models cleanup
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Dobromir Popov
318
enhanced_rl_diagnostic.py
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318
enhanced_rl_diagnostic.py
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#!/usr/bin/env python3
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"""
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Enhanced RL Diagnostic and Setup Script
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This script:
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1. Diagnoses why Enhanced RL shows as DISABLED
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2. Explains model management and training progression
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3. Sets up clean training environment
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4. Provides solutions for the reward function issues
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"""
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import sys
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import json
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import logging
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from datetime import datetime
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from pathlib import Path
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logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
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logger = logging.getLogger(__name__)
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def check_enhanced_rl_availability():
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"""Check what's causing Enhanced RL to be disabled"""
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logger.info("🔍 DIAGNOSING ENHANCED RL AVAILABILITY")
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logger.info("=" * 50)
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issues = []
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solutions = []
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# Test 1: Enhanced components import
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try:
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from core.enhanced_orchestrator import EnhancedTradingOrchestrator
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logger.info("✅ EnhancedTradingOrchestrator imports successfully")
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except ImportError as e:
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issues.append(f"❌ Cannot import EnhancedTradingOrchestrator: {e}")
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solutions.append("Fix: Check core/enhanced_orchestrator.py exists and is valid")
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# Test 2: Unified data stream import
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try:
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from core.unified_data_stream import UnifiedDataStream, TrainingDataPacket, UIDataPacket
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logger.info("✅ Unified data stream components import successfully")
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except ImportError as e:
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issues.append(f"❌ Cannot import unified data stream: {e}")
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solutions.append("Fix: Check core/unified_data_stream.py exists and is valid")
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# Test 3: Universal data adapter import
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try:
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from core.universal_data_adapter import UniversalDataAdapter
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logger.info("✅ UniversalDataAdapter imports successfully")
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except ImportError as e:
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issues.append(f"❌ Cannot import UniversalDataAdapter: {e}")
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solutions.append("Fix: Check core/universal_data_adapter.py exists and is valid")
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# Test 4: Dashboard initialization logic
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logger.info("🔍 Checking dashboard initialization logic...")
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# Simulate dashboard initialization
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try:
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from core.enhanced_orchestrator import EnhancedTradingOrchestrator
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from core.data_provider import DataProvider
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data_provider = DataProvider()
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enhanced_orchestrator = EnhancedTradingOrchestrator(
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data_provider=data_provider,
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symbols=['ETH/USDT'],
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enhanced_rl_training=True
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)
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# Check the isinstance condition
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if isinstance(enhanced_orchestrator, EnhancedTradingOrchestrator):
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logger.info("✅ EnhancedTradingOrchestrator isinstance check passes")
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else:
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issues.append("❌ isinstance(orchestrator, EnhancedTradingOrchestrator) fails")
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solutions.append("Fix: Ensure dashboard is initialized with EnhancedTradingOrchestrator")
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except Exception as e:
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issues.append(f"❌ Cannot create EnhancedTradingOrchestrator: {e}")
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solutions.append("Fix: Check orchestrator initialization parameters")
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# Test 5: Main startup script
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logger.info("🔍 Checking main startup configuration...")
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main_file = Path("main_clean.py")
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if main_file.exists():
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content = main_file.read_text()
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if "EnhancedTradingOrchestrator" in content:
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logger.info("✅ main_clean.py uses EnhancedTradingOrchestrator")
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else:
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issues.append("❌ main_clean.py not using EnhancedTradingOrchestrator")
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solutions.append("Fix: Update main_clean.py to use EnhancedTradingOrchestrator")
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return issues, solutions
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def analyze_model_management():
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"""Analyze current model management setup"""
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logger.info("📊 ANALYZING MODEL MANAGEMENT")
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logger.info("=" * 50)
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models_dir = Path("models")
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# Count different model types
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model_counts = {
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"CNN models": len(list(models_dir.glob("**/cnn*.pt*"))),
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"RL models": len(list(models_dir.glob("**/trading_agent*.pt*"))),
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"Backup models": len(list(models_dir.glob("**/*.backup"))),
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"Total model files": len(list(models_dir.glob("**/*.pt*")))
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}
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for model_type, count in model_counts.items():
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logger.info(f" {model_type}: {count}")
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# Check for training progression system
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progress_file = models_dir / "training_progress.json"
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if progress_file.exists():
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logger.info("✅ Training progression file exists")
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try:
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with open(progress_file) as f:
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progress = json.load(f)
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logger.info(f" Created: {progress.get('created', 'Unknown')}")
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logger.info(f" Version: {progress.get('version', 'Unknown')}")
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except Exception as e:
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logger.warning(f"⚠️ Cannot read progression file: {e}")
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else:
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logger.info("❌ No training progression tracking found")
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# Check for conflicting models
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conflicting_models = [
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"models/cnn_final_20250331_001817.pt.pt",
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"models/cnn_best.pt.pt",
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"models/trading_agent_final.pt",
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"models/trading_agent_best_pnl.pt"
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]
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conflicts = [model for model in conflicting_models if Path(model).exists()]
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if conflicts:
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logger.warning(f"⚠️ Found {len(conflicts)} potentially conflicting model files")
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for conflict in conflicts:
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logger.warning(f" {conflict}")
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else:
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logger.info("✅ No obvious model conflicts detected")
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def analyze_reward_function():
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"""Analyze the reward function and training issues"""
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logger.info("🎯 ANALYZING REWARD FUNCTION ISSUES")
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logger.info("=" * 50)
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# Read recent dashboard logs to understand the -0.5 reward issue
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log_file = Path("dashboard.log")
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if log_file.exists():
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try:
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with open(log_file, 'r') as f:
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lines = f.readlines()
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# Look for reward patterns
