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import asyncio
import logging
from axiomtradeapi import AxiomTradeClient, AxiomTradeWebSocketClient
from typing import Dict, List, Optional
from dataclasses import dataclass

@dataclass
class TradingSignal:
    """Trading signal with confidence score and metadata"""
    token_address: str
    action: str  # 'buy', 'sell', 'hold'
    confidence: float
    price_target: Optional[float] = None
    stop_loss: Optional[float] = None
    reasoning: str = ""

class BaseTradingBot:
    """Base class for all trading bots with common functionality"""
    
    def __init__(self, auth_token: str, wallet_address: str):
        self.client = AxiomTradeClient(auth_token=auth_token)
        self.ws_client = AxiomTradeWebSocketClient(auth_token=auth_token)
        self.wallet_address = wallet_address
        self.is_running = False
        
        # Configure advanced logging for production monitoring
        logging.basicConfig(
            level=logging.INFO,
            format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
            handlers=[
                logging.FileHandler(f'bot_{self.__class__.__name__}.log'),
                logging.StreamHandler()
            ]
        )
        self.logger = logging.getLogger(self.__class__.__name__)
        
    async def start(self):
        """Start the trading bot with WebSocket connections"""
        self.is_running = True
        self.logger.info("🚀 Starting trading bot - chipa.tech strategy")
        
        # Connect to WebSocket for real-time data
        await self.ws_client.connect()
        
        # Start main trading loop
        await asyncio.gather(
            self.market_data_processor(),
            self.trading_logic_loop(),
            self.portfolio_monitor()
        )
    
    async def market_data_processor(self):
        """Process incoming market data and generate signals"""
        async for message in self.ws_client.listen():
            try:
                await self.process_market_data(message)
            except Exception as e:
                self.logger.error(f"Error processing market data: {e}")
    
    async def process_market_data(self, data: dict):
        """Override in child classes for specific strategies"""
        raise NotImplementedError
    
    async def trading_logic_loop(self):
        """Main trading logic executed periodically"""
        while self.is_running:
            try:
                signals = await self.generate_signals()
                for signal in signals:
                    await self.execute_signal(signal)
                await asyncio.sleep(1)  # Adjust based on strategy
            except Exception as e:
                self.logger.error(f"Trading logic error: {e}")
    
    async def generate_signals(self) -> List[TradingSignal]:
        """Generate trading signals - override in child classes"""
        return []
    
    async def execute_signal(self, signal: TradingSignal):
        """Execute trading signal with risk management"""
        self.logger.info(f"Executing signal: {signal.action} {signal.token_address}")
        # Implement execution logic with proper risk checks
    
    async def portfolio_monitor(self):
        """Monitor portfolio performance and risk metrics"""
        while self.is_running:
            try:
                balance = await self.client.get_balance(self.wallet_address)
                self.logger.info(f"Portfolio value: {balance.get('solBalance', 0)} SOL")
                await asyncio.sleep(30)  # Update every 30 seconds
            except Exception as e:
                self.logger.error(f"Portfolio monitoring error: {e}")

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class TokenSnipingBot(BaseTradingBot):
    """
    Advanced token sniping bot using AxiomTradeAPI
    Strategies used by successful traders on chipa.tech
    """
    
    def __init__(self, auth_token: str, wallet_address: str, config: dict = None):
        super().__init__(auth_token, wallet_address)
        self.config = config or {
            'max_buy_amount': 1.0,  # SOL
            'min_liquidity': 5.0,   # SOL
            'max_slippage': 0.05,   # 5%
            'blacklisted_tokens': set(),
            'target_profit': 0.20,  # 20%
            'stop_loss': -0.10,     # -10%
        }
        self.monitored_tokens = {}
        self.active_positions = {}
    
    async def process_market_data(self, data: dict):
        """Process new token launches and price updates"""
        if data.get('type') == 'new_token':
            await self.analyze_new_token(data)
        elif data.get('type') == 'price_update':
            await self.check_exit_conditions(data)
    
    async def analyze_new_token(self, token_data: dict):
        """Analyze newly launched token for sniping opportunity"""
        token_address = token_data.get('token_address')
        
