working training

NN/models/cnn_model_pytorch.py

@@ -24,60 +24,67 @@ logger = logging.getLogger(__name__)
 class CNNPyTorch(nn.Module):
     """PyTorch CNN model for time series analysis"""
     
-    def __init__(self, input_shape, output_size=3):
+    def __init__(self, input_shape, output_size=5):
         """
-        Initialize the CNN model.
+        Initialize the enhanced CNN model.
         
         Args:
             input_shape (tuple): Shape of input data (window_size, features)
-            output_size (int): Size of output (1 for regression, 3 for classification)
+            output_size (int): Always 5 for our trading signals
         """
         super(CNNPyTorch, self).__init__()
         
         window_size, num_features = input_shape
-        
+        kernel_size = 5
-        # Architecture parameters
-        filters = [32, 64, 128]
-        kernel_sizes = [3, 5, 7]
-        lstm_units = 100
-        dense_units = 64
         dropout_rate = 0.3
         
-        # Create parallel convolutional pathways
+        # Enhanced CNN Architecture
-        self.conv_paths = nn.ModuleList()
+        self.conv_layers = nn.Sequential(
-        
+            # Block 1
-        for f, k in zip(filters, kernel_sizes):
+            nn.Conv1d(num_features, 64, kernel_size, padding='same'),
-            path = nn.Sequential(
+            nn.BatchNorm1d(64),
-                nn.Conv1d(num_features, f, kernel_size=k, padding='same'),
-                nn.ReLU(),
-                nn.BatchNorm1d(f),
-                nn.MaxPool1d(kernel_size=2, stride=1, padding=1),
-                nn.Dropout(dropout_rate)
-            )
-            self.conv_paths.append(path)
-        
-        # Calculate output size from conv paths
-        conv_output_size = sum(filters) * window_size
-        
-        # LSTM layer
-        self.lstm = nn.LSTM(
-            input_size=sum(filters),
-            hidden_size=lstm_units,
-            batch_first=True,
-            bidirectional=True
-        )
-        
-        # Dense layers
-        self.flatten = nn.Flatten()
-        self.dense1 = nn.Sequential(
-            nn.Linear(lstm_units * 2 * window_size, dense_units),
             nn.ReLU(),
-            nn.BatchNorm1d(dense_units),
+            
-            nn.Dropout(dropout_rate)
+            # Block 2  
+            nn.Conv1d(64, 128, kernel_size, padding='same'),
+            nn.BatchNorm1d(128),
+            nn.ReLU(),
+            nn.MaxPool1d(2),
+            
+            # Block 3
+            nn.Conv1d(128, 256, kernel_size, padding='same'),
+            nn.BatchNorm1d(256),
+            nn.ReLU(),
+            
+            # Block 4
+            nn.Conv1d(256, 512, kernel_size, padding='same'),
+            nn.BatchNorm1d(512),
+            nn.ReLU(),
+            nn.MaxPool1d(2)
         )
         
-        # Output layer
+        # Calculate flattened size after conv and pooling
-        self.output = nn.Linear(dense_units, output_size)
+        conv_output_size = 512 * (window_size // 4)
+        
+        # Enhanced dense layers
+        self.dense_block = nn.Sequential(
+            nn.Flatten(),
+            nn.Linear(conv_output_size, 512),
+            nn.BatchNorm1d(512),
+            nn.ReLU(),
+            nn.Dropout(dropout_rate),
+            
+            nn.Linear(512, 256),
+            nn.BatchNorm1d(256),
+            nn.ReLU(),
+            nn.Dropout(dropout_rate),
+            
+            nn.Linear(256, 128),
+            nn.BatchNorm1d(128),
+            nn.ReLU(),
+            
+            nn.Linear(128, output_size)
+        )
         
         # Activation based on output size
         if output_size == 1:
@@ -89,7 +96,7 @@ class CNNPyTorch(nn.Module):
     
     def forward(self, x):
         """
-        Forward pass through the network.
+        Forward pass through enhanced network.
         
