wip

NN/models/cnn_model_pytorch.py

@@ -263,8 +263,12 @@ class CNNModelPyTorch:
                 local_min_mask = (price_changes < -0.001).to(torch.bool)  # -0.1% threshold for local minimum
                 
                 # Apply masks to set retrospective targets using torch.where
-                retrospective_targets = torch.where(local_max_mask, torch.zeros_like(targets), retrospective_targets)  # SELL at local max
+                # Use indices where the masks have True values
-                retrospective_targets = torch.where(local_min_mask, 2 * torch.ones_like(targets), retrospective_targets)  # BUY at local min
+                for i in range(len(retrospective_targets)):
+                    if local_max_mask[i]:
+                        retrospective_targets[i] = 0  # SELL at local max
+                    elif local_min_mask[i]:
+                        retrospective_targets[i] = 2  # BUY at local min
                 
                 # Calculate retrospective loss with higher weight for profitable signals
                 retrospective_loss = self.criterion(outputs, retrospective_targets)
@@ -372,8 +376,12 @@ class CNNModelPyTorch:
                     local_min_mask = (price_changes < -0.001).to(torch.bool)  # -0.1% threshold for local minimum
                     
                     # Apply masks to set retrospective targets using torch.where
-                    retrospective_targets = torch.where(local_max_mask, torch.zeros_like(targets), retrospective_targets)  # SELL at local max
+                    # Use indices where the masks have True values
-                    retrospective_targets = torch.where(local_min_mask, 2 * torch.ones_like(targets), retrospective_targets)  # BUY at local min
+                    for i in range(len(retrospective_targets)):
+                        if local_max_mask[i]:
+                            retrospective_targets[i] = 0  # SELL at local max
+                        elif local_min_mask[i]:
+                            retrospective_targets[i] = 2  # BUY at local min
                     
                     # Calculate retrospective loss with higher weight for profitable signals
                     retrospective_loss = self.criterion(outputs, retrospective_targets)
@@ -403,13 +411,29 @@ class CNNModelPyTorch:
     def predict(self, X):
         """Make predictions on input data"""
         self.model.eval()
+        
+        # Convert to tensor if not already
+        if not isinstance(X, torch.Tensor):
             X_tensor = torch.tensor(X, dtype=torch.float32).to(self.device)
+        else:
+            X_tensor = X.to(self.device)
         
         with torch.no_grad():
             outputs = self.model(X_tensor)
-            # To maintain compatibility with the transformer model, return the action probs
+            
-            # And a dummy price prediction of zeros
+            # Get the current close prices from the input
-            return outputs.cpu().numpy(), np.zeros((len(X), 1))
+            current_prices = X_tensor[:, -1, 3].cpu().numpy()  # Last timestamp's close price
+            
+            # For price predictions, we'll estimate based on the action probabilities
+            # Buy (2) means price likely to go up, Sell (0) means price likely to go down
+            action_probs = outputs.cpu().numpy()
+            price_directions = np.argmax(action_probs, axis=1) - 1  # -1, 0, or 1
+            
+            # Simple price prediction: current price + small change based on predicted direction
+            # Use 0.001 (0.1%) as a baseline change
+            price_preds = current_prices * (1 + price_directions * 0.001)
+            
+            return action_probs, price_preds.reshape(-1, 1)
 
     def predict_next_candles(self, X, n_candles=3):
         """

NN/realtime_main.py

@@ -198,16 +198,44 @@ def train(data_interface, model, args):
                 val_action_probs, val_price_preds = model.predict(X_val)
                 
                 # Calculate PnL and win rates
+                try:
                     train_pnl, train_win_rate, train_trades = data_interface.calculate_pnl(
-                    train_action_probs, train_prices, position_size=1.0
+                        train_preds, train_prices, position_size=1.0
                     )
                     val_pnl, val_win_rate, val_trades = data_interface.calculate_pnl(
-                    val_action_probs, val_prices, position_size=1.0
+                        val_preds, val_prices, position_size=1.0
                     )
+                except Exception as e:
+                    logger.error(f"Error calculating PnL: {str(e)}")
+                    train_pnl, train_win_rate, val_pnl, val_win_rate = 0, 0, 0, 0
+                    train_trades, val_trades = [], []
                 
