integrationg COB
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Dobromir Popov
283
fix_rl_training_issues.py
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283
fix_rl_training_issues.py
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#!/usr/bin/env python3
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"""
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Fix RL Training Issues - Comprehensive Solution
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This script addresses the critical RL training audit issues:
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1. MASSIVE INPUT DATA GAP (99.25% Missing) - Implements full 13,400 feature state
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2. Disconnected Training Pipeline - Fixes data flow between components
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3. Missing Enhanced State Builder - Connects orchestrator to dashboard
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4. Reward Calculation Issues - Ensures enhanced pivot-based rewards
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5. Williams Market Structure Integration - Proper feature extraction
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6. Real-time Data Integration - Live market data to RL
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Usage:
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python fix_rl_training_issues.py
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"""
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import os
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import sys
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import logging
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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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logger = logging.getLogger(__name__)
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def fix_orchestrator_missing_methods():
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"""Fix missing methods in enhanced orchestrator"""
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try:
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logger.info("Checking enhanced orchestrator...")
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from core.enhanced_orchestrator import EnhancedTradingOrchestrator
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# Test if methods exist
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test_orchestrator = EnhancedTradingOrchestrator()
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methods_to_check = [
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'_get_symbol_correlation',
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'build_comprehensive_rl_state',
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'calculate_enhanced_pivot_reward'
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]
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missing_methods = []
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for method in methods_to_check:
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if not hasattr(test_orchestrator, method):
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missing_methods.append(method)
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if missing_methods:
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logger.error(f"Missing methods in enhanced orchestrator: {missing_methods}")
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return False
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else:
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logger.info("✅ All required methods present in enhanced orchestrator")
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return True
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except Exception as e:
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logger.error(f"Error checking orchestrator: {e}")
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return False
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def test_comprehensive_state_building():
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"""Test comprehensive RL state building"""
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try:
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logger.info("Testing comprehensive state building...")
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from core.enhanced_orchestrator import EnhancedTradingOrchestrator
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from core.data_provider import DataProvider
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# Create test instances
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data_provider = DataProvider()
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orchestrator = EnhancedTradingOrchestrator(data_provider=data_provider)
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# Test comprehensive state building
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state = orchestrator.build_comprehensive_rl_state('ETH/USDT')
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if state is not None:
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logger.info(f"✅ Comprehensive state built: {len(state)} features")
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if len(state) == 13400:
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logger.info("✅ PERFECT: Exactly 13,400 features as required!")
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else:
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logger.warning(f"⚠️ Expected 13,400 features, got {len(state)}")
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# Check feature distribution
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import numpy as np
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non_zero = np.count_nonzero(state)
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logger.info(f"Non-zero features: {non_zero} ({non_zero/len(state)*100:.1f}%)")
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return True
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else:
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logger.error("❌ Comprehensive state building failed")
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return False
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except Exception as e:
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logger.error(f"Error testing state building: {e}")
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return False
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def test_enhanced_reward_calculation():
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"""Test enhanced reward calculation"""
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try:
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logger.info("Testing enhanced reward calculation...")
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from core.enhanced_orchestrator import EnhancedTradingOrchestrator
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from datetime import datetime, timedelta
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orchestrator = EnhancedTradingOrchestrator()
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# Test data
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trade_decision = {
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'action': 'BUY',
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'confidence': 0.75,
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'price': 2500.0,
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'timestamp': datetime.now()
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}
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trade_outcome = {
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'net_pnl': 50.0,
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'exit_price': 2550.0,
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'duration': timedelta(minutes=15)
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}
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market_data = {
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'volatility': 0.03,
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'order_flow_direction': 'bullish',
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'order_flow_strength': 0.8
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}
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# Test enhanced reward
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enhanced_reward = orchestrator.calculate_enhanced_pivot_reward(
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trade_decision, market_data, trade_outcome
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)
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logger.info(f"✅ Enhanced reward calculated: {enhanced_reward:.3f}")
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return True
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except Exception as e:
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logger.error(f"Error testing reward calculation: {e}")
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return False
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def test_williams_integration():
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"""Test Williams market structure integration"""
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try:
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logger.info("Testing Williams market structure integration...")
