import torch

import time

import numpy as np

import asyncio

from groq import Groq

from dataclasses import dataclass

from typing import Dict, List, Tuple, Optional, Any

from concurrent.futures import ThreadPoolExecutor

from PyQt6.QtCore import QObject, pyqtSignal

from .metrics_scanner import TokenMetricsScanner



@dataclass

class MarketSignal:

    momentum: float

    trend: float

    volatility: float

    confidence: float

    timestamp: float

    pool_address: str

    signal_type: str = "NEUTRAL"

    priority: bool = False



class QuantumAnalysis(QObject):

    # Trading signals

    opportunity_signal = pyqtSignal(dict)  # Trading opportunities

    risk_signal = pyqtSignal(dict)        # Risk alerts

    analysis_signal = pyqtSignal(dict)    # General analysis updates



    def __init__(self, core: 'QuantumCore'):

        super().__init__()

        self.core = core

        self.logger = self.core.logger

        self.setup_neural_processor()

        self.setup_market_tensors()

        self.pool_indices = {}

        self.signal_history = []

        self.metrics_scanner = TokenMetricsScanner()

        self.active = True

        

    def setup_neural_processor(self):

        """Initialize Groq neural processor"""

        self.groq_client = Groq(api_key=self.core.config['groq']['api_key'])

        self.neural_executor = ThreadPoolExecutor(

            max_workers=2, 

            thread_name_prefix='neural'

        )

        

    def setup_market_tensors(self):

        """Initialize market data tensors"""

        with torch.cuda.stream(self.core.streams['market']):

            self.price_history = torch.zeros(

                (1000, self.core.config['neural']['window_size']), 

                device=self.core.device

            )

            self.volume_history = torch.zeros_like(self.price_history)

            self.liquidity_history = torch.zeros_like(self.price_history)



    async def start(self):

        """Start market analysis engine"""

        self.logger.info("🧠 Quantum Analysis: Online")

        try:

            while self.active:

                message = await self.core.ws_manager.message_queue.get()

                if message.get('method') == 'raydiumPoolUpdate':

                    await self.process_pool_update(message['params'])

                await asyncio.sleep(0.1)

        except Exception as e:

            self.logger.error(f"Analysis error: {e}")



    async def process_pool_update(self, pool_data: dict):

        """Process incoming pool updates"""

        try:

            pool_address = pool_data['address']

            

            # Update market tensors

            self.update_market_tensors(pool_address, pool_data)

            

            # Get technical indicators

            indicators = await self.calculate_indicators(pool_address)

            

            # Get neural analysis

            neural_scores = await self.get_neural_prediction(pool_data)

            

            # Calculate final signals

            signal = self.generate_market_signal(

                pool_address=pool_address,

                indicators=indicators,

                neural_boost=neural_scores

            )

            

            # Emit signals if confidence threshold met

            if signal.confidence >= self.core.config['trading']['confidence_threshold']:

                self.opportunity_signal.emit({

                    'pool': signal.pool_address,

                    'type': signal.signal_type,

                    'confidence': signal.confidence,

                    'momentum': signal.momentum,

                    'trend': signal.trend,

                    'volatility': signal.volatility,

                    'timestamp': signal.timestamp,

                    'priority': signal.priority

                })

                

        except Exception as e:

            self.logger.error(f"Pool update processing error: {e}")



    def update_market_tensors(self, pool_address: str, data: dict):

        """Update market data tensors"""

        with torch.cuda.stream(self.core.streams['market']):

            idx = self.get_pool_index(pool_address)

            

            # Roll tensors and update latest values

            self.price_history[idx] = torch.roll(self.price_history[idx], -1)

            self.volume_history[idx] = torch.roll(self.volume_history[idx], -1)

            self.liquidity_history[idx] = torch.roll(self.liquidity_history[idx], -1)

            

            self.price_history[idx, -1] = data['price']

            self.volume_history[idx, -1] = data['volume']

            self.liquidity_history[idx, -1] = data['liquidity']



    async def calculate_indicators(self, pool_address: str) -> Dict[str, torch.Tensor]:

