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.mdsafety · automated scan (low risk)
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- pip install
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source content
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:
- Python 3.8+ is installed
- requests library is available:
pip install requests - Internet connectivity for API access (Alternative.me, CoinGecko)
- Optional:
skill for enhanced news analysiscrypto-news-aggregator
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
| Option | Description | Default |
|---|---|---|
| Analyze specific coin (BTC, ETH, etc.) | All market |
| Time period (1h, 4h, 24h, 7d) | 24h |
| Show full component breakdown | false |
| Output format (table, json, csv) | table |
| Output file path | stdout |
| Custom weights (e.g., "news:0.5,fng:0.3,momentum:0.2") | Default |
| Enable verbose output | false |
Sentiment Classifications
| Score Range | Classification | Description |
|---|---|---|
| 0-20 | Extreme Fear | Market panic, potential bottom |
| 21-40 | Fear | Cautious sentiment, bearish |
| 41-60 | Neutral | Balanced, no strong bias |
| 61-80 | Greed | Optimistic, bullish sentiment |
| 81-100 | Extreme Greed | Euphoria, 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.
| Error | Cause | Solution |
|---|---|---|
| Fear & Greed unavailable | API down | Uses cached value with warning |
| News fetch failed | Network issue | Reduces weight of news component |
| Invalid coin | Unknown symbol | Proceeds 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
- Alternative.me Fear & Greed Index: https://alternative.me/crypto/fear-and-greed-index/
- CoinGecko API: https://www.coingecko.com/en/api
- Sentiment Analysis Theory: Contrarian indicator - extreme readings often precede reversals
- See
for configuration options{baseDir}/config/settings.yaml