organize my script in the proper order please and clean up imports if you dont mind include all of the code just remove redudancies and put it in the proper order please import torch
import groq
import numpy as np
from PyQt6.QtWidgets import *
from PyQt6.QtCore import *
from PyQt6.QtCharts import *
from ta import *
import pandas as pd
from pathlib import Path
from transformers import pipeline
from sentence_transformers import SentenceTransformer
import spacy
from solana.rpc.async_api import AsyncClient
import ray

class QuantumTradingPlatform(QMainWindow):
    def __init__(self):
        super().__init__()
        self.setup_core_systems()
        self.init_quantum_interface()
        
    def setup_core_systems(self):
        # Initialize GPU acceleration
        self.device = torch.device("cuda:0")  # RTX 4070 Ti
        print(f"🚀 GPU Accelerator: {torch.cuda.get_device_name(0)}")
        
        # Initialize AI systems
        self.groq_client = groq.Client(api_key=os.environ['GROQ_API_KEY'])
        self.nlp = spacy.load("en_core_web_sm")
        self.sentiment_analyzer = pipeline("sentiment-analysis", device=self.device)
        
        # Initialize trading systems
        self.setup_trading_engine()
        self.load_trading_history()
        
    def setup_trading_engine(self):
        # Initialize Solana client
        self.solana = AsyncClient("https://marga-e56m84-fast-mainnet.helius-rpc.com")
        
        # Initialize Ray for parallel processing
        ray.init(ignore_reinit_error=True)
        
        # Setup technical indicators
        self.indicators = {
            'rsi': lambda df: ta.momentum.RSIIndicator(df['close']).rsi(),
            'macd': lambda df: ta.trend.MACD(df['close']).macd(),
            'bb': lambda df: ta.volatility.BollingerBands(df['close'])
        }
import torch
import groq
import numpy as np
from PyQt6.QtWidgets import *
from PyQt6.QtCore import *
from PyQt6.QtCharts import *
from ta import *
import pandas as pd
from pathlib import Path
from transformers import pipeline
from sentence_transformers import SentenceTransformer
import spacy
from solana.rpc.async_api import AsyncClient
import ray

@ray.remote
class QuantumCore:
    def __init__(self):
        self.device = torch.device("cuda:0")
        self.groq = groq.Client(api_key=os.environ['GROQ_API_KEY'])
        self.setup_neural_engine()
        
    def setup_neural_engine(self):
        self.model = torch.jit.load("models/quantum_trader.pt")
        self.model.to(self.device)

class EliteQuantumPlatform(QMainWindow):
    def __init__(self):
        super().__init__()
        self.quantum_core = QuantumCore.remote()
        self.setup_elite_interface()
        
    def setup_elite_interface(self):
        # Holographic trading interface
        self.setStyleSheet("""
            QMainWindow {
                background: qlineargradient(x1:0, y1:0, x2:1, y2:1,
                    stop:0 #050510,
                    stop:1 #0A0A1F);
            }
            .quantum-panel {
                background: rgba(5, 14, 30, 0.85);
                border: 1px solid rgba(0, 247, 255, 0.2);
                border-radius: 4px;
            }
        """)
import torch
import groq
import numpy as np
from PyQt6.QtWidgets import *
from PyQt6.QtCore import *
from PyQt6.QtCharts import *
from ta import *
import pandas as pd
from pathlib import Path
import ray

class EliteQuantumPlatform(QMainWindow):
    def __init__(self):
        super().__init__()
        self.setup_quantum_core()
        self.init_elite_interface()
        
    def setup_quantum_core(self):
        self.device = torch.device("cuda:0")
        print(f"🚀 GPU: {torch.cuda.get_device_name(0)}")
        self.groq = groq.Client(api_key=os.environ['GROQ_API_KEY'])
        self.load_trading_brain()
        
    def init_elite_interface(self):
        self.setWindowTitle("∞ QUANTUM CORE ∞")
        self.setup_main_layout()
        self.create_control_panels()
        self.init_data_visualization()
        
    def setup_main_layout(self):
        central = QWidget()
        self.setCentralWidget(central)
        layout = QVBoxLayout(central)
        
        # Holographic chart
        self.chart = QuantumChart(self)
        self.chart.setMinimumHeight(400)
        layout.addWidget(self.chart)
        
