gogo2/ENHANCED_SYSTEM_STATUS.md
2025-05-26 16:02:40 +03:00

4.5 KiB

Enhanced Trading System Status

System Successfully Configured

The enhanced trading system is now properly configured with both RL training and CNN pattern learning pipelines active.

🧠 Learning Systems Active

1. RL (Reinforcement Learning) Pipeline

  • Status: Active and Ready
  • Agents: 2 agents (ETH/USDT, BTC/USDT)
  • Learning Method: Continuous learning from every trading decision
  • Training Frequency: Every 5 minutes (300 seconds)
  • Features:
    • Prioritized experience replay
    • Market regime adaptation
    • Double DQN with dueling architecture
    • Epsilon-greedy exploration with decay

2. CNN (Convolutional Neural Network) Pipeline

  • Status: Active and Ready
  • Learning Method: Training on "perfect moves" with known outcomes
  • Training Frequency: Every hour (3600 seconds)
  • Features:
    • Multi-timeframe pattern recognition
    • Retrospective learning from market data
    • Enhanced CNN with attention mechanisms
    • Confidence scoring for predictions

🎯 Enhanced Orchestrator

  • Status: Operational
  • Confidence Threshold: 0.6 (60%)
  • Decision Frequency: 30 seconds
  • Symbols: ETH/USDT, BTC/USDT
  • Timeframes: 1s, 1m, 1h, 1d

📊 Training Configuration

training:
  # CNN specific training
  cnn_training_interval: 3600    # Train CNN every hour
  min_perfect_moves: 50          # Reduced for faster learning
  
  # RL specific training  
  rl_training_interval: 300      # Train RL every 5 minutes
  min_experiences: 50            # Reduced for faster learning
  training_steps_per_cycle: 20   # Increased for more learning
  
  # Continuous learning settings
  continuous_learning: true
  learning_from_trades: true
  pattern_recognition: true
  retrospective_learning: true

🚀 How It Works

Real-Time Learning Loop:

  1. Trading Decisions: Enhanced orchestrator makes coordinated decisions every 30 seconds
  2. RL Learning: Every trading decision is queued for RL evaluation and learning
  3. Perfect Move Detection: Significant market moves (>2% price change) are marked as "perfect moves"
  4. CNN Training: CNN trains on accumulated perfect moves every hour
  5. Continuous Adaptation: Both systems continuously adapt to market conditions

Learning From Trading:

  • RL Agents: Learn from the outcome of every trading decision
  • CNN Models: Learn from retrospective analysis of optimal moves
  • Market Adaptation: Both systems adapt to changing market regimes (trending, ranging, volatile)

🎮 Dashboard Integration

The enhanced dashboard is working and connected to:

  • Real-time trading decisions
  • RL training pipeline
  • CNN pattern learning
  • Performance monitoring
  • Learning progress tracking

🔧 Key Components

Enhanced Trading Main (enhanced_trading_main.py)

  • Main system coordinator
  • Manages all learning loops
  • Performance tracking
  • Graceful shutdown handling

Enhanced Orchestrator (core/enhanced_orchestrator.py)

  • Multi-modal decision making
  • Perfect move marking
  • RL evaluation queuing
  • Market state management

Enhanced CNN Trainer (training/enhanced_cnn_trainer.py)

  • Trains on perfect moves with known outcomes
  • Multi-timeframe pattern recognition
  • Confidence scoring

Enhanced RL Trainer (training/enhanced_rl_trainer.py)

  • Continuous learning from trading decisions
  • Prioritized experience replay
  • Market regime adaptation

📈 Performance Tracking

The system tracks:

  • Total trading decisions made
  • Profitable decisions
  • Perfect moves identified
  • CNN training sessions completed
  • RL training steps
  • Success rate percentage

🎯 Next Steps

  1. Run Enhanced Dashboard: Use the working enhanced dashboard for monitoring
  2. Start Live Learning: The system will learn and improve with every trade
  3. Monitor Performance: Track learning progress through the dashboard
  4. Scale Up: Add more symbols or timeframes as needed

🏆 Achievement Summary

Model Cleanup: Removed outdated models, kept only the best performers RL Pipeline: Active continuous learning from trading decisions
CNN Pipeline: Active pattern learning from perfect moves Enhanced Orchestrator: Coordinating multi-modal decisions Dashboard Integration: Working enhanced dashboard Performance Monitoring: Comprehensive metrics tracking Graceful Scaling: Optimized for 8GB GPU memory constraint

The enhanced trading system is now ready for live trading with continuous learning capabilities!