```python
import logging
import numpy as np
import pandas as pd
import ta
from typing import Dict, Any
from datetime import datetime
from sklearn.cluster import KMeans
from sklearn.preprocessing import StandardScaler
from sklearn.decomposition import PCA
from groq import Groq

class AITrader:
    def __init__(self, config):
        self.client = Groq(api_key=config["groq"]["api_key"])
        self.model = config["groq"]["model"]
        self.tps_limit = config["trading"]["scalping"]["tps_limit"]
        self.last_trade_time = None
        self.confidence_threshold = config["neural"]["confidence_threshold"]

    async def get_trading_decision(self, market_data, indicators):
        current_time = datetime.now()
        
        if (self.last_trade_time is None or 
            (current_time - self.last_trade_time).total_seconds() >= 1/self.tps_limit):
            
            prompt = f"""
            As a trading AI, analyze this market data and provide a trading decision.
            Current market conditions:
            - Price: {market_data.get('price', 'N/A')}
            - RSI: {indicators['metrics'].get('rsi', 'N/A')}
            - MACD: {indicators['metrics'].get('macd', 'N/A')}
            - Bollinger Bands: {indicators['metrics'].get('bollinger_bands', 'N/A')}
            - Volume Profile: {indicators['metrics'].get('volume_profile', 'N/A')}
            - Market Pattern Cluster: {indicators.get('ml_analysis', {}).get('cluster', 'N/A')}
            
            Provide decision in format:
            ACTION: [BUY/SELL/HOLD]
            CONFIDENCE: [0-1]
            REASONING: [Brief explanation]
            """
            
            response = self.client.chat.completions.create(
                model=self.model,
                messages=[{"role": "user", "content": prompt}],
                temperature=0.1,
                max_tokens=150
            )
            
            self.last_trade_time = current_time
            return response.choices[0].message.content
            
        return "ACTION: HOLD\nCONFIDENCE: 1.0\nREASONING: TPS limit enforced"

class MarketDataProcessor:
    def __init__(self, config):
        self.config = config
        self.vector_dim = config["neural"]["vector_dimension"]
        self.window_size = config["neural"]["window_size"]
        self.price_history = []
        self.volume_history = []
        
        # ML components
        self.kmeans = KMeans(n_clusters=5, random_state=42)
        self.scaler = StandardScaler()
        self.pca = PCA(n_components=3)
        
        # AI Trader
        self.ai_trader = AITrader(config)
        
    def _calculate_indicators(self, price_data: pd.Series, volume_data: pd.Series) -> Dict:
        df = pd.DataFrame({
            "close": price_data,
            "volume": volume_data,
            "high": price_data,
            "low": price_data,
            "open": price_data
        })
        
        indicators = {
            # Trend
            "ema_fast": ta.trend.ema_indicator(df.close, window=self.config["indicators"]["ema_fast"]),
            "ema_slow": ta.trend.ema_indicator(df.close, window=self.config["indicators"]["ema_slow"]),
            "macd": ta.trend.macd(df.close, 
                                window_fast=self.config["indicators"]["macd_fast"],
                                window_slow=self.config["indicators"]["macd_slow"]),
            "macd_signal": ta.trend.macd_signal(df.close),
            
            # Momentum
            "rsi": ta.momentum.rsi(df.close, window=self.config["indicators"]["rsi_period"]),
            "stoch": ta.momentum.stoch(df.high, df.low, df.close),
            "stoch_signal": ta.momentum.stoch_signal(df.high, df.low, df.close),
            
            # Volatility
            "bollinger_high": ta.volatility.bollinger_hband(df.close),
            "bollinger_low": ta.volatility.bollinger_lband(df.close),
            "bollinger_mavg": ta.volatility.bollinger_mavg(df.close),
            "atr": ta.volatility.average_true_range(df.high, df.low, df.close),
            
            # Volume
            "vwap": ta.volume.volume_weighted_average_price(df.high, df.low, df.close, df.volume),
            "force_index": ta.volume.force_index(df.close, df.volume),
            
            # Advanced
            "keltner_high": ta.volatility.keltner_channel_hband(df.high, df.low, df.close),
            "keltner_low": ta.volatility.keltner_channel_lband(df.high, df.low, df.close),
            "ichimoku_a": ta.trend.ichimoku_a(df.high, df.low),
            "ichimoku_b": ta.trend.ichimoku_b(df.high, df.low)
        }
        
        return indicators
        
    def _vectorize_data(self, data: Dict[str, Any]) -> np.ndarray:
        price = float(data.get("price", 0))
        volume = float(data.get("volume", 0))
        
        self.price_history.append(price)
        self.volume_history.append(volume)
        
        if len(self.price_history) > self.window_size:
            self.price_history.pop(0)
            self.volume_history.pop(0)
            
        price_series = pd.Series(self.price_history)
        volume_series = pd.Series(self.volume_history)
        
        # Calculate indicators
        indicators = self._calculate_indicators(price_series, volume_series)
        
        feature_vector = np.concatenate([
            np.array(self.price_history),
            np.array(self.volume_history),