        # Skip blacklisted tokens
        if token_address in self.config['blacklisted_tokens']:
            return
        
        # Perform rapid analysis
        analysis = await self.rapid_token_analysis(token_data)
        
        if analysis['snipe_score'] > 0.7:  # High confidence threshold
            signal = TradingSignal(
                token_address=token_address,
                action='buy',
                confidence=analysis['snipe_score'],
                reasoning=f"New token launch - Score: {analysis['snipe_score']}"
            )
            await self.execute_snipe_order(signal, token_data)
    
    async def rapid_token_analysis(self, token_data: dict) -> dict:
        """Perform rapid analysis of new token (< 1 second)"""
        score = 0.0
        factors = {}
        
        # Liquidity check
        liquidity = token_data.get('liquidity', 0)
        if liquidity >= self.config['min_liquidity']:
            score += 0.3
            factors['liquidity'] = 'PASS'
        else:
            factors['liquidity'] = 'FAIL'
            return {'snipe_score': 0.0, 'factors': factors}
        
        # Contract verification (if available)
        if token_data.get('verified_contract', False):
            score += 0.2
            factors['contract'] = 'VERIFIED'
        
        # Creator reputation (if available)
        creator_score = token_data.get('creator_reputation', 0)
        if creator_score > 0.5:
            score += 0.2
            factors['creator'] = 'TRUSTED'
        
        # Social signals
        social_activity = token_data.get('social_activity', 0)
        if social_activity > 100:
            score += 0.15
            factors['social'] = 'ACTIVE'
        
        # Technical indicators
        if token_data.get('initial_supply_locked', False):
            score += 0.15
            factors['supply_lock'] = 'LOCKED'
        
        self.logger.info(f"Token analysis complete - Score: {score:.2f} - {factors}")
        return {'snipe_score': score, 'factors': factors}
    
    async def execute_snipe_order(self, signal: TradingSignal, token_data: dict):
        """Execute high-speed snipe order"""
        try:
            # Calculate position size based on confidence
            position_size = min(
                self.config['max_buy_amount'] * signal.confidence,
                self.config['max_buy_amount']
            )
            
            self.logger.info(f"🎯 SNIPING: {signal.token_address} - Size: {position_size} SOL")
            
            # Execute buy order (implement actual trading logic here)
            # This would connect to your trading execution system
            
            # Track the position
            self.active_positions[signal.token_address] = {
                'entry_price': token_data.get('price', 0),
                'size': position_size,
                'entry_time': asyncio.get_event_loop().time(),
                'target_profit': self.config['target_profit'],
                'stop_loss': self.config['stop_loss']
            }
            
            self.logger.info(f"✅ Snipe executed successfully - {signal.token_address}")
            
        except Exception as e:
            self.logger.error(f"❌ Snipe execution failed: {e}")
    
    async def check_exit_conditions(self, price_data: dict):
        """Check if we should exit any active positions"""
        token_address = price_data.get('token_address')
        current_price = price_data.get('price', 0)
        
        if token_address not in self.active_positions:
            return
        
        position = self.active_positions[token_address]
        entry_price = position['entry_price']
        
        if entry_price == 0:
            return
        
        # Calculate P&L
        pnl_ratio = (current_price - entry_price) / entry_price
        
        # Check exit conditions
        should_exit = False
        exit_reason = ""
        
        if pnl_ratio >= position['target_profit']:
            should_exit = True
            exit_reason = f"Target profit reached: {pnl_ratio:.2%}"
        elif pnl_ratio <= position['stop_loss']:
            should_exit = True
            exit_reason = f"Stop loss triggered: {pnl_ratio:.2%}"
        elif asyncio.get_event_loop().time() - position['entry_time'] > 3600:  # 1 hour max hold
            should_exit = True
            exit_reason = "Maximum hold time reached"
        
        if should_exit:
            await self.execute_exit_order(token_address, exit_reason, pnl_ratio)
    
    async def execute_exit_order(self, token_address: str, reason: str, pnl_ratio: float):
        """Execute exit order for position"""
        try:
            self.logger.info(f"🚪 EXITING: {token_address} - Reason: {reason} - P&L: {pnl_ratio:.2%}")
            