        # Neural controls
        controls = QHBoxLayout()
        controls.addWidget(self.create_strategy_panel())
        controls.addWidget(self.create_execution_panel())
        controls.addWidget(self.create_analysis_panel())
        layout.addLayout(controls)
        
    def create_strategy_panel(self):
        panel = QuantumPanel("STRATEGY MATRIX")
        layout = QVBoxLayout(panel)
        
        # Strategy selector with neural optimization
        self.strategy_selector = QuantumComboBox()
        self.strategy_selector.addItems([
            "QUANTUM MOMENTUM",
            "NEURAL GRID",
            "ATOMIC SWAP",
            "CUSTOM NEURAL NET"
        ])
        
        # Parameter controls
        self.params = {
            'position_size': QuantumSlider("Position Size", 0.1, 10.0),
            'risk_level': QuantumSlider("Risk %", 0.1, 5.0),
            'execution_speed': QuantumSlider("Speed", 0.1, 1.0)
        }
        
        layout.addWidget(self.strategy_selector)
        for param in self.params.values():
            layout.addWidget(param)
            
        return panel
        
    def create_execution_panel(self):
        panel = QuantumPanel("EXECUTION MATRIX")
        layout = QVBoxLayout(panel)
        
        # Neural execution buttons
        execute = QuantumButton("EXECUTE", 
            glow_color="#00F7FF",
            pulse_effect="quantum_wave"
        )
        execute.clicked.connect(self.execute_strategy)
        
        # Performance metrics
        self.metrics = QuantumMetrics()
        
        layout.addWidget(execute)
        layout.addWidget(self.metrics)
        return panel
        
    def create_analysis_panel(self):
        panel = QuantumPanel("QUANTUM ANALYSIS")
        layout = QVBoxLayout(panel)
        
        # AI insights display
        self.ai_display = QuantumDisplay()
        
        # Technical indicators
        self.indicators = QuantumIndicators()
        
        layout.addWidget(self.ai_display)
        layout.addWidget(self.indicators)
        return panel
        
    async def execute_strategy(self):
        # Get AI analysis
        market_data = await self.get_market_data()
        analysis = await self.get_quantum_analysis(market_data)
        
        # Neural execution
        params = {name: slider.value() for name, slider in self.params.items()}
        result = await self.execute_neural_trade(params)
        
        # Update displays
        self.update_interface(result)
        
    async def get_quantum_analysis(self, data):
        response = await self.groq.chat.create(
            model="mixtral-8x7b-32768",
            messages=[{
                "role": "user",
                "content": f"Analyze market conditions: {data}"
            }]
        )
        return response.choices[0].message.content
        
    def update_interface(self, result):
        self.chart.update_data(result)
        self.metrics.update(result)
        self.ai_display.update(result)
        self.indicators.update()

class QuantumPanel(QWidget):
    def __init__(self, title):
        super().__init__()
        self.setStyleSheet("""
            QWidget {
                background: rgba(5, 14, 30, 0.85);
                border: 1px solid rgba(0, 247, 255, 0.2);
                border-radius: 4px;
                color: #00F7FF;
                font-family: 'BlenderPro';
            }
        """)

class QuantumButton(QPushButton):
    def __init__(self, text, glow_color, pulse_effect):
        super().__init__(text)
        self.setStyleSheet(f"""
            QPushButton {{
                background: rgba(0, 247, 255, 0.1);
                border: 1px solid {glow_color};
                border-radius: 4px;
                color: {glow_color};
                padding: 10px;
                font-weight: bold;
            }}
            QPushButton:hover {{
                background: rgba(0, 247, 255, 0.2);
            }}
        """)

if __name__ == "__main__":
    app = QApplication([])
    window = EliteQuantumPlatform()
    window.show()
    app.exec()
class QuantumChart(QWidget):
    def __init__(self, parent):
        super().__init__(parent)
        self.setup_chart()
        self.setup_neural_indicators()
        
    def setup_chart(self):
        self.chart = QChart()
        self.chart_view = QChartView(self.chart)
        self.chart_view.setRenderHint(QPainter.RenderHint.Antialiasing)
        
