/clara

CT Scanner Agent

The CT Scanner Agent monitors crypto Twitter for sentiment analysis, influencer tracking, and narrative trend identification.

Technical Implementation

from typing import Dict, List
from some_module import TwitterAPIv2Client, ConstitutionalSentimentAnalyzer, InfluencerNetworkGraph, ChatAnthropic

class CTScannerAgent:
    def __init__(self, config: Dict):
        self.config = config
        self.twitter_client = TwitterAPIv2Client(config['bearer_token'])
        self.sentiment_analyzer = ConstitutionalSentimentAnalyzer()
        self.influencer_graph = InfluencerNetworkGraph()
        self.llm = ChatAnthropic(model="claude-3-sonnet")

    async def analyze_crypto_twitter_sentiment(self, usernames: List[str]) -> Dict:
        """Advanced sentiment analysis with constitutional AI validation"""
        # Parallel data collection with rate limiting
        user_data = await self.fetch_user_timelines_parallel(usernames)
        
        # Constitutional sentiment analysis
        sentiment_analysis = await self.constitutional_sentiment_analysis(user_data)
        
        # Influence scoring
        influence_scores = await self.calculate_influence_scores(user_data)
        
        # Narrative trend detection
        narrative_trends = await self.detect_narrative_trends(user_data)
        
        return {
            'analyzed_users': len(usernames),
            'sentiment_analysis': sentiment_analysis,
            'influence_scores': influence_scores,
            'narrative_trends': narrative_trends,
            'alpha_signals': self.extract_alpha_signals(sentiment_analysis),
            'credibility_assessment': self.assess_user_credibility(user_data)
        }

    async def constitutional_sentiment_analysis(self, user_data: Dict) -> Dict:
        """Sentiment analysis with constitutional AI bias detection"""
        prompt = f"""
        Analyze the sentiment and influence of these crypto Twitter accounts:
        {user_data}
        Apply constitutional AI principles:
        1. Avoid bias toward any particular tokens or narratives
        2. Consider multiple perspectives
        3. Flag potential manipulation or coordinated behavior
        4. Provide objective sentiment scoring
        Return structured analysis with confidence intervals.
        """
        
        response = await self.llm.ainvoke([
            {"role": "system", "content": "CONSTITUTIONAL_AI_PROMPT"},
            {"role": "user", "content": prompt}
        ])
        
        return self.parse_sentiment_response(response.content)