243 lines
8.7 KiB
Python
243 lines
8.7 KiB
Python
#!/usr/bin/env python3
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"""
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Test Enhanced Trading System Improvements
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This script tests:
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1. Color-coded position display ([LONG] green, [SHORT] red)
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2. Enhanced model training detection and retrospective learning
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3. Lower confidence thresholds for closing positions (0.25 vs 0.6 for opening)
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4. Perfect opportunity detection and learning
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"""
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import asyncio
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import logging
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import time
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from datetime import datetime, timedelta
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from core.data_provider import DataProvider
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from core.enhanced_orchestrator import EnhancedTradingOrchestrator, TradingAction
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from web.scalping_dashboard import RealTimeScalpingDashboard, TradingSession
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# Setup logging
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logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s')
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logger = logging.getLogger(__name__)
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def test_color_coded_positions():
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"""Test color-coded position display functionality"""
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logger.info("=== Testing Color-Coded Position Display ===")
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# Create trading session
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session = TradingSession()
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# Simulate some positions
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session.positions = {
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'ETH/USDT': {
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'side': 'LONG',
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'size': 0.1,
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'entry_price': 2558.15
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},
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'BTC/USDT': {
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'side': 'SHORT',
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'size': 0.05,
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'entry_price': 45123.45
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}
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}
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logger.info("Created test positions:")
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logger.info(f"ETH/USDT: LONG 0.1 @ $2558.15")
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logger.info(f"BTC/USDT: SHORT 0.05 @ $45123.45")
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# Test position display logic (simulating dashboard logic)
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live_prices = {'ETH/USDT': 2565.30, 'BTC/USDT': 45050.20}
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for symbol, pos in session.positions.items():
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side = pos['side']
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size = pos['size']
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entry_price = pos['entry_price']
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current_price = live_prices.get(symbol, entry_price)
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# Calculate unrealized P&L
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if side == 'LONG':
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unrealized_pnl = (current_price - entry_price) * size
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color_class = "text-success" # Green for LONG
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side_display = "[LONG]"
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else: # SHORT
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unrealized_pnl = (entry_price - current_price) * size
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color_class = "text-danger" # Red for SHORT
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side_display = "[SHORT]"
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position_text = f"{side_display} {size:.3f} @ ${entry_price:.2f} | P&L: ${unrealized_pnl:+.2f}"
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logger.info(f"Position Display: {position_text} (Color: {color_class})")
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logger.info("✅ Color-coded position display test completed")
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def test_confidence_thresholds():
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"""Test different confidence thresholds for opening vs closing"""
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logger.info("=== Testing Confidence Thresholds ===")
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# Create orchestrator
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data_provider = DataProvider()
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orchestrator = EnhancedTradingOrchestrator(data_provider)
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logger.info(f"Opening threshold: {orchestrator.confidence_threshold_open}")
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logger.info(f"Closing threshold: {orchestrator.confidence_threshold_close}")
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# Test opening action with medium confidence
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test_confidence = 0.45
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logger.info(f"\nTesting opening action with confidence {test_confidence}")
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if test_confidence >= orchestrator.confidence_threshold_open:
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logger.info("✅ Would OPEN position (confidence above opening threshold)")
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else:
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logger.info("❌ Would NOT open position (confidence below opening threshold)")
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# Test closing action with same confidence
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logger.info(f"Testing closing action with confidence {test_confidence}")
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if test_confidence >= orchestrator.confidence_threshold_close:
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logger.info("✅ Would CLOSE position (confidence above closing threshold)")
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else:
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logger.info("❌ Would NOT close position (confidence below closing threshold)")
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logger.info("✅ Confidence threshold test completed")
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def test_retrospective_learning():
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"""Test retrospective learning and perfect opportunity detection"""
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logger.info("=== Testing Retrospective Learning ===")
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# Create orchestrator
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data_provider = DataProvider()
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orchestrator = EnhancedTradingOrchestrator(data_provider)
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# Simulate perfect moves
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from core.enhanced_orchestrator import PerfectMove
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perfect_move = PerfectMove(
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symbol='ETH/USDT',
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timeframe='1m',
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timestamp=datetime.now(),
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optimal_action='BUY',
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actual_outcome=0.025, # 2.5% price increase