         Args:
             x: Input tensor of shape [batch_size, window_size, features]
@@ -97,35 +104,15 @@ class CNNPyTorch(nn.Module):
         Returns:
             Output tensor of shape [batch_size, output_size]
         """
-        batch_size, window_size, num_features = x.shape
-        
         # Transpose for conv1d: [batch, features, window]
         x_t = x.transpose(1, 2)
         
-        # Process through parallel conv paths
+        # Process through all CNN layers
-        conv_outputs = []
+        conv_out = self.conv_layers(x_t)
-        for path in self.conv_paths:
-            conv_outputs.append(path(x_t))
         
-        # Concatenate conv outputs
+        # Process through dense layers
-        conv_concat = torch.cat(conv_outputs, dim=1)
+        output = self.dense_block(conv_out)
         
-        # Transpose back for LSTM: [batch, window, features]
-        conv_concat = conv_concat.transpose(1, 2)
-        
-        # LSTM processing
-        lstm_out, _ = self.lstm(conv_concat)
-        
-        # Flatten
-        flattened = self.flatten(lstm_out)
-        
-        # Dense processing
-        dense_out = self.dense1(flattened)
-        
-        # Output
-        output = self.output(dense_out)
-        
-        # Apply activation
         return self.activation(output)
 
 
@@ -137,7 +124,7 @@ class CNNModelPyTorch:
     predictions with the CNN model.
     """
     
-    def __init__(self, window_size, num_features, output_size=3, timeframes=None):
+    def __init__(self, window_size, num_features, output_size=5, timeframes=None):
         """
         Initialize the CNN model.
         
@@ -506,41 +493,27 @@ class CNNModelPyTorch:
     
     def extract_hidden_features(self, X):
         """
-        Extract hidden features from the model.
+        Extract hidden features from the model - outputs from last dense layer before output.
         
         Args:
             X: Input data
             
         Returns:
-            Hidden features
+            Hidden features (output from penultimate dense layer)
         """
         # Convert to PyTorch tensor
         X_tensor = torch.tensor(X, dtype=torch.float32).to(self.device)
         
-        # Forward pass through the model up to the last hidden layer
+        # Forward pass through the model
         self.model.eval()
         with torch.no_grad():
-            # Get features before the output layer
+            # Get features through CNN layers
             x_t = X_tensor.transpose(1, 2)
+            conv_out = self.model.conv_layers(x_t)
             
-            # Process through parallel conv paths
+            # Process through all dense layers except the output layer
-            conv_outputs = []
+            features = conv_out
-            for path in self.model.conv_paths:
+            for layer in self.model.dense_block[:-2]:  # Exclude last linear layer and dropout
-                conv_outputs.append(path(x_t))
+                features = layer(features)
-            
-            # Concatenate conv outputs
-            conv_concat = torch.cat(conv_outputs, dim=1)
-            
-            # Transpose back for LSTM
-            conv_concat = conv_concat.transpose(1, 2)
-            
-            # LSTM processing
-            lstm_out, _ = self.model.lstm(conv_concat)
-            
-            # Flatten
-            flattened = self.model.flatten(lstm_out)
-            
-            # Dense processing
-            hidden_features = self.model.dense1(flattened)
         
-        return hidden_features.cpu().numpy() 
+        return features.cpu().numpy()

NN/realtime-main.py

@@ -118,9 +118,7 @@ def main():
         logger.info("Initializing data interface...")
         data_interface = DataInterface(
             symbol=args.symbol,
-            timeframes=args.timeframes,
+            timeframes=args.timeframes
-            window_size=args.window_size,
-            output_size=args.output_size
         )
     except Exception as e:
         logger.error(f"Failed to initialize data interface: {str(e)}")

NN/utils/realtime_analyzer.py

@@ -10,6 +10,12 @@ import numpy as np
 from threading import Thread
 from queue import Queue
 from datetime import datetime
+import asyncio
+import websockets
+import json
+import os
+import pandas as pd
+from collections import deque
 
 logger = logging.getLogger(__name__)
 
@@ -19,7 +25,8 @@ class RealtimeAnalyzer:
     
