403 lines
16 KiB
Python
403 lines
16 KiB
Python
"""
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Enhanced CNN Integration for Dashboard
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This module integrates the EnhancedCNNAdapter with the dashboard, providing real-time
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training and inference capabilities.
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"""
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import logging
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import threading
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import time
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from datetime import datetime
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from typing import Dict, List, Optional, Any, Union
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import os
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from .enhanced_cnn_adapter import EnhancedCNNAdapter
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from .standardized_data_provider import StandardizedDataProvider
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from .data_models import BaseDataInput, ModelOutput, create_model_output
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logger = logging.getLogger(__name__)
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class EnhancedCNNIntegration:
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"""
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Integration of EnhancedCNNAdapter with the dashboard
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This class:
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1. Manages the EnhancedCNNAdapter lifecycle
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2. Provides real-time training and inference
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3. Collects and reports performance metrics
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4. Integrates with the dashboard's model visualization
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"""
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def __init__(self, data_provider: StandardizedDataProvider, checkpoint_dir: str = "models/enhanced_cnn"):
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"""
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Initialize the EnhancedCNNIntegration
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Args:
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data_provider: StandardizedDataProvider instance
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checkpoint_dir: Directory to store checkpoints
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"""
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self.data_provider = data_provider
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self.checkpoint_dir = checkpoint_dir
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self.model_name = "enhanced_cnn_v1"
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# Create checkpoint directory if it doesn't exist
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os.makedirs(checkpoint_dir, exist_ok=True)
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# Initialize CNN adapter
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self.cnn_adapter = EnhancedCNNAdapter(checkpoint_dir=checkpoint_dir)
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# Load best checkpoint if available
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self.cnn_adapter.load_best_checkpoint()
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# Performance tracking
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self.inference_times = []
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self.training_times = []
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self.total_inferences = 0
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self.total_training_runs = 0
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self.last_inference_time = None
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self.last_training_time = None
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self.inference_rate = 0.0
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self.training_rate = 0.0
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self.daily_inferences = 0
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self.daily_training_runs = 0
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# Training settings
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self.training_enabled = True
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self.inference_enabled = True
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self.training_frequency = 10 # Train every N inferences
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self.training_batch_size = 32
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self.training_epochs = 1
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# Latest prediction
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self.latest_prediction = None
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self.latest_prediction_time = None
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# Training metrics
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self.current_loss = 0.0
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self.initial_loss = None
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self.best_loss = None
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self.current_accuracy = 0.0
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self.improvement_percentage = 0.0
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# Training thread
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self.training_thread = None
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self.training_active = False
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self.stop_training = False
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logger.info(f"EnhancedCNNIntegration initialized with model: {self.model_name}")
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def start_continuous_training(self):
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"""Start continuous training in a background thread"""
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if self.training_thread is not None and self.training_thread.is_alive():
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logger.info("Continuous training already running")
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return
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self.stop_training = False
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self.training_thread = threading.Thread(target=self._continuous_training_loop, daemon=True)
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self.training_thread.start()
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logger.info("Started continuous training thread")
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def stop_continuous_training(self):
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"""Stop continuous training"""
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self.stop_training = True
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logger.info("Stopping continuous training thread")
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def _continuous_training_loop(self):
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"""Continuous training loop"""
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try:
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self.training_active = True
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logger.info("Starting continuous training loop")
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while not self.stop_training:
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# Check if training is enabled
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if not self.training_enabled:
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time.sleep(5)
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continue
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# Check if we have enough training samples
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if len(self.cnn_adapter.training_data) < self.training_batch_size:
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logger.debug(f"Not enough training samples: {len(self.cnn_adapter.training_data)}/{self.training_batch_size}")
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time.sleep(5)
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continue
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# Train model
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start_time = time.time()
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metrics = self.cnn_adapter.train(epochs=self.training_epochs)
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training_time = time.time() - start_time
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# Update metrics
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self.training_times.append(training_time)
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if len(self.training_times) > 100:
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self.training_times.pop(0)
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self.total_training_runs += 1
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self.daily_training_runs += 1
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self.last_training_time = datetime.now()
