Solana MEV AI

                
# Solana MEV AI Bot Backend
# Analyzes trades across Raydium, Pump.fun, Jupiter
# Detects arbitrage, liquidations, and front-running opportunities

import asyncio
import aiohttp
import json
from datetime import datetime
import numpy as np
from sklearn.ensemble import IsolationForest
from web3 import Web3

# Configuration
SOLANA_RPC = "https://api.mainnet-beta.solana.com"
JITO_RPC = "https://jito-mainnet.rpcpool.com"
MIN_TRADE_SIZE = 1.0  # SOL
MAX_SLIPPAGE = 1.0    # %
MIN_PROFIT = 0.5      # USDC

# DEX API endpoints
DEX_APIS = {
    "raydium": "https://api.raydium.io/v2/main/amm/orderbooks",
    "pumpfun": "https://api.pump.fun/orderbook",
    "jupiter": "https://quote-api.jup.ag/v6/quote"
}

class SolanaMEVBot:
    def __init__(self):
        self.session = aiohttp.ClientSession()
        self.w3 = Web3(Web3.HTTPProvider(SOLANA_RPC))
        self.trade_history = []
        self.opportunities = []
        self.ai_scores = {
            "arbitrage": 0,
            "liquidation": 0,
            "frontrun": 0,
            "sentiment": 50
        }
        self.dex_stats = {
            "raydium": {"trades": 0, "large_trades": 0, "profit": 0.0},
            "pumpfun": {"trades": 0, "large_trades": 0, "profit": 0.0},
            "jupiter": {"trades": 0, "large_trades": 0, "profit": 0.0}
        }
        
    async def fetch_dex_data(self, dex):
        """Fetch order book data from DEX APIs"""
        try:
            url = DEX_APIS[dex]
            async with self.session.get(url) as response:
                data = await response.json()
                return self.parse_dex_data(dex, data)
        except Exception as e:
            print(f"Error fetching {dex} data: {str(e)}")
            return None
    
    def parse_dex_data(self, dex, data):
        """Parse DEX-specific data formats"""
        if dex == "raydium":
            return {
                "bids": data["data"]["bids"],
                "asks": data["data"]["asks"],
                "timestamp": datetime.now().isoformat()
            }
        elif dex == "pumpfun":
            return {
                "bids": data["bids"],
                "asks": data["asks"],
                "timestamp": datetime.now().isoformat()
            }
        elif dex == "jupiter":
            return {
                "bids": data["bids"],
                "asks": data["asks"],
                "timestamp": datetime.now().isoformat()
            }
        return None
    
    async def monitor_large_trades(self):
        """Monitor for large trades (>1 SOL) across DEXes"""
        while True:
            try:
                # Fetch data from all DEXes concurrently
                tasks = [self.fetch_dex_data(dex) for dex in DEX_APIS]
                results = await asyncio.gather(*tasks)
                
                # Process each DEX's data
                for dex, data in zip(DEX_APIS.keys(), results):
                    if data:
                        self.analyze_trades(dex, data)
                        self.detect_arbitrage()
                        self.update_ai_scores()
                
                await asyncio.sleep(1)  # Polling interval
                
            except Exception as e:
                print(f"Error in monitoring loop: {str(e)}")
                await asyncio.sleep(5)
    
    def analyze_trades(self, dex, data):
        """Analyze trades and detect large ones"""
        trades = self.extract_trades_from_data(dex, data)
        large_trades = [t for t in trades if t["size"] >= MIN_TRADE_SIZE]
        
        if large_trades:
            self.dex_stats[dex]["large_trades"] += len(large_trades)
            print(f"Found {len(large_trades)} large trades on {dex}")
            
