gogo2/run_scalping_dashboard.py

#!/usr/bin/env python3
"""
Run Ultra-Fast Scalping Dashboard (500x Leverage)

This script starts the custom scalping dashboard with:
- Full-width 1s ETH/USDT candlestick chart
- 3 small ETH charts: 1m, 1h, 1d
- 1 small BTC 1s chart
- Ultra-fast 100ms updates for scalping
- Real-time PnL tracking and logging
"""

import logging
import asyncio
from threading import Thread
import time
import random
from datetime import datetime, timedelta
from dataclasses import dataclass
from typing import Dict, List, Optional

from core.config import get_config, setup_logging
from core.data_provider import DataProvider
from core.enhanced_orchestrator import EnhancedTradingOrchestrator, TradingAction, TimeframePrediction
from web.scalping_dashboard import ScalpingDashboard

# Setup logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

@dataclass
class Trade:
    """Individual trade tracking for PnL calculation"""
    trade_id: int
    symbol: str
    action: str  # 'BUY', 'SELL'
    entry_price: float
    quantity: float
    entry_time: datetime
    confidence: float
    exit_price: Optional[float] = None
    exit_time: Optional[datetime] = None
    pnl: Optional[float] = None
    fees: Optional[float] = None
    leverage: int = 500
    is_closed: bool = False

class UltraFastScalpingRunner:
    """Ultra-fast scalping dashboard runner with 500x leverage simulation and PnL tracking"""
    
    def __init__(self):
        """Initialize the ultra-fast scalping system with PnL tracking"""
        self.config = get_config()
        self.data_provider = DataProvider(self.config)
        self.orchestrator = EnhancedTradingOrchestrator(self.data_provider)
        
        # Create the specialized scalping dashboard
        self.dashboard = ScalpingDashboard(
            data_provider=self.data_provider,
            orchestrator=self.orchestrator
        )
        
        # Ultra-fast simulation state
        self.running = False
        self.simulation_thread = None
        self.exit_monitor_thread = None
        self.trade_count = 0
        
        # PnL Tracking System
        self.open_positions: Dict[int, Trade] = {}  # Track open positions by trade_id
        self.closed_trades: List[Trade] = []  # History of closed trades
        self.total_pnl = 0.0
        self.total_fees = 0.0
        self.win_count = 0
        self.loss_count = 0
        self.leverage = 500  # 500x leverage
        self.trading_fee = 0.0002  # 0.02% per trade
        self.balance = 10000.0  # Starting balance
        
        # Price tracking for PnL calculation
        self.current_prices = {
            'ETH/USDT': 3050.0,
            'BTC/USDT': 67000.0
        }
        
        # Scalping parameters
        self.min_exit_time = 2  # Minimum 2 seconds before exit
        self.max_exit_time = 15  # Maximum 15 seconds before forced exit
        
        logger.info("🚀 Ultra-Fast Scalping Runner with PnL Tracking initialized")
        logger.info("⚡ 500x Leverage Mode Activated")
        logger.info(f"💰 Starting Balance: ${self.balance:.2f}")
        logger.info(f"📊 Leverage: {self.leverage}x")
        logger.info(f"💳 Trading Fee: {self.trading_fee*100:.3f}% per trade")
        logger.info(f"⏱️  Trade Duration: {self.min_exit_time}-{self.max_exit_time} seconds")
        logger.info("📊 Timeframes: 1s (primary), 1m, 1h, 1d")
    
    def start_ultra_fast_simulation(self):
        """Start ultra-fast trading simulation for 500x leverage scalping"""
        self.running = True
        self.simulation_thread = Thread(target=self._ultra_fast_loop, daemon=True)
        self.exit_monitor_thread = Thread(target=self._monitor_exits, daemon=True)
        
        self.simulation_thread.start()
        self.exit_monitor_thread.start()
        
        logger.info("🚀 Ultra-fast scalping simulation started")
        logger.info("⚡ Generating trades every 3-8 seconds")
        logger.info("📊 Monitoring trade exits for PnL calculation")
    
    def _ultra_fast_loop(self):
        """Ultra-fast scalping simulation loop with trade entry"""
        while self.running:
            try:
                # Update current prices with realistic movement
                self._update_prices()
                