            np.array(indicators["ema_fast"].fillna(0)),
            np.array(indicators["rsi"].fillna(0)),
            np.array(indicators["bollinger_high"].fillna(0)),
            np.array(indicators["bollinger_low"].fillna(0)),
            np.array(indicators["macd"].fillna(0)),
            np.array(indicators["force_index"].fillna(0)),
            np.array(indicators["vwap"].fillna(0))
        ])
        
        return feature_vector
        
    def _cluster_analysis(self, feature_vector: np.ndarray):
        if len(self.price_history) >= self.window_size:
            scaled_features = self.scaler.fit_transform(feature_vector.reshape(-1, 1))
            pca_features = self.pca.fit_transform(scaled_features.reshape(1, -1))
            cluster = self.kmeans.fit_predict(pca_features)
            
            return {
                "cluster": int(cluster[0]),
                "pca_components": pca_features.tolist(),
                "cluster_centers": self.kmeans.cluster_centers_.tolist()
            }
        return None
        
    async def process_market_data(self, data):
        try:
            feature_vector = self._vectorize_data(data)
            cluster_data = self._cluster_analysis(feature_vector)
            
            latest_indicators = self._calculate_indicators(
                pd.Series(self.price_history), 
                pd.Series(self.volume_history)
            )
            
            processed_data = {
                "timestamp": data.get("timestamp", datetime.now().isoformat()),
                "metrics": {
                    "price": float(self.price_history[-1]),
                    "volume": float(self.volume_history[-1]),
                    "volatility": float(np.std(feature_vector)),
                    "momentum": float(np.mean(np.diff(feature_vector))),
                    "volume_profile": float(np.mean(self.volume_history)),
                    "rsi": float(latest_indicators["rsi"].iloc[-1]),
                    "macd": float(latest_indicators["macd"].iloc[-1]),
                    "bollinger_bands": {
                        "upper": float(latest_indicators["bollinger_high"].iloc[-1]),
                        "lower": float(latest_indicators["bollinger_low"].iloc[-1]),
                        "middle": float(latest_indicators["bollinger_mavg"].iloc[-1])
                    },
                    "ml_analysis": cluster_data
                },
                "ai_decision": await self.ai_trader.get_trading_decision(data, processed_data)
            }
            
            return processed_data
            
        except Exception as e:
            logging.error(f"Error processing market data: {str(e)}")
            return None

import asyncio
import logging
import time
from typing import Dict, Any, Optional
from collections import defaultdict, deque
import numpy as np
from datetime import datetime
import psutil
import torch

class TurboTrader:
    def __init__(self, config: Dict[str, Any], trading_core, websocket):
        self.config = config
        self.trading_core = trading_core
        self.websocket = websocket
        self.start_time = time.time()
        
        # Trading state
        self.active = True
        self.active_trades = {}
        self.trade_history = defaultdict(list)
        self.last_prices = {}
        self.profit_tracker = defaultdict(float)
        
        # Queue management
        self.trade_queue = deque(maxlen=1000)
        
        # Performance tracking
        self.execution_times = []
        self.slippage_history = []
        self.success_rate = []
        
        logging.info("TurboTrader initialized")

    async def start(self):
        """Start the trading engine"""
        logging.info("Starting TurboTrader")
        try:
            await asyncio.gather(
                self.monitor_trades(),
                self.process_queue(),
                self.update_metrics()
            )
        except Exception as e:
            logging.error(f"TurboTrader startup error: {str(e)}")
            raise

    async def monitor_trades(self):
        """Monitor active trades and manage positions"""
        while self.active:
            try:
                for trade_id, trade in list(self.active_trades.items()):
                    current_price = await self.trading_core.get_current_price(trade_id)
                    self.last_prices[trade_id] = current_price
                    await self._check_exit_conditions(trade_id, trade)
                    await self._update_trade_metrics(trade_id, current_price)
                await asyncio.sleep(0.1)
            except Exception as e:
                logging.error(f"Trade monitoring error: {str(e)}")
                await asyncio.sleep(1)

    async def process_queue(self):
        """Process the trading queue"""
        while self.active:
            try:
                if self.trade_queue:
                    trade = self.trade_queue.popleft()
                    await self.execute_trade(trade)
                await asyncio.sleep(0.1)
            except Exception as e:
                logging.error(f"Queue processing error: {str(e)}")

    async def update_metrics(self):
        """Update trading metrics periodically"""
        while self.active:
            try:
                metrics = self.get_metrics()
                await self.websocket.broadcast_metrics({
                    "type": "metrics_update",
