wip....
This commit is contained in:
Dobromir Popov
@ -130,7 +130,7 @@ class DQNAgent:
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result = load_best_checkpoint(self.model_name)
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if result:
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file_path, metadata = result
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checkpoint = torch.load(file_path, map_location=self.device)
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checkpoint = torch.load(file_path, map_location=self.device, weights_only=False)
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# Load model states
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if 'policy_net_state_dict' in checkpoint:
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@ -1212,7 +1212,7 @@ class DQNAgent:
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# Load agent state
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try:
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agent_state = torch.load(f"{path}_agent_state.pt", map_location=self.device)
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agent_state = torch.load(f"{path}_agent_state.pt", map_location=self.device, weights_only=False)
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self.epsilon = agent_state['epsilon']
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self.update_count = agent_state['update_count']
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self.losses = agent_state['losses']
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@ -2351,7 +2351,7 @@ class TradingOrchestrator:
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except Exception as e:
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logger.debug(f"No decision fusion checkpoint found: {e}")
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logger.info("🧠 Decision fusion network initialized in orchestrator - TRAINING ON EVERY SIGNAL!")
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logger.info("Decision fusion network initialized in orchestrator - TRAINING ON EVERY SIGNAL!")
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except Exception as e:
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logger.error(f"Error initializing decision fusion: {e}")
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69
test_cob_data_format.py
Normal file
69
test_cob_data_format.py
Normal file
@ -0,0 +1,69 @@
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#!/usr/bin/env python3
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"""
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Test COB Data Format - Check what data is actually available
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"""
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import time
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import asyncio
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from core.multi_exchange_cob_provider import MultiExchangeCOBProvider
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async def test_cob_data_format():
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"""Test what COB data format is actually available"""
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print("=== COB DATA FORMAT TEST ===")
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# Create COB provider directly (same as dashboard)
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cob_provider = MultiExchangeCOBProvider(
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symbols=['ETH/USDT', 'BTC/USDT'],
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bucket_size_bps=1.0
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)
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# Add callback to capture data
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captured_data = {}
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def capture_callback(symbol: str, cob_snapshot):
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captured_data[symbol] = cob_snapshot
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print(f"Captured COB data for {symbol}:")
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print(f" Type: {type(cob_snapshot)}")
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print(f" Attributes: {dir(cob_snapshot)}")
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# Check key attributes
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if hasattr(cob_snapshot, 'consolidated_bids'):
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print(f" Bids count: {len(cob_snapshot.consolidated_bids)}")
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if hasattr(cob_snapshot, 'consolidated_asks'):
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print(f" Asks count: {len(cob_snapshot.consolidated_asks)}")
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if hasattr(cob_snapshot, 'spread_bps'):
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print(f" Spread: {cob_snapshot.spread_bps}")
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if hasattr(cob_snapshot, 'exchanges_active'):
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print(f" Active exchanges: {len(cob_snapshot.exchanges_active)}")
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print()
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cob_provider.subscribe_to_cob_updates(capture_callback)
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# Start COB provider
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print("Starting COB provider...")
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await cob_provider.start_streaming()
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# Wait for data
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print("Waiting for COB data...")
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for i in range(30):
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await asyncio.sleep(1)
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if captured_data:
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break
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if i % 5 == 0:
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print(f" Waiting... {i}s")
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if captured_data:
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print("SUCCESS: COB data captured!")
