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Dobromir Popov
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test_improved_data_integration.py
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test_improved_data_integration.py
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#!/usr/bin/env python3
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"""
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Test Improved Data Integration
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Test the enhanced data integration with fallback strategies
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"""
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import time
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from core.orchestrator import TradingOrchestrator
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from core.data_provider import DataProvider
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def test_enhanced_data_population():
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"""Test enhanced data population with fallback strategies"""
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print("=== Testing Enhanced Data Population ===")
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try:
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data_provider = DataProvider()
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orchestrator = TradingOrchestrator(data_provider)
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# Wait for initial population
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print("Waiting 5 seconds for enhanced data population...")
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time.sleep(5)
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# Check detailed queue status
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print("\nDetailed queue status after enhanced population:")
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orchestrator.log_queue_status(detailed=True)
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# Check minimum data requirements
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symbols_to_check = ['ETH/USDT', 'BTC/USDT']
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timeframes_to_check = ['1s', '1m', '1h', '1d']
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min_requirements = {'1s': 100, '1m': 50, '1h': 20, '1d': 10}
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print(f"\nChecking minimum data requirements with fallback:")
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all_requirements_met = True
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for symbol in symbols_to_check:
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print(f"\n{symbol}:")
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symbol_requirements_met = True
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for timeframe in timeframes_to_check:
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min_count = min_requirements.get(timeframe, 10)
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has_min = orchestrator.ensure_minimum_data(f'ohlcv_{timeframe}', symbol, min_count)
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actual_count = 0
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if f'ohlcv_{timeframe}' in orchestrator.data_queues and symbol in orchestrator.data_queues[f'ohlcv_{timeframe}']:
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with orchestrator.data_queue_locks[f'ohlcv_{timeframe}'][symbol]:
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actual_count = len(orchestrator.data_queues[f'ohlcv_{timeframe}'][symbol])
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status = "✅" if has_min else "❌"
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print(f" {timeframe}: {status} {actual_count}/{min_count}")
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if not has_min:
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symbol_requirements_met = False
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all_requirements_met = False
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# Check technical indicators
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indicators_count = 0
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if 'technical_indicators' in orchestrator.data_queues and symbol in orchestrator.data_queues['technical_indicators']:
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with orchestrator.data_queue_locks['technical_indicators'][symbol]:
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indicators_data = list(orchestrator.data_queues['technical_indicators'][symbol])
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if indicators_data:
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indicators_count = len(indicators_data[-1]) # Latest indicators dict
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indicators_status = "✅" if indicators_count > 0 else "❌"
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print(f" indicators: {indicators_status} {indicators_count} calculated")
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# Test BaseDataInput building
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print(f"\nTesting BaseDataInput building with fallback:")
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for symbol in ['ETH/USDT', 'BTC/USDT']:
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base_data = orchestrator.build_base_data_input(symbol)
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if base_data:
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features = base_data.get_feature_vector()
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print(f" ✅ {symbol}: BaseDataInput built successfully")
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print(f" Feature vector size: {len(features)}")
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print(f" OHLCV 1s bars: {len(base_data.ohlcv_1s)}")
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print(f" OHLCV 1m bars: {len(base_data.ohlcv_1m)}")
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print(f" OHLCV 1h bars: {len(base_data.ohlcv_1h)}")
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print(f" OHLCV 1d bars: {len(base_data.ohlcv_1d)}")
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print(f" BTC bars: {len(base_data.btc_ohlcv_1s)}")
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print(f" Technical indicators: {len(base_data.technical_indicators)}")
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# Validate feature vector
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if len(features) == 7850:
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print(f" ✅ Feature vector has correct size (7850)")
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else:
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print(f" ❌ Feature vector size mismatch: {len(features)} != 7850")
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else:
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print(f" ❌ {symbol}: Failed to build BaseDataInput")
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return all_requirements_met
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except Exception as e:
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print(f"❌ Test failed: {e}")
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return False
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def test_fallback_strategies():
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"""Test specific fallback strategies"""
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print("\n=== Testing Fallback Strategies ===")
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try:
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data_provider = DataProvider()
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orchestrator = TradingOrchestrator(data_provider)
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# Wait for initial population
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time.sleep(3)
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# Check if fallback strategies were used
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print("Checking fallback strategy usage:")
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# Check ETH/USDT 1s data (likely to need fallback)
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eth_1s_count = 0