     Features:
     - Connects to real-time data sources (websockets)
-    - Processes incoming data through the neural network
+    - Processes tick data into multiple timeframes (1s, 1m, 1h, 1d)
+    - Uses trained models to analyze all timeframes
     - Generates trading signals
     - Manages risk and position sizing
     - Logs all trading decisions
@@ -33,17 +40,26 @@ class RealtimeAnalyzer:
             data_interface (DataInterface): Preconfigured data interface
             model: Trained neural network model
             symbol (str): Trading pair symbol
-            timeframes (list): List of timeframes to monitor
+            timeframes (list): List of timeframes to monitor (default: ['1s', '1m', '1h', '1d'])
         """
         self.data_interface = data_interface
         self.model = model
         self.symbol = symbol
-        self.timeframes = timeframes or ['1h']
+        self.timeframes = timeframes or ['1s', '1m', '1h', '1d']
         self.running = False
         self.data_queue = Queue()
-        self.prediction_interval = 60  # Seconds between predictions
+        self.prediction_interval = 10  # Seconds between predictions
+        self.ws_url = f"wss://stream.binance.com:9443/ws/{symbol.replace('/', '').lower()}@trade"
+        self.ws = None
+        self.tick_storage = deque(maxlen=10000)  # Store up to 10,000 ticks
+        self.candle_cache = {
+            '1s': deque(maxlen=5000),
+            '1m': deque(maxlen=5000),
+            '1h': deque(maxlen=5000),
+            '1d': deque(maxlen=5000)
+        }
         
-        logger.info(f"RealtimeAnalyzer initialized for {symbol}")
+        logger.info(f"RealtimeAnalyzer initialized for {symbol} with timeframes: {self.timeframes}")
 
     def start(self):
         """Start the realtime analysis process."""
@@ -53,9 +69,13 @@ class RealtimeAnalyzer:
             
         self.running = True
         
-        # Start data collection thread
+        # Start WebSocket connection thread
-        self.data_thread = Thread(target=self._collect_data, daemon=True)
+        self.ws_thread = Thread(target=self._run_websocket, daemon=True)
-        self.data_thread.start()
+        self.ws_thread.start()
+        
+        # Start data processing thread
+        self.processing_thread = Thread(target=self._process_data, daemon=True)
+        self.processing_thread.start()
         
         # Start analysis thread
         self.analysis_thread = Thread(target=self._analyze_data, daemon=True)
@@ -66,42 +86,128 @@ class RealtimeAnalyzer:
     def stop(self):
         """Stop the realtime analysis process."""
         self.running = False
-        if hasattr(self, 'data_thread'):
+        if self.ws:
-            self.data_thread.join(timeout=1)
+            asyncio.run(self.ws.close())
+        if hasattr(self, 'ws_thread'):
+            self.ws_thread.join(timeout=1)
+        if hasattr(self, 'processing_thread'):
+            self.processing_thread.join(timeout=1)
         if hasattr(self, 'analysis_thread'):
             self.analysis_thread.join(timeout=1)
         logger.info("Realtime analysis stopped")
 
-    def _collect_data(self):
+    def _run_websocket(self):
-        """Thread function for collecting real-time data."""
+        """Thread function for running WebSocket connection."""
-        logger.info("Starting data collection thread")
+        loop = asyncio.new_event_loop()
-        
+        asyncio.set_event_loop(loop)
-        # In a real implementation, this would connect to websockets/API
+        loop.run_until_complete(self._connect_websocket())
-        # For now, we'll simulate data collection from the data interface
+
+    async def _connect_websocket(self):
+        """Connect to WebSocket and receive data."""
         while self.running:
             try:
-                # Get latest data for each timeframe
+                logger.info(f"Connecting to WebSocket: {self.ws_url}")
-                for timeframe in self.timeframes:
+                async with websockets.connect(self.ws_url) as ws:
-                    # Get recent data (simulating real-time updates)
+                    self.ws = ws
-                    X, timestamp = self.data_interface.prepare_realtime_input(
+                    logger.info("WebSocket connected")
-                        timeframe=timeframe,
-                        n_candles=30,
-                        window_size=self.data_interface.window_size
-                    )
                     
