cash works again!
This commit is contained in:
Dobromir Popov
@@ -6,163 +6,113 @@ This script starts the web dashboard with the enhanced trading system
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for real-time monitoring and visualization.
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"""
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import logging
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import asyncio
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from threading import Thread
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import logging
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import sys
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import time
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from datetime import datetime
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from pathlib import Path
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from threading import Thread
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# Add project root to path
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project_root = Path(__file__).parent
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sys.path.insert(0, str(project_root))
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from core.config import get_config, setup_logging
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from core.data_provider import DataProvider
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from core.enhanced_orchestrator import EnhancedTradingOrchestrator
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from web.dashboard import TradingDashboard
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from web.scalping_dashboard import run_scalping_dashboard
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# Setup logging
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logging.basicConfig(level=logging.INFO)
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setup_logging()
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logger = logging.getLogger(__name__)
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class EnhancedDashboardRunner:
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"""Enhanced dashboard runner with mock trading simulation"""
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def __init__(self):
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"""Initialize the enhanced dashboard"""
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self.config = get_config()
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self.data_provider = DataProvider(self.config)
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self.orchestrator = EnhancedTradingOrchestrator(self.data_provider)
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# Create dashboard with enhanced orchestrator
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self.dashboard = TradingDashboard(
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data_provider=self.data_provider,
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orchestrator=self.orchestrator
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)
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# Simulation state
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self.running = False
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self.simulation_thread = None
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logger.info("Enhanced dashboard runner initialized")
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def start_simulation(self):
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"""Start background simulation for demonstration"""
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self.running = True
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self.simulation_thread = Thread(target=self._simulation_loop, daemon=True)
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self.simulation_thread.start()
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logger.info("Started enhanced trading simulation")
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def _simulation_loop(self):
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"""Background simulation loop"""
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import random
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from datetime import datetime
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from core.enhanced_orchestrator import TradingAction, TimeframePrediction
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action_count = 0
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while self.running:
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try:
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# Simulate trading decisions for demonstration
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for symbol in self.config.symbols:
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# Create mock timeframe predictions
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timeframe_predictions = []
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for timeframe in ['1h', '4h', '1d']:
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# Random but realistic predictions
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action_probs = [
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random.uniform(0.1, 0.4), # SELL
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random.uniform(0.3, 0.6), # HOLD
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random.uniform(0.1, 0.4) # BUY
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]
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# Normalize probabilities
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total = sum(action_probs)
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action_probs = [p/total for p in action_probs]
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best_action_idx = action_probs.index(max(action_probs))
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actions = ['SELL', 'HOLD', 'BUY']
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best_action = actions[best_action_idx]
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tf_pred = TimeframePrediction(
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timeframe=timeframe,
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action=best_action,
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confidence=random.uniform(0.5, 0.9),
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probabilities={
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'SELL': action_probs[0],
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'HOLD': action_probs[1],
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'BUY': action_probs[2]
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},
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timestamp=datetime.now(),
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market_features={
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'volatility': random.uniform(0.01, 0.05),
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'volume': random.uniform(1000, 10000),
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'trend_strength': random.uniform(0.3, 0.8)
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}
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)
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timeframe_predictions.append(tf_pred)
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# Create mock trading action
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if random.random() > 0.7: # 30% chance of action
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action_count += 1
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mock_action = TradingAction(
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symbol=symbol,
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action=random.choice(['BUY', 'SELL']),
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quantity=random.uniform(0.01, 0.1),
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confidence=random.uniform(0.6, 0.9),
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price=random.uniform(2000, 4000) if 'ETH' in symbol else random.uniform(40000, 70000),
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timestamp=datetime.now(),
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reasoning={
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'model': 'Enhanced Multi-Modal',
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'timeframe_consensus': 'Strong',
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'market_regime': random.choice(['trending', 'ranging', 'volatile']),
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'action_count': action_count
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},
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timeframe_analysis=timeframe_predictions
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)
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# Add to dashboard
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self.dashboard.add_trading_decision(mock_action)
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logger.info(f"Simulated {mock_action.action} for {symbol} "
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f"(confidence: {mock_action.confidence:.2f})")
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# Sleep for next iteration
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time.sleep(10) # Update every 10 seconds
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except Exception as e:
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logger.error(f"Error in simulation loop: {e}")
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time.sleep(5)
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def run_dashboard(self, host='127.0.0.1', port=8050):
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"""Run the enhanced dashboard"""
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logger.info(f"Starting enhanced trading dashboard at http://{host}:{port}")
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logger.info("Features:")
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logger.info("- Multi-modal CNN + RL predictions")
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logger.info("- Multi-timeframe analysis")
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logger.info("- Real-time market regime detection")
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logger.info("- Perfect move tracking for CNN training")
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logger.info("- RL feedback loop evaluation")
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# Start simulation
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self.start_simulation()
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# Run dashboard
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try:
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self.dashboard.run(host=host, port=port, debug=False)
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except KeyboardInterrupt:
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logger.info("Dashboard stopped by user")
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finally:
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self.running = False
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if self.simulation_thread:
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self.simulation_thread.join(timeout=2)
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def main():
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"""Main function"""
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def validate_real_data_connection(data_provider: DataProvider) -> bool:
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"""
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CRITICAL: Validate that we have a real data connection
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Returns False if any synthetic data is detected or connection fails
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"""
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try:
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logger.info("=== ENHANCED TRADING DASHBOARD ===")
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logger.info("🔍 VALIDATING REAL MARKET DATA CONNECTION...")
