Breakdown: A table showing the score for each of the 6 components.
  • Key Takeaway: A short, human-readable summary of the current breadth situation.

Step 3: Present Findings to User

Synthesize the Markdown report into a concise, clear answer.

Example Response: "Current market breadth is healthy, with a composite score of 78.5 out of 100. This is above the 3-month average of 65.2, indicating an improving trend.

  • Strengths: A high percentage of stocks are trading above their 50-day (85/100) and 200-day (90/100) moving averages.
  • Weakness: Advance-Decline Line momentum is only moderate (60/100).

Overall, this suggests the current market rally is broad-based and well-supported."

Interpretation Guide

  • > 70 (Healthy): Strong participation. Rally is likely sustainable.
  • 50-70 (Moderate): Decent participation, but some signs of narrowing.
  • 30-50 (Weak): Narrow, selective market. High risk of reversal.
  • < 30 (Very Weak): Extremely poor participation. Market is vulnerable.

A divergence (e.g., S&P 500 making new highs while the breadth score is falling) is a significant warning sign.

适合场景

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能力概览

能力 1

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能力 2

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能力 3

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能力 4

补充不同宿主或平台的使用分布数据

能力 5

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

OpenClaw

94.85%
按下载量换算962

安全审计

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通过

ClawScan

可疑

Static analysis

通过

权限和风险

敏感数据

该 Skill 可能接触密钥、Token、环境变量或敏感配置,应进入高风险复核队列,默认不自动发布。

安装前确认

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来源信息

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