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for symbol, cob_snapshot in captured_data.items():
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print(f"\n{symbol} COB snapshot:")
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print(f" Type: {type(cob_snapshot)}")
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print(f" Has consolidated_bids: {hasattr(cob_snapshot, 'consolidated_bids')}")
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print(f" Has consolidated_asks: {hasattr(cob_snapshot, 'consolidated_asks')}")
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else:
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print("No COB data captured")
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# Stop COB provider
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await cob_provider.stop_streaming()
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if __name__ == "__main__":
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asyncio.run(test_cob_data_format())
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@ -1670,19 +1670,40 @@ class CleanTradingDashboard:
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return {'error': str(e), 'cob_status': 'Error Getting Status', 'orchestrator_type': 'Unknown'}
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def _get_cob_snapshot(self, symbol: str) -> Optional[Any]:
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"""Get COB snapshot for symbol from unified orchestrator"""
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"""Get COB snapshot for symbol - PERFORMANCE OPTIMIZED: Use orchestrator's COB integration"""
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try:
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# Unified orchestrator with COB integration
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if hasattr(self.orchestrator, 'get_cob_snapshot'):
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snapshot = self.orchestrator.get_cob_snapshot(symbol)
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# PERFORMANCE FIX: Use orchestrator's COB integration instead of separate dashboard integration
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# This eliminates redundant COB providers and improves performance
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if hasattr(self.orchestrator, 'cob_integration') and self.orchestrator.cob_integration:
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snapshot = self.orchestrator.cob_integration.get_cob_snapshot(symbol)
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if snapshot:
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logger.debug(f"COB snapshot available for {symbol}")
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logger.debug(f"COB snapshot available for {symbol} from orchestrator COB integration")
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return snapshot
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else:
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logger.debug(f"No COB snapshot available for {symbol}")
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logger.debug(f"No COB snapshot available for {symbol} from orchestrator COB integration")
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return None
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# Fallback: Use cached COB data if orchestrator integration not available
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elif symbol in self.latest_cob_data:
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cob_data = self.latest_cob_data[symbol]
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logger.debug(f"COB snapshot available for {symbol} from cached data (fallback)")
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# Create a simple snapshot object from the cached data
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class COBSnapshot:
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def __init__(self, data):
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self.consolidated_bids = data.get('bids', [])
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self.consolidated_asks = data.get('asks', [])
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stats = data.get('stats', {})
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self.spread_bps = stats.get('spread_bps', 0)
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self.volume_weighted_mid = stats.get('mid_price', 0)
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self.liquidity_imbalance = stats.get('imbalance', 0)
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self.total_bid_liquidity = stats.get('total_bid_liquidity', 0)
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self.total_ask_liquidity = stats.get('total_ask_liquidity', 0)
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self.exchanges_active = stats.get('exchanges_active', [])
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return COBSnapshot(cob_data)
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else:
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logger.debug(f"No COB integration available for {symbol}")
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logger.debug(f"No COB snapshot available for {symbol} - no orchestrator integration or cached data")
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return None
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except Exception as e:
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@ -1739,8 +1760,9 @@ class CleanTradingDashboard:
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dqn_training_enabled = getattr(self, 'dqn_training_enabled', True) # Default: enabled
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dqn_checkpoint_loaded = dqn_state.get('checkpoint_loaded', False)
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# DQN is active if checkpoint is loaded AND inference is enabled
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dqn_active = dqn_checkpoint_loaded and dqn_inference_enabled
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# DQN is active if checkpoint is loaded AND inference is enabled AND orchestrator has the model
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dqn_model_available = self.orchestrator and hasattr(self.orchestrator, 'rl_agent') and self.orchestrator.rl_agent is not None
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dqn_active = dqn_checkpoint_loaded and dqn_inference_enabled and dqn_model_available
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dqn_prediction_count = len(self.recent_decisions) if signal_generation_active else 0
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if signal_generation_active and len(self.recent_decisions) > 0:
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@ -1759,13 +1781,14 @@ class CleanTradingDashboard:
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'action': last_action,
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'confidence': last_confidence
