Claude-skill-registry analyzing-market-sentiment

install
source · Clone the upstream repo
git clone https://github.com/majiayu000/claude-skill-registry
Claude Code · Install into ~/.claude/skills/
T=$(mktemp -d) && git clone --depth=1 https://github.com/majiayu000/claude-skill-registry "$T" && mkdir -p ~/.claude/skills && cp -r "$T/skills/data/analyzing-market-sentiment" ~/.claude/skills/majiayu000-claude-skill-registry-analyzing-market-sentiment && rm -rf "$T"
manifest: skills/data/analyzing-market-sentiment/SKILL.md
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Analyzing Market Sentiment

Overview

This skill provides comprehensive cryptocurrency market sentiment analysis by combining multiple data sources:

  • Fear & Greed Index: Market-wide sentiment from Alternative.me
  • News Sentiment: Keyword-based analysis of recent crypto news
  • Market Momentum: Price and volume trends from CoinGecko

Key Capabilities:

  • Composite sentiment score (0-100) with classification
  • Coin-specific sentiment analysis
  • Detailed breakdown of sentiment components
  • Multiple output formats (table, JSON, CSV)

Prerequisites

Before using this skill, ensure:

  1. Python 3.8+ is installed
  2. requests library is available:
    pip install requests
  3. Internet connectivity for API access (Alternative.me, CoinGecko)
  4. Optional:
    crypto-news-aggregator
    skill for enhanced news analysis

Instructions

Step 1: Assess User Intent

Determine what sentiment analysis the user needs:

  • Overall market: No specific coin, general sentiment
  • Coin-specific: Extract coin symbol (BTC, ETH, etc.)
  • Quick vs detailed: Quick score or full breakdown

Step 2: Execute Sentiment Analysis

Run the sentiment analyzer with appropriate options:

# Quick sentiment check (default)
python {baseDir}/scripts/sentiment_analyzer.py

# Coin-specific sentiment
python {baseDir}/scripts/sentiment_analyzer.py --coin BTC

# Detailed analysis with component breakdown
python {baseDir}/scripts/sentiment_analyzer.py --detailed

# Export to JSON
python {baseDir}/scripts/sentiment_analyzer.py --format json --output sentiment.json

# Custom time period
python {baseDir}/scripts/sentiment_analyzer.py --period 7d --detailed

Step 3: Present Results

Format and present the sentiment analysis:

  • Show composite score and classification
  • Explain what the sentiment means
  • Highlight any extreme readings
  • For detailed mode, show component breakdown

Command-Line Options

OptionDescriptionDefault
--coin
Analyze specific coin (BTC, ETH, etc.)All market
--period
Time period (1h, 4h, 24h, 7d)24h
--detailed
Show full component breakdownfalse
--format
Output format (table, json, csv)table
--output
Output file pathstdout
--weights
Custom weights (e.g., "news:0.5,fng:0.3,momentum:0.2")Default
--verbose
Enable verbose outputfalse

Sentiment Classifications

Score RangeClassificationDescription
0-20Extreme FearMarket panic, potential bottom
21-40FearCautious sentiment, bearish
41-60NeutralBalanced, no strong bias
61-80GreedOptimistic, bullish sentiment
81-100Extreme GreedEuphoria, potential top

Output

Table Format (Default)

==============================================================================
  MARKET SENTIMENT ANALYZER                         Updated: 2026-01-14 15:30
==============================================================================

  COMPOSITE SENTIMENT
------------------------------------------------------------------------------
  Score: 65.5 / 100                         Classification: GREED

  Component Breakdown:
  - Fear & Greed Index:  72.0  (weight: 40%)  → 28.8 pts
  - News Sentiment:      58.5  (weight: 40%)  → 23.4 pts
  - Market Momentum:     66.5  (weight: 20%)  → 13.3 pts

  Interpretation: Market is moderately greedy. Consider taking profits or
  reducing position sizes. Watch for reversal signals.

==============================================================================

JSON Format

{
  "composite_score": 65.5,
  "classification": "Greed",
  "components": {
    "fear_greed": {
      "score": 72,
      "classification": "Greed",
      "weight": 0.40,
      "contribution": 28.8
    },
    "news_sentiment": {
      "score": 58.5,
      "articles_analyzed": 25,
      "positive": 12,
      "negative": 5,
      "neutral": 8,
      "weight": 0.40,
      "contribution": 23.4
    },
    "market_momentum": {
      "score": 66.5,
      "btc_change_24h": 3.5,
      "weight": 0.20,
      "contribution": 13.3
    }
  },
  "meta": {
    "timestamp": "2026-01-14T15:30:00Z",
    "period": "24h"
  }
}

Error Handling

See

{baseDir}/references/errors.md
for comprehensive error handling.

ErrorCauseSolution
Fear & Greed unavailableAPI downUses cached value with warning
News fetch failedNetwork issueReduces weight of news component
Invalid coinUnknown symbolProceeds with market-wide analysis

Examples

See

{baseDir}/references/examples.md
for detailed examples.

Quick Examples

# Quick market sentiment check
python {baseDir}/scripts/sentiment_analyzer.py

# Bitcoin-specific sentiment
python {baseDir}/scripts/sentiment_analyzer.py --coin BTC

# Detailed analysis
python {baseDir}/scripts/sentiment_analyzer.py --detailed

# Export for trading model
python {baseDir}/scripts/sentiment_analyzer.py --format json --output sentiment.json

# Custom weights (emphasize news)
python {baseDir}/scripts/sentiment_analyzer.py --weights "news:0.5,fng:0.3,momentum:0.2"

# Weekly sentiment comparison
python {baseDir}/scripts/sentiment_analyzer.py --period 7d --detailed

Resources