                    "data": metrics
                })
                await asyncio.sleep(1)
            except Exception as e:
                logging.error(f"Metrics update error: {str(e)}")

    async def execute_trade(self, trade_data: Dict[str, Any]) -> Dict[str, Any]:
        """Execute a trade with advanced position management"""
        try:
            start_time = time.time()
            
            if not self._validate_trade(trade_data):
                return {"success": False, "reason": "validation_failed"}

            position_size = self._calculate_position_size(trade_data)
            execution_result = await self._execute_with_protection(trade_data, position_size)
            
            execution_time = time.time() - start_time
            self.execution_times.append(execution_time)
            
            if execution_result["success"]:
                await self._track_trade(execution_result, trade_data)
            
            return execution_result

        except Exception as e:
            logging.error(f"Trade execution error: {str(e)}")
            return {"success": False, "error": str(e)}

    def _validate_trade(self, trade_data: Dict[str, Any]) -> bool:
        """Validate trade parameters and risk limits"""
        try:
            if len(self.active_trades) >= self.config["risk"]["max_open_trades"]:
                return False
                
            daily_pnl = sum(self.profit_tracker.values())
            if daily_pnl <= -self.config["risk"]["max_daily_loss"]:
                return False
                
            if not self._check_market_conditions(trade_data):
                return False
                
            return True
            
        except Exception as e:
            logging.error(f"Trade validation error: {str(e)}")
            return False

    def _calculate_position_size(self, trade_data: Dict[str, Any]) -> float:
        """Calculate optimal position size"""
        base_size = self.config["trading"]["position_size"]
        vol_factor = self._calculate_volatility_factor(trade_data)
        conviction = trade_data.get("conviction", 0.5)
        adjusted_size = base_size * vol_factor * conviction
        max_size = self.config["trading"]["max_position"]
        return min(adjusted_size, max_size)

    def _calculate_volatility_factor(self, trade_data: Dict[str, Any]) -> float:
        """Calculate volatility adjustment factor"""
        try:
            recent_prices = trade_data.get("recent_prices", [])
            if len(recent_prices) < 2:
                return 1.0
            volatility = np.std(recent_prices) / np.mean(recent_prices)
            return max(0.1, min(1.0, 1.0 - volatility))
        except Exception as e:
            logging.error(f"Volatility calculation error: {str(e)}")
            return 1.0

    async def _execute_with_protection(self, trade_data: Dict[str, Any], position_size: float) -> Dict[str, Any]:
        """Execute trade with slippage and liquidity protection"""
        price_impact = self._calculate_price_impact(trade_data, position_size)
        if price_impact > self.config["trading"]["max_price_impact"]:
            return {"success": False, "reason": "high_impact"}

        execution_params = {
            "size": position_size,
            "price": trade_data["price"],
            "slippage_limit": self.config["trading"]["max_slippage"],
            "compute_limit": self.config["trading"]["compute_limit"]
        }
        
        result = await self.trading_core.execute_order(execution_params)
        
        if result["success"]:
            self._analyze_execution(result)
        
        return result

    def _calculate_price_impact(self, trade_data: Dict[str, Any], position_size: float) -> float:
        """Calculate estimated price impact"""
        try:
            market_depth = trade_data.get("market_depth", 1000000)
            return (position_size / market_depth) * 100
        except Exception as e:
            logging.error(f"Price impact calculation error: {str(e)}")
            return 0.0

    def get_metrics(self) -> Dict[str, Any]:
        """Get real-time trading metrics"""
        return {
            "active_trades": len(self.active_trades),
            "total_trades": len(self.trade_history),
            "total_profit": sum(self.profit_tracker.values()),
            "performance": {
                "avg_execution_time": np.mean(self.execution_times) if self.execution_times else 0,
                "success_rate": np.mean(self.success_rate) if self.success_rate else 0,
                "avg_slippage": np.mean(self.slippage_history) if self.slippage_history else 0
            },
            "risk_metrics": {
                "max_drawdown": self._calculate_drawdown(),
                "sharpe_ratio": self._calculate_sharpe(),
                "current_exposure": self._calculate_exposure()
            },
            "system_stats": {
                "uptime": time.time() - self.start_time,
                "memory_usage": self._get_memory_usage(),
                "gpu_utilization": self._get_gpu_stats()
            }
        }

    def _calculate_drawdown(self) -> float:
        """Calculate maximum drawdown"""
        if not self.profit_tracker:
            return 0.0
        cumulative = np.cumsum(list(self.profit_tracker.values()))
        peak = np.maximum.accumulate(cumulative)