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},
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'loss_5ma': dqn_state.get('current_loss', dqn_state.get('initial_loss', 0.2850)),
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# FIXED: Get REAL loss values from orchestrator model, not placeholders
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'loss_5ma': self._get_real_model_loss('dqn'),
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'initial_loss': dqn_state.get('initial_loss', 0.2850),
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'best_loss': dqn_state.get('best_loss', dqn_state.get('initial_loss', 0.2850)),
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'best_loss': self._get_real_best_loss('dqn'),
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'improvement': safe_improvement_calc(
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dqn_state.get('initial_loss', 0.2850),
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dqn_state.get('current_loss', dqn_state.get('initial_loss', 0.2850)),
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0.0 if not dqn_active else 94.9 # No improvement if not training
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self._get_real_model_loss('dqn'),
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0.0 if not dqn_active else 94.9 # Default if no real improvement available
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),
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'checkpoint_loaded': dqn_checkpoint_loaded,
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'model_type': 'DQN',
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@ -3034,6 +3057,81 @@ class CleanTradingDashboard:
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return default
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except Exception:
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return default
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def _get_real_model_loss(self, model_name: str) -> float:
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"""Get REAL current loss from the actual model, not placeholders"""
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try:
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if not self.orchestrator:
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return 0.2850 # Default fallback
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if model_name == 'dqn' and hasattr(self.orchestrator, 'rl_agent') and self.orchestrator.rl_agent:
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# Get real loss from DQN agent
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agent = self.orchestrator.rl_agent
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if hasattr(agent, 'losses') and len(agent.losses) > 0:
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# Average of last 50 losses for current loss
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recent_losses = agent.losses[-50:]
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return sum(recent_losses) / len(recent_losses)
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elif hasattr(agent, 'current_loss'):
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return agent.current_loss
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elif model_name == 'cnn' and hasattr(self.orchestrator, 'cnn_model') and self.orchestrator.cnn_model:
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# Get real loss from CNN model
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model = self.orchestrator.cnn_model
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if hasattr(model, 'training_losses') and len(model.training_losses) > 0:
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recent_losses = model.training_losses[-50:]
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return sum(recent_losses) / len(recent_losses)
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elif hasattr(model, 'current_loss'):
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return model.current_loss
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elif model_name == 'decision' and hasattr(self.orchestrator, 'decision_fusion_network'):
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# Get real loss from decision fusion
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if hasattr(self.orchestrator, 'fusion_training_data') and len(self.orchestrator.fusion_training_data) > 0:
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recent_losses = [entry['loss'] for entry in self.orchestrator.fusion_training_data[-50:]]
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if recent_losses:
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return sum(recent_losses) / len(recent_losses)
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# Fallback to model states
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model_states = self.orchestrator.get_model_states() if hasattr(self.orchestrator, 'get_model_states') else {}
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state = model_states.get(model_name, {})
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return state.get('current_loss', 0.2850)
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except Exception as e:
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logger.debug(f"Error getting real loss for {model_name}: {e}")
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return 0.2850 # Safe fallback
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def _get_real_best_loss(self, model_name: str) -> float:
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"""Get REAL best loss from the actual model"""
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try:
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if not self.orchestrator:
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return 0.0145 # Default fallback
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if model_name == 'dqn' and hasattr(self.orchestrator, 'rl_agent') and self.orchestrator.rl_agent:
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agent = self.orchestrator.rl_agent
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if hasattr(agent, 'best_loss'):
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return agent.best_loss
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elif hasattr(agent, 'losses') and len(agent.losses) > 0:
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return min(agent.losses)
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elif model_name == 'cnn' and hasattr(self.orchestrator, 'cnn_model') and self.orchestrator.cnn_model:
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model = self.orchestrator.cnn_model
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if hasattr(model, 'best_loss'):
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return model.best_loss
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elif hasattr(model, 'training_losses') and len(model.training_losses) > 0:
