/aria

Token Meta Agent

The Token Meta Agent provides comprehensive fundamental analysis, combining on-chain metrics with market sentiment and narrative categorization.

Technical Implementation

import asyncio
from typing import Dict
from some_module import DexScreenerClient, TwitterAPIClient, ChatAnthropic, TokenKnowledgeGraph

class TokenMetaAgent:
    def __init__(self, config: Dict):
        self.config = config
        self.dex_screener = DexScreenerClient()
        self.twitter_client = TwitterAPIClient(config['twitter_bearer'])
        self.llm = ChatAnthropic(model="claude-3-sonnet")
        self.knowledge_graph = TokenKnowledgeGraph()

    async def comprehensive_token_analysis(self, token_address: str) -> Dict:
        """
        Multi-dimensional token analysis with narrative categorization
        """
        market_data, social_data, on_chain_data = await asyncio.gather(
            self.fetch_market_data(token_address),
            self.fetch_social_sentiment(token_address),
            self.fetch_on_chain_metrics(token_address)
        )

        analysis = await self.perform_constitutional_analysis({
            'market_data': market_data,
            'social_data': social_data,
            'on_chain_data': on_chain_data
        })

        narrative_category = await self.categorize_token_narrative(
            market_data, social_data
        )

        risk_assessment = await self.assess_token_risk(
            analysis, narrative_category
        )

        return {
            'token_address': token_address,
            'market_analysis': analysis['market_insights'],
            'social_sentiment': analysis['sentiment_analysis'],
            'narrative_category': narrative_category,
            'risk_assessment': risk_assessment,
            'alpha_score': self.calculate_alpha_score(analysis),
            'recommendations': analysis['trading_recommendations']
        }

    async def categorize_token_narrative(self, market_data: Dict, social_data: Dict) -> Dict:
        """
        AI-powered narrative categorization using constitutional reasoning
        """
        prompt = f"""
        Analyze this token data and categorize it into meta trends:
        Market Data: {market_data}
        Social Data: {social_data}
        Categories: AI, DeFi, Gaming, Memes, Infrastructure, Privacy, etc.
        Provide reasoning and confidence scores.
        """

        response = await self.llm.ainvoke([
            {"role": "system", "content": "CONSTITUTIONAL_AI_PROMPT"},
            {"role": "user", "content": prompt}
        ])

        return self.parse_narrative_response(response.content)