        drawdown = (peak - cumulative) / peak
        return float(np.max(drawdown)) if len(drawdown) > 0 else 0.0

    def _calculate_sharpe(self) -> float:
        """Calculate Sharpe ratio"""
        if not self.profit_tracker:
            return 0.0
        returns = np.array(list(self.profit_tracker.values()))
        if len(returns) < 2:
            return 0.0
        return float(np.mean(returns) / np.std(returns) if np.std(returns) != 0 else 0)

    def _calculate_exposure(self) -> float:
        """Calculate current market exposure"""
        return sum(trade["position_size"] for trade in self.active_trades.values())

    def _get_memory_usage(self) -> Dict[str, float]:
        """Get memory usage statistics"""
        return {
            "ram_used": psutil.Process().memory_info().rss / 1024 / 1024,
            "gpu_vram": torch.cuda.memory_allocated() / 1024 / 1024 if torch.cuda.is_available() else 0
        }

    def _get_gpu_stats(self) -> Dict[str, float]:
        """Get GPU utilization statistics"""
        if not torch.cuda.is_available():
            return {"utilization": 0.0}
        return {
            "utilization": float(torch.cuda.utilization()) if hasattr(torch.cuda, 'utilization') else 0.0
        }

    async def _track_trade(self, execution: Dict[str, Any], trade_data: Dict[str, Any]):
        """Track trade performance and update metrics"""
        trade_id = execution["trade_id"]
        self.active_trades[trade_id] = {
            "entry_price": execution["price"],
            "position_size": execution["size"],
            "entry_time": time.time(),
            "metadata": trade_data
        }
        
        await self.websocket.broadcast_metrics({
            "type": "trade_opened",
            "trade_id": trade_id,
            "price": execution["price"],
            "size": execution["size"]
        })

    async def _check_exit_conditions(self, trade_id: str, trade: Dict[str, Any]):
        """Check and execute trade exit conditions"""
        current_price = self.last_prices.get(trade_id)
        if not current_price:
            return

        entry_price = trade["entry_price"]
        position_size = trade["position_size"]
        profit_pct = ((current_price - entry_price) / entry_price) * 100

        if profit_pct >= self.config["trading"]["scalping"]["profit_target"]:
            await self._close_position(trade_id, "profit_target", profit_pct)
            return

        if profit_pct <= -self.config["risk"]["stop_loss"]:
            await self._close_position(trade_id, "stop_loss", profit_pct)
            return

        if self._check_trailing_stop(trade, current_price):
            await self._close_position(trade_id, "trailing_stop", profit_pct)

    async def _close_position(self, trade_id: str, reason: str, profit_pct: float):
        """Close a trading position"""
        try:
            trade = self.active_trades.pop(trade_id)
            self.trade_history[trade_id].append({
                "exit_reason": reason,
                "profit_pct": profit_pct,
                "exit_time": time.time()
            })
            
            self.profit_tracker[trade_id] = profit_pct
            
            await self.websocket.broadcast_metrics({
                "type": "trade_closed",
                "trade_id": trade_id,
                "reason": reason,
                "profit_pct": profit_pct
            })
        except Exception as e:
            logging.error(f"Position closing error: {str(e)}")

    async def cleanup(self):
        """Cleanup resources"""
        try:
            await self.pause_trading()
            self.trade_queue.clear()
            self.active_trades.clear()
            logging.info("TurboTrader cleanup completed")
        except Exception as e:
            logging.error(f"Cleanup error: {str(e)}")

    async def pause_trading(self):
        """Pause trading activities"""
        self.active = False
        logging.warning("Trading paused")
        for trade_id in list(self.active_trades.keys()):
            await self._close_position(trade_id, "trading_paused", 0)

    async def resume_trading(self):
        """Resume trading activities"""
        self.active = True
        logging.info("Trading resumed")

import asyncio
import websockets
import json
import logging
import time
from collections import deque
import numpy as np
from datetime import datetime
from typing import Dict, Any, Set, Optional
from dataclasses import dataclass

@dataclass
class MarketMetrics:
    price_volatility: float
    volume_trend: float
    recent_trades: list

class WebSocketHandler:
    def __init__(self, config: Dict[str, Any]):
        self.config = config
        self.clients: Set[websockets.WebSocketServerProtocol] = set()
        self.ws_endpoint = config["helius"]["ws_endpoint"]
        
        # Initialize data structures
        self._init_data_structures()
        
        # Performance tracking
        self.latency_tracker = deque(maxlen=1000)
        self.message_count = 0
        self.last_broadcast = time.time()
        
        # Connection management
        self.market_feed: Optional[websockets.WebSocketClientProtocol] = None
        self.reconnect_delay = 1.0
        
        logging.info("WebSocket Handler Initialized")

    def _init_data_structures(self):
        config = self.config["websocket"]["history_size"]