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# Create and run dashboard
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runner = EnhancedDashboardRunner()
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runner.run_dashboard()
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# Test multiple symbols and timeframes
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test_symbols = ['ETH/USDT', 'BTC/USDT']
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test_timeframes = ['1m', '5m']
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for symbol in test_symbols:
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for timeframe in test_timeframes:
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# Force fresh data fetch (no cache)
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data = data_provider.get_historical_data(symbol, timeframe, limit=50, refresh=True)
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if data is None or data.empty:
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logger.error(f"❌ CRITICAL: No real data for {symbol} {timeframe}")
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return False
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# Validate data authenticity
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if len(data) < 10:
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logger.error(f"❌ CRITICAL: Insufficient real data for {symbol} {timeframe}")
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return False
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# Check for realistic price ranges (basic sanity check)
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prices = data['close'].values
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if 'ETH' in symbol and (prices.min() < 100 or prices.max() > 10000):
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logger.error(f"❌ CRITICAL: Unrealistic ETH prices detected - possible synthetic data")
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return False
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elif 'BTC' in symbol and (prices.min() < 10000 or prices.max() > 200000):
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logger.error(f"❌ CRITICAL: Unrealistic BTC prices detected - possible synthetic data")
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return False
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logger.info(f"✅ Real data validated: {symbol} {timeframe} - {len(data)} candles")
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logger.info("✅ ALL REAL MARKET DATA CONNECTIONS VALIDATED")
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return True
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except Exception as e:
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logger.error(f"Fatal error: {e}")
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import traceback
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traceback.print_exc()
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logger.error(f"❌ CRITICAL: Data validation failed: {e}")
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return False
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def main():
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"""Enhanced dashboard with REAL MARKET DATA ONLY"""
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logger.info("🚀 STARTING ENHANCED DASHBOARD - 100% REAL MARKET DATA")
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try:
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# Initialize data provider
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data_provider = DataProvider()
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# CRITICAL: Validate real data connection
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if not validate_real_data_connection(data_provider):
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logger.error("❌ CRITICAL: Real data validation FAILED")
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logger.error("❌ Dashboard will NOT start without verified real market data")
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logger.error("❌ NO SYNTHETIC DATA FALLBACK ALLOWED")
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return 1
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# Initialize orchestrator with validated real data
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orchestrator = EnhancedTradingOrchestrator(data_provider)
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# Final check: Ensure orchestrator has real data
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logger.info("🔍 Final validation: Testing orchestrator with real data...")
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try:
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# Test orchestrator analysis with real data
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analysis = orchestrator.analyze_market_conditions('ETH/USDT')
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if analysis is None:
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logger.error("❌ CRITICAL: Orchestrator analysis failed - no real data")
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return 1
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logger.info("✅ Orchestrator validated with real market data")
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except Exception as e:
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logger.error(f"❌ CRITICAL: Orchestrator validation failed: {e}")
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return 1
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logger.info("🎯 LAUNCHING DASHBOARD WITH 100% REAL MARKET DATA")
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logger.info("🚫 ZERO SYNTHETIC DATA - REAL TRADING DECISIONS ONLY")
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# Start the dashboard with real data only
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run_scalping_dashboard(data_provider, orchestrator)
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except Exception as e:
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logger.error(f"❌ CRITICAL ERROR: {e}")
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logger.error("❌ Dashboard stopped - NO SYNTHETIC DATA FALLBACK")
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return 1
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if __name__ == "__main__":
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main()
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exit_code = main()
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sys.exit(exit_code if exit_code else 0)
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