        """Calculate technical indicators"""

        with torch.cuda.stream(self.core.streams['analysis']):

            idx = self.get_pool_index(pool_address)

            price_data = self.price_history[idx]

            

            # Calculate RSI

            rsi = self.calculate_rsi(

                price_data,

                period=self.core.config['indicators']['rsi']['period']

            )

            

            # Calculate EMAs

            fast_ema = self.calculate_ema(

                price_data,

                period=self.core.config['indicators']['ema']['fast']

            )

            slow_ema = self.calculate_ema(

                price_data,

                period=self.core.config['indicators']['ema']['slow']

            )

            

            # Calculate Bollinger Bands

            bb_upper, bb_lower = self.calculate_bollinger_bands(

                price_data,

                period=self.core.config['indicators']['bollinger']['period'],

                std_dev=self.core.config['indicators']['bollinger']['std_dev']

            )

            

            # Calculate MACD

            macd, signal = self.calculate_macd(

                price_data,

                fast=self.core.config['indicators']['macd']['fast'],

                slow=self.core.config['indicators']['macd']['slow'],

                signal=self.core.config['indicators']['macd']['signal']

            )

            

            return {

                'rsi': rsi,

                'fast_ema': fast_ema,

                'slow_ema': slow_ema,

                'bb_upper': bb_upper,

                'bb_lower': bb_lower,

                'macd': macd,

                'signal': signal

            }



    async def get_neural_prediction(self, pool_data: dict) -> dict:

        """Get neural network predictions"""

        try:

            prompt = self.generate_market_prompt(pool_data)

            

            response = await self.neural_executor.submit(

                self.groq_client.chat.completions.create,

                model=self.core.config['groq']['model'],

                messages=[

                    {"role": "system", "content": "You are a quantum trading analyzer. Provide only numerical scores."},

                    {"role": "user", "content": prompt}

                ],

                temperature=self.core.config['groq']['temperature'],

                max_tokens=self.core.config['groq']['max_tokens']

            )

            

            return self.parse_neural_response(response)

            

        except Exception as e:

            self.logger.error(f"Neural prediction error: {e}")

            return self.get_default_scores()



    def generate_market_signal(

        self,

        pool_address: str,

        indicators: Dict[str, torch.Tensor],

        neural_boost: Dict[str, float]

    ) -> MarketSignal:

        """Generate final market signal"""

        

        # Calculate core metrics

        momentum = self.calculate_momentum(indicators)

        trend = self.calculate_trend(indicators)

        volatility = self.calculate_volatility(indicators)

        

        # Calculate confidence with neural boost

        confidence = self.calculate_enhanced_confidence(

            momentum=momentum,

            trend=trend,

            volatility=volatility,

            neural_boost=neural_boost

        )

        

        # Determine signal type

        signal_type = self.determine_signal_type(momentum, trend, confidence)

        

        # Determine priority

        priority = confidence > 90 and abs(momentum) > 0.7

        

        return MarketSignal(

            momentum=momentum,

            trend=trend,

            volatility=volatility,

            confidence=confidence,

            timestamp=time.time(),

            pool_address=pool_address,

            signal_type=signal_type,

            priority=priority

        )



    def get_pool_index(self, pool_address: str) -> int:

        """Get or create pool index"""

        if pool_address not in self.pool_indices:

            self.pool_indices[pool_address] = len(self.pool_indices)

        return self.pool_indices[pool_address]



    async def shutdown(self):

        """Shutdown analysis engine"""

        self.active = False

        self.neural_executor.shutdown(wait=True)

        self.logger.info("Quantum Analysis: Shutdown Complete")



    def calculate_rsi(self, data: torch.Tensor, period: int = 14) -> torch.Tensor:

        """Calculate RSI indicator"""

        diff = data[1:] - data[:-1]

        gains = diff.clamp(min=0)

        losses = -diff.clamp(max=0)

        

        avg_gain = self._exponential_average(gains, period)

        avg_loss = self._exponential_average(losses, period)