        # Holographic price series
        self.price_series = QLineSeries()
        self.price_series.setPen(QPen(QColor("#00F7FF"), 2))
        self.chart.addSeries(self.price_series)
        
        # Neural prediction overlay
        self.prediction_series = QLineSeries()
        self.prediction_series.setPen(QPen(QColor("#7B42FF"), 2))
        
    def setup_neural_indicators(self):
        self.indicators = {
            'momentum': self.create_momentum_indicator(),
            'volume_profile': self.create_volume_profile(),
            'market_depth': self.create_depth_map()
        }
        
class QuantumNeuralEngine:
    def __init__(self, device):
        self.device = device
        self.setup_models()
        
    def setup_models(self):
        # Price prediction model
        self.price_predictor = torch.nn.Sequential(
            torch.nn.Linear(128, 64),
            torch.nn.ReLU(),
            torch.nn.Linear(64, 32),
            torch.nn.ReLU(),
            torch.nn.Linear(32, 1)
        ).to(self.device)
        
        # Market analysis model
        self.market_analyzer = torch.nn.TransformerEncoder(
            torch.nn.TransformerEncoderLayer(
                d_model=128, nhead=8
            ), num_layers=6
        ).to(self.device)
class QuantumBacktester:
    def __init__(self, device):
        self.device = device
        self.history = pd.DataFrame()
        self.setup_performance_metrics()
        
    def run_backtest(self, strategy, params, timeframe):
        results = torch.zeros(len(self.history)).to(self.device)
        
        # GPU-accelerated backtesting
        with torch.cuda.amp.autocast():
            for i, row in enumerate(self.history.itertuples()):
                signal = strategy.get_signal(row)
                results[i] = self.execute_virtual_trade(signal, params)
                
        return self.analyze_results(results)
        
class QuantumPerformanceAnalytics:
    def __init__(self):
        self.metrics = {
            'sharpe': self.calculate_sharpe,
            'sortino': self.calculate_sortino,
            'max_drawdown': self.calculate_drawdown,
            'win_rate': self.calculate_win_rate
        }
        
    def analyze_strategy(self, results):
        return {
            name: metric(results) 
            for name, metric in self.metrics.items()
        }
class QuantumMarketDepth:
    def __init__(self, device):
        self.device = device
        self.setup_visualization()
        
    def setup_visualization(self):
        # Holographic depth map
        self.depth_map = QuantumHeatmap(
            glow_intensity=0.8,
            update_frequency=0.1,
            particle_density=1000
        )
        
        # Order flow indicators
        self.flow_indicators = {
            'buy_pressure': self.calculate_buy_pressure,
            'sell_pressure': self.calculate_sell_pressure,
            'liquidity_zones': self.identify_liquidity_zones
        }

class QuantumOptimizer:
    def __init__(self, device):
        self.device = device
        self.setup_optimization_engine()
        
    def setup_optimization_engine(self):
        # Neural parameter optimizer
        self.optimizer = torch.optim.Adam(
            self.strategy.parameters(),
            lr=0.001
        )
        
        # Real-time performance tracking
        self.performance_tracker = QuantumPerformanceTracker(
            update_frequency=0.1,
            metrics=['sharpe', 'sortino', 'alpha']
        )
        
    async def optimize_strategy(self, strategy, market_data):
        with torch.cuda.amp.autocast():
            # Real-time strategy optimization
            optimized_params = await self.neural_optimization(
                strategy=strategy,
                market_data=market_data,
                iterations=100
            )
            return optimized_params
class QuantumNeuralOptimizer:
    def __init__(self, device):
        self.device = device
        self.setup_neural_engine()
        
    def setup_neural_engine(self):
        # Advanced neural architecture
        self.neural_net = torch.nn.Sequential(
            torch.nn.LSTM(input_size=128, hidden_size=256, num_layers=4),
            torch.nn.Dropout(0.2),
            torch.nn.Linear(256, 64),
            torch.nn.ReLU(),
            torch.nn.Linear(64, 32)
        ).to(self.device)
        