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if 'ohlcv_1s' in orchestrator.data_queues and 'ETH/USDT' in orchestrator.data_queues['ohlcv_1s']:
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with orchestrator.data_queue_locks['ohlcv_1s']['ETH/USDT']:
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eth_1s_count = len(orchestrator.data_queues['ohlcv_1s']['ETH/USDT'])
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if eth_1s_count >= 100:
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print(f" ✅ ETH/USDT 1s data: {eth_1s_count} bars (fallback likely used)")
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else:
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print(f" ❌ ETH/USDT 1s data: {eth_1s_count} bars (fallback may have failed)")
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# Check ETH/USDT 1h data (likely to need fallback)
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eth_1h_count = 0
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if 'ohlcv_1h' in orchestrator.data_queues and 'ETH/USDT' in orchestrator.data_queues['ohlcv_1h']:
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with orchestrator.data_queue_locks['ohlcv_1h']['ETH/USDT']:
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eth_1h_count = len(orchestrator.data_queues['ohlcv_1h']['ETH/USDT'])
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if eth_1h_count >= 20:
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print(f" ✅ ETH/USDT 1h data: {eth_1h_count} bars (fallback likely used)")
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else:
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print(f" ❌ ETH/USDT 1h data: {eth_1h_count} bars (fallback may have failed)")
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# Test manual fallback strategy
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print(f"\nTesting manual fallback strategy:")
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missing_data = [('ohlcv_1s', 0, 100), ('ohlcv_1h', 0, 20)]
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fallback_success = orchestrator._try_fallback_data_strategy('ETH/USDT', missing_data)
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print(f" Manual fallback result: {'✅ SUCCESS' if fallback_success else '❌ FAILED'}")
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return eth_1s_count >= 100 and eth_1h_count >= 20
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except Exception as e:
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print(f"❌ Test failed: {e}")
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return False
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def test_model_predictions():
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"""Test that models can now make predictions with the improved data"""
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print("\n=== Testing Model Predictions ===")
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try:
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data_provider = DataProvider()
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orchestrator = TradingOrchestrator(data_provider)
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# Wait for data population
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time.sleep(5)
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# Try to make predictions
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print("Testing model prediction capability:")
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# Test CNN prediction
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try:
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base_data = orchestrator.build_base_data_input('ETH/USDT')
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if base_data:
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print(" ✅ BaseDataInput available for CNN")
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# Test feature vector
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features = base_data.get_feature_vector()
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if len(features) == 7850:
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print(" ✅ Feature vector has correct size for CNN")
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print(" ✅ CNN should be able to make predictions without rebuilding")
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else:
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print(f" ❌ Feature vector size issue: {len(features)} != 7850")
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else:
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print(" ❌ BaseDataInput not available for CNN")
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except Exception as e:
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print(f" ❌ CNN prediction test failed: {e}")
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# Test RL prediction
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try:
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base_data = orchestrator.build_base_data_input('ETH/USDT')
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if base_data:
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print(" ✅ BaseDataInput available for RL")
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# Test state features
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state_features = base_data.get_feature_vector()
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if len(state_features) == 7850:
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print(" ✅ State features have correct size for RL")
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else:
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print(f" ❌ State features size issue: {len(state_features)} != 7850")
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else:
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print(" ❌ BaseDataInput not available for RL")
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except Exception as e:
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print(f" ❌ RL prediction test failed: {e}")
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return base_data is not None
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except Exception as e:
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print(f"❌ Test failed: {e}")
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return False
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def main():
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"""Run all enhanced data integration tests"""
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print("=== Enhanced Data Integration Test Suite ===")
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test1_passed = test_enhanced_data_population()
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test2_passed = test_fallback_strategies()
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test3_passed = test_model_predictions()
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print(f"\n=== Results ===")
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print(f"Enhanced data population: {'✅ PASSED' if test1_passed else '❌ FAILED'}")
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print(f"Fallback strategies: {'✅ PASSED' if test2_passed else '❌ FAILED'}")
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print(f"Model predictions: {'✅ PASSED' if test3_passed else '❌ FAILED'}")
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if test1_passed and test2_passed and test3_passed:
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print("\n✅ ALL TESTS PASSED!")
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print("✅ Enhanced data integration is working!")
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print("✅ Fallback strategies provide missing data")
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print("✅ Models should be able to make predictions")
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print("✅ No more 'Insufficient data' errors expected")
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else:
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print("\n⚠️ Some tests failed, but system may still work")
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print("⚠️ Check specific failures above")
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if __name__ == "__main__":
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main()
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