        self.trade_history = deque(maxlen=config["trades"])
        self.price_history = deque(maxlen=config["prices"])
        self.volume_history = deque(maxlen=config["volumes"])

    async def start(self):
        """Start WebSocket server and initialize connections"""
        logging.info(f"Starting WebSocket server on port {self.config['websocket']['port']}")
        
        server = await websockets.serve(
            self.handle_client,
            self.config["websocket"]["host"],
            self.config["websocket"]["port"]
        )
        
        # Start market feed connection in background
        market_task = asyncio.create_task(self._maintain_market_feed())
        
        try:
            await asyncio.gather(server.wait_closed(), market_task)
        except Exception as e:
            logging.error(f"WebSocket server error: {str(e)}")
            raise

    async def broadcast_metrics(self, metrics: Dict[str, Any]):
        """Broadcast metrics to all connected clients"""
        if not self.clients:
            return
            
        try:
            enriched_metrics = self._enrich_metrics(metrics)
            message = json.dumps(enriched_metrics)
            
            # Concurrent broadcast to all clients
            broadcast_tasks = [
                asyncio.create_task(client.send(message))
                for client in self.clients
            ]
            await asyncio.gather(*broadcast_tasks, return_exceptions=True)
            
            # Performance tracking
            self._update_performance_metrics()
            
        except Exception as e:
            logging.error(f"Broadcast error: {str(e)}")

    def _enrich_metrics(self, metrics: Dict[str, Any]) -> Dict[str, Any]:
        """Enrich metrics with additional data"""
        enriched = {
            **metrics,
            "timestamp": datetime.now().isoformat(),
            "analytics": {
                "message_count": self.message_count,
                "avg_latency": np.mean(self.latency_tracker) if self.latency_tracker else 0,
                "connected_clients": len(self.clients)
            }
        }

        if self.price_history:
            enriched["market"] = self._calculate_market_metrics()
        
        return enriched

    def _calculate_market_metrics(self) -> MarketMetrics:
        """Calculate market metrics"""
        return MarketMetrics(
            price_volatility=np.std([p["price"] for p in self.price_history]),
            volume_trend=np.mean([v["volume"] for v in self.volume_history[-10:]]),
            recent_trades=list(self.trade_history)[-5:]
        )

    async def handle_client(self, websocket: websockets.WebSocketServerProtocol):
        """Handle individual client connections"""
        self.clients.add(websocket)
        client_id = id(websocket)
        logging.info(f"New client connected. Total clients: {len(self.clients)}")
        
        try:
            await self._send_initial_state(websocket)
            await self._handle_client_messages(websocket)
        except Exception as e:
            logging.error(f"Client error: {str(e)}")
        finally:
            self.clients.remove(websocket)
            logging.info(f"Client {client_id} disconnected. Remaining: {len(self.clients)}")

    async def _send_initial_state(self, websocket: websockets.WebSocketServerProtocol):
        """Send initial state to newly connected client"""
        initial_state = {
            "type": "initial_state",
            "trade_history": list(self.trade_history),
            "price_history": list(self.price_history),
            "volume_history": list(self.volume_history),
            "market_metrics": self._calculate_market_metrics().__dict__ if self.price_history else None
        }
        await websocket.send(json.dumps(initial_state))

    async def _handle_client_messages(self, websocket: websockets.WebSocketServerProtocol):
        """Handle incoming client messages"""
        async for message in websocket:
            try:
                data = json.loads(message)
                self.message_count += 1

                if data["type"] == "subscribe":
                    await self._handle_subscription(websocket, data)
                elif data["type"] == "unsubscribe":
                    await self._handle_unsubscribe(websocket, data)
                elif data["type"] == "request_data":
                    await self._handle_data_request(websocket, data)
            except json.JSONDecodeError:
                logging.error("Invalid JSON received")
            except Exception as e:
                logging.error(f"Error processing client message: {str(e)}")

    async def _maintain_market_feed(self):
        """Maintain persistent market data feed connection"""
        while True:
            try:
                async with websockets.connect(self.ws_endpoint) as ws:
                    self.market_feed = ws
                    await self._handle_market_feed()
            except Exception as e:
                logging.error(f"Market feed error: {str(e)}")
                await asyncio.sleep(self.reconnect_delay)
                self.reconnect_delay = min(self.reconnect_delay * 2, 60)

    async def _handle_market_feed(self):
        """Handle market feed messages"""
        await self._subscribe_to_market()
        
        while True:
            msg = await self.market_feed.recv()