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# Calculate training rate
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if self.training_times:
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avg_training_time = sum(self.training_times) / len(self.training_times)
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self.training_rate = 1.0 / avg_training_time if avg_training_time > 0 else 0.0
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# Update loss and accuracy
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self.current_loss = metrics.get('loss', 0.0)
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self.current_accuracy = metrics.get('accuracy', 0.0)
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# Update initial loss if not set
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if self.initial_loss is None:
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self.initial_loss = self.current_loss
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# Update best loss
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if self.best_loss is None or self.current_loss < self.best_loss:
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self.best_loss = self.current_loss
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# Calculate improvement percentage
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if self.initial_loss is not None and self.initial_loss > 0:
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self.improvement_percentage = ((self.initial_loss - self.current_loss) / self.initial_loss) * 100
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logger.info(f"Training completed: loss={self.current_loss:.4f}, accuracy={self.current_accuracy:.4f}, samples={metrics.get('samples', 0)}")
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# Sleep before next training
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time.sleep(10)
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except Exception as e:
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logger.error(f"Error in continuous training loop: {e}")
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finally:
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self.training_active = False
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def predict(self, symbol: str) -> Optional[ModelOutput]:
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"""
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Make a prediction using the EnhancedCNN model
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Args:
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symbol: Trading symbol
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Returns:
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ModelOutput: Standardized model output
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"""
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try:
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# Check if inference is enabled
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if not self.inference_enabled:
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return None
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# Get standardized input data
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base_data = self.data_provider.get_base_data_input(symbol)
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if base_data is None:
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logger.warning(f"Failed to get base data input for {symbol}")
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return None
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# Make prediction
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start_time = time.time()
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model_output = self.cnn_adapter.predict(base_data)
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inference_time = time.time() - start_time
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# Update metrics
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self.inference_times.append(inference_time)
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if len(self.inference_times) > 100:
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self.inference_times.pop(0)
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self.total_inferences += 1
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self.daily_inferences += 1
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self.last_inference_time = datetime.now()
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# Calculate inference rate
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if self.inference_times:
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avg_inference_time = sum(self.inference_times) / len(self.inference_times)
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self.inference_rate = 1.0 / avg_inference_time if avg_inference_time > 0 else 0.0
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# Store latest prediction
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self.latest_prediction = model_output
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self.latest_prediction_time = datetime.now()
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# Store model output in data provider
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self.data_provider.store_model_output(model_output)
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# Add training sample if we have a price
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current_price = self._get_current_price(symbol)
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if current_price and current_price > 0:
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# Simulate market feedback based on price movement
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# In a real system, this would be replaced with actual market performance data
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action = model_output.predictions['action']
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# For demonstration, we'll use a simple heuristic:
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# - If price is above 3000, BUY is good
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# - If price is below 3000, SELL is good
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# - Otherwise, HOLD is good
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if current_price > 3000:
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best_action = 'BUY'
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elif current_price < 3000:
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best_action = 'SELL'
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else:
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best_action = 'HOLD'
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# Calculate reward based on whether the action matched the best action
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if action == best_action:
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reward = 0.05 # Positive reward for correct action
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else:
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reward = -0.05 # Negative reward for incorrect action
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# Add training sample
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self.cnn_adapter.add_training_sample(base_data, best_action, reward)
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logger.debug(f"Added training sample for {symbol}, action: {action}, best_action: {best_action}, reward: {reward:.4f}")
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return model_output
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except Exception as e:
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logger.error(f"Error making prediction: {e}")
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return None
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def _get_current_price(self, symbol: str) -> Optional[float]:
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"""Get current price for a symbol"""
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try:
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# Try to get price from data provider
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if hasattr(self.data_provider, 'current_prices'):
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binance_symbol = symbol.replace('/', '').upper()
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if binance_symbol in self.data_provider.current_prices:
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return self.data_provider.current_prices[binance_symbol]
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# Try to get price from latest OHLCV data
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df = self.data_provider.get_historical_data(symbol, '1s', 1)
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if df is not None and not df.empty:
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return float(df.iloc[-1]['close'])
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return None
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except Exception as e:
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logger.error(f"Error getting current price: {e}")
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return None
