Analyzing Market Sentiment

Analyze cryptocurrency market sentiment using Fear & Greed Index, news analysis, and market momentum. Use when gauging overall market mood, checking if markets are fearful or greedy, or analyzing sentiment for specific coins. Trigger with phrases like "analyze crypto sentiment", "check market mood", "is the market fearful", "sentiment for Bitcoin", or "Fear and Greed index".

aibot88 Updated Sep 9, 2026 3 repo stars

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$ npx skillmds@latest add aibot88/analyzing-market-sentiment

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Last audited Sep 24, 2026 · commit 32d4fcb

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Makes network calls
The instructions fetch remote URLs or call external services.

License: MIT
Works with: Claude Code Claude.ai OpenAI Codex +

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Analyzing Market Sentiment

Overview

Cryptocurrency market sentiment analysis combining Fear & Greed Index, news keyword analysis, and price/volume momentum into a composite 0-100 score.

Prerequisites

Python 3.8+ installed
Dependencies: pip install requests
Internet connectivity for API access (Alternative.me, CoinGecko)
Optional: crypto-news-aggregator skill for enhanced news analysis

Instructions

Assess user intent - determine what analysis is needed:
- Overall market: no specific coin, general sentiment
- Coin-specific: extract symbol (BTC, ETH, etc.)
- Quick vs detailed: quick score or full component breakdown

Run sentiment analysis with appropriate options:

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

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

# Detailed breakdown with all components
python ${CLAUDE_SKILL_DIR}/scripts/sentiment_analyzer.py --detailed

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

Export results for trading models or analysis:

python ${CLAUDE_SKILL_DIR}/scripts/sentiment_analyzer.py --format json --output sentiment.json

Present results to the user:
- Show composite score and classification prominently
- Explain what the sentiment reading means
- Highlight extreme readings (potential contrarian signals)
- For detailed mode, show component breakdown with weights

Output

Composite sentiment score (0-100) with classification and weighted component breakdown. Extreme readings serve as contrarian indicators:

==============================================================================
  MARKET SENTIMENT ANALYZER                         Updated: 2026-01-14 15:30  # 2026 - current year timestamp
==============================================================================

  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.

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

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

See ${CLAUDE_SKILL_DIR}/references/errors.md for comprehensive error handling.

Examples

Sentiment analysis patterns from quick checks to custom-weighted deep analysis:

# Quick market sentiment
python ${CLAUDE_SKILL_DIR}/scripts/sentiment_analyzer.py

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

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

# Custom weights emphasizing news
python ${CLAUDE_SKILL_DIR}/scripts/sentiment_analyzer.py --weights "news:0.5,fng:0.3,momentum:0.2"

# Weekly sentiment trend
python ${CLAUDE_SKILL_DIR}/scripts/sentiment_analyzer.py --period 7d --detailed

Resources

${CLAUDE_SKILL_DIR}/references/implementation.md - CLI options, classifications, JSON format, contrarian theory
${CLAUDE_SKILL_DIR}/references/errors.md - Comprehensive error handling
${CLAUDE_SKILL_DIR}/references/examples.md - Detailed usage examples
Alternative.me Fear & Greed: https://alternative.me/crypto/fear-and-greed-index/
CoinGecko API: https://www.coingecko.com/en/api
${CLAUDE_SKILL_DIR}/config/settings.yaml - Configuration options

