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BytesAgain β€Ί Skills β€Ί Crypto Sentiment Monitor
πŸ¦€ ClawHub

Crypto Sentiment Monitor

by @dzxiatian-crypto

Real-time crypto market sentiment analysis. Aggregates Twitter/X, Reddit, Google Trends, and exchange data. Detects FOMO/FUD cycles and whale movements. Trig...

Versionv1.0.0
Downloads703
#crypto-defi#social-media#legal
TERMINAL
clawhub install crypto-sentiment-monitor

πŸ“– About This Skill

name: crypto-sentiment-monitor description: > Real-time crypto market sentiment analysis. Aggregates Twitter/X, Reddit, Google Trends, and exchange data. Detects FOMO/FUD cycles and whale movements. Triggers: "crypto", "比特币", "sentiment", "ζƒ…η»ͺ", "FOMO", "FUD". version: 1.0.0 tags: - latest - crypto - trading - sentiment

Crypto Sentiment Monitor

Real-time cryptocurrency market sentiment analysis combining social media, search trends, and exchange data.

Features

  • Social Sentiment: Twitter/X, Reddit, Telegram channel analysis
  • Search Trends: Google Trends, Baidu Index for crypto keywords
  • Exchange Data: Funding rates, open interest, whale transactions
  • FOMO/FUD Detection: Fear & Greed index calculation

    • Usage

    Twitter Sentiment

    xreach search "$BTC OR #Bitcoin OR $ETH" -n 50 --json | \
  python3 analyze_sentiment.py

    Fear & Greed Index

    def calculate_fear_greed():
    """Calculate Crypto Fear & Greed Index (0-100)"""
    components = {
        "volatility": get_volatility(),      # 25%
        "market_momentum": get_momentum(),  # 25%
        "social_volume": get_social_vol(),  # 15%
        "dominant": get_btc_dominance(),    # 10%
        "trends": get_google_trends(),      # 10%
        "whale_ratio": get_whale_ratio(),   # 15%
    }
    score = sum(c["weight"] * c["value"] 
                for c in components.values())
    
    if score < 25: return "Extreme Fear 😱"
    elif score < 45: return "Fear 😰"
    elif score < 55: return "Neutral 😐"
    elif score < 75: return "Greed 😊"
    else: return "Extreme Greed πŸ€‘"

    Whale Alert Detection

    def detect_whale_movements():
    """Detect large wallet transactions"""
    alerts = get_whale_alerts(min_usd=1000000)
    for alert in alerts:
        if alert["amount_usd"] > 10000000:
            print(f"πŸ‹ ${alert['amount_usd']/1e6:.1f}M moved: "
                  f"{alert['from']} β†’ {alert['to']}")

    Sources

  • Twitter/X: xreach tool
  • Reddit: r/CryptoCurrency and r/Bitcoin hot posts
  • Google Trends: crypto, bitcoin, ethereum
  • Whale Alert: whale-alert.io (free API)

    • Tags

    crypto bitcoin sentiment trading fear-greed whale-alert twitter

    πŸ’‘ Examples

    Twitter Sentiment

    xreach search "$BTC OR #Bitcoin OR $ETH" -n 50 --json | \
  python3 analyze_sentiment.py

    Fear & Greed Index

    def calculate_fear_greed():
    """Calculate Crypto Fear & Greed Index (0-100)"""
    components = {
        "volatility": get_volatility(),      # 25%
        "market_momentum": get_momentum(),  # 25%
        "social_volume": get_social_vol(),  # 15%
        "dominant": get_btc_dominance(),    # 10%
        "trends": get_google_trends(),      # 10%
        "whale_ratio": get_whale_ratio(),   # 15%
    }
    score = sum(c["weight"] * c["value"] 
                for c in components.values())
    
    if score < 25: return "Extreme Fear 😱"
    elif score < 45: return "Fear 😰"
    elif score < 55: return "Neutral 😐"
    elif score < 75: return "Greed 😊"
    else: return "Extreme Greed πŸ€‘"

    Whale Alert Detection

    def detect_whale_movements():
    """Detect large wallet transactions"""
    alerts = get_whale_alerts(min_usd=1000000)
    for alert in alerts:
        if alert["amount_usd"] > 10000000:
            print(f"πŸ‹ ${alert['amount_usd']/1e6:.1f}M moved: "
                  f"{alert['from']} β†’ {alert['to']}")