                # Ultra-fast scalping - trades every 3-8 seconds
                for symbol in ['ETH/USDT', 'BTC/USDT']:
                    # 40% chance of action (very active scalping)
                    if random.random() > 0.6:
                        self._execute_trade(symbol)
                
                # Ultra-fast interval (3-8 seconds between trades)
                sleep_time = random.uniform(3, 8)
                time.sleep(sleep_time)
                
            except Exception as e:
                logger.error(f"Error in ultra-fast scalping loop: {e}")
                time.sleep(2)
    
    def _execute_trade(self, symbol: str):
        """Execute a new scalping trade"""
        self.trade_count += 1
        
        # Create ultra-fast timeframe predictions
        timeframe_predictions = []
        
        # Focus on 1s predictions (primary for scalping)
        for tf, weight in [('1s', 0.6), ('1m', 0.2), ('1h', 0.15), ('1d', 0.05)]:
            # More aggressive probabilities for scalping
            if tf == '1s':  # Primary scalping signal
                action_probs = [
                    random.uniform(0.05, 0.25),  # SELL
                    random.uniform(0.20, 0.40),  # HOLD
                    random.uniform(0.35, 0.75)   # BUY (bias for bull market)
                ]
            else:
                action_probs = [
                    random.uniform(0.1, 0.4),   # SELL
                    random.uniform(0.3, 0.6),   # HOLD
                    random.uniform(0.1, 0.4)    # BUY
                ]
            
            # Normalize probabilities
            total = sum(action_probs)
            action_probs = [p/total for p in action_probs]
            
            best_action_idx = action_probs.index(max(action_probs))
            actions = ['SELL', 'HOLD', 'BUY']
            best_action = actions[best_action_idx]
            
            tf_pred = TimeframePrediction(
                timeframe=tf,
                action=best_action,
                confidence=random.uniform(0.65, 0.95),  # High confidence for scalping
                probabilities={
                    'SELL': action_probs[0],
                    'HOLD': action_probs[1], 
                    'BUY': action_probs[2]
                },
                timestamp=datetime.now(),
                market_features={
                    'volatility': random.uniform(0.005, 0.02),  # Higher volatility for 1s
                    'volume': random.uniform(2000, 15000),      # High volume for scalping
                    'trend_strength': random.uniform(0.4, 0.9),
                    'leverage': '500x',
                    'scalping_signal': tf == '1s'
                }
            )
            timeframe_predictions.append(tf_pred)
        
        # Create scalping action (focus on non-HOLD actions)
        primary_action = timeframe_predictions[0].action  # Use 1s timeframe
        if primary_action == 'HOLD':
            primary_action = random.choice(['BUY', 'SELL'])  # Force action for demo
        
        # Get current price and calculate trade details
        entry_price = self.current_prices[symbol]
        quantity = random.uniform(0.01, 0.05)  # Small quantities for scalping
        confidence = random.uniform(0.70, 0.95)
        
        # Create trade record
        trade = Trade(
            trade_id=self.trade_count,
            symbol=symbol,
            action=primary_action,
            entry_price=entry_price,
            quantity=quantity,
            entry_time=datetime.now(),
            confidence=confidence,
            leverage=self.leverage
        )
        
        # Store open position
        self.open_positions[self.trade_count] = trade
        
        # Calculate position value and fees
        position_value = quantity * entry_price
        leveraged_value = position_value * self.leverage
        entry_fee = position_value * self.trading_fee
        
        # Create ultra-fast trading action for dashboard
        scalping_action = TradingAction(
            symbol=symbol,
            action=primary_action,
            quantity=quantity,
            confidence=confidence,
            price=entry_price,
            timestamp=datetime.now(),
            reasoning={
                'model': 'Ultra-Fast Scalping AI',
                'leverage': f'{self.leverage}x',
                'timeframe_primary': '1s',
                'scalping_mode': True,
                'trade_id': self.trade_count,
                'expected_duration': f"{random.uniform(2, 8):.1f}s",
                'market_regime': random.choice(['trending_up', 'momentum', 'breakout']),
                'position_value': f"${leveraged_value:.2f}",
                'entry_fee': f"${entry_fee:.2f}"
            },
            timeframe_analysis=timeframe_predictions
        )
        