            await self._process_market_data(msg)

    async def _subscribe_to_market(self):
        """Subscribe to market data"""
        subscription_message = {
            "jsonrpc": "2.0",
            "id": 1,
            "method": "programSubscribe",
            "params": [
                self.config["raydium"]["program_id"],
                {"encoding": "base64", "commitment": "processed"}
            ]
        }
        await self.market_feed.send(json.dumps(subscription_message))

    async def _process_market_data(self, msg: str):
        """Process incoming market data"""
        try:
            data = json.loads(msg)
            
            if "params" in data:
                params = data["params"]
                
                if "price" in params:
                    self.price_history.append({
                        "timestamp": time.time(),
                        "price": params["price"]
                    })
                
                if "volume" in params:
                    self.volume_history.append({
                        "timestamp": time.time(),
                        "volume": params["volume"]
                    })
                
                if "trade" in params:
                    self.trade_history.append({
                        "timestamp": time.time(),
                        "trade": params["trade"]
                    })
                
                if self._should_broadcast():
                    await self.broadcast_metrics({
                        "type": "market_update",
                        "data": params
                    })
                    
        except Exception as e:
            logging.error(f"Error processing market data: {str(e)}")

    async def _handle_subscription(self, websocket: websockets.WebSocketServerProtocol, data: Dict[str, Any]):
        """Handle client subscription requests"""
        topic = data.get("topic")
        if topic:
            await websocket.send(json.dumps({
                "type": "subscription_confirmed",
                "topic": topic
            }))

    async def _handle_unsubscribe(self, websocket: websockets.WebSocketServerProtocol, data: Dict[str, Any]):
        """Handle client unsubscribe requests"""
        topic = data.get("topic")
        if topic:
            await websocket.send(json.dumps({
                "type": "unsubscribe_confirmed",
                "topic": topic
            }))

    async def _handle_data_request(self, websocket: websockets.WebSocketServerProtocol, data: Dict[str, Any]):
        """Handle client data requests"""
        request_type = data.get("request_type")
        response = {
            "type": "data_response",
            "request_type": request_type,
            "data": None
        }
        
        if request_type == "market_metrics":
            response["data"] = self._calculate_market_metrics().__dict__
        elif request_type == "trade_history":
            response["data"] = list(self.trade_history)
        
        await websocket.send(json.dumps(response))

    def _should_broadcast(self) -> bool:
        """Determine if we should broadcast based on rate limiting"""
        return (time.time() - self.last_broadcast) >= self.config["websocket"]["broadcast_interval"]

    def _update_performance_metrics(self):
        """Update performance tracking metrics"""
        broadcast_time = time.time() - self.last_broadcast
        self.latency_tracker.append(broadcast_time)
        self.last_broadcast = time.time()
    
    async def close_all(self):
        """Close all websocket connections"""
        close_tasks = [client.close() for client in self.clients]
        await asyncio.gather(*close_tasks, return_exceptions=True)
        self.clients.clear()
        if self.market_feed:
            await self.market_feed.close()

import torch
import logging
from typing import Dict, Any, Optional, List, Tuple
import asyncio
from datetime import datetime
import time
from collections import deque
import aiohttp

class TradingCore:
    def __init__(self, config: Dict[str, Any], device: torch.device):
        self.config = config
        self.device = device
        
        # Initialize neural network components
        self.feature_size = 640
        self.hidden_size = 1280
        self.model = self._initialize_model()
        
        # Setup CUDA optimizations
        torch.backends.cudnn.benchmark = True
        torch.backends.cuda.matmul.allow_tf32 = True
        torch.backends.cudnn.allow_tf32 = True
        
        # Setup GPU streams for parallel processing
        self.gpu_streams = [torch.cuda.Stream() for _ in range(16)]
        
        # Initialize optimizer and performance tracking
        self.optimizer = torch.optim.Adam(
            self.model.parameters(),
            lr=config["neural"]["learning_rate"]
        )
        
        # Initialize automatic mixed precision scaler
        self.scaler = torch.amp.GradScaler()
        
        # Performance tracking
        self.metrics = {
            "execution_times": deque(maxlen=1000),
            "accuracy": deque(maxlen=1000),
            "predictions": deque(maxlen=1000),
            "profits": deque(maxlen=1000)
        }
        
        # Market state tracking
        self.market_state = {
            "last_price": 0.0,
            "volume_24h": 0.0,
            "volatility": 0.0,
            "bid_ask_spread": 0.0