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return min(model.training_losses)
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elif model_name == 'decision' and hasattr(self.orchestrator, 'fusion_training_data'):
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if len(self.orchestrator.fusion_training_data) > 0:
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all_losses = [entry['loss'] for entry in self.orchestrator.fusion_training_data]
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return min(all_losses) if all_losses else 0.0065
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# Fallback to model states
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model_states = self.orchestrator.get_model_states() if hasattr(self.orchestrator, 'get_model_states') else {}
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state = model_states.get(model_name, {})
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return state.get('best_loss', 0.0145)
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except Exception as e:
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logger.debug(f"Error getting best loss for {model_name}: {e}")
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return 0.0145 # Safe fallback
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def _clear_old_signals_for_tick_range(self):
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"""Clear old signals that are outside the current tick cache time range - VERY CONSERVATIVE"""
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@ -3305,99 +3403,98 @@ class CleanTradingDashboard:
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def _create_cob_ladder_display(self, symbol: str) -> List:
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"""Create real COB ladder display showing order book"""
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try:
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# Get COB data from the working integration
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cob_data = self.get_cob_data(symbol)
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# FIXED: Use cached COB data from the working integration
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cob_data = self.latest_cob_data.get(symbol)
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if not cob_data:
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return [
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html.Div([
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html.H6(f"{symbol} - COB", className="text-muted mb-2"),
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html.P("COB data not available", className="text-warning small"),
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html.P("Initializing connections...", className="text-muted small")
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html.H6(f"{symbol.replace('/USDT', '')} Order Book", className="text-muted mb-2"),
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html.P("Connecting to exchanges...", className="text-warning small"),
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html.P("Binance • Coinbase • Kraken", className="text-muted small")
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])
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]
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components = []
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# Header with symbol and status
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# Get data from cached COB data
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stats = cob_data.get('stats', {})
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spread_bps = stats.get('spread_bps', 0)
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spread_color = "text-success" if spread_bps < 5 else "text-warning" if spread_bps < 10 else "text-danger"
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components.append(html.Div([
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html.H6(f"{symbol} - Order Book", className="text-info mb-2"),
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html.Small(f"Last update: {datetime.now().strftime('%H:%M:%S')}", className="text-muted")
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html.H6(f"{symbol.replace('/USDT', '')} Order Book", className="text-info mb-1"),
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html.Div([
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html.Small(f"Spread: {spread_bps:.1f} bps", className=f"{spread_color} me-2"),
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html.Small(f"Exchanges: {len(cob_data.get('exchanges_active', []))}", className="text-muted")
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])
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]))
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# Get order book data
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bids = cob_data.get('bids', [])
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# Get order book data from cached data
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asks = cob_data.get('asks', [])
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stats = cob_data.get('stats', {})
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bids = cob_data.get('bids', [])
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mid_price = stats.get('mid_price', 0)
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# Display key statistics
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if stats:
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spread = stats.get('spread_bps', 0)
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imbalance = stats.get('imbalance', 0)
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components.append(html.Div([
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html.P([
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html.Span("Spread: ", className="text-muted small"),
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html.Span(f"{spread:.1f} bps", className="text-warning small fw-bold")
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], className="mb-1"),
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html.P([
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html.Span("Imbalance: ", className="text-muted small"),
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html.Span(f"{imbalance:.3f}", className="text-info small fw-bold")
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], className="mb-2")
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]))
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# Order book ladder - Asks (top, descending)
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# Order book ladder - Asks (top, descending, reversed for proper display)
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if asks:
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# Show top 5 asks in descending price order (highest price at top)