---name: analyzing-market-sentimentdescription: Analyze cryptocurrency market sentiment using Fear & Greed Index, news analysis, and market momentum. Use when gauging overall market mood, checking if markets are fearful or greedy, or analyzing sentiment for specific coins. Trigger with phrases like "analyze crypto sentiment", "check market mood", "is the market fearful", "sentiment for Bitcoin", or "Fear and Greed index".license: MIT---# Analyzing Market Sentiment ## Overview Cryptocurrency market sentiment analysis combining Fear & Greed Index, news keyword analysis, and price/volume momentum into a composite 0-100 score. ## Prerequisites 1. **Python 3.8+** installed 2. **Dependencies**: `pip install requests` 3. Internet connectivity for API access (Alternative.me, CoinGecko) 4. Optional: `crypto-news-aggregator` skill for enhanced news analysis ## Instructions 1. **Assess user intent** - determine what analysis is needed: - Overall market: no specific coin, general sentiment - Coin-specific: extract symbol (BTC, ETH, etc.) - Quick vs detailed: quick score or full component breakdown 2. **Run sentiment analysis** with appropriate options: ```bash # Quick market sentiment check python ${CLAUDE_SKILL_DIR}/scripts/sentiment_analyzer.py # Coin-specific sentiment python ${CLAUDE_SKILL_DIR}/scripts/sentiment_analyzer.py --coin BTC # Detailed breakdown with all components python ${CLAUDE_SKILL_DIR}/scripts/sentiment_analyzer.py --detailed # Custom time period python ${CLAUDE_SKILL_DIR}/scripts/sentiment_analyzer.py --period 7d --detailed ``` 3. **Export results** for trading models or analysis: ```bash python ${CLAUDE_SKILL_DIR}/scripts/sentiment_analyzer.py --format json --output sentiment.json ``` 4. **Present results** to the user: - Show composite score and classification prominently - Explain what the sentiment reading means - Highlight extreme readings (potential contrarian signals) - For detailed mode, show component breakdown with weights ## Output Composite sentiment score (0-100) with classification and weighted component breakdown. Extreme readings serve as contrarian indicators: ``` ============================================================================== MARKET SENTIMENT ANALYZER Updated: 2026-01-14 15:30 # 2026 - current year timestamp ============================================================================== COMPOSITE SENTIMENT ------------------------------------------------------------------------------ Score: 65.5 / 100 Classification: GREED Component Breakdown: - Fear & Greed Index: 72.0 (weight: 40%) -> 28.8 pts - News Sentiment: 58.5 (weight: 40%) -> 23.4 pts - Market Momentum: 66.5 (weight: 20%) -> 13.3 pts Interpretation: Market is moderately greedy. Consider taking profits or reducing position sizes. Watch for reversal signals. ============================================================================== ``` ## Error Handling | Error | Cause | Solution | |-------|-------|----------| | Fear & Greed unavailable | API down | Uses cached value with warning | | News fetch failed | Network issue | Reduces weight of news component | | Invalid coin | Unknown symbol | Proceeds with market-wide analysis | See `${CLAUDE_SKILL_DIR}/references/errors.md` for comprehensive error handling. ## Examples Sentiment analysis patterns from quick checks to custom-weighted deep analysis: ```bash # Quick market sentiment python ${CLAUDE_SKILL_DIR}/scripts/sentiment_analyzer.py # Bitcoin-specific sentiment python ${CLAUDE_SKILL_DIR}/scripts/sentiment_analyzer.py --coin BTC # Detailed analysis with component breakdown python ${CLAUDE_SKILL_DIR}/scripts/sentiment_analyzer.py --detailed # Custom weights emphasizing news python ${CLAUDE_SKILL_DIR}/scripts/sentiment_analyzer.py --weights "news:0.5,fng:0.3,momentum:0.2" # Weekly sentiment trend python ${CLAUDE_SKILL_DIR}/scripts/sentiment_analyzer.py --period 7d --detailed ``` ## Resources - `${CLAUDE_SKILL_DIR}/references/implementation.md` - CLI options, classifications, JSON format, contrarian theory - `${CLAUDE_SKILL_DIR}/references/errors.md` - Comprehensive error handling - `${CLAUDE_SKILL_DIR}/references/examples.md` - Detailed usage examples - Alternative.me Fear & Greed: https://alternative.me/crypto/fear-and-greed-index/ - CoinGecko API: https://www.coingecko.com/en/api - `${CLAUDE_SKILL_DIR}/config/settings.yaml` - Configuration options

aibot88/sec_skill_store/tree/main/skills/claudskills/analyzing-market-sentiment commit 32d4fcbe30

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