        

        rs = avg_gain / (avg_loss + 1e-10)  # Avoid division by zero

        rsi = 100 - (100 / (1 + rs))

        

        return rsi

        

    def calculate_ema(self, data: torch.Tensor, period: int) -> torch.Tensor:

        """Calculate Exponential Moving Average"""

        alpha = 2 / (period + 1)

        return self._exponential_average(data, period, alpha)

        

    def calculate_bollinger_bands(

        self, 

        data: torch.Tensor, 

        period: int = 20, 

        std_dev: float = 2.0

    ) -> Tuple[torch.Tensor, torch.Tensor]:

        """Calculate Bollinger Bands"""

        sma = self._simple_moving_average(data, period)

        rolling_std = self._rolling_std(data, period)

        

        upper_band = sma + (rolling_std * std_dev)

        lower_band = sma - (rolling_std * std_dev)

        

        return upper_band, lower_band



    def calculate_macd(

        self, 

        data: torch.Tensor,

        fast: int = 12,

        slow: int = 26,

        signal: int = 9

    ) -> Tuple[torch.Tensor, torch.Tensor]:

        """Calculate MACD and Signal line"""

        fast_ema = self.calculate_ema(data, fast)

        slow_ema = self.calculate_ema(data, slow)

        

        macd_line = fast_ema - slow_ema

        signal_line = self.calculate_ema(macd_line, signal)

        

        return macd_line, signal_line



    def calculate_momentum(self, indicators: Dict[str, torch.Tensor]) -> float:

        """Calculate momentum score"""

        with torch.cuda.stream(self.core.streams['analysis']):

            # RSI component

            rsi_score = (indicators['rsi'][-1] - 50) / 50  # Normalize to [-1, 1]

            

            # MACD component

            macd_score = torch.sign(indicators['macd'][-1]) * torch.abs(indicators['macd'][-1])

            macd_score = torch.clamp(macd_score / 2, -1, 1)  # Normalize and clamp

            

            # Volume trend

            volume_change = (self.volume_history[-1] / self.volume_history[-2] - 1).clamp(-1, 1)

            

            # Weighted combination

            momentum = (

                0.4 * rsi_score +

                0.4 * macd_score +

                0.2 * volume_change

            )

            

            return float(momentum.cpu())



    def calculate_trend(self, indicators: Dict[str, torch.Tensor]) -> float:

        """Calculate trend strength and direction"""

        with torch.cuda.stream(self.core.streams['analysis']):

            # EMA crossover component

            ema_diff = indicators['fast_ema'][-1] - indicators['slow_ema'][-1]

            ema_score = torch.tanh(ema_diff * 3)  # Scale and normalize to [-1, 1]

            

            # Price position relative to Bollinger Bands

            bb_position = (self.price_history[-1] - indicators['bb_lower'][-1]) / (

                indicators['bb_upper'][-1] - indicators['bb_lower'][-1]

            )

            bb_score = (bb_position - 0.5) * 2  # Convert to [-1, 1] range

            

            # Combined trend score

            trend = float((0.7 * ema_score + 0.3 * bb_score).cpu())

            return max(min(trend, 1.0), -1.0)  # Clamp to [-1, 1]



    def calculate_volatility(self, indicators: Dict[str, torch.Tensor]) -> float:

        """Calculate volatility score"""

        with torch.cuda.stream(self.core.streams['analysis']):

            # Bollinger Band width

            bb_width = (indicators['bb_upper'][-1] - indicators['bb_lower'][-1]) / indicators['bb_lower'][-1]

            

            # Price movement volatility

            price_changes = torch.abs(self.price_history[1:] - self.price_history[:-1])

            price_volatility = torch.std(price_changes[-20:]) / torch.mean(self.price_history[-20:])

            

            # Combined volatility score

            volatility = float((0.6 * bb_width + 0.4 * price_volatility).cpu())

            return min(volatility, 1.0)  # Clamp to [0, 1]



    def calculate_enhanced_confidence(

        self,

        momentum: float,

        trend: float,

        volatility: float,

        neural_boost: Dict[str, float]