-                    if X is not None:
+                    while self.running:
-                        self.data_queue.put({
+                        try:
-                            'timeframe': timeframe,
+                            message = await ws.recv()
-                            'data': X,
+                            data = json.loads(message)
-                            'timestamp': timestamp
+                            
-                        })
+                            if 'e' in data and data['e'] == 'trade':
+                                tick = {
+                                    'timestamp': data['T'],
+                                    'price': float(data['p']),
+                                    'volume': float(data['q']),
+                                    'symbol': self.symbol
+                                }
+                                self.tick_storage.append(tick)
+                                self.data_queue.put(tick)
+                                
+                        except websockets.exceptions.ConnectionClosed:
+                            logger.warning("WebSocket connection closed")
+                            break
+                        except Exception as e:
+                            logger.error(f"Error receiving WebSocket message: {str(e)}")
+                            time.sleep(1)
+                            
+            except Exception as e:
+                logger.error(f"WebSocket connection error: {str(e)}")
+                time.sleep(5)  # Wait before reconnecting
+
+    def _process_data(self):
+        """Process incoming tick data into candles for all timeframes."""
+        logger.info("Starting data processing thread")
+        
+        while self.running:
+            try:
+                # Process any new ticks
+                while not self.data_queue.empty():
+                    tick = self.data_queue.get()
+                    
+                    # Convert timestamp to datetime
+                    timestamp = datetime.fromtimestamp(tick['timestamp'] / 1000)
+                    
+                    # Process for each timeframe
+                    for timeframe in self.timeframes:
+                        interval = self._get_interval_seconds(timeframe)
+                        if interval is None:
+                            continue
+                            
+                        # Round timestamp to nearest candle interval
+                        candle_ts = int(tick['timestamp'] // (interval * 1000)) * (interval * 1000)
+                        
+                        # Get or create candle for this timeframe
+                        if not self.candle_cache[timeframe]:
+                            # First candle for this timeframe
+                            candle = {
+                                'timestamp': candle_ts,
+                                'open': tick['price'],
+                                'high': tick['price'],
+                                'low': tick['price'],
+                                'close': tick['price'],
+                                'volume': tick['volume']
+                            }
+                            self.candle_cache[timeframe].append(candle)
+                        else:
+                            # Update existing candle
+                            last_candle = self.candle_cache[timeframe][-1]
+                            
+                            if last_candle['timestamp'] == candle_ts:
+                                # Update current candle
+                                last_candle['high'] = max(last_candle['high'], tick['price'])
+                                last_candle['low'] = min(last_candle['low'], tick['price'])
+                                last_candle['close'] = tick['price']
+                                last_candle['volume'] += tick['volume']
+                            else:
+                                # New candle
+                                candle = {
+                                    'timestamp': candle_ts,
+                                    'open': tick['price'],
+                                    'high': tick['price'],
+                                    'low': tick['price'],
+                                    'close': tick['price'],
+                                    'volume': tick['volume']
+                                }
+                                self.candle_cache[timeframe].append(candle)
                 
-                # Throttle data collection
+                time.sleep(0.1)
-                time.sleep(1)
                 
             except Exception as e:
-                logger.error(f"Error in data collection: {str(e)}")
+                logger.error(f"Error in data processing: {str(e)}")
-                time.sleep(5)  # Wait before retrying
+                time.sleep(1)
+
+    def _get_interval_seconds(self, timeframe):
+        """Convert timeframe string to seconds."""
+        intervals = {
+            '1s': 1,
+            '1m': 60,
+            '1h': 3600,
+            '1d': 86400
+        }
+        return intervals.get(timeframe)
 
     def _analyze_data(self):
         """Thread function for analyzing data and generating signals."""
@@ -118,27 +224,56 @@ class RealtimeAnalyzer:
                     time.sleep(0.1)
                     continue
                 