        # Real-time visualization components
        self.visualizer = QuantumVisualizer(
            chart_type="holographic",
            particle_effects=True,
            depth_perception=True
        )
        
    async def optimize_live(self, market_data):
        with torch.cuda.amp.autocast():
            predictions = self.neural_net(market_data)
            self.visualizer.update(predictions)
            return self.generate_signals(predictions)

class QuantumVisualizer:
    def __init__(self, **kwargs):
        self.setup_advanced_charts()
        
    def setup_advanced_charts(self):
        # 3D market visualization
        self.market_view = Quantum3DView(
            rotation_speed=0.5,
            glow_intensity=0.8
        )
        
        # Neural network state visualization
        self.network_view = QuantumNetworkView(
            node_count=1000,
            edge_thickness=2,
            update_freq=0.1
        )
class QuantumSignalGenerator:
    def __init__(self, device):
        self.device = device
        self.setup_signal_engine()
        
    def setup_signal_engine(self):
        # Real-time signal processing
        self.processor = torch.jit.script(
            self.build_signal_processor()
        ).to(self.device)
        
        # Live trade execution signals
        self.signals = {
            'entry': self.generate_entry_signals,
            'exit': self.generate_exit_signals,
            'risk': self.calculate_risk_levels,
            'size': self.optimize_position_size
        }

class Quantum3DMarketView:
    def __init__(self):
        self.setup_3d_engine()
        
    def setup_3d_engine(self):
        # Holographic price surface
        self.price_surface = QuantumSurface(
            resolution=(100, 100),
            color_map='quantum_flow',
            height_scale=2.0
        )
        
        # Volume profile visualization
        self.volume_cloud = QuantumParticleCloud(
            particle_count=10000,
            glow_intensity=0.8,
            flow_speed=1.2
        )
        
        # Market depth topology
        self.depth_topology = QuantumTopology(
            grid_size=(50, 50),
            elevation_scale=1.5,
            update_freq=0.1
        )
class QuantumParticleSystem:
    def __init__(self, device):
        self.device = device
        self.setup_particle_engine()
        
    def setup_particle_engine(self):
        # GPU-accelerated particle physics
        self.particles = torch.zeros(10000, 4).to(self.device)  # x, y, z, velocity
        self.forces = torch.zeros(10000, 3).to(self.device)  # market forces
        
        # Market flow visualization
        self.flow_field = {
            'buy_pressure': self.create_flow_field(color="#00F7FF"),
            'sell_pressure': self.create_flow_field(color="#FF00A7"),
            'volume_flow': self.create_flow_field(color="#7B42FF")
        }

class QuantumTopologyMapper:
    def __init__(self):
        self.setup_topology_engine()
        
    def setup_topology_engine(self):
        # Real-time market topology
        self.surface = QuantumSurface3D(
            resolution=(200, 200),
            height_map="adaptive",
            color_gradient=[
                "#050510",  # Deep space
                "#0A1A2F",  # Dark matter
                "#00F7FF"   # Quantum glow
            ]
        )
        
        # Support/Resistance mapping
        self.level_detector = QuantumLevelDetector(
            sensitivity=0.8,
            confidence_threshold=0.75,
            update_frequency=0.1
        )
        
    async def update_topology(self, market_data):
        levels = await self.level_detector.scan(market_data)
        self.surface.update_height_map(levels)
        return self.generate_trading_zones(levels)
class QuantumTradingZones:
    def __init__(self, device):
        self.device = device
        self.setup_zone_engine()
        
    def setup_zone_engine(self):
        # Neural zone detection
        self.zone_detector = torch.jit.script(self.build_zone_detector()).to(self.device)
        
        # Live visualization
        self.zone_display = {
            'accumulation': QuantumZone(color="#00F7FF", opacity=0.4),
            'distribution': QuantumZone(color="#FF00A7", opacity=0.4),
            'equilibrium': QuantumZone(color="#7B42FF", opacity=0.2)
        }
        
    @torch.cuda.amp.autocast()
    def detect_zones(self, market_data):
        zones = self.zone_detector(market_data)
        self.update_visualization(zones)
        return self.generate_trade_signals(zones)

class QuantumLevelDetector:
    def __init__(self):
        self.setup_detection_matrix()
        
    def setup_detection_matrix(self):
        # Real-time support/resistance
        self.matrix = QuantumMatrix(
            dimensions=(100, 100),
            sensitivity=0.85,
            confidence_threshold=0.75
        )
        
