better clean dash
This commit is contained in:
Dobromir Popov
@ -1,72 +1,188 @@
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#!/usr/bin/env python3
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"""
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Run Clean Trading Dashboard
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Simple runner for the modular dashboard implementation
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Run Clean Trading Dashboard with Full Training Pipeline
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Integrated system with both training loop and clean web dashboard
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"""
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import os
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# Fix OpenMP library conflicts before importing other modules
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os.environ['KMP_DUPLICATE_LIB_OK'] = 'TRUE'
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os.environ['OMP_NUM_THREADS'] = '4'
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import asyncio
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import logging
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import sys
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import os
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import threading
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import time
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from pathlib import Path
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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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# Import checkpoint management
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from utils.checkpoint_manager import get_checkpoint_manager
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from utils.training_integration import get_training_integration
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# Setup logging
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logging.basicConfig(
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level=logging.INFO,
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format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
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)
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setup_logging()
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logger = logging.getLogger(__name__)
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def main():
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"""Main function to run the clean dashboard"""
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async def start_training_pipeline(orchestrator, trading_executor):
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"""Start the training pipeline in the background"""
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logger.info("=" * 70)
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logger.info("STARTING TRAINING PIPELINE WITH CLEAN DASHBOARD")
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logger.info("=" * 70)
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# Initialize checkpoint management
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checkpoint_manager = get_checkpoint_manager()
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training_integration = get_training_integration()
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# Training statistics
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training_stats = {
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'iteration_count': 0,
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'total_decisions': 0,
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'successful_trades': 0,
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'best_performance': 0.0,
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'last_checkpoint_iteration': 0
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}
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try:
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logger.info("Starting Clean Trading Dashboard...")
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# Start real-time processing
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await orchestrator.start_realtime_processing()
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logger.info("✅ Real-time processing started")
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# Import core components
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# Start COB integration
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if hasattr(orchestrator, 'start_cob_integration'):
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await orchestrator.start_cob_integration()
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logger.info("✅ COB integration started")
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# Main training loop
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iteration = 0
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last_checkpoint_time = time.time()
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while True:
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try:
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iteration += 1
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training_stats['iteration_count'] = iteration
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# Get symbols to process
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symbols = orchestrator.symbols if hasattr(orchestrator, 'symbols') else ['ETH/USDT']
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# Process each symbol
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for symbol in symbols:
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try:
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# Make trading decision (this triggers model training)
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decision = await orchestrator.make_trading_decision(symbol)
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if decision:
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training_stats['total_decisions'] += 1
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logger.debug(f"[{symbol}] Decision: {decision.action} @ {decision.confidence:.1%}")
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except Exception as e:
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logger.warning(f"Error processing {symbol}: {e}")
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# Status logging every 100 iterations
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if iteration % 100 == 0:
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current_time = time.time()
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elapsed = current_time - last_checkpoint_time
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logger.info(f"[TRAINING] Iteration {iteration}, Decisions: {training_stats['total_decisions']}, Time: {elapsed:.1f}s")
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# Models will save their own checkpoints when performance improves
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training_stats['last_checkpoint_iteration'] = iteration
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last_checkpoint_time = current_time
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# Brief pause to prevent overwhelming the system
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await asyncio.sleep(0.1) # 100ms between iterations
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except Exception as e:
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logger.error(f"Training loop error: {e}")
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await asyncio.sleep(5) # Wait longer on error
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except Exception as e:
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logger.error(f"Training pipeline error: {e}")
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import traceback
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logger.error(traceback.format_exc())
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def start_clean_dashboard_with_training():
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"""Start clean dashboard with full training pipeline"""
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try:
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logger.info("=" * 80)
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logger.info("CLEAN TRADING DASHBOARD + FULL TRAINING PIPELINE")
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logger.info("=" * 80)
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logger.info("Features: Real-time Training, COB Integration, Clean UI")
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logger.info("GPU Training: ENABLED")
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logger.info("Multi-symbol: ETH/USDT, BTC/USDT")
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logger.info("Dashboard: http://127.0.0.1:8051")
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logger.info("=" * 80)
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# Get configuration
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config = get_config()
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# Initialize core components
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from core.data_provider import DataProvider
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from core.orchestrator import TradingOrchestrator
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from core.enhanced_orchestrator import EnhancedTradingOrchestrator
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from core.trading_executor import TradingExecutor
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# Create data provider
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data_provider = DataProvider()
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# Create enhanced orchestrator with full training capabilities
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orchestrator = EnhancedTradingOrchestrator(
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data_provider=data_provider,
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symbols=['ETH/USDT', 'BTC/USDT'],
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enhanced_rl_training=True, # Enable RL training
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model_registry={}
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)
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logger.info("✅ Enhanced Trading Orchestrator created with training enabled")
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# Create trading executor
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trading_executor = TradingExecutor()
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# Import clean dashboard
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from web.clean_dashboard import create_clean_dashboard
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# Initialize components
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logger.info("Initializing trading components...")
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data_provider = DataProvider()
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# Try to use enhanced orchestrator if available
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try:
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from core.enhanced_orchestrator import EnhancedTradingOrchestrator
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orchestrator = EnhancedTradingOrchestrator(
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data_provider=data_provider,
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symbols=['ETH/USDT', 'BTC/USDT']
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)
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logger.info("Using Enhanced Trading Orchestrator")
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except ImportError:
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orchestrator = TradingOrchestrator(data_provider=data_provider)
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logger.info("Using Standard Trading Orchestrator")
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trading_executor = TradingExecutor()
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# Create and run dashboard
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logger.info("Creating clean dashboard...")
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# Create clean dashboard
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dashboard = create_clean_dashboard(
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data_provider=data_provider,
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orchestrator=orchestrator,
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trading_executor=trading_executor
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)
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logger.info("✅ Clean Trading Dashboard created")
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logger.info("Dashboard created successfully!")
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logger.info("Starting server on http://127.0.0.1:8051")
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# Start training pipeline in background thread
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def training_worker():
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"""Run training pipeline in background"""
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try:
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asyncio.run(start_training_pipeline(orchestrator, trading_executor))
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except Exception as e:
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logger.error(f"Training worker error: {e}")
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# Run the dashboard
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training_thread = threading.Thread(target=training_worker, daemon=True)
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training_thread.start()
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logger.info("✅ Training pipeline started in background")
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# Wait a moment for training to initialize
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time.sleep(3)
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# Start dashboard server (this blocks)
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logger.info("🚀 Starting Clean Dashboard Server...")
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dashboard.run_server(host='127.0.0.1', port=8051, debug=False)
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except KeyboardInterrupt:
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logger.info("Dashboard stopped by user")
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logger.info("System stopped by user")
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except Exception as e:
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logger.error(f"Error running clean dashboard: {e}")
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logger.error(f"Error running clean dashboard with training: {e}")
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import traceback
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traceback.print_exc()
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sys.exit(1)
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def main():
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"""Main function"""
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start_clean_dashboard_with_training()
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if __name__ == "__main__":
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main()
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