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top_asks = sorted(asks[:10], key=lambda x: x['price'], reverse=True)[:5]
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components.append(html.Div([
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html.H6("ASKS", className="text-danger small mb-1"),
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html.Div([
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html.Div([
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html.Span(f"${ask['price']:.2f}", className="text-danger small me-2"),
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html.Span(f"{ask['size']:.4f}", className="text-muted small")
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], className="d-flex justify-content-between mb-1")
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for ask in asks[:5] # Top 5 asks
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], className="border-start border-danger ps-2 mb-2")
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]))
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html.Span(f"${ask['price']:.2f}", className="text-danger small fw-bold", style={"width": "60px"}),
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html.Span(f"{ask['size']:.3f}", className="text-muted small", style={"textAlign": "right"})
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], className="d-flex justify-content-between py-1",
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style={"borderBottom": "1px solid rgba(220,53,69,0.2)"})
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for ask in top_asks
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])
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], className="mb-2"))
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# Current price (mid)
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if bids and asks:
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mid_price = (bids[0]['price'] + asks[0]['price']) / 2
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# Current price/spread indicator with mid price
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if mid_price > 0:
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components.append(html.Div([
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html.Hr(className="my-1"),
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html.P([
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html.Strong(f"${mid_price:.2f}", className="text-primary")
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], className="text-center mb-1"),
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html.Hr(className="my-1")
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]))
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html.Div([
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html.Span(f"${mid_price:.2f}", className="text-warning fw-bold"),
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html.Small(f" ({spread_bps:.1f} bps)", className="text-muted")
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], className="text-center py-2",
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style={"backgroundColor": "rgba(255,193,7,0.1)", "border": "1px solid rgba(255,193,7,0.3)"})
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], className="mb-2"))
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# Order book ladder - Bids (bottom, descending)
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if bids:
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# Show top 5 bids in descending price order
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top_bids = sorted(bids[:10], key=lambda x: x['price'], reverse=True)[:5]
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components.append(html.Div([
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html.H6("BIDS", className="text-success small mb-1"),
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html.Div([
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html.Div([
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html.Span(f"${bid['price']:.2f}", className="text-success small me-2"),
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html.Span(f"{bid['size']:.4f}", className="text-muted small")
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], className="d-flex justify-content-between mb-1")
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for bid in bids[:5] # Top 5 bids
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], className="border-start border-success ps-2")
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html.Span(f"${bid['price']:.2f}", className="text-success small fw-bold", style={"width": "60px"}),
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html.Span(f"{bid['size']:.3f}", className="text-muted small", style={"textAlign": "right"})
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], className="d-flex justify-content-between py-1",
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style={"borderBottom": "1px solid rgba(25,135,84,0.2)"})
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for bid in top_bids
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])
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]))
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# Summary stats
|
||||
# Summary stats - liquidity and imbalance
|
||||
if bids and asks:
|
||||
total_bid_volume = sum(bid['size'] * bid['price'] for bid in bids[:10])
|
||||
total_ask_volume = sum(ask['size'] * ask['price'] for ask in asks[:10])
|
||||
total_bid_liquidity = stats.get('total_bid_liquidity', sum(bid['total'] for bid in bids[:10] if 'total' in bid))
|
||||
total_ask_liquidity = stats.get('total_ask_liquidity', sum(ask['total'] for ask in asks[:10] if 'total' in ask))
|
||||
total_liquidity = total_bid_liquidity + total_ask_liquidity
|
||||
|
||||
# Calculate imbalance
|
||||
bid_ratio = (total_bid_liquidity / total_liquidity * 100) if total_liquidity > 0 else 50
|
||||
ask_ratio = (total_ask_liquidity / total_liquidity * 100) if total_liquidity > 0 else 50
|
||||
|
||||
components.append(html.Div([
|
||||
html.Hr(className="my-2"),
|
||||
html.P([
|
||||
html.Span("Bid Vol: ", className="text-muted small"),
|
||||
html.Span(f"${total_bid_volume:,.0f}", className="text-success small")
|
||||
html.Div([
|
||||
html.Small("Liquidity:", className="text-muted"),
|
||||
html.Small(f" ${total_liquidity:,.0f}", className="text-info fw-bold")
|
||||
], className="mb-1"),
|
||||
html.P([
|
||||
html.Span("Ask Vol: ", className="text-muted small"),
|
||||
html.Span(f"${total_ask_volume:,.0f}", className="text-danger small")
|
||||
], className="mb-1")
|
||||
html.Div([
|
||||
html.Small(f"Bids: {bid_ratio:.0f}%", className="text-success small me-2"),
|
||||
html.Small(f"Asks: {ask_ratio:.0f}%", className="text-danger small")
|
||||
])
|
||||
]))
|
||||
|
||||
return components
|
||||
|
||||
Reference in New Issue
Block a user