        # Price action analysis
        self.analyzers = {
            'volume_profile': self.analyze_volume_clusters,
            'time_clusters': self.analyze_time_distribution,
            'price_rejection': self.analyze_rejection_points
        }
import torch
import groq
import numpy as np
import pandas as pd
from PyQt6.QtWidgets import *
from PyQt6.QtCore import *
from PyQt6.QtCharts import *
from pathlib import Path
import asyncio

class QuantumMasterSystem(QMainWindow):
    def __init__(self):
        super().__init__()
        self.device = torch.device("cuda:0")
        self.groq = groq.Client(api_key=os.environ['GROQ_API_KEY'])
        self.setup_complete_system()
        
    def setup_complete_system(self):
        # Neural Core
        self.neural_core = QuantumNeuralCore(self.device)
        self.market_analyzer = QuantumMarketAnalyzer(self.device)
        self.signal_generator = QuantumSignalGenerator(self.device)
        
        # Trading Systems
        self.strategy_engine = QuantumStrategyEngine(
            neural_core=self.neural_core,
            market_analyzer=self.market_analyzer
        )
        
        # Data Management
        self.data_manager = QuantumDataManager(
            save_path=Path("data/trading_history.csv"),
            backup_frequency=300  # 5 minutes
        )
        
        # Performance Tracking
        self.performance_tracker = QuantumPerformanceTracker(
            metrics=['sharpe', 'sortino', 'alpha', 'beta'],
            update_frequency=0.1
        )
        
        # Initialize UI
        self.setup_quantum_interface()
        
    def setup_quantum_interface(self):
        self.setWindowTitle("∞ QUANTUM MASTER ∞")
        self.setup_main_layout()
        self.connect_signals()
        
    def setup_main_layout(self):
        central = QWidget()
        self.setCentralWidget(central)
        layout = QVBoxLayout(central)
        
        # Advanced Chart System
        self.chart_system = QuantumChartSystem(
            parent=self,
            indicators=self.market_analyzer.indicators,
            zones=self.strategy_engine.trading_zones
        )
        layout.addWidget(self.chart_system)
        
        # Control Matrix
        controls = QHBoxLayout()
        controls.addWidget(self.create_strategy_matrix())
        controls.addWidget(self.create_execution_matrix())
        controls.addWidget(self.create_analysis_matrix())
        layout.addLayout(controls)
        
    async def execute_strategy(self):
        # Get market analysis
        market_data = await self.market_analyzer.get_current_state()
        analysis = await self.get_quantum_analysis(market_data)
        
        # Generate and execute signals
        signals = self.signal_generator.generate_signals(analysis)
        result = await self.strategy_engine.execute_signals(signals)
        
        # Update everything
        self.update_system_state(result)
        
    def update_system_state(self, result):
        # Update visuals
        self.chart_system.update(result)
        self.performance_tracker.update(result)
        
        # Save data
        self.data_manager.save_trade_data(result)
        
        # Update AI models
        self.neural_core.update_models(result)

class QuantumChartSystem(QWidget):
    def __init__(self, parent, indicators, zones):
        super().__init__(parent)
        self.setup_chart_system()
        
    def setup_chart_system(self):
        # Main price chart
        self.price_chart = QuantumPriceChart(
            chart_type="holographic",
            update_freq=0.1
        )
        
        # Indicator overlays
        self.indicator_matrix = QuantumIndicatorMatrix(
            grid_size=(3, 3),
            update_freq=0.1
        )
        
        # Trading zones
        self.zone_visualizer = QuantumZoneVisualizer(
            opacity=0.4,
            glow_intensity=0.8
        )

class QuantumStrategyEngine:
    def __init__(self, neural_core, market_analyzer):
        self.neural_core = neural_core
        self.market_analyzer = market_analyzer
        self.setup_strategies()
        
    def setup_strategies(self):
        self.strategies = {
            'momentum': QuantumMomentumStrategy(),
            'mean_reversion': QuantumMeanReversionStrategy(),
            'breakout': QuantumBreakoutStrategy(),
            'custom': QuantumCustomStrategy()
        }
        
    async def execute_signals(self, signals):
        strategy = self.strategies[self.current_strategy]
        return await strategy.execute(signals)

if __name__ == "__main__":
    app = QApplication([])
    
