# Arbitrage Strategy
import logging
import asyncio
from typing import List, Dict, Optional, Tuple
from dataclasses import dataclass
from datetime import datetime

logger = logging.getLogger(__name__)

@dataclass
class ArbitrageOpportunity:
    symbol: str
    buy_exchange: str
    sell_exchange: str
    buy_price: float
    sell_price: float
    spread_pct: float
    profit_pct: float
    max_volume: float
    execution_time_ms: int
    type: str  # 'cross_exchange', 'triangular', 'funding'

class ArbitrageStrategy:
    """
    Multi-market arbitrage strategy.
    Exploits price discrepancies across exchanges and markets.
    """
    
    def __init__(self, config: dict):
        self.config = config
        self.min_spread_pct = config.get('min_spread_pct', 0.1)  # 0.1%
        self.max_execution_time_ms = config.get('max_execution_time_ms', 500)
        self.enabled = config.get('enabled', False)
        
        # Track prices across exchanges
        self.price_cache: Dict[str, Dict[str, float]] = {}
        
        logger.info(f"ArbitrageStrategy initialized: min_spread={self.min_spread_pct}%")
    
    async def find_opportunities(self, market_data: dict) -> List[ArbitrageOpportunity]:
        """Find arbitrage opportunities"""
        if not self.enabled:
            return []
        
        opportunities = []
        
        # Update price cache
        await self._update_price_cache(market_data)
        
        # Cross-exchange arbitrage
        cross_ex = await self._find_cross_exchange_arbitrage()
        opportunities.extend(cross_ex)
        
        # Triangular arbitrage (for forex/crypto)
        triangular = await self._find_triangular_arbitrage()
        opportunities.extend(triangular)
        
        # Funding rate arbitrage (for perpetuals)
        funding = await self._find_funding_arbitrage()
        opportunities.extend(funding)
        
        # Sort by profit potential
        opportunities.sort(key=lambda x: x.profit_pct, reverse=True)
        
        return opportunities
    
    async def _update_price_cache(self, market_data: dict):
        """Update price cache from market data"""
        for symbol, data in market_data.items():
            exchange = data.get('exchange', 'unknown')
            price = data.get('close', data.get('price', 0))
            
            if symbol not in self.price_cache:
                self.price_cache[symbol] = {}
            
            self.price_cache[symbol][exchange] = {
                'price': price,
                'bid': data.get('bid', price),
                'ask': data.get('ask', price),
                'timestamp': datetime.utcnow()
            }
    
    async def _find_cross_exchange_arbitrage(self) -> List[ArbitrageOpportunity]:
        """Find price differences across exchanges"""
        opportunities = []
        
        for symbol, exchanges in self.price_cache.items():
            if len(exchanges) < 2:
                continue
            
            # Find best buy and sell prices
            best_buy = None
            best_sell = None
            
            for exchange, data in exchanges.items():
                if best_buy is None or data['ask'] < best_buy['price']:
                    best_buy = {'exchange': exchange, 'price': data['ask']}
                
                if best_sell is None or data['bid'] > best_sell['price']:
                    best_sell = {'exchange': exchange, 'price': data['bid']}
            
            if best_buy and best_sell and best_buy['exchange'] != best_sell['exchange']:
                spread = best_sell['price'] - best_buy['price']
                spread_pct = (spread / best_buy['price']) * 100
                
                if spread_pct >= self.min_spread_pct:
                    # Account for fees (assume 0.1% per trade)
                    fees = 0.2
                    profit_pct = spread_pct - fees
                    
                    if profit_pct > 0:
                        opportunities.append(ArbitrageOpportunity(
                            symbol=symbol,
                            buy_exchange=best_buy['exchange'],
                            sell_exchange=best_sell['exchange'],
                            buy_price=best_buy['price'],
                            sell_price=best_sell['price'],
                            spread_pct=spread_pct,
                            profit_pct=profit_pct,
                            max_volume=0,  # Calculate from order books
                            execution_time_ms=self.max_execution_time_ms,
                            type='cross_exchange'
                        ))
        
        return opportunities
    
    async def _find_triangular_arbitrage(self) -> List[ArbitrageOpportunity]:
        """Find triangular arbitrage opportunities"""
        # For forex: EUR/USD * USD/GBP = EUR/GBP
        # For crypto: BTC/ETH * ETH/USDT = BTC/USDT
        
        opportunities = []
        # Implementation requires order book depth analysis
        
        return opportunities
    
    async def _find_funding_arbitrage(self) -> List[ArbitrageOpportunity]:
        """Find funding rate arbitrage (spot vs perpetual)"""
        # Buy spot, short perpetual when funding is positive
        # Sell spot, long perpetual when funding is negative
        
        opportunities = []
        # Implementation requires funding rate data
        
        return opportunities
    
    async def execute_arbitrage(self, opportunity: ArbitrageOpportunity) -> bool:
        """Execute arbitrage trade"""
        logger.info(f"Executing arbitrage: {opportunity}")
        
        try:
            # Simultaneous execution on both exchanges
            # Buy on buy_exchange, sell on sell_exchange
            
            # In production:
            # 1. Lock in both legs simultaneously
            # 2. Handle partial fills
            # 3. Transfer assets if needed
            
            return True
            
        except Exception as e:
            logger.error(f"Arbitrage execution failed: {e}")
            return False