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reward_lines = [line for line in lines if "Reward:" in line]
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if reward_lines:
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recent_rewards = reward_lines[-10:] # Last 10 rewards
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negative_rewards = [line for line in recent_rewards if "-0.5" in line]
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logger.info(f"Recent rewards found: {len(recent_rewards)}")
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logger.info(f"Negative -0.5 rewards: {len(negative_rewards)}")
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if len(negative_rewards) > 5:
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logger.warning("⚠️ High number of -0.5 rewards detected")
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logger.info("This suggests blocked signals are being penalized with fees")
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logger.info("Solution: Update _queue_signal_for_training to handle blocked signals better")
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# Look for blocked signal patterns
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blocked_signals = [line for line in lines if "NOT_EXECUTED" in line]
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if blocked_signals:
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logger.info(f"Blocked signals found: {len(blocked_signals)}")
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recent_blocked = blocked_signals[-5:]
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for line in recent_blocked:
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logger.info(f" {line.strip()}")
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except Exception as e:
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logger.warning(f"Cannot analyze log file: {e}")
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else:
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logger.info("No dashboard.log found for analysis")
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def provide_solutions():
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"""Provide comprehensive solutions"""
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logger.info("💡 COMPREHENSIVE SOLUTIONS")
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logger.info("=" * 50)
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solutions = {
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"Enhanced RL DISABLED Issue": [
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"1. Update main_clean.py to use EnhancedTradingOrchestrator (already done)",
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"2. Restart the dashboard with: python main_clean.py web",
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"3. Verify Enhanced RL: ENABLED appears in logs"
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],
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"Williams Repeated Initialization": [
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"1. Dashboard reuses Williams instance now (already fixed)",
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"2. Default strengths changed from [2,3,5,8,13] to [2,3,5] (already done)",
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"3. No more repeated 'Williams Market Structure initialized' logs"
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],
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"Model Management": [
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"1. Run: python cleanup_and_setup_models.py",
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"2. This will backup old models and create clean structure",
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"3. Set up training progression tracking",
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"4. Initialize fresh training environment"
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],
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"Reward Function (-0.5 Issue)": [
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"1. Blocked signals now get small negative reward (-0.1) instead of fee penalty",
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"2. Synthetic signals handled separately from real trades",
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"3. Reward calculation improved for better learning"
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],
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"CNN Training Sessions": [
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"1. CNN training is disabled by default (no TensorFlow)",
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"2. Williams pivot detection works without CNN",
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"3. Enable CNN when TensorFlow available for enhanced predictions"
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]
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}
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for category, steps in solutions.items():
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logger.info(f"\n{category}:")
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for step in steps:
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logger.info(f" {step}")
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def create_startup_script():
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"""Create an optimal startup script"""
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startup_script = """#!/usr/bin/env python3
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# Enhanced RL Trading Dashboard Startup Script
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import logging
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logging.basicConfig(level=logging.INFO)
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def main():
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try:
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# Import enhanced components
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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 core.trading_executor import TradingExecutor
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from web.dashboard import TradingDashboard
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from config import get_config
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config = get_config()
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# Initialize with enhanced RL support
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data_provider = DataProvider()
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enhanced_orchestrator = EnhancedTradingOrchestrator(
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data_provider=data_provider,
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symbols=config.get('symbols', ['ETH/USDT']),
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enhanced_rl_training=True
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)
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trading_executor = TradingExecutor()
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# Create dashboard with enhanced components
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dashboard = TradingDashboard(
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data_provider=data_provider,
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orchestrator=enhanced_orchestrator, # Enhanced RL enabled
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trading_executor=trading_executor
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)
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print("Enhanced RL Trading Dashboard Starting...")
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print("Enhanced RL: ENABLED")
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print("Williams Pivot Detection: ENABLED")
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print("Real Market Data: ENABLED")
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print("Access at: http://127.0.0.1:8050")
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dashboard.run(host='127.0.0.1', port=8050, debug=False)
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except Exception as e:
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print(f"Startup failed: {e}")
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import traceback
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traceback.print_exc()
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if __name__ == "__main__":
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main()
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"""
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with open("start_enhanced_dashboard.py", "w", encoding='utf-8') as f:
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f.write(startup_script)
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logger.info("Created start_enhanced_dashboard.py for optimal startup")
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def main():
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"""Main diagnostic function"""
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print("🔬 ENHANCED RL DIAGNOSTIC AND SETUP")
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print("=" * 60)
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print("Analyzing Enhanced RL issues and providing solutions...")
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print("=" * 60)
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# Run diagnostics
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issues, solutions = check_enhanced_rl_availability()
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analyze_model_management()
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analyze_reward_function()
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provide_solutions()
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create_startup_script()
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# Summary
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print("\n" + "=" * 60)
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print("📋 SUMMARY")
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print("=" * 60)
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if issues:
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print("❌ Issues found:")
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for issue in issues:
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print(f" {issue}")
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print("\n💡 Solutions:")
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for solution in solutions:
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print(f" {solution}")
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else:
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print("✅ No critical issues detected!")
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print("\n🚀 NEXT STEPS:")
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print("1. Run model cleanup: python cleanup_and_setup_models.py")
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print("2. Start enhanced dashboard: python start_enhanced_dashboard.py")
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print("3. Verify 'Enhanced RL: ENABLED' in dashboard")
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print("4. Check Williams pivot detection on chart")
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print("5. Monitor training episodes (should not all be -0.5 reward)")
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if __name__ == "__main__":
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main()
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