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Dobromir Popov
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tests/test_nn_driven_trading.py
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tests/test_nn_driven_trading.py
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#!/usr/bin/env python3
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"""
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Test NN-Driven Trading System
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Demonstrates how the system now makes decisions using Neural Networks instead of algorithms
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"""
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import logging
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import asyncio
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from datetime import datetime
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import numpy as np
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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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async def test_nn_driven_system():
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"""Test the NN-driven trading system"""
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logger.info("=== TESTING NN-DRIVEN TRADING SYSTEM ===")
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try:
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# Import core components
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from core.config import get_config
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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.nn_decision_fusion import ModelPrediction, MarketContext
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# Initialize components
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config = get_config()
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data_provider = DataProvider()
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# Initialize NN-driven orchestrator
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orchestrator = EnhancedTradingOrchestrator(
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data_provider=data_provider,
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symbols=['ETH/USDT', 'BTC/USDT'],
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enhanced_rl_training=True
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)
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logger.info("✅ NN-driven orchestrator initialized")
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# Test 1: Add mock CNN prediction
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cnn_prediction = ModelPrediction(
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model_name="williams_cnn",
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prediction_type="direction",
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value=0.6, # Bullish signal
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confidence=0.8,
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timestamp=datetime.now(),
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metadata={'timeframe': '1h', 'feature_importance': [0.2, 0.3, 0.5]}
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)
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orchestrator.neural_fusion.add_prediction(cnn_prediction)
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logger.info("🔮 Added CNN prediction: BULLISH (0.6) with 80% confidence")
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# Test 2: Add mock RL prediction
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rl_prediction = ModelPrediction(
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model_name="dqn_agent",
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prediction_type="action",
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value=0.4, # Moderate buy signal
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confidence=0.7,
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timestamp=datetime.now(),
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metadata={'action_probs': [0.4, 0.2, 0.4]} # [BUY, SELL, HOLD]
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)
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orchestrator.neural_fusion.add_prediction(rl_prediction)
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logger.info("🔮 Added RL prediction: MODERATE_BUY (0.4) with 70% confidence")
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# Test 3: Add mock COB RL prediction
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cob_prediction = ModelPrediction(
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model_name="cob_rl",
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prediction_type="direction",
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value=0.3, # Slightly bullish
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confidence=0.85,
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timestamp=datetime.now(),
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metadata={'cob_imbalance': 0.1, 'liquidity_depth': 150000}
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)
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orchestrator.neural_fusion.add_prediction(cob_prediction)
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logger.info("🔮 Added COB RL prediction: SLIGHT_BULLISH (0.3) with 85% confidence")
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# Test 4: Create market context
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market_context = MarketContext(
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symbol='ETH/USDT',
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current_price=2441.50,
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price_change_1m=0.002, # 0.2% up in 1m
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price_change_5m=0.008, # 0.8% up in 5m
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volume_ratio=1.2, # 20% above average volume
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volatility=0.015, # 1.5% volatility
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timestamp=datetime.now()
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)
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logger.info(f"📊 Market Context: ETH/USDT at ${market_context.current_price}")
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logger.info(f" 📈 Price changes: 1m: {market_context.price_change_1m:.3f}, 5m: {market_context.price_change_5m:.3f}")
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logger.info(f" 📊 Volume ratio: {market_context.volume_ratio:.2f}, Volatility: {market_context.volatility:.3f}")
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# Test 5: Make NN decision
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fusion_decision = orchestrator.neural_fusion.make_decision(
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symbol='ETH/USDT',
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market_context=market_context,
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min_confidence=0.25
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)
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if fusion_decision:
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logger.info("🧠 === NN DECISION RESULT ===")
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logger.info(f" Action: {fusion_decision.action}")
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logger.info(f" Confidence: {fusion_decision.confidence:.3f}")
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logger.info(f" Expected Return: {fusion_decision.expected_return:.3f}")
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logger.info(f" Risk Score: {fusion_decision.risk_score:.3f}")
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logger.info(f" Position Size: {fusion_decision.position_size:.4f} ETH")
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logger.info(f" Reasoning: {fusion_decision.reasoning}")
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logger.info(" Model Contributions:")
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for model, contribution in fusion_decision.model_contributions.items():
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logger.info(f" - {model}: {contribution:.1%}")
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else:
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logger.warning("❌ No NN decision generated")
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# Test 6: Test coordinated decisions
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logger.info("\n🎯 Testing coordinated NN decisions...")
