scalping dash also works initially
This commit is contained in:
Dobromir Popov
@ -11,19 +11,16 @@ This script starts the custom scalping dashboard with:
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- Enhanced orchestrator with real AI model decisions
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"""
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import asyncio
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import argparse
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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.config import 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.scalping_dashboard import create_scalping_dashboard
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@ -32,184 +29,45 @@ from web.scalping_dashboard import create_scalping_dashboard
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setup_logging()
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logger = logging.getLogger(__name__)
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def validate_real_market_connection(data_provider: DataProvider) -> bool:
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"""
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CRITICAL: Validate real market data connection
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Returns False if connection fails or data seems synthetic
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"""
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try:
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logger.info("VALIDATING REAL MARKET DATA CONNECTION...")
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# Test primary trading symbols
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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 quality for trading
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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"CRITICAL: Market data validation failed: {e}")
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return False
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class RealTradingEngine:
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"""
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Real trading engine that makes decisions based on live market analysis
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NO SYNTHETIC DATA - Uses orchestrator for real market analysis
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"""
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def __init__(self, data_provider: DataProvider, orchestrator: EnhancedTradingOrchestrator):
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self.data_provider = data_provider
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self.orchestrator = orchestrator
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self.running = False
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self.trade_count = 0
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def start(self):
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"""Start real trading analysis"""
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self.running = True
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trading_thread = Thread(target=self._run_async_trading_loop, daemon=True)
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trading_thread.start()
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logger.info("REAL TRADING ENGINE STARTED - NO SYNTHETIC DATA")
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def stop(self):
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"""Stop trading analysis"""
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self.running = False
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logger.info("Real trading engine stopped")
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def _run_async_trading_loop(self):
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"""Run the async trading loop in a separate thread"""
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asyncio.run(self._real_trading_loop())
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async def _real_trading_loop(self):
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"""
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Real trading analysis loop using live market data ONLY
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"""
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logger.info("Starting REAL trading analysis loop...")
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while self.running:
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try:
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# Make coordinated decisions using the orchestrator
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decisions = await self.orchestrator.make_coordinated_decisions()
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for symbol, decision in decisions.items():
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if decision and decision.action in ['BUY', 'SELL']:
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self.trade_count += 1
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logger.info(f"REAL TRADING DECISION #{self.trade_count}:")
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logger.info(f" {decision.action} {symbol} @ ${decision.price:.2f}")
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logger.info(f" Confidence: {decision.confidence:.1%}")
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logger.info(f" Quantity: {decision.quantity:.6f}")
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logger.info(f" Based on REAL market analysis")
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logger.info(f" Time: {datetime.now().strftime('%H:%M:%S')}")
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# Log timeframe analysis
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for tf_pred in decision.timeframe_analysis:
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logger.info(f" {tf_pred.timeframe}: {tf_pred.action} "
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f"(conf: {tf_pred.confidence:.3f})")
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# Evaluate past actions for RL learning
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await self.orchestrator.evaluate_actions_with_rl()
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# Wait between real analysis cycles (60 seconds for enhanced decisions)
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await asyncio.sleep(60)
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except Exception as e:
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logger.error(f"Error in real trading analysis: {e}")
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await asyncio.sleep(30) # Wait on error
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def test_orchestrator_simple(orchestrator: EnhancedTradingOrchestrator) -> bool:
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"""Simple test to verify orchestrator can make basic decisions"""
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try:
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# Run a simple async test
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loop = asyncio.new_event_loop()
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asyncio.set_event_loop(loop)
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# Test making coordinated decisions
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decisions = loop.run_until_complete(orchestrator.make_coordinated_decisions())
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loop.close()
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# Check if we got any results
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if isinstance(decisions, dict):
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logger.info(f"Orchestrator test successful - got decisions for {len(decisions)} symbols")
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return True
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else:
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logger.error("Orchestrator test failed - no decisions returned")
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return False
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except Exception as e:
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logger.error(f"Orchestrator test failed: {e}")
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return False
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def main():
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"""Main function for scalping dashboard with REAL DATA ONLY"""
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logger.info("STARTING SCALPING DASHBOARD - 100% REAL MARKET DATA")
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logger.info("Ultra-fast scalping with live market analysis")
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logger.info("ZERO SYNTHETIC DATA - REAL DECISIONS ONLY")
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"""Main function for scalping dashboard"""
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# Parse command line arguments
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parser = argparse.ArgumentParser(description='Ultra-Fast Scalping Dashboard (500x Leverage)')
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parser.add_argument('--episodes', type=int, default=1000, help='Number of episodes (for compatibility)')
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parser.add_argument('--max-position', type=float, default=0.1, help='Maximum position size')
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parser.add_argument('--leverage', type=int, default=500, help='Leverage multiplier')
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parser.add_argument('--port', type=int, default=8051, help='Dashboard port')
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parser.add_argument('--host', type=str, default='127.0.0.1', help='Dashboard host')
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parser.add_argument('--debug', action='store_true', help='Enable debug mode')
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args = parser.parse_args()
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logger.info("STARTING SCALPING DASHBOARD")
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logger.info("Session-based trading with $100 starting balance")
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logger.info(f"Configuration: Leverage={args.leverage}x, Max Position={args.max_position}, Port={args.port}")
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try:
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# Initialize data provider
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# Initialize components
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logger.info("Initializing data provider...")
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data_provider = DataProvider()
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# CRITICAL: Validate real market data connection
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if not validate_real_market_connection(data_provider):
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logger.error("CRITICAL: Real market data validation FAILED")
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logger.error("Scalping dashboard will NOT start without verified real 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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logger.info("Initializing trading orchestrator...")
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orchestrator = EnhancedTradingOrchestrator(data_provider)
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# Test orchestrator with a simple test
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logger.info("Testing orchestrator with real market data...")
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if not test_orchestrator_simple(orchestrator):
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logger.error("CRITICAL: Orchestrator validation failed")
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return 1
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logger.info("LAUNCHING DASHBOARD")
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logger.info(f"Dashboard will be available at http://{args.host}:{args.port}")
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logger.info("Orchestrator validated with real market data")
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# Initialize real trading engine
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trading_engine = RealTradingEngine(data_provider, orchestrator)
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trading_engine.start()
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logger.info("LAUNCHING SCALPING DASHBOARD WITH 100% REAL DATA")
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logger.info("Real-time scalping decisions from live market analysis")
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# Start the scalping dashboard with real data
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# Start the dashboard
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dashboard = create_scalping_dashboard(data_provider, orchestrator)
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dashboard.run(host='127.0.0.1', port=8051, debug=False)
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dashboard.run(host=args.host, port=args.port, debug=args.debug)
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except KeyboardInterrupt:
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logger.info("Scalping dashboard stopped by user")
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logger.info("Dashboard stopped by user")
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return 0
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except Exception as e:
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logger.error(f"CRITICAL ERROR: {e}")
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logger.error("Scalping dashboard stopped - NO SYNTHETIC DATA FALLBACK")
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logger.error(f"ERROR: {e}")
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import traceback
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traceback.print_exc()
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return 1
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if __name__ == "__main__":
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