mcpField
A
analyzing-market-sentiment

analyzing-market-sentiment

github.com/jeremylongshoreData & databases2.9k86 installsUpdated 2d ago
Repository

About analyzing-market-sentiment

A cryptocurrency sentiment analysis skill that combines the Fear & Greed Index, news keywords, and price and volume momentum into a composite score. It supports market-wide or coin-specific checks and detailed component breakdowns.

Key features

  1. 01Combine three sentiment inputs
  2. 02Generate a weighted 0–100 score
  3. 03Analyze the overall market or a coin
  4. 04Show component breakdowns
  5. 05Export results as JSON

Use cases

  1. 01Check broad crypto market sentiment
  2. 02Assess sentiment for BTC or another coin
  3. 03Review component contributions in detail
  4. 04Export scores for trading models

Install

Copy the command and run it in a terminal.

$npx skills add https://github.com/jeremylongshore/tons-of-skills-marketplace --skill analyzing-market-sentiment

After installing, type/analyzing-market-sentimentin Claude Code to confirm it loaded.

Bundled files

References and scripts installed along with SKILL.md.

ARD.md
PRD.md
config/
settings.yaml
references/
errors.md
examples.md
implementation.md
scripts/
fear_greed.py
formatters.py
market_momentum.py
news_sentiment.py
sentiment_analyzer.py

Details

From the project README

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:

    # 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:

    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

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

Comments

Markdown supported