    # Enable GPU acceleration
    app.setAttribute(Qt.ApplicationAttribute.AA_UseHighDpiPixmaps)
    app.setAttribute(Qt.ApplicationAttribute.AA_EnableHighDpiScaling)
    
    # Launch quantum system
    window = QuantumMasterSystem()
    window.show()
    
    # Start event loop
    asyncio.get_event_loop().run_forever()
    app.exec()
# Previous code remains the same, now adding ALL the implementation details:

class QuantumNeuralCore:
    def __init__(self, device):
        self.device = device
        self.setup_neural_models()
        
    def setup_neural_models(self):
        self.models = {
            'price_predictor': self.build_price_predictor(),
            'signal_generator': self.build_signal_generator(),
            'risk_analyzer': self.build_risk_analyzer()
        }
        
    def build_price_predictor(self):
        return torch.nn.Sequential(
            torch.nn.LSTM(128, 256, num_layers=4),
            torch.nn.Linear(256, 64),
            torch.nn.ReLU(),
            torch.nn.Linear(64, 1)
        ).to(self.device)

class QuantumMarketAnalyzer:
    def __init__(self, device):
        self.device = device
        self.setup_analyzers()
        
    def setup_analyzers(self):
        self.indicators = {
            'momentum': self.calculate_momentum,
            'volume_profile': self.analyze_volume,
            'price_action': self.analyze_price_action
        }
# Previous classes remain, adding ALL core implementations:

class QuantumSignalGenerator:
    def __init__(self, device):
        self.device = device
        self.setup_signal_processor()
        
    def setup_signal_processor(self):
        self.processor = torch.jit.script(self.build_processor()).to(self.device)
        
    def generate_signals(self, market_data):
        with torch.cuda.amp.autocast():
            return self.processor(market_data)

class QuantumDataManager:
    def __init__(self, save_path, backup_frequency):
        self.save_path = save_path
        self.backup_frequency = backup_frequency
        self.setup_storage()
        
    def setup_storage(self):
        self.history = pd.DataFrame(columns=[
            'timestamp', 'action', 'price', 'size', 
            'execution_time', 'profit_loss'
        ])
        
    def save_trade_data(self, trade_data):
        self.history = pd.concat([self.history, pd.DataFrame([trade_data])])
        self.history.to_csv(self.save_path, index=False)

class QuantumPerformanceTracker:
    def __init__(self, metrics, update_frequency):
        self.metrics = metrics
        self.update_frequency = update_frequency
        self.setup_tracking()
        
    def calculate_metrics(self, data):
        return {
            'sharpe': self.calculate_sharpe(data),
            'sortino': self.calculate_sortino(data),
            'alpha': self.calculate_alpha(data),
            'beta': self.calculate_beta(data)
        }

class QuantumStrategyExecutor:
    def __init__(self, neural_core):
        self.neural_core = neural_core
        self.setup_execution_engine()
        
    async def execute_trade(self, signals):
        position_size = self.calculate_position_size(signals)
        entry_price = await self.get_best_entry(signals)
        return await self.submit_trade(position_size, entry_price)

class QuantumMomentumStrategy:
    def __init__(self):
        self.setup_momentum_indicators()
        
    async def execute(self, signals):
        momentum = self.calculate_momentum(signals)
        if momentum > self.threshold:
            return await self.execute_long()
        elif momentum < -self.threshold:
            return await self.execute_short()

class QuantumBreakoutStrategy:
    def __init__(self):
        self.setup_breakout_detector()
        
    async def execute(self, signals):
        breakout = self.detect_breakout(signals)
        if breakout['direction'] == 'up':
            return await self.execute_long(breakout['level'])
        elif breakout['direction'] == 'down':
            return await self.execute_short(breakout['level'])

# Main execution
if __name__ == "__main__":
    app = QApplication([])
    window = QuantumMasterSystem()
    window.show()
    asyncio.get_event_loop().run_forever()
    app.exec()
# Test a quantum momentum strategy
strategy = quantum_system.strategies['momentum']
results = await strategy.execute_backtest(
    timeframe='1h',
    leverage=3,
    position_size=1.0
)

# View the performance metrics
print(quantum_system.performance_tracker.calculate_metrics(results))