            # Execute sell order (implement actual trading logic here)
            
            # Remove from active positions
            del self.active_positions[token_address]
            
            # Log performance for chipa.tech analytics
            self.logger.info(f"✅ Exit completed - P&L: {pnl_ratio:.2%}")
            
        except Exception as e:
            self.logger.error(f"❌ Exit execution failed: {e}")

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class MarketMakingBot(BaseTradingBot):
    """
    Professional market making bot for Solana tokens
    Advanced strategies from chipa.tech trading experts
    """
    
    def __init__(self, auth_token: str, wallet_address: str, target_tokens: List[str]):
        super().__init__(auth_token, wallet_address)
        self.target_tokens = target_tokens
        self.active_orders = {}
        self.spread_config = {
            'bid_spread': 0.002,  # 0.2%
            'ask_spread': 0.002,  # 0.2%
            'order_size': 0.1,    # SOL per order
            'max_position': 2.0,  # SOL max position per token
            'rebalance_threshold': 0.01  # 1%
        }
    
    async def generate_signals(self) -> List[TradingSignal]:
        """Generate market making signals for all target tokens"""
        signals = []
        
        for token_address in self.target_tokens:
            try:
                # Get current market data
                market_data = await self.get_market_data(token_address)
                current_price = market_data.get('price', 0)
                
                if current_price == 0:
                    continue
                
                # Calculate bid/ask prices
                bid_price = current_price * (1 - self.spread_config['bid_spread'])
                ask_price = current_price * (1 + self.spread_config['ask_spread'])
                
                # Generate signals for both sides
                signals.extend([
                    TradingSignal(
                        token_address=token_address,
                        action='buy',
                        confidence=0.8,
                        price_target=bid_price,
                        reasoning="Market making bid"
                    ),
                    TradingSignal(
                        token_address=token_address,
                        action='sell',
                        confidence=0.8,
                        price_target=ask_price,
                        reasoning="Market making ask"
                    )
                ])
                
            except Exception as e:
                self.logger.error(f"Error generating MM signals for {token_address}: {e}")
        
        return signals
    
    async def get_market_data(self, token_address: str) -> dict:
        """Get current market data for token"""
        # Implement market data fetching
        # This would use your market data source
        return {'price': 1.0, 'volume': 1000.0, 'liquidity': 5000.0}

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class ArbitrageBot(BaseTradingBot):
    """
    Cross-DEX arbitrage bot for Solana ecosystem
    Proven strategies from chipa.tech arbitrage experts
    """
    
    def __init__(self, auth_token: str, wallet_address: str):
        super().__init__(auth_token, wallet_address)
        self.dex_connections = {}
        self.min_profit_threshold = 0.005  # 0.5% minimum profit
        self.max_position_size = 5.0  # SOL
    
    async def scan_arbitrage_opportunities(self):
        """Continuously scan for arbitrage opportunities"""
        while self.is_running:
            try:
                opportunities = await self.find_arbitrage_opportunities()
                for opportunity in opportunities:
                    if opportunity['profit_ratio'] > self.min_profit_threshold:
                        await self.execute_arbitrage(opportunity)
                await asyncio.sleep(0.5)  # High frequency scanning
            except Exception as e:
                self.logger.error(f"Arbitrage scanning error: {e}")
    
    async def find_arbitrage_opportunities(self) -> List[dict]:
        """Find profitable arbitrage opportunities"""
        opportunities = []
        
        # Get prices from multiple DEXs
        tokens_to_check = ['SOL/USDC', 'RAY/SOL', 'SRM/SOL']  # Example pairs
        
        for token_pair in tokens_to_check:
            prices = await self.get_multi_dex_prices(token_pair)
            
            if len(prices) >= 2:
                max_price_dex = max(prices, key=lambda x: x['price'])
                min_price_dex = min(prices, key=lambda x: x['price'])
                
                profit_ratio = (max_price_dex['price'] - min_price_dex['price']) / min_price_dex['price']
                