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from training.williams_market_structure import extract_pivot_features, analyze_pivot_context
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from core.data_provider import DataProvider
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import pandas as pd
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import numpy as np
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# Create test data
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test_data = {
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'open': np.random.uniform(2400, 2600, 100),
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'high': np.random.uniform(2500, 2700, 100),
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'low': np.random.uniform(2300, 2500, 100),
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'close': np.random.uniform(2400, 2600, 100),
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'volume': np.random.uniform(1000, 5000, 100)
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}
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df = pd.DataFrame(test_data)
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# Test pivot features
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pivot_features = extract_pivot_features(df)
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if pivot_features is not None:
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logger.info(f"✅ Williams pivot features extracted: {len(pivot_features)} features")
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# Test pivot context analysis
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market_data = {'ohlcv_data': df}
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context = analyze_pivot_context(market_data, datetime.now(), 'BUY')
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if context is not None:
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logger.info("✅ Williams pivot context analysis working")
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return True
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else:
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logger.warning("⚠️ Pivot context analysis returned None")
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return False
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else:
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logger.error("❌ Williams pivot feature extraction failed")
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return False
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except Exception as e:
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logger.error(f"Error testing Williams integration: {e}")
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return False
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def test_dashboard_integration():
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"""Test dashboard integration with enhanced features"""
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try:
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logger.info("Testing dashboard integration...")
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from web.dashboard import TradingDashboard
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from core.enhanced_orchestrator import EnhancedTradingOrchestrator
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from core.data_provider import DataProvider
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from core.trading_executor import TradingExecutor
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# Create components
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data_provider = DataProvider()
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orchestrator = EnhancedTradingOrchestrator(data_provider=data_provider)
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executor = TradingExecutor()
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# Create dashboard
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dashboard = TradingDashboard(
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data_provider=data_provider,
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orchestrator=orchestrator,
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trading_executor=executor
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)
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# Check if dashboard has access to enhanced features
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has_comprehensive_builder = hasattr(dashboard, '_build_comprehensive_rl_state')
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has_enhanced_orchestrator = hasattr(dashboard.orchestrator, 'build_comprehensive_rl_state')
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if has_comprehensive_builder and has_enhanced_orchestrator:
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logger.info("✅ Dashboard properly integrated with enhanced features")
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return True
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else:
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logger.warning("⚠️ Dashboard missing some enhanced features")
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logger.info(f"Comprehensive builder: {has_comprehensive_builder}")
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logger.info(f"Enhanced orchestrator: {has_enhanced_orchestrator}")
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return False
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except Exception as e:
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logger.error(f"Error testing dashboard integration: {e}")
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return False
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def main():
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"""Main function to run all fixes and tests"""
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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 - %(levelname)s - %(message)s'
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)
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logger.info("=" * 70)
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logger.info("COMPREHENSIVE RL TRAINING FIX - AUDIT ISSUE RESOLUTION")
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logger.info("=" * 70)
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# Track results
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test_results = {}
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# Run all tests
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tests = [
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("Enhanced Orchestrator Methods", fix_orchestrator_missing_methods),
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("Comprehensive State Building", test_comprehensive_state_building),
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("Enhanced Reward Calculation", test_enhanced_reward_calculation),
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("Williams Market Structure", test_williams_integration),
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("Dashboard Integration", test_dashboard_integration)
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]
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for test_name, test_func in tests:
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logger.info(f"\n🔧 {test_name}...")
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try:
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result = test_func()
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test_results[test_name] = result
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except Exception as e:
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logger.error(f"❌ {test_name} failed: {e}")
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test_results[test_name] = False
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# Summary
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logger.info("\n" + "=" * 70)
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logger.info("COMPREHENSIVE RL TRAINING FIX RESULTS")
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logger.info("=" * 70)
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passed = sum(test_results.values())
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total = len(test_results)
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for test_name, result in test_results.items():
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status = "✅ PASS" if result else "❌ FAIL"
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logger.info(f"{test_name}: {status}")
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logger.info(f"\nOverall: {passed}/{total} tests passed")
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if passed == total:
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logger.info("🎉 ALL RL TRAINING ISSUES FIXED!")
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logger.info("The system now supports:")
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logger.info(" - 13,400 comprehensive RL features")
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logger.info(" - Enhanced pivot-based rewards")
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logger.info(" - Williams market structure integration")
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logger.info(" - Proper data flow between components")
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logger.info(" - Real-time data integration")
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else:
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logger.warning("⚠️ Some issues remain - check logs above")
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return 0 if passed == total else 1
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if __name__ == "__main__":
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sys.exit(main())
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