        # Add to dashboard
        self.dashboard.add_trading_decision(scalping_action)
        
        # Log trade entry with detailed information
        logger.info(f"🔥 TRADE #{self.trade_count} OPENED:")
        logger.info(f"   📊 {primary_action} {symbol} @ ${entry_price:.2f}")
        logger.info(f"   📈 Quantity: {quantity:.4f} | Confidence: {confidence:.1%}")
        logger.info(f"   💰 Position Value: ${leveraged_value:.2f} ({self.leverage}x leverage)")
        logger.info(f"   💳 Entry Fee: ${entry_fee:.4f}")
        logger.info(f"   ⏱️  Expected Exit: {self.min_exit_time}-{self.max_exit_time}s")
    
    def _monitor_exits(self):
        """Monitor open positions and execute exits for PnL calculation"""
        while self.running:
            try:
                current_time = datetime.now()
                positions_to_close = []
                
                for trade_id, trade in self.open_positions.items():
                    time_elapsed = (current_time - trade.entry_time).total_seconds()
                    
                    # Check if trade should be closed
                    should_close = False
                    
                    # Force close after max time
                    if time_elapsed >= self.max_exit_time:
                        should_close = True
                    
                    # Probabilistic close after min time (scalping style)
                    elif time_elapsed >= self.min_exit_time:
                        close_probability = (time_elapsed - self.min_exit_time) / (self.max_exit_time - self.min_exit_time)
                        if random.random() < close_probability * 0.3:  # 30% max probability per check
                            should_close = True
                    
                    if should_close:
                        positions_to_close.append(trade_id)
                
                # Close positions and calculate PnL
                for trade_id in positions_to_close:
                    self._close_position(trade_id)
                
                time.sleep(0.5)  # Check every 500ms for ultra-fast scalping
                
            except Exception as e:
                logger.error(f"Error in exit monitoring: {e}")
                time.sleep(1)
    
    def _close_position(self, trade_id: int):
        """Close a position and calculate PnL"""
        if trade_id not in self.open_positions:
            return
        
        trade = self.open_positions[trade_id]
        
        # Get current exit price
        exit_price = self.current_prices[trade.symbol]
        trade.exit_price = exit_price
        trade.exit_time = datetime.now()
        
        # Calculate PnL based on trade direction
        if trade.action == 'BUY':
            # Long position: profit when price goes up
            price_change = (exit_price - trade.entry_price) / trade.entry_price
        else:  # SELL
            # Short position: profit when price goes down
            price_change = (trade.entry_price - exit_price) / trade.entry_price
        
        # Calculate leveraged PnL
        position_value = trade.quantity * trade.entry_price
        raw_pnl = position_value * price_change * self.leverage
        
        # Calculate fees (entry + exit)
        entry_fee = position_value * self.trading_fee
        exit_fee = trade.quantity * exit_price * self.trading_fee
        total_fees = entry_fee + exit_fee
        
        # Net PnL after fees
        net_pnl = raw_pnl - total_fees
        
        # Update trade record
        trade.pnl = net_pnl
        trade.fees = total_fees
        trade.is_closed = True
        
        # Update totals
        self.total_pnl += net_pnl
        self.total_fees += total_fees
        
        if net_pnl > 0:
            self.win_count += 1
        else:
            self.loss_count += 1
        
        # Update dashboard metrics
        self.dashboard.scalping_metrics['total_pnl'] = self.total_pnl
        self.dashboard.scalping_metrics['win_rate'] = self.win_count / (self.win_count + self.loss_count) if (self.win_count + self.loss_count) > 0 else 0
        
        # Move to closed trades
        self.closed_trades.append(trade)
        del self.open_positions[trade_id]
        
        # Calculate trade duration
        duration = (trade.exit_time - trade.entry_time).total_seconds()
        
        # Log detailed PnL information
        pnl_color = "🟢" if net_pnl > 0 else "🔴"
        logger.info(f"{pnl_color} TRADE #{trade_id} CLOSED:")
        logger.info(f"   📊 {trade.action} {trade.symbol}: ${trade.entry_price:.2f} → ${exit_price:.2f}")
        logger.info(f"   📈 Price Change: {price_change*100:+.3f}%")
        logger.info(f"   ⏱️  Duration: {duration:.1f}s")
        logger.info(f"   💰 Raw PnL: ${raw_pnl:+.2f} ({self.leverage}x leverage)")
        logger.info(f"   💳 Total Fees: ${total_fees:.4f}")
        logger.info(f"   🎯 Net PnL: ${net_pnl:+.2f}")
        logger.info(f"   📊 Total PnL: ${self.total_pnl:+.2f} | Win Rate: {self.dashboard.scalping_metrics['win_rate']*100:.1f}%")
        logger.info("   " + "="*50)
    
    def _update_prices(self):
        """Update current prices with realistic movement"""
        for symbol in self.current_prices:
            # Small random price movement (typical for 1s intervals)
            current_price = self.current_prices[symbol]
            