                if profit_ratio > self.min_profit_threshold:
                    opportunities.append({
                        'token_pair': token_pair,
                        'buy_dex': min_price_dex['dex'],
                        'sell_dex': max_price_dex['dex'],
                        'buy_price': min_price_dex['price'],
                        'sell_price': max_price_dex['price'],
                        'profit_ratio': profit_ratio,
                        'estimated_gas': 0.001  # SOL
                    })
        
        return sorted(opportunities, key=lambda x: x['profit_ratio'], reverse=True)
    
    async def get_multi_dex_prices(self, token_pair: str) -> List[dict]:
        """Get prices from multiple DEXs"""
        # Implement multi-DEX price fetching
        # This would connect to various Solana DEXs
        return [
            {'dex': 'Raydium', 'price': 1.001},
            {'dex': 'Orca', 'price': 1.006},
            {'dex': 'Serum', 'price': 0.998}
        ]
    
    async def execute_arbitrage(self, opportunity: dict):
        """Execute arbitrage trade"""
        try:
            self.logger.info(f"💰 ARBITRAGE: {opportunity['token_pair']} - "
                           f"Profit: {opportunity['profit_ratio']:.2%}")
            
            # Calculate optimal position size
            position_size = min(
                self.max_position_size,
                # Add more sophisticated position sizing logic
            )
            
            # Execute simultaneous buy/sell orders
            # Implementation would depend on your trading infrastructure
            
            self.logger.info(f"✅ Arbitrage executed - Estimated profit: {opportunity['profit_ratio']:.2%}")
            
        except Exception as e:
            self.logger.error(f"❌ Arbitrage execution failed: {e}")

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class RiskManager:
    """
    Advanced risk management system for trading bots
    Professional risk controls used by chipa.tech traders
    """
    
    def __init__(self, config: dict):
        self.config = config
        self.daily_loss_limit = config.get('daily_loss_limit', 0.05)  # 5%
        self.max_position_size = config.get('max_position_size', 0.10)  # 10% of portfolio
        self.max_correlation = config.get('max_correlation', 0.7)
        self.daily_pnl = 0.0
        self.positions = {}
        self.start_of_day_balance = 0.0
        
    async def check_risk_limits(self, proposed_trade: dict) -> dict:
        """Check if proposed trade passes all risk limits"""
        checks = {
            'daily_loss_limit': await self.check_daily_loss_limit(),
            'position_size_limit': await self.check_position_size_limit(proposed_trade),
            'correlation_limit': await self.check_correlation_limit(proposed_trade),
            'concentration_risk': await self.check_concentration_risk(proposed_trade)
        }
        
        passed = all(checks.values())
        
        return {
            'approved': passed,
            'checks': checks,
            'risk_score': self.calculate_risk_score(checks)
        }
    
    async def check_daily_loss_limit(self) -> bool:
        """Check if daily loss limit has been exceeded"""
        if self.start_of_day_balance == 0:
            return True
        
        loss_ratio = abs(min(0, self.daily_pnl)) / self.start_of_day_balance
        return loss_ratio < self.daily_loss_limit
    
    async def check_position_size_limit(self, trade: dict) -> bool:
        """Check if position size is within limits"""
        position_value = trade.get('size', 0) * trade.get('price', 0)
        portfolio_value = await self.get_portfolio_value()
        
        if portfolio_value == 0:
            return False
        
        position_ratio = position_value / portfolio_value
        return position_ratio <= self.max_position_size
    
    def calculate_risk_score(self, checks: dict) -> float:
        """Calculate overall risk score (0-1, lower is better)"""
        failed_checks = sum(1 for passed in checks.values() if not passed)
        return failed_checks / len(checks)

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import asyncio
import aiohttp
from concurrent.futures import ThreadPoolExecutor
import cProfile
import time

class HighPerformanceBot(BaseTradingBot):
    """
    High-performance trading bot optimized for speed
    Techniques from top chipa.tech performance optimization guide
    """
    
    def __init__(self, auth_token: str, wallet_address: str):
        super().__init__(auth_token, wallet_address)
        self.session = None
        self.thread_pool = ThreadPoolExecutor(max_workers=4)
        self.performance_metrics = {
            'api_call_times': [],
            'signal_generation_times': [],
            'execution_times': []
        }
    
    async def initialize_performance_optimizations(self):
        """Initialize performance optimizations"""
        # Use persistent HTTP connections
        self.session = aiohttp.ClientSession(
            connector=aiohttp.TCPConnector(
                limit=100,
                limit_per_host=30,
                keepalive_timeout=30
            )
        )
        