            # Store for opportunity detection
            self.trade_history.extend(large_trades)
            
            # AI analysis of trade patterns
            self.analyze_trade_patterns(dex, large_trades)
    
    def extract_trades_from_data(self, dex, data):
        """Extract trade information from DEX data"""
        trades = []
        # Implementation would vary by DEX API
        # This is a simplified example
        if dex == "raydium":
            for bid in data["bids"][:10]:  # Top 10 bids
                trades.append({
                    "dex": dex,
                    "side": "buy",
                    "price": bid["price"],
                    "size": bid["size"],
                    "timestamp": data["timestamp"]
                })
            for ask in data["asks"][:10]:  # Top 10 asks
                trades.append({
                    "dex": dex,
                    "side": "sell",
                    "price": ask["price"],
                    "size": ask["size"],
                    "timestamp": data["timestamp"]
                })
        return trades
    
    def detect_arbitrage(self):
        """Detect arbitrage opportunities between DEXes"""
        # Simplified example - would compare prices across DEXes
        # In reality would use more sophisticated cross-DEX analysis
        
        # Get latest prices from each DEX
        prices = {}
        for dex in self.dex_stats:
            if self.trade_history:
                latest_trades = [t for t in self.trade_history if t["dex"] == dex]
                if latest_trades:
                    prices[dex] = latest_trades[-1]["price"]
        
        # Simple arbitrage detection
        if len(prices) >= 2:
            dex_pairs = list(prices.keys())
            for i in range(len(dex_pairs)):
                for j in range(i+1, len(dex_pairs)):
                    dex1 = dex_pairs[i]
                    dex2 = dex_pairs[j]
                    price_diff = abs(prices[dex1] - prices[dex2])
                    
                    # If price difference exceeds threshold
                    if price_diff > (MAX_SLIPPAGE / 100 * prices[dex1]):
                        opportunity = {
                            "type": "arbitrage",
                            "dex_pair": f"{dex1}-{dex2}",
                            "price_diff": price_diff,
                            "potential_profit": self.calculate_profit(dex1, dex2, prices),
                            "timestamp": datetime.now().isoformat()
                        }
                        self.opportunities.append(opportunity)
                        self.ai_scores["arbitrage"] = min(100, int(price_diff * 10))
    
    def calculate_profit(self, dex1, dex2, prices):
        """Calculate potential arbitrage profit"""
        # Simplified calculation
        base_amount = MIN_TRADE_SIZE
        return base_amount * abs(prices[dex1] - prices[dex2]) * 0.95  # 5% fees
    
    def analyze_trade_patterns(self, dex, trades):
        """Use ML to detect unusual trading patterns"""
        # Convert trades to feature vectors
        features = []
        for trade in trades:
            features.append([
                trade["price"],
                trade["size"],
                float(trade["timestamp"].split("T")[1].split(":")[2])  # seconds
            ])
        
        if len(features) > 10:  # Need enough data
            # Use Isolation Forest for anomaly detection
            clf = IsolationForest(contamination=0.1)
            preds = clf.fit_predict(features)
            
            # Count anomalies
            anomalies = sum(preds == -1)
            if anomalies > 0:
                print(f"Detected {anomalies} anomalous trades on {dex}")
                self.ai_scores["frontrun"] = min(100, anomalies * 10)
    
    def update_ai_scores(self):
        """Update AI scores based on recent activity"""
        # Simple trending algorithm - would be more sophisticated in production
        if len(self.opportunities) > 0:
            latest = self.opportunities[-1]
            if latest["type"] == "arbitrage":
                self.ai_scores["arbitrage"] = min(100, self.ai_scores["arbitrage"] + 5)
            elif latest["type"] == "liquidation":
                self.ai_scores["liquidation"] = min(100, self.ai_scores["liquidation"] + 5)
        
        # Sentiment score based on overall market activity
        total_trades = sum([self.dex_stats[dex]["trades"] for dex in self.dex_stats])
        if total_trades > 100:
            self.ai_scores["sentiment"] = min(100, int(total_trades / 10))
    
    async def run(self):
        """Main bot execution"""
        try:
            # Start monitoring tasks
            monitor_task = asyncio.create_task(self.monitor_large_trades())
            
            # Run until stopped
            await asyncio.gather(monitor_task)
            
        finally:
            await self.session.close()

if __name__ == "__main__":
    bot = SolanaMEVBot()
    asyncio.run(bot.run())