            # More volatile movement for realistic scalping
            if symbol == 'ETH/USDT':
                change_percent = random.normalvariate(0, 0.0008)  # ~0.08% standard deviation
            else:  # BTC/USDT
                change_percent = random.normalvariate(0, 0.0006)  # ~0.06% standard deviation
            
            new_price = current_price * (1 + change_percent)
            
            # Keep prices within reasonable bounds
            if symbol == 'ETH/USDT':
                new_price = max(3000, min(3100, new_price))
            else:  # BTC/USDT
                new_price = max(66000, min(68000, new_price))
            
            self.current_prices[symbol] = new_price
    
    def _get_realistic_price(self, symbol: str) -> float:
        """Get realistic price for symbol"""
        return self.current_prices[symbol]
    
    def run_scalping_dashboard(self, host='127.0.0.1', port=8051):
        """Run the ultra-fast scalping dashboard"""
        logger.info("🔥 ULTRA-FAST SCALPING DASHBOARD WITH PnL TRACKING 🔥")
        logger.info(f"🌐 Starting at http://{host}:{port}")
        logger.info("📊 Dashboard Features:")
        logger.info("   • Full-width 1s ETH/USDT candlestick chart")
        logger.info("   • 3 small ETH charts: 1m, 1h, 1d")
        logger.info("   • 1 small BTC 1s chart")
        logger.info("   • 100ms ultra-fast updates")
        logger.info("   • 500x leverage simulation")
        logger.info("   • Real-time PnL tracking and logging")
        logger.info("")
        logger.info("🎯 Optimized for ultra-fast scalping trades")
        logger.info("⚡ Generating trading signals every 3-8 seconds")
        logger.info("💰 Real-time PnL calculation with fees and leverage")
        
        # Start ultra-fast simulation
        self.start_ultra_fast_simulation()
        
        # Run dashboard
        try:
            self.dashboard.run(host=host, port=port, debug=False)
        except KeyboardInterrupt:
            logger.info("🛑 Scalping dashboard stopped by user")
        finally:
            self.running = False
            if self.simulation_thread:
                self.simulation_thread.join(timeout=2)
            if self.exit_monitor_thread:
                self.exit_monitor_thread.join(timeout=2)
            
            # Final session summary
            total_trades = len(self.closed_trades)
            logger.info("💼 FINAL SCALPING SESSION SUMMARY:")
            logger.info("="*60)
            logger.info(f"   📊 Total Trades: {total_trades}")
            logger.info(f"   🎯 Total PnL: ${self.total_pnl:+.2f}")
            logger.info(f"   💳 Total Fees: ${self.total_fees:.2f}")
            logger.info(f"   🟢 Wins: {self.win_count} | 🔴 Losses: {self.loss_count}")
            logger.info(f"   📈 Win Rate: {self.dashboard.scalping_metrics['win_rate']*100:.1f}%")
            logger.info(f"   💰 Starting Balance: ${self.balance:.2f}")
            logger.info(f"   💰 Final Balance: ${self.balance + self.total_pnl:.2f}")
            logger.info(f"   📊 Return: {((self.balance + self.total_pnl) / self.balance - 1) * 100:+.2f}%")
            logger.info("="*60)

def main():
    """Main function"""
    try:
        logger.info("=== ULTRA-FAST SCALPING SYSTEM WITH PnL TRACKING ===")
        logger.info("💰 500x Leverage Mode")
        logger.info("⚡ Optimized for 1s-8s trades")
        logger.info("📊 Real-time PnL calculation and logging")
        
        # Create and run scalping dashboard
        runner = UltraFastScalpingRunner()
        runner.run_scalping_dashboard()
        
    except Exception as e:
        logger.error(f"Fatal error: {e}")
        import traceback
        traceback.print_exc()

if __name__ == "__main__":
    main()