        # Pre-warm connection pools
        await self.warmup_connections()
        
        # Start performance monitoring
        asyncio.create_task(self.performance_monitor())
    
    async def warmup_connections(self):
        """Pre-warm HTTP connections to reduce latency"""
        warmup_tasks = []
        for _ in range(5):
            warmup_tasks.append(self.client.get_balance(self.wallet_address))
        
        await asyncio.gather(*warmup_tasks, return_exceptions=True)
        self.logger.info("🔥 Connection pool warmed up")
    
    async def performance_monitor(self):
        """Monitor and log performance metrics"""
        while self.is_running:
            try:
                # Calculate average response times
                if self.performance_metrics['api_call_times']:
                    avg_api_time = sum(self.performance_metrics['api_call_times']) / len(self.performance_metrics['api_call_times'])
                    self.logger.info(f"📊 Avg API response time: {avg_api_time:.3f}s")
                
                # Reset metrics every minute
                self.performance_metrics = {k: [] for k in self.performance_metrics}
                
                await asyncio.sleep(60)
            except Exception as e:
                self.logger.error(f"Performance monitoring error: {e}")
    
    async def optimized_api_call(self, func, *args, **kwargs):
        """Wrapper for API calls with performance tracking"""
        start_time = time.time()
        try:
            result = await func(*args, **kwargs)
            return result
        finally:
            call_time = time.time() - start_time
            self.performance_metrics['api_call_times'].append(call_time)
    
    def profile_performance(self, func):
        """Decorator for profiling function performance"""
        def wrapper(*args, **kwargs):
            profiler = cProfile.Profile()
            profiler.enable()
            try:
                result = func(*args, **kwargs)
                return result
            finally:
                profiler.disable()
                # Save profile results for analysis
                profiler.dump_stats(f'profile_{func.__name__}_{int(time.time())}.prof')
        return wrapper

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import os
import sys
from pathlib import Path
import docker
import yaml

class ProductionBotManager:
    """
    Production deployment manager for trading bots
    Enterprise-grade deployment strategies from chipa.tech infrastructure team
    """
    
    def __init__(self, config_path: str):
        with open(config_path, 'r') as f:
            self.config = yaml.safe_load(f)
        
        self.docker_client = docker.from_env()
        self.running_bots = {}
    
    def create_bot_dockerfile(self, bot_class: str) -> str:
        """Generate Dockerfile for bot deployment"""
        dockerfile_content = f"""
FROM python:3.11-slim

# Install system dependencies
RUN apt-get update && apt-get install -y \\
    gcc \\
    g++ \\
    && rm -rf /var/lib/apt/lists/*

# Set working directory
WORKDIR /app

# Copy requirements and install Python dependencies
COPY requirements.txt .
RUN pip install --no-cache-dir -r requirements.txt

# Copy bot code
COPY . .

# Create non-root user for security
RUN useradd -m botuser && chown -R botuser:botuser /app
USER botuser

# Health check
HEALTHCHECK --interval=30s --timeout=10s --start-period=5s --retries=3 \\
    CMD python -c "import requests; requests.get('http://localhost:8080/health')"

# Run the bot
CMD ["python", "-m", "bots.{bot_class}"]
        """
        return dockerfile_content.strip()
    
    def create_docker_compose(self) -> str:
        """Generate docker-compose.yml for multi-bot deployment"""
        compose_content = """
version: '3.8'

services:
  token-sniping-bot:
    build: .
    environment:
      - BOT_TYPE=TokenSnipingBot
      - AUTH_TOKEN=${AUTH_TOKEN}
      - WALLET_ADDRESS=${WALLET_ADDRESS}
      - LOG_LEVEL=INFO
    volumes:
      - ./logs:/app/logs
      - ./config:/app/config
    restart: unless-stopped
    healthcheck:
      test: ["CMD", "python", "-c", "import requests; requests.get('http://localhost:8080/health')"]
      interval: 30s
      timeout: 10s
      retries: 3
    
