Agent Studio

from c0gni import Agent, Strategy, MarketData
from c0gni.indicators import RSI, MACD
import numpy as np

class AdvancedSniperStrategy(Strategy):
    def __init__(self):
        super().__init__()
        self.rsi = RSI(period=14)
        self.macd = MACD(fast=12, slow=26, signal=9)
        self.min_liquidity = 10  # ETH
        self.confidence_threshold = 0.8
        
    async def on_new_token(self, token_data):
        # Multi-factor analysis for new token launches
        liquidity_score = self.analyze_liquidity(token_data)
        technical_score = await self.technical_analysis(token_data)
        sentiment_score = await self.sentiment_analysis(token_data)
        
        # Weighted confidence calculation
        confidence = (
            liquidity_score * 0.4 +
            technical_score * 0.3 +
            sentiment_score * 0.3
        )
        
        if confidence > self.confidence_threshold:
            position_size = self.kelly_criterion(confidence)
            return self.buy_signal(
                token=token_data.address,
                amount=position_size,
                confidence=confidence
            )
        
        return None
    
    def analyze_liquidity(self, token_data):
        if token_data.liquidity < self.min_liquidity:
            return 0.0
        
        # Liquidity depth analysis
        bid_ask_spread = token_data.ask_price - token_data.bid_price
        spread_ratio = bid_ask_spread / token_data.mid_price
        
        return max(0, 1 - (spread_ratio * 10))
    
    async def technical_analysis(self, token_data):
        # Get price history for technical indicators
        history = await self.get_price_history(token_data.address, periods=100)
        
        if len(history) < 50:
            return 0.5  # Neutral for insufficient data
        
        # RSI analysis
        rsi_value = self.rsi.calculate(history.close)
        rsi_score = 1 - abs(rsi_value - 50) / 50  # Closer to 50 is better
        
        # MACD analysis  
        macd_line, signal_line, histogram = self.macd.calculate(history.close)
        macd_score = 1 if macd_line[-1] > signal_line[-1] else 0
        
        return (rsi_score + macd_score) / 2