-                # Get latest data from queue
+                # Prepare input data from all timeframes
-                if not self.data_queue.empty():
+                input_data = {}
-                    data_item = self.data_queue.get()
+                valid = True
+                
+                for timeframe in self.timeframes:
+                    if not self.candle_cache[timeframe]:
+                        logger.warning(f"No data available for timeframe {timeframe}")
+                        valid = False
+                        break
+                        
+                    # Get last N candles for this timeframe
+                    candles = list(self.candle_cache[timeframe])[-self.data_interface.window_size:]
                     
-                    # Make prediction
+                    # Convert to numpy array
-                    prediction = self.model.predict(data_item['data'])
+                    ohlcv = np.array([
+                        [c['open'], c['high'], c['low'], c['close'], c['volume']] 
+                        for c in candles
+                    ])
+                    
+                    # Normalize data
+                    ohlcv_normalized = (ohlcv - ohlcv.mean(axis=0)) / (ohlcv.std(axis=0) + 1e-8)
+                    input_data[timeframe] = ohlcv_normalized
+                
+                if not valid:
+                    time.sleep(0.1)
+                    continue
+                
+                # Make prediction using the model
+                try:
+                    prediction = self.model.predict(input_data)
+                    
+                    # Get latest timestamp from 1s timeframe
+                    latest_ts = self.candle_cache['1s'][-1]['timestamp'] if self.candle_cache['1s'] else int(time.time() * 1000)
                     
                     # Process prediction
                     self._process_prediction(
                         prediction=prediction,
-                        timeframe=data_item['timeframe'],
+                        timeframe='multi',
-                        timestamp=data_item['timestamp']
+                        timestamp=latest_ts
                     )
                     
                     last_prediction_time = current_time
+                except Exception as e:
+                    logger.error(f"Error making prediction: {str(e)}")
                 
                 time.sleep(0.1)
                 
             except Exception as e:
                 logger.error(f"Error in analysis: {str(e)}")
-                time.sleep(1)  # Wait before retrying
+                time.sleep(1)
 
     def _process_prediction(self, prediction, timeframe, timestamp):
         """
@@ -146,17 +281,23 @@ class RealtimeAnalyzer:
         
         Args:
             prediction: Model prediction output
-            timeframe (str): Timeframe the prediction is for
+            timeframe (str): Timeframe the prediction is for ('multi' for combined)
-            timestamp: Timestamp of the prediction
+            timestamp: Timestamp of the prediction (ms)
         """
         # Convert prediction to trading signal
-        signal = self._prediction_to_signal(prediction)
+        signal, confidence = self._prediction_to_signal(prediction)
         
-        # Log the signal
+        # Convert timestamp to datetime
+        try:
+            dt = datetime.fromtimestamp(timestamp / 1000)
+        except:
+            dt = datetime.now()
+        
+        # Log the signal with all timeframes
         logger.info(
-            f"Signal generated - Timeframe: {timeframe}, "
+            f"Signal generated - Timeframes: {', '.join(self.timeframes)}, "
-            f"Timestamp: {timestamp}, "
+            f"Timestamp: {dt}, "
-            f"Signal: {signal}"
+            f"Signal: {signal} (Confidence: {confidence:.2f})"
         )
         
         # In a real implementation, we would execute trades here
@@ -164,19 +305,60 @@ class RealtimeAnalyzer:
 
     def _prediction_to_signal(self, prediction):
         """
-        Convert model prediction to trading signal.
+        Convert model prediction to trading signal and confidence.
         