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decisions = await orchestrator.make_coordinated_decisions()
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if decisions:
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logger.info(f"✅ Generated {len(decisions)} NN-driven trading decisions:")
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for i, decision in enumerate(decisions):
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logger.info(f" Decision {i+1}: {decision.symbol} {decision.action} "
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f"({decision.confidence:.3f} confidence, "
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f"{decision.quantity:.4f} size)")
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if hasattr(decision, 'metadata') and decision.metadata:
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if decision.metadata.get('nn_driven'):
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logger.info(f" 🧠 NN-DRIVEN: {decision.metadata.get('reasoning', 'No reasoning')}")
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else:
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logger.info("ℹ️ No trading decisions generated (insufficient confidence)")
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# Test 7: Check NN system status
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nn_status = orchestrator.neural_fusion.get_status()
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logger.info("\n📊 NN System Status:")
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logger.info(f" Device: {nn_status['device']}")
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logger.info(f" Training Mode: {nn_status['training_mode']}")
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logger.info(f" Registered Models: {nn_status['registered_models']}")
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logger.info(f" Recent Predictions: {nn_status['recent_predictions']}")
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logger.info(f" Model Parameters: {nn_status['model_parameters']:,}")
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# Test 8: Demonstrate different confidence scenarios
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logger.info("\n🔬 Testing different confidence scenarios...")
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# Low confidence scenario
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low_conf_prediction = ModelPrediction(
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model_name="williams_cnn",
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prediction_type="direction",
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value=0.1, # Weak signal
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confidence=0.2, # Low confidence
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timestamp=datetime.now()
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)
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orchestrator.neural_fusion.add_prediction(low_conf_prediction)
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low_conf_decision = orchestrator.neural_fusion.make_decision(
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symbol='ETH/USDT',
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market_context=market_context,
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min_confidence=0.25
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)
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if low_conf_decision:
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logger.info(f" Low confidence result: {low_conf_decision.action} (should be HOLD)")
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else:
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logger.info(" ✅ Low confidence correctly resulted in no decision")
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# High confidence scenario
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high_conf_prediction = ModelPrediction(
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model_name="williams_cnn",
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prediction_type="direction",
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value=0.8, # Strong signal
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confidence=0.95, # Very high confidence
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timestamp=datetime.now()
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)
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orchestrator.neural_fusion.add_prediction(high_conf_prediction)
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high_conf_decision = orchestrator.neural_fusion.make_decision(
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symbol='ETH/USDT',
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market_context=market_context,
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min_confidence=0.25
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)
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if high_conf_decision:
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logger.info(f" High confidence result: {high_conf_decision.action} "
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f"(conf: {high_conf_decision.confidence:.3f}, "
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f"size: {high_conf_decision.position_size:.4f})")
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logger.info("\n✅ NN-DRIVEN TRADING SYSTEM TEST COMPLETE")
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logger.info("🎯 Key Benefits Demonstrated:")
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logger.info(" 1. Multiple NN models provide predictions")
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logger.info(" 2. Central NN fusion makes final decisions")
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logger.info(" 3. Market context influences decisions")
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logger.info(" 4. Confidence thresholds prevent bad trades")
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logger.info(" 5. Position sizing based on NN outputs")
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logger.info(" 6. Clear reasoning for every decision")
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logger.info(" 7. Model contribution tracking")
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except Exception as e:
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logger.error(f"Error in NN-driven system test: {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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asyncio.run(test_nn_driven_system())
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