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def get_model_state(self) -> Dict[str, Any]:
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"""
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Get model state for dashboard display
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Returns:
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Dict[str, Any]: Model state
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"""
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try:
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# Format prediction for display
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prediction_info = "FRESH"
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confidence = 0.0
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if self.latest_prediction:
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action = self.latest_prediction.predictions.get('action', 'UNKNOWN')
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confidence = self.latest_prediction.confidence
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# Map action to display text
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if action == 'BUY':
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prediction_info = "BUY_SIGNAL"
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elif action == 'SELL':
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prediction_info = "SELL_SIGNAL"
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elif action == 'HOLD':
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prediction_info = "HOLD_SIGNAL"
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else:
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prediction_info = "PATTERN_ANALYSIS"
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# Format timing information
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inference_timing = "None"
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training_timing = "None"
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if self.last_inference_time:
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inference_timing = self.last_inference_time.strftime('%H:%M:%S')
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if self.last_training_time:
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training_timing = self.last_training_time.strftime('%H:%M:%S')
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# Calculate improvement percentage
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improvement = 0.0
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if self.initial_loss is not None and self.initial_loss > 0 and self.current_loss > 0:
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improvement = ((self.initial_loss - self.current_loss) / self.initial_loss) * 100
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return {
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'model_name': self.model_name,
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'model_type': 'cnn',
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'parameters': 50000000, # 50M parameters
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'status': 'ACTIVE' if self.inference_enabled else 'DISABLED',
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'checkpoint_loaded': True, # Assume checkpoint is loaded
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'last_prediction': prediction_info,
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'confidence': confidence * 100, # Convert to percentage
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'last_inference_time': inference_timing,
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'last_training_time': training_timing,
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'inference_rate': self.inference_rate,
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'training_rate': self.training_rate,
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'daily_inferences': self.daily_inferences,
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'daily_training_runs': self.daily_training_runs,
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'initial_loss': self.initial_loss,
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'current_loss': self.current_loss,
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'best_loss': self.best_loss,
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'current_accuracy': self.current_accuracy,
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'improvement_percentage': improvement,
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'training_active': self.training_active,
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'training_enabled': self.training_enabled,
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'inference_enabled': self.inference_enabled,
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'training_samples': len(self.cnn_adapter.training_data)
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}
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except Exception as e:
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logger.error(f"Error getting model state: {e}")
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return {
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'model_name': self.model_name,
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'model_type': 'cnn',
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'parameters': 50000000, # 50M parameters
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'status': 'ERROR',
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'error': str(e)
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}
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def get_pivot_prediction(self) -> Dict[str, Any]:
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"""
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Get pivot prediction for dashboard display
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Returns:
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Dict[str, Any]: Pivot prediction
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"""
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try:
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if not self.latest_prediction:
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return {
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'next_pivot': 0.0,
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'pivot_type': 'UNKNOWN',
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'confidence': 0.0,
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'time_to_pivot': 0
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}
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# Extract pivot prediction from model output
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extrema_pred = self.latest_prediction.predictions.get('extrema', [0, 0, 0])
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# Determine pivot type (0=bottom, 1=top, 2=neither)
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pivot_type_idx = extrema_pred.index(max(extrema_pred))
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pivot_types = ['BOTTOM', 'TOP', 'RANGE_CONTINUATION']
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pivot_type = pivot_types[pivot_type_idx]
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# Get current price
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current_price = self._get_current_price('ETH/USDT') or 0.0
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# Calculate next pivot price (simple heuristic for demonstration)
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if pivot_type == 'BOTTOM':
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next_pivot = current_price * 0.95 # 5% below current price
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elif pivot_type == 'TOP':
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next_pivot = current_price * 1.05 # 5% above current price
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else:
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next_pivot = current_price # Same as current price
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# Calculate confidence
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confidence = max(extrema_pred) * 100 # Convert to percentage
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# Calculate time to pivot (simple heuristic for demonstration)
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time_to_pivot = 5 # 5 minutes
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return {
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'next_pivot': next_pivot,
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'pivot_type': pivot_type,
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'confidence': confidence,
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'time_to_pivot': time_to_pivot
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}
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except Exception as e:
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logger.error(f"Error getting pivot prediction: {e}")
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return {
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'next_pivot': 0.0,
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'pivot_type': 'ERROR',
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'confidence': 0.0,
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'time_to_pivot': 0
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} |