         Args:
-            prediction: Model prediction output
+            prediction: Model prediction output (can be dict for multi-timeframe)
             
         Returns:
-            str: Trading signal (BUY, SELL, HOLD)
+            tuple: (signal, confidence) where signal is BUY/SELL/HOLD,
+                   confidence is probability (0-1)
         """
-        # Simple threshold-based signal generation
+        if isinstance(prediction, dict):
-        if len(prediction.shape) == 1:
+            # Multi-timeframe prediction - combine signals
-            # Binary classification
+            signals = []
-            return "BUY" if prediction[0] > 0.5 else "SELL"
+            confidences = []
+            
+            for tf, pred in prediction.items():
+                if len(pred.shape) == 1:
+                    # Binary classification
+                    signal = "BUY" if pred[0] > 0.5 else "SELL"
+                    confidence = pred[0] if signal == "BUY" else 1 - pred[0]
+                else:
+                    # Multi-class
+                    class_idx = np.argmax(pred)
+                    signal = ["SELL", "HOLD", "BUY"][class_idx]
+                    confidence = pred[class_idx]
+                
+                signals.append(signal)
+                confidences.append(confidence)
+            
+            # Simple voting system - count BUY/SELL signals
+            buy_count = signals.count("BUY")
+            sell_count = signals.count("SELL")
+            
+            if buy_count > sell_count:
+                final_signal = "BUY"
+                final_confidence = np.mean([c for s, c in zip(signals, confidences) if s == "BUY"])
+            elif sell_count > buy_count:
+                final_signal = "SELL"
+                final_confidence = np.mean([c for s, c in zip(signals, confidences) if s == "SELL"])
+            else:
+                final_signal = "HOLD"
+                final_confidence = np.mean(confidences)
+                
+            return final_signal, final_confidence
+            
         else:
-            # Multi-class classification (3 outputs)
+            # Single prediction
-            class_idx = np.argmax(prediction)
+            if len(prediction.shape) == 1:
-            return ["SELL", "HOLD", "BUY"][class_idx]
+                # Binary classification
+                signal = "BUY" if prediction[0] > 0.5 else "SELL"
+                confidence = prediction[0] if signal == "BUY" else 1 - prediction[0]
+            else:
+                # Multi-class
+                class_idx = np.argmax(prediction)
+                signal = ["SELL", "HOLD", "BUY"][class_idx]
+                confidence = prediction[class_idx]
+            
+            return signal, confidence

_notes.md

@@ -36,4 +36,11 @@ Backend tkagg is interactive backend. Turning interactive mode on.
 
 remodel our NN architecture. we should support up to 3 pairs simultaniously. so input can be 3 pairs: each pair will have up to 5 timeframes 1s(ticks, unspecified length), 1m, 1h, 1d + one additionall. we should normalize them in a way that preserves the relations between them (one price should be normalized to the same value across all tieframes). additionally to the 5 features OHLCV we will add up to 20 additional features for various technical indcators. 1s timeframe will be streamed in realtime. the MOE model should handle all that. we still need to access latest of the CNN hidden layers in the MOe model so we can extract learned features recognition
 .
-now let's run our "NN Training Pipeline" debug config. for now we start with single pair - BTC/USD. later we'll add up to 3 pairs for context. the NN will always have only 1 "main" pair - where the buy/sell actions are applied and which price prediction is calculater for each frame. we'll also try to predict the next local extrema that will help us be profitable
+now let's run our "NN Training Pipeline" debug config. for now we start with single pair - BTC/USD. later we'll add up to 3 pairs for context. the NN will always have only 1 "main" pair - where the buy/sell actions are applied and which price prediction is calculater for each frame. we'll also try to predict the next local extrema that will help us be profitable
+
+python -c "import sys; sys.path.append('f:/projects/gogo2'); from NN.realtime_main import main; main()" --mode train --model-type cnn --framework pytorch
+python -c "import sys; sys.path.append('f:/projects/gogo2'); from NN.realtime_main import main; main()" --mode train --model-type cnn --framework pytorch --epochs 1000
+python -c "import sys; sys.path.append('f:/projects/gogo2'); from NN.realtime_main import main; main()" --mode train --model-type cnn --framework pytorch --epochs 1000 --symbol BTC/USDT --timeframes 1m 5m 1h 4h --epochs 10 --batch-size 32 --window-size 20 --output-size 3
+
+python NN/realtime-main.py --mode train --model-type cnn --framework pytorch --symbol BTC/USDT --timeframes 1m 5m 1h 4h --epochs 10 --batch-size 32 --window-size 20 --output-size 3
+