                 # Calculate price prediction error
-                train_price_mae = np.mean(np.abs(train_price_preds - train_future_prices))
+                if train_future_prices is not None and train_price_preds is not None:
-                val_price_mae = np.mean(np.abs(val_price_preds - val_future_prices))
+                    # Ensure arrays have the same shape and are numpy arrays
+                    train_future_prices_np = np.array(train_future_prices) if not isinstance(train_future_prices, np.ndarray) else train_future_prices
+                    train_price_preds_np = np.array(train_price_preds) if not isinstance(train_price_preds, np.ndarray) else train_price_preds
+                    
+                    if len(train_price_preds_np) > 0 and len(train_future_prices_np) > 0:
+                        min_len = min(len(train_price_preds_np), len(train_future_prices_np))
+                        train_price_mae = np.mean(np.abs(train_price_preds_np[:min_len] - train_future_prices_np[:min_len]))
+                    else:
+                        train_price_mae = float('inf')
+                else:
+                    train_price_mae = float('inf')
+                    
+                if val_future_prices is not None and val_price_preds is not None:
+                    # Ensure arrays have the same shape and are numpy arrays
+                    val_future_prices_np = np.array(val_future_prices) if not isinstance(val_future_prices, np.ndarray) else val_future_prices
+                    val_price_preds_np = np.array(val_price_preds) if not isinstance(val_price_preds, np.ndarray) else val_price_preds
+                    
+                    if len(val_price_preds_np) > 0 and len(val_future_prices_np) > 0:
+                        min_len = min(len(val_price_preds_np), len(val_future_prices_np))
+                        val_price_mae = np.mean(np.abs(val_price_preds_np[:min_len] - val_future_prices_np[:min_len]))
+                    else:
+                        val_price_mae = float('inf')
+                else:
+                    val_price_mae = float('inf')
                 
                 # Monitor action distribution
                 train_actions = np.bincount(np.argmax(train_action_probs, axis=1), minlength=3)
@@ -233,7 +261,7 @@ def train(data_interface, model, args):
                     writer.add_scalar(f'Actions/val_{action}', val_actions[i], epoch)
                 
                 # Save best model based on validation metrics
-                if val_pnl > best_val_pnl or (val_pnl == best_val_pnl and val_acc > best_val_acc):
+                if np.isscalar(val_pnl) and np.isscalar(best_val_pnl) and (val_pnl > best_val_pnl or (np.isclose(val_pnl, best_val_pnl) and val_acc > best_val_acc)):
                     best_val_pnl = val_pnl
                     best_val_acc = val_acc
                     best_win_rate = val_win_rate

NN/utils/data_interface.py

@@ -12,6 +12,15 @@ from datetime import datetime, timedelta
 import json
 import pickle
 from sklearn.preprocessing import MinMaxScaler
+import sys
+
+# Add project root to sys.path
+project_root = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+if project_root not in sys.path:
+    sys.path.append(project_root)
+
+# Import BinanceHistoricalData from the root module
+from realtime import BinanceHistoricalData
 
 logger = logging.getLogger(__name__)
 
@@ -39,6 +48,9 @@ class DataInterface:
         self.data_dir = data_dir
         self.scalers = {}  # Store scalers for each timeframe
         
+        # Initialize the historical data fetcher
+        self.historical_data = BinanceHistoricalData()
+        
         # Create data directory if it doesn't exist
         os.makedirs(self.data_dir, exist_ok=True)
         