        }
        
        logging.info("Trading Core initialized with GPU optimization")

    def _initialize_model(self) -> torch.nn.Module:
        model = torch.nn.Sequential(
            torch.nn.Linear(self.feature_size, self.hidden_size),
            torch.nn.LeakyReLU(negative_slope=0.1),
            torch.nn.BatchNorm1d(self.hidden_size),
            torch.nn.Dropout(0.2),
            
            torch.nn.Linear(self.hidden_size, self.hidden_size * 2),
            torch.nn.LeakyReLU(negative_slope=0.1),
            torch.nn.BatchNorm1d(self.hidden_size * 2),
            torch.nn.Dropout(0.2),
            
            torch.nn.Linear(self.hidden_size * 2, self.hidden_size),
            torch.nn.LeakyReLU(negative_slope=0.1),
            torch.nn.BatchNorm1d(self.hidden_size),
            
            torch.nn.Linear(self.hidden_size, 128),
            torch.nn.LeakyReLU(negative_slope=0.1),
            torch.nn.BatchNorm1d(128),
            
            torch.nn.Linear(128, 1),
            torch.nn.Sigmoid()
        ).to(self.device).half()

        # Initialize weights for optimal performance
        for m in model.modules():
            if isinstance(m, torch.nn.Linear):
                torch.nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
                if m.bias is not None:
                    torch.nn.init.zeros_(m.bias)
            elif isinstance(m, torch.nn.BatchNorm1d):
                torch.nn.init.ones_(m.weight)
                torch.nn.init.zeros_(m.bias)

        return model

    async def process_market_data(self, data: Dict[str, Any]) -> Dict[str, Any]:
        try:
            start_time = time.time()
            
            # Update market state
            self._update_market_state(data)
            
            # Prepare features
            features = self._prepare_features(data)
            
            # Split processing across GPU streams
            predictions = []
            for stream, feature_batch in zip(self.gpu_streams, torch.chunk(features, len(self.gpu_streams))):
                with torch.cuda.stream(stream):
                    with torch.cuda.amp.autocast():
                        pred = self.model(feature_batch)
                        predictions.append(pred)
            
            torch.cuda.synchronize()
            prediction = torch.cat(predictions).mean()
            
            # Calculate confidence and signals
            confidence = float(prediction.sigmoid().item())
            execution_time = time.time() - start_time
            
            # Track metrics
            self.metrics["execution_times"].append(execution_time)
            self.metrics["predictions"].append(confidence)
            
            return {
                "prediction": prediction.item(),
                "confidence": confidence,
                "execution_time": execution_time,
                "market_state": self.market_state,
                "timestamp": datetime.now().isoformat()
            }
            
        except Exception as e:
            logging.error(f"Market data processing error: {str(e)}")
            raise

    def _prepare_features(self, data: Dict[str, Any]) -> torch.Tensor:
        """Prepare neural network input features"""
        # Market data features
        market_features = np.array([
            data.get("price", 0) / self.market_state["last_price"] - 1,  # Price change
            data.get("volume", 0) / self.market_state["volume_24h"],     # Volume ratio
            data.get("bid_size", 0),
            data.get("ask_size", 0),
            self.market_state["volatility"],
            self.market_state["bid_ask_spread"]
        ], dtype=np.float32)
        
        # Technical indicators
        tech_features = self._calculate_indicators(data)
        
        # Combine features
        features = np.concatenate([market_features, tech_features])
        
        # Pad to feature_size if needed
        if len(features) < self.feature_size:
            features = np.pad(features, (0, self.feature_size - len(features)))
        
        return torch.from_numpy(features).to(self.device).half()

    def _calculate_indicators(self, data: Dict[str, Any]) -> np.ndarray:
        """Calculate technical indicators"""
        price = data.get("price", 0)
        volume = data.get("volume", 0)
        
        # Example indicators (expand based on your strategy)
        rsi = self._calculate_rsi(price)
        volume_ma = self._calculate_volume_ma(volume)
        macd = self._calculate_macd(price)
        
        return np.array([rsi, volume_ma, macd], dtype=np.float32)

    def _calculate_rsi(self, price: float, period: int = 14) -> float:
        """Calculate Relative Strength Index"""
        if len(self.metrics["predictions"]) < period:
            return 50.0
        
        changes = np.diff([p for p in self.metrics["predictions"]][-period:])
        gains = np.sum(np.maximum(changes, 0))
        losses = -np.sum(np.minimum(changes, 0))
        
        if losses == 0:
            return 100.0
        
        rs = gains / losses
        return 100.0 - (100.0 / (1 + rs))

    def _calculate_volume_ma(self, volume: float, period: int = 20) -> float:
        """Calculate Volume Moving Average"""
        recent_volumes = [p for p in self.metrics["predictions"]][-period:]
        if not recent_volumes:
            return volume
