Sentiment Analyzer Skill

Multi-source sentiment analysis providing comprehensive market sentiment insights for trading decisions.

Overview

This skill provides:

• News article sentiment scoring from multiple financial sources
• Social media sentiment aggregation (Twitter, Reddit, StockTwits)
• Market microstructure sentiment (order flow, volatility indicators)
• Composite sentiment scores with confidence intervals
• Trend detection and anomaly flagging
• Real-time sentiment monitoring

Sentiment Scoring Methodology

Score Range: -1.0 to +1.0

• 0.7 to 1.0: Very Positive (Strong Buy Signal)
• 0.3 to 0.7: Positive (Buy Signal)
• -0.3 to 0.3: Neutral (Hold)
• -0.7 to -0.3: Negative (Sell Signal)
• -1.0 to -0.7: Very Negative (Strong Sell Signal)

Confidence Levels

• High (>0.8): Strong agreement across sources
• Medium (0.6-0.8): Moderate agreement
• Low (<0.6): Mixed signals, use caution

Tools

1. analyze_news_sentiment

Performs sentiment analysis on financial news articles.

Parameters:

• symbols (required): List of ticker symbols (e.g., ["AAPL", "MSFT"])
• time_window_hours (optional): Analysis window in hours (default: 24)
• sources (optional): Specific news sources to include (default: all available)

Returns:

{
  "success": true,
  "sentiment": {
    "AAPL": {
      "overall_score": 0.72,
      "label": "positive",
      "confidence": 0.85,
      "article_count": 15,
      "breakdown": {
        "positive": 11,
        "neutral": 3,
        "negative": 1
      },
      "sources": {
        "yahoo": 0.75,
        "alphavantage": 0.68,
        "grok_twitter": 0.80
      },
      "trends": {
        "direction": "improving",
        "momentum": 0.12
      },
      "key_topics": [
        {"topic": "earnings", "sentiment": 0.85, "mentions": 8},
        {"topic": "product_launch", "sentiment": 0.70, "mentions": 5}
      ]
    }
  },
  "timestamp": "2025-11-25T10:00:00Z"
}

Usage:

python scripts/sentiment_analyzer.py analyze_news_sentiment --symbols AAPL MSFT --time-window-hours 24

2. analyze_social_sentiment

Aggregates sentiment from social media platforms.

Parameters:

• symbols (required): List of ticker symbols
• platforms (optional): Platforms to analyze (["twitter", "reddit", "stocktwits"], default: all)
• time_window_hours (optional): Analysis window (default: 6)
• min_mentions (optional): Minimum mention threshold (default: 10)

Returns:

{
  "success": true,
  "sentiment": {
    "AAPL": {
      "overall_score": 0.65,
      "label": "positive",
      "confidence": 0.72,
      "total_mentions": 3420,
      "platforms": {
        "twitter": {
          "score": 0.68,
          "mentions": 1850,
          "trending": true
        },
        "reddit": {
          "score": 0.62,
          "mentions": 950,
          "trending": false
        },
        "stocktwits": {
          "score": 0.64,
          "mentions": 620,
          "trending": false
        }
      },
      "influencer_sentiment": 0.75,
      "retail_sentiment": 0.63,
      "volume_trend": "increasing",
      "anomalies": []
    }
  },
  "timestamp": "2025-11-25T10:00:00Z"
}

3. get_composite_sentiment

Generates weighted composite sentiment from all sources.

Parameters:

• symbols (required): List of ticker symbols
• weights (optional): Custom source weights object (default: balanced weights)
• include_market_sentiment (optional): Include technical indicators (default: true)

Returns:

{
  "success": true,
  "composite_sentiment": {
    "AAPL": {
      "score": 0.68,
      "label": "positive",
      "confidence": 0.80,
      "components": {
        "news_sentiment": {
          "score": 0.72,
          "weight": 0.40,
          "contribution": 0.288
        },
        "social_sentiment": {
          "score": 0.65,
          "weight": 0.30,
          "contribution": 0.195
        },
        "market_sentiment": {
          "score": 0.70,
          "weight": 0.30,
          "contribution": 0.210
        }
      },
      "signal_strength": "strong",
      "recommendation": "buy",
      "risk_factors": [
        "High social media volatility",
        "Mixed sector sentiment"
      ]
    }
  },
  "timestamp": "2025-11-25T10:00:00Z"
}

4. detect_sentiment_anomalies

Identifies unusual sentiment patterns or rapid changes.

Parameters:

• symbols (required): List of ticker symbols
• lookback_hours (optional): Historical comparison window (default: 72)
• sensitivity (optional): "low", "medium", "high" (default: "medium")

Returns:

{
  "success": true,
  "anomalies": [
    {
      "symbol": "AAPL",
      "type": "rapid_shift",
      "severity": "high",
      "description": "Sentiment shifted from 0.35 to 0.85 in 2 hours",
      "current_sentiment": 0.85,
      "previous_sentiment": 0.35,
      "trigger": "Breaking news: Positive earnings surprise",
      "recommendation": "Wait for stabilization before trading",
      "timestamp": "2025-11-25T09:30:00Z"
    }
  ]
}

Data Sources

News Sources (Priority Order)

1. Yahoo Finance News
2. Alpha Vantage News Sentiment
3. Grok Twitter (Real-time)
4. StockTwits
5. Reddit (r/wallstreetbets, r/stocks, r/investing)

Social Media Platforms

1. Twitter/X (via Grok API)
2. Reddit (r/wallstreetbets, r/stocks, r/investing)
3. StockTwits

Market Indicators

1. Put/Call Ratio
2. VIX (Fear Index)
3. Advance/Decline Line
4. On-Balance Volume (OBV)

Integration with Trading System

This skill integrates with:

• src/utils/news_sentiment.py - NewsSentimentAggregator
• src/utils/sentiment_loader.py - Historical sentiment loading
• src/rag/sentiment_store.py - RAG-based sentiment storage
• src/utils/reddit_sentiment.py - Reddit sentiment analysis

Rate Limiting & Caching

• News API: 100 requests/hour
• Social APIs: Variable by platform
• Results cached for 15 minutes
• Real-time updates for breaking news

Usage Example

from claude_skills import load_skill

sentiment_skill = load_skill("sentiment_analyzer")

# Get comprehensive sentiment before trade decision
composite = sentiment_skill.get_composite_sentiment(
    symbols=["AAPL"],
    include_market_sentiment=True
)

# Check for anomalies that might affect timing
anomalies = sentiment_skill.detect_sentiment_anomalies(
    symbols=["AAPL"],
    lookback_hours=24,
    sensitivity="high"
)

if composite["composite_sentiment"]["AAPL"]["score"] > 0.6 and not anomalies:
    print("Strong positive sentiment - consider buy")

CLI Usage

# Analyze news sentiment
python scripts/sentiment_analyzer.py analyze_news_sentiment --symbols AAPL MSFT

# Get composite sentiment
python scripts/sentiment_analyzer.py get_composite_sentiment --symbols AAPL

# Detect anomalies
python scripts/sentiment_analyzer.py detect_sentiment_anomalies --symbols AAPL --sensitivity high

Safety & Best Practices

• Always verify sentiment with multiple sources
• Consider confidence levels when making trading decisions
• Watch for sentiment anomalies that may indicate manipulation
• Combine sentiment with technical analysis for best results
• Cache results to avoid rate limiting