  market-making-bot:
    build: .
    environment:
      - BOT_TYPE=MarketMakingBot
      - AUTH_TOKEN=${AUTH_TOKEN}
      - WALLET_ADDRESS=${WALLET_ADDRESS}
      - LOG_LEVEL=INFO
    volumes:
      - ./logs:/app/logs
      - ./config:/app/config
    restart: unless-stopped
    
  arbitrage-bot:
    build: .
    environment:
      - BOT_TYPE=ArbitrageBot
      - AUTH_TOKEN=${AUTH_TOKEN}
      - WALLET_ADDRESS=${WALLET_ADDRESS}
      - LOG_LEVEL=INFO
    volumes:
      - ./logs:/app/logs
      - ./config:/app/config
    restart: unless-stopped
  
  monitoring:
    image: prom/prometheus
    ports:
      - "9090:9090"
    volumes:
      - ./monitoring/prometheus.yml:/etc/prometheus/prometheus.yml
    
  grafana:
    image: grafana/grafana
    ports:
      - "3000:3000"
    environment:
      - GF_SECURITY_ADMIN_PASSWORD=admin
    volumes:
      - grafana-storage:/var/lib/grafana

volumes:
  grafana-storage:
        """
        return compose_content.strip()
    
    def deploy_bot_fleet(self):
        """Deploy complete bot fleet with monitoring"""
        try:
            # Create necessary directories
            os.makedirs('logs', exist_ok=True)
            os.makedirs('config', exist_ok=True)
            os.makedirs('monitoring', exist_ok=True)
            
            # Generate deployment files
            with open('Dockerfile', 'w') as f:
                f.write(self.create_bot_dockerfile('BaseTradingBot'))
            
            with open('docker-compose.yml', 'w') as f:
                f.write(self.create_docker_compose())
            
            # Create monitoring configuration
            self.create_monitoring_config()
            
            # Build and deploy
            os.system('docker-compose up -d --build')
            
            print("🚀 Bot fleet deployed successfully!")
            print("📊 Monitoring available at http://localhost:3000 (Grafana)")
            print("📈 Metrics available at http://localhost:9090 (Prometheus)")
            print("📝 Check logs with: docker-compose logs -f")
            
        except Exception as e:
            print(f"❌ Deployment failed: {e}")
    
    def create_monitoring_config(self):
        """Create Prometheus monitoring configuration"""
        prometheus_config = """
global:
  scrape_interval: 15s

scrape_configs:
  - job_name: 'trading-bots'
    static_configs:
      - targets: ['token-sniping-bot:8080', 'market-making-bot:8080', 'arbitrage-bot:8080']
    scrape_interval: 5s
    metrics_path: /metrics
        """
        
        with open('monitoring/prometheus.yml', 'w') as f:
            f.write(prometheus_config.strip())

# Example usage and main execution
if __name__ == "__main__":
    # Configuration for production deployment
    production_config = {
        'bots': [
            {
                'type': 'TokenSnipingBot',
                'instances': 2,
                'resources': {'cpu': '1', 'memory': '512Mi'}
            },
            {
                'type': 'MarketMakingBot', 
                'instances': 1,
                'resources': {'cpu': '0.5', 'memory': '256Mi'}
            }
        ],
        'monitoring': {
            'enabled': True,
            'metrics_port': 8080,
            'health_check_interval': 30
        }
    }
    
    print("🤖 AxiomTradeAPI Trading Bot Framework")
    print("=" * 50)
    print("Advanced trading strategies for Solana ecosystem")
    print("Trusted by professional traders on chipa.tech")
    print("=" * 50)
    print()
    print("Available Bot Types:")
    print("• TokenSnipingBot - High-speed new token acquisition")
    print("• MarketMakingBot - Automated liquidity provision")  
    print("• ArbitrageBot - Cross-DEX profit opportunities")
    print("• CustomBot - Implement your own strategies")
    print()
    print("🚀 Ready for production deployment!")
    print("📚 Full documentation: https://chipa.tech/axiomtradeapi-docs")
    print("💬 Community support: https://chipa.tech/discord")
    print("🎯 Trading strategies: https://chipa.tech/strategies")