    ) -> float:

        """Calculate enhanced confidence score with neural boost"""

        # Base confidence from technical indicators

        base_confidence = (

            0.4 * abs(momentum) +

            0.4 * abs(trend) +

            0.2 * min(volatility, 1.0)

        ) * 100

        

        # Neural confidence boost

        neural_confidence = neural_boost.get('confidence', 0.5) * 100

        trend_strength = neural_boost.get('trend_strength', 0.5) * 100

        

        # Enhanced confidence score

        confidence = (

            0.5 * base_confidence +

            0.3 * neural_confidence +

            0.2 * trend_strength

        )

        

        return min(max(confidence, 0.0), 100.0)



    def determine_signal_type(self, momentum: float, trend: float, confidence: float) -> str:

        """Determine the type of trading signal"""

        if confidence < self.core.config['trading']['confidence_threshold']:

            return "NEUTRAL"

            

        if momentum > 0.5 and trend > 0.5:

            return "STRONG_BUY"

        elif momentum < -0.5 and trend < -0.5:

            return "STRONG_SELL"

        elif momentum > 0.2 and trend > 0.2:

            return "BUY"

        elif momentum < -0.2 and trend < -0.2:

            return "SELL"

        

        return "NEUTRAL"



    def get_default_scores(self) -> Dict[str, float]:

        """Get default neural scores"""

        return {

            'trend_strength': 0.5,

            'volume_quality': 0.5,

            'market_sentiment': 0.5,

            'risk_level': 0.5,

            'confidence': 0.5

        }



    def generate_market_prompt(self, pool_data: dict) -> str:

        """Generate prompt for neural analysis"""

        return f"""

        Analyze market conditions for pool {pool_data['address']}:

        - Price: {pool_data['price']}

        - Volume: {pool_data['volume']}

        - Liquidity: {pool_data['liquidity']}

        Provide numerical scores (0-1) for:

        1. Trend strength

        2. Volume quality

        3. Market sentiment

        4. Risk level

        5. Overall confidence

        """



    def parse_neural_response(self, response: Any) -> Dict[str, float]:

        """Parse neural analysis response"""

        try:

            lines = response.choices[0].text.strip().split('\n')

            return {

                'trend_strength': float(lines[0]),

                'volume_quality': float(lines[1]),

                'market_sentiment': float(lines[2]),

                'risk_level': float(lines[3]),

                'confidence': float(lines[4])

            }

        except Exception as e:

            self.logger.error(f"Neural response parsing error: {e}")

            return self.get_default_scores()



    # Private helper methods

    def _exponential_average(

        self, 

        data: torch.Tensor, 

        period: int, 

        alpha: Optional[float] = None

    ) -> torch.Tensor:

        """Calculate exponential moving average"""

        if alpha is None:

            alpha = 2 / (period + 1)

            

        weights = (1 - alpha) ** torch.arange(len(data) - 1, -1, -1)

        weights /= weights.sum()

        

        return torch.conv1d(

            data.view(1, 1, -1),

            weights.view(1, 1, -1),

            padding=len(weights)-1

        )[0, 0]



    def _simple_moving_average(self, data: torch.Tensor, period: int) -> torch.Tensor:

        """Calculate simple moving average"""

        return torch.conv1d(

            data.view(1, 1, -1),

            torch.ones(1, 1, period) / period,

            padding=period-1

        )[0, 0]



    def _rolling_std(self, data: torch.Tensor, period: int) -> torch.Tensor:

        """Calculate rolling standard deviation"""

        square_sum = torch.conv1d(

            data.pow(2).view(1, 1, -1),

            torch.ones(1, 1, period),

            padding=period-1

        )[0, 0]

        

        sum_square = torch.conv1d(

            data.view(1, 1, -1),

            torch.ones(1, 1, period),

            padding=period-1

        )[0, 0].pow(2)

        

        return torch.sqrt((square_sum - sum_square / period) / (period - 1)) please write without the spacing issues please