@@ -59,138 +71,39 @@ class DataInterface:
         Returns:
             pd.DataFrame: DataFrame with OHLCV data
         """
-        cache_file = os.path.join(self.data_dir, f"{self.symbol.replace('/', '_')}_{timeframe}.csv")
+        # Map timeframe string to seconds for BinanceHistoricalData
-        
+        timeframe_to_seconds = {
-        # For 1s timeframe, always fetch fresh data
+            '1s': 1,
-        if timeframe == '1s':
-            use_cache = False
-        
-        # Check if cached data exists and is recent
-        if use_cache and os.path.exists(cache_file):
-            try:
-                df = pd.read_csv(cache_file, parse_dates=['timestamp'])
-                # If we have enough data and it's recent, use it
-                if len(df) >= n_candles:
-                    logger.info(f"Using cached data for {self.symbol} {timeframe} ({len(df)} candles)")
-                    self.dataframes[timeframe] = df
-                    return df.tail(n_candles)
-            except Exception as e:
-                logger.error(f"Error reading cached data: {str(e)}")
-        
-        # If we get here, we need to fetch data
-        try:
-            logger.info(f"Fetching historical data for {self.symbol} {timeframe}")
-            
-            # For 1s timeframe, we need more data points
-            if timeframe == '1s':
-                n_candles = min(n_candles * 60, 10000)  # Up to 10k ticks
-            
-            # Placeholder for real data fetching
-            self._fetch_data_from_exchange(timeframe, n_candles)
-            
-            # Save to cache (except for 1s timeframe)
-            if self.dataframes[timeframe] is not None and timeframe != '1s':
-                self.dataframes[timeframe].to_csv(cache_file, index=False)
-                return self.dataframes[timeframe]
-            else:
-                # Create dummy data as fallback
-                logger.warning(f"Could not fetch data for {self.symbol} {timeframe}, using dummy data")
-                df = self._create_dummy_data(timeframe, n_candles)
-                self.dataframes[timeframe] = df
-                return df
-        except Exception as e:
-            logger.error(f"Error fetching data: {str(e)}")
-            return None
-    
-    def _fetch_data_from_exchange(self, timeframe, n_candles):
-        """
-        Placeholder method for fetching data from an exchange.
-        In a real implementation, this would connect to an exchange API.
-        """
-        # This is a placeholder - in a real implementation this would make API calls
-        # to a cryptocurrency exchange to fetch OHLCV data
-        
-        # For now, just generate dummy data
-        self.dataframes[timeframe] = self._create_dummy_data(timeframe, n_candles)
-    
-    def _create_dummy_data(self, timeframe, n_candles):
-        """
-        Create dummy OHLCV data for testing purposes.
-        
-        Args:
-            timeframe (str): Timeframe to create data for
-            n_candles (int): Number of candles to create
-            
-        Returns:
-            pd.DataFrame: DataFrame with dummy OHLCV data
-        """
-        # Map timeframe to seconds
-        tf_seconds = {
-            '1s': 1,  # Added 1s timeframe
             '1m': 60,
             '5m': 300,
             '15m': 900,
+            '30m': 1800,
             '1h': 3600,
             '4h': 14400,
             '1d': 86400
         }
-        seconds = tf_seconds.get(timeframe, 3600)  # Default to 1h
         
-        # Create timestamps
+        interval_seconds = timeframe_to_seconds.get(timeframe, 3600)  # Default to 1h if not found
-        end_time = datetime.now()
-        timestamps = [end_time - timedelta(seconds=seconds * i) for i in range(n_candles)]
-        timestamps.reverse()  # Oldest first
         
-        # Generate random price data with realistic patterns
+        try:
-        np.random.seed(42)  # For reproducibility
+            # Fetch data using BinanceHistoricalData
-        
+            df = self.historical_data.get_historical_candles(
-        # Start price
+                symbol=self.symbol,
-        price = 50000  # For BTC/USDT
+                interval_seconds=interval_seconds,
-        prices = []
+                limit=n_candles
-        volumes = []
-        
-        for i in range(n_candles):
-            # Random walk with drift and volatility based on timeframe
-            drift = 0.0001 * seconds  # Larger drift for larger timeframes
-            volatility = 0.01 * np.sqrt(seconds / 3600)  # Scale volatility by sqrt of time
-            
-            # Daily/weekly patterns
-            if timeframe in ['1d', '4h']:
-                # Add some cyclical patterns
-                cycle = np.sin(i / 7 * np.pi) * 0.02  # Weekly cycle
-            else:
-                cycle = np.sin(i / 24 * np.pi) * 0.01  # Daily cycle
-            
-            # Calculate price change with random walk + cycles (clamped to prevent overflow)
-            price_change = price * np.clip(drift + volatility * np.random.randn() + cycle, -0.1, 0.1)
-            price = np.clip(price + price_change, 1000, 100000)  # Keep price in reasonable range
-            
-            # Generate OHLC from the price
-            open_price = price
-            high_price = price * (1 + abs(0.005 * np.random.randn()))
-            low_price = price * (1 - abs(0.005 * np.random.randn()))
-            close_price = price * (1 + 0.002 * np.random.randn())
-            
-            # Ensure high >= open, close, low and low <= open, close
-            high_price = max(high_price, open_price, close_price)
-            low_price = min(low_price, open_price, close_price)
-            
-            # Generate volume (higher for larger price movements) with safe calculation
-            volume = 10000 + 5000 * np.random.rand() + abs(price_change)/price * 10000
-            
-            prices.append((open_price, high_price, low_price, close_price))
-            volumes.append(volume)
-            
-            # Update price for next iteration
-            price = close_price
-        
-        # Create DataFrame
-        df = pd.DataFrame(
-            [(t, o, h, l, c, v) for t, (o, h, l, c), v in zip(timestamps, prices, volumes)],
-            columns=['timestamp', 'open', 'high', 'low', 'close', 'volume']
             )
             