        return np.mean(recent_volumes)

    def _calculate_macd(self, price: float) -> float:
        """Calculate MACD indicator"""
        if len(self.metrics["predictions"]) < 26:
            return 0.0
            
        prices = [p for p in self.metrics["predictions"]][-26:]
        ema12 = np.mean(prices[-12:])
        ema26 = np.mean(prices)
        return ema12 - ema26

    def _update_market_state(self, data: Dict[str, Any]):
        """Update internal market state tracking"""
        self.market_state["last_price"] = data.get("price", self.market_state["last_price"])
        self.market_state["volume_24h"] = data.get("volume_24h", self.market_state["volume_24h"])
        
        # Calculate volatility
        if len(self.metrics["predictions"]) >= 20:
            recent_prices = [p for p in self.metrics["predictions"]][-20:]
            self.market_state["volatility"] = np.std(recent_prices)
        
        # Update bid-ask spread
        if "bid" in data and "ask" in data:
            self.market_state["bid_ask_spread"] = (data["ask"] - data["bid"]) / data["bid"]

    async def adjust_model(self):
        """Adjust model parameters based on performance"""
        logging.info("Adjusting model parameters")
        
        if len(self.metrics["accuracy"]) < 100:
            return

        recent_accuracy = np.mean(list(self.metrics["accuracy"])[-100:])
        
        if recent_accuracy < 0.55:
            # Increase learning rate to adapt faster
            self.optimizer.param_groups[0]['lr'] *= 1.2
            logging.info("Increasing learning rate for faster adaptation")
        elif recent_accuracy > 0.65:
            # Decrease learning rate to stabilize
            self.optimizer.param_groups[0]['lr'] *= 0.8
            logging.info("Decreasing learning rate to stabilize")

 
    async def execute_order(self, params: Dict[str, Any]) -> Dict[str, Any]:
        """Execute trading order with Jupiter/Raydium integration"""
        try:
            start_time = time.time()
        
            # Validate order parameters
            if not self._validate_order(params):
                return {"success": False, "reason": "invalid_parameters"}

            # Prepare transaction parameters
            tx_params = {
                "inputMint": self.config["trading"]["token_mint"],
                "amount": str(int(params["size"] * 1e9)),  # Convert to lamports
                "slippageBps": str(int(params["slippage_limit"] * 100)),
                "computeLimit": self.config["trading"]["compute_limit"],
                "priorityFee": self.config["trading"]["priority_fee"]
            }

            # Get quote from Jupiter
            if self.config["trading"]["execution"]["use_jupiter"]:
                async with aiohttp.ClientSession() as session:
                    quote_url = f"{self.config['jupiter']['quote_api']}/quote"
                    async with session.get(quote_url, params=tx_params) as resp:
                        if resp.status != 200:
                            return {"success": False, "reason": "quote_failed"}
                        quote = await resp.json()

                    # Validate price impact
                    price_impact = float(quote["priceImpactPct"])
                    if price_impact > self.config["jupiter"]["max_slippage"]:
                        return {"success": False, "reason": "high_impact"}

                    # Build and send transaction
                    tx = await self._build_jupiter_transaction(quote)
                    signature = await self._submit_transaction(tx)

            # Use Raydium for execution
            else:
                pool_info = await self._get_raydium_pool_info()
                tx = await self._build_raydium_transaction(pool_info, params)
                signature = await self._submit_transaction(tx)

            # Wait for confirmation
            success = await self._confirm_transaction(signature)
            if not success:
               return {"success": False, "reason": "tx_failed"}

            # Record execution metrics
            execution_time = time.time() - start_time
            order_result = {
                "success": True,
                "trade_id": signature,
                "price": params["price"],
                "size": params["size"],
                "execution_time": execution_time,
                "price_impact": price_impact if 'price_impact' in locals() else 0,
               "fee": self._calculate_transaction_fee(tx)
            }

            # Update metrics
            self.metrics["execution_times"].append(execution_time)
            self._update_trade_metrics(order_result)

            return order_result

        except Exception as e:
            logging.error(f"Order execution error: {str(e)}")
            return {"success": False, "error": str(e)}

    async def _build_jupiter_transaction(self, quote: Dict[str, Any]) -> Dict[str, Any]:
        """Build Jupiter swap transaction"""
        try:
            async with aiohttp.ClientSession() as session:
                # Get swap instruction
                swap_url = f"{self.config['jupiter']['quote_api']}/swap"
                swap_data = {
                    "userPublicKey": self.config["wallet"]["address"],
                    "quoteResponse": quote,
                    "computeLimit": self.config["trading"]["compute_limit"],
                    "