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market-sentiment

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Crypto Market Sentiment

加密货币市场情绪分析

Overview

概述

This skill enables aggregation of news from popular cryptocurrency RSS feeds, performs sentiment analysis on the articles, and computes a market sentiment score ranging from -1 (highly negative) to +1 (highly positive), along with evidence-based explanations.
此技能可聚合热门加密货币RSS源的新闻，对文章进行情绪分析，并计算范围从-1（极度负面）到+1（极度正面）的市场情绪得分，同时提供基于证据的说明。

Workflow

工作流程

Follow these steps to analyze crypto market sentiment:
  1. Select RSS Feeds: Choose popular crypto RSS feeds (see references/rss_feeds.md for a curated list).
  2. Fetch News: Retrieve recent articles from the selected feeds.
  3. Analyze Sentiment: Classify each article's sentiment as positive (+1), negative (-1), or neutral (0) based on content keywords and context.
  4. Calculate Score: Compute the average sentiment score across all articles.
  5. Generate Explanation: Provide evidence from the news items supporting the score.
遵循以下步骤分析加密货币市场情绪：
  1. 选择RSS源：选择热门加密货币RSS源（参考references/rss_feeds.md中的精选列表）。
  2. 获取新闻：从选定的源中获取近期文章。
  3. 情绪分析：根据内容关键词和上下文将每篇文章的情绪分类为正面（+1）、负面（-1）或中性（0）。
  4. 计算得分：计算所有文章的平均情绪得分。
  5. 生成说明：提供支持该得分的新闻条目证据。

Sentiment Classification Guidelines

情绪分类准则

  • Positive (+1): News about adoption, launches, partnerships, ETF approvals, price rallies, regulatory wins, or technological breakthroughs.
  • Negative (-1): News about hacks, crashes, regulatory crackdowns, liquidations, delays, or criticisms.
  • Neutral (0): Factual updates, mixed outcomes, or speculative content without clear bias.
  • 正面（+1）：关于采用、推出、合作、ETF获批、价格上涨、监管利好或技术突破的新闻。
  • 负面（-1）：关于黑客攻击、崩盘、监管打压、清算、延迟或批评的新闻。
  • 中性（0）：事实性更新、混合结果或无明确倾向的推测性内容。

Output Format

输出格式

The skill outputs:
  • Sentiment Score: Numerical value between -1 and 1.
  • Explanation: Breakdown by feed/source, key positive/negative drivers, and overall market implications.
该技能输出：
  • 情绪得分：介于-1和1之间的数值。
  • 说明：按源/渠道分类的细分情况、关键正面/负面驱动因素，以及整体市场影响。

Resources

资源

scripts/

scripts/

  • sentiment_analyzer.py
    : Python script to fetch RSS feeds, parse articles, and compute sentiment score. Run with 
    python sentiment_analyzer.py
    to get automated results.
  • sentiment_analyzer.py
    ：用于获取RSS源、解析文章并计算情绪得分的Python脚本。运行
    python sentiment_analyzer.py
    即可获得自动化结果。

references/

references/

  • rss_feeds.md
    : List of popular crypto RSS feeds with URLs and descriptions.
  • sentiment_examples.md
    : Examples of sentiment classification for common news types.
  • rss_feeds.md
    ：包含URL和描述的热门加密货币RSS源列表。
  • sentiment_examples.md
    ：常见新闻类型的情绪分类示例。