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market_state_before=None,
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market_state_after=None,
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confidence_should_have_been=0.85
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)
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orchestrator.perfect_moves.append(perfect_move)
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orchestrator.retrospective_learning_active = True
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logger.info(f"Added perfect move: {perfect_move.optimal_action} {perfect_move.symbol}")
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logger.info(f"Outcome: {perfect_move.actual_outcome*100:+.2f}%")
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logger.info(f"Confidence should have been: {perfect_move.confidence_should_have_been:.3f}")
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# Test performance metrics
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metrics = orchestrator.get_performance_metrics()
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retro_metrics = metrics['retrospective_learning']
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logger.info(f"Retrospective learning active: {retro_metrics['active']}")
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logger.info(f"Recent perfect moves: {retro_metrics['perfect_moves_recent']}")
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logger.info(f"Average confidence needed: {retro_metrics['avg_confidence_needed']:.3f}")
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logger.info("✅ Retrospective learning test completed")
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async def test_tick_pattern_detection():
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"""Test tick pattern detection for violent moves"""
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logger.info("=== Testing Tick Pattern Detection ===")
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# Create orchestrator
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data_provider = DataProvider()
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orchestrator = EnhancedTradingOrchestrator(data_provider)
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# Simulate violent tick
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from core.tick_aggregator import RawTick
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violent_tick = RawTick(
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timestamp=datetime.now(),
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price=2560.0,
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volume=1000.0,
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quantity=0.5,
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side='buy',
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trade_id='test123',
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time_since_last=25.0, # Very fast tick (25ms)
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price_change=5.0, # $5 price jump
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volume_intensity=3.5 # High volume
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)
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# Add symbol attribute for testing
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violent_tick.symbol = 'ETH/USDT'
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logger.info(f"Simulating violent tick:")
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logger.info(f"Price change: ${violent_tick.price_change:+.2f}")
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logger.info(f"Time since last: {violent_tick.time_since_last:.0f}ms")
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logger.info(f"Volume intensity: {violent_tick.volume_intensity:.1f}x")
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# Process the tick
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orchestrator._handle_raw_tick(violent_tick)
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# Check if perfect move was created
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if orchestrator.perfect_moves:
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latest_move = orchestrator.perfect_moves[-1]
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logger.info(f"✅ Perfect move detected: {latest_move.optimal_action}")
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logger.info(f"Confidence: {latest_move.confidence_should_have_been:.3f}")
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else:
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logger.info("❌ No perfect move detected")
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logger.info("✅ Tick pattern detection test completed")
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def test_dashboard_integration():
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"""Test dashboard integration with new features"""
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logger.info("=== Testing Dashboard Integration ===")
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# Create components
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data_provider = DataProvider()
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orchestrator = EnhancedTradingOrchestrator(data_provider)
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# Test model training status
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metrics = orchestrator.get_performance_metrics()
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logger.info("Model Training Metrics:")
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logger.info(f"Perfect moves: {metrics['perfect_moves']}")
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logger.info(f"RL queue size: {metrics['rl_queue_size']}")
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logger.info(f"Retrospective learning: {metrics['retrospective_learning']}")
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logger.info(f"Position tracking: {metrics['position_tracking']}")
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logger.info(f"Thresholds: {metrics['thresholds']}")
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logger.info("✅ Dashboard integration test completed")
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async def main():
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"""Run all tests"""
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logger.info("🚀 Starting Enhanced Trading System Tests")
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logger.info("=" * 60)
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try:
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# Run tests
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test_color_coded_positions()
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print()
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test_confidence_thresholds()
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print()
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test_retrospective_learning()
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print()
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await test_tick_pattern_detection()
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print()
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test_dashboard_integration()
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print()
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logger.info("=" * 60)
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logger.info("🎉 All tests completed successfully!")
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logger.info("Key improvements verified:")
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logger.info("✅ Color-coded positions ([LONG] green, [SHORT] red)")
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logger.info("✅ Lower closing thresholds (0.25 vs 0.6)")
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logger.info("✅ Retrospective learning on perfect opportunities")
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logger.info("✅ Enhanced model training detection")
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logger.info("✅ Violent move pattern detection")
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except Exception as e:
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logger.error(f"❌ Test failed: {e}")
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import traceback
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logger.error(traceback.format_exc())
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if __name__ == "__main__":
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asyncio.run(main()) |