+            if not df.empty:
+                logger.info(f"Using data for {self.symbol} {timeframe} ({len(df)} candles)")
+                self.dataframes[timeframe] = df
                 return df
+            else:
+                logger.error(f"No data available for {self.symbol} {timeframe}")
+                return None
+                
+        except Exception as e:
+            logger.error(f"Error fetching data for {self.symbol} {timeframe}: {str(e)}")
+            return None
     
     def prepare_nn_input(self, timeframes=None, n_candles=500, window_size=20):
         """
@@ -459,24 +372,27 @@ class DataInterface:
             n_candles (int): Number of future candles to look at
             
         Returns:
-            np.ndarray: Future prices array
+            np.ndarray: Future prices array (1D array)
         """
-        if len(prices) < n_candles + 1:
+        if prices is None or len(prices) < n_candles + 1:
             return None
         
-        # For each price point, get the maximum price in the next n_candles
+        # Convert to numpy array if it's not already
-        future_prices = np.zeros(len(prices))
+        prices_np = np.array(prices).flatten() if not isinstance(prices, np.ndarray) else prices.flatten()
         
-        for i in range(len(prices) - n_candles):
+        # For each price point, get the maximum price in the next n_candles
+        future_prices = np.zeros(len(prices_np))
+        
+        for i in range(len(prices_np) - n_candles):
             # Get the next n candles
-            next_candles = prices[i+1:i+n_candles+1]
+            next_candles = prices_np[i+1:i+n_candles+1]
             # Use the maximum price as the future price
             future_prices[i] = np.max(next_candles)
             
         # For the last n_candles points, use the last available price
-        future_prices[-n_candles:] = prices[-1]
+        future_prices[-n_candles:] = prices_np[-1]
         
-        return future_prices
+        return future_prices.flatten()  # Ensure it's a 1D array
         
     def prepare_training_data(self, refresh=False, refresh_interval=300):
         """

_notes.md

@@ -42,6 +42,7 @@ python -c "import sys; sys.path.append('f:/projects/gogo2'); from NN.realtime_ma
 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 -c "import sys; sys.path.append('f:/projects/gogo2'); from NN.realtime_main import main; main()" --mode train --model-type cnn --framework pytorch --epochs 10 --symbol BTC/USDT --timeframes 1s 1m 1h 1d --batch-size 32 --window-size 20 --output-size 3
+python NN/realtime_main.py --mode train --model-type cnn --epochs 1 --symbol BTC/USDT --timeframes 1s 1m --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
 

realtime.py

@@ -912,8 +912,16 @@ class BinanceHistoricalData:
         """Convert interval seconds to Binance interval string"""
         if interval_seconds == 60:  # 1m
             return "1m"
+        elif interval_seconds == 300:  # 5m
+            return "5m"
+        elif interval_seconds == 900:  # 15m
+            return "15m"
+        elif interval_seconds == 1800:  # 30m
+            return "30m"
         elif interval_seconds == 3600:  # 1h
             return "1h"
+        elif interval_seconds == 14400:  # 4h
+            return "4h"
         elif interval_seconds == 86400:  # 1d
             return "1d"
         else: