trying to fix training
This commit is contained in:
Dobromir Popov
parent
2255a8363a
commit
ebbc0ed2d7
22
.vscode/launch.json
vendored
22
.vscode/launch.json
vendored
@ -108,22 +108,25 @@
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"name": "NN Training Pipeline",
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"type": "python",
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"request": "launch",
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"program": "-m",
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"module": "NN.realtime_main",
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"args": [
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"NN.realtime-main",
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"--mode",
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"train",
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"--model-type",
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"cnn",
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"--framework",
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"pytorch",
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"--symbol",
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"BTC/USDT",
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"--timeframes",
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"1m", "5m", "1h", "4h",
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"--epochs",
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"100",
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"--batch_size",
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"64",
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"--window_size",
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"30",
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"--output_size",
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"10",
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"--batch-size",
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"32",
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"--window-size",
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"20",
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"--output-size",
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"3"
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],
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"console": "integratedTerminal",
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@ -132,6 +135,7 @@
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"PYTHONUNBUFFERED": "1",
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"TF_CPP_MIN_LOG_LEVEL": "2"
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},
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"pythonArgs": ["-c", "import sys; sys.path.append('f:/projects/gogo2')"],
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"postDebugTask": "Start TensorBoard"
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},
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{
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@ -150,4 +154,4 @@
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}
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}
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]
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}
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}
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@ -178,6 +178,148 @@ class CNNModelPyTorch:
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logger.info(f"Model built successfully with {sum(p.numel() for p in self.model.parameters())} parameters")
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def train_epoch(self, X_train, y_train, batch_size=32):
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"""Train for one epoch and return loss and accuracy"""
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# Convert to PyTorch tensors
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X_train_tensor = torch.tensor(X_train, dtype=torch.float32).to(self.device)
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if self.output_size == 1:
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y_train_tensor = torch.tensor(y_train, dtype=torch.float32).to(self.device)
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else:
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y_train_tensor = torch.tensor(y_train, dtype=torch.long).to(self.device)
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# Create DataLoader
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train_dataset = TensorDataset(X_train_tensor, y_train_tensor)
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train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)
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self.model.train()
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running_loss = 0.0
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correct = 0
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total = 0
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for inputs, targets in train_loader:
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# Zero gradients
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self.optimizer.zero_grad()
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# Forward pass
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outputs = self.model(inputs)
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# Calculate loss
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if self.output_size == 1:
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loss = self.criterion(outputs, targets.unsqueeze(1))
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else:
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loss = self.criterion(outputs, targets)
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# Backward pass and optimize
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loss.backward()
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self.optimizer.step()
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# Statistics
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running_loss += loss.item()
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if self.output_size > 1:
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_, predicted = torch.max(outputs, 1)
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total += targets.size(0)
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correct += (predicted == targets).sum().item()
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epoch_loss = running_loss / len(train_loader)
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epoch_acc = correct / total if total > 0 else 0
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return epoch_loss, epoch_acc
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def evaluate(self, X_val, y_val):
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"""Evaluate on validation data and return loss and accuracy"""
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X_val_tensor = torch.tensor(X_val, dtype=torch.float32).to(self.device)
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if self.output_size == 1:
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y_val_tensor = torch.tensor(y_val, dtype=torch.float32).to(self.device)
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else:
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y_val_tensor = torch.tensor(y_val, dtype=torch.long).to(self.device)
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val_dataset = TensorDataset(X_val_tensor, y_val_tensor)
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val_loader = DataLoader(val_dataset, batch_size=32)
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self.model.eval()
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val_loss = 0.0
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correct = 0
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total = 0
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with torch.no_grad():
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for inputs, targets in val_loader:
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# Forward pass
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outputs = self.model(inputs)
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# Calculate loss
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if self.output_size == 1:
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loss = self.criterion(outputs, targets.unsqueeze(1))
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else:
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loss = self.criterion(outputs, targets)
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val_loss += loss.item()
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# Calculate accuracy
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if self.output_size > 1:
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_, predicted = torch.max(outputs, 1)
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total += targets.size(0)
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correct += (predicted == targets).sum().item()
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return val_loss / len(val_loader), correct / total if total > 0 else 0
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def predict(self, X):
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"""Make predictions on input data"""
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self.model.eval()
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X_tensor = torch.tensor(X, dtype=torch.float32).to(self.device)
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with torch.no_grad():
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outputs = self.model(X_tensor)
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if self.output_size > 1:
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_, predicted = torch.max(outputs, 1)
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return predicted.cpu().numpy()
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else:
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return outputs.cpu().numpy()
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def predict_next_candles(self, X, n_candles=3):
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"""
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Predict the next n candles for each timeframe.
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Args:
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X: Input data of shape [batch_size, window_size, features]
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n_candles: Number of future candles to predict
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Returns:
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Dictionary of predictions for each timeframe
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"""
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self.model.eval()
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X_tensor = torch.tensor(X, dtype=torch.float32).to(self.device)
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with torch.no_grad():
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# Get the last window of data
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last_window = X_tensor[-1:] # [1, window_size, features]
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# Initialize predictions
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predictions = {}
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# For each timeframe, predict next n candles
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for i, tf in enumerate(self.timeframes):
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# Extract features for this timeframe
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tf_features = last_window[:, :, i*5:(i+1)*5] # [1, window_size, 5]
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# Predict next n candles
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tf_predictions = []
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current_window = tf_features
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for _ in range(n_candles):
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# Get prediction for next candle
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output = self.model(current_window)
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tf_predictions.append(output.cpu().numpy())
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# Update window for next prediction
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current_window = torch.cat([
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current_window[:, 1:, :],
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output.unsqueeze(1)
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], dim=1)
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predictions[tf] = np.concatenate(tf_predictions, axis=0)
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return predictions
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def train(self, X_train, y_train, X_val=None, y_val=None, batch_size=32, epochs=100):
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"""
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Train the CNN model.
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@ -259,7 +401,7 @@ class CNNModelPyTorch:
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# Validation phase
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if val_loader is not None:
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val_loss, val_acc = self._validate(val_loader)
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val_loss, val_acc = self.evaluate(X_val, y_val)
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logger.info(f"Epoch {epoch+1}/{epochs} - "
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f"loss: {epoch_loss:.4f} - acc: {epoch_acc:.4f} - "
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@ -281,51 +423,12 @@ class CNNModelPyTorch:
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logger.info("Training completed")
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return self.history
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def _validate(self, val_loader):
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"""Validate the model using the validation set"""
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self.model.eval()
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val_loss = 0.0
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correct = 0
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total = 0
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with torch.no_grad():
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for inputs, targets in val_loader:
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# Forward pass
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outputs = self.model(inputs)
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# Calculate loss
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if self.output_size == 1:
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loss = self.criterion(outputs, targets.unsqueeze(1))
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else:
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loss = self.criterion(outputs, targets)
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val_loss += loss.item()
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# Calculate accuracy
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if self.output_size > 1:
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_, predicted = torch.max(outputs, 1)
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total += targets.size(0)
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correct += (predicted == targets).sum().item()
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return val_loss / len(val_loader), correct / total if total > 0 else 0
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def evaluate(self, X_test, y_test):
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def evaluate_metrics(self, X_test, y_test):
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"""
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Evaluate the model on test data.
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Args:
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X_test: Test input data
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y_test: Test target data
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Returns:
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dict: Evaluation metrics
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Calculate and return comprehensive evaluation metrics as dict
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"""
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logger.info(f"Evaluating model on {len(X_test)} samples")
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# Convert to PyTorch tensors
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X_test_tensor = torch.tensor(X_test, dtype=torch.float32).to(self.device)
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# Get predictions
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self.model.eval()
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with torch.no_grad():
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y_pred = self.model(X_test_tensor)
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@ -336,70 +439,15 @@ class CNNModelPyTorch:
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else:
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y_pred_class = (y_pred.cpu().numpy() > 0.5).astype(int).flatten()
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# Calculate metrics
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if self.output_size > 1:
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accuracy = accuracy_score(y_test, y_pred_class)
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precision = precision_score(y_test, y_pred_class, average='weighted')
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recall = recall_score(y_test, y_pred_class, average='weighted')
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f1 = f1_score(y_test, y_pred_class, average='weighted')
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metrics = {
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'accuracy': accuracy,
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'precision': precision,
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'recall': recall,
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'f1_score': f1
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}
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else:
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accuracy = accuracy_score(y_test, y_pred_class)
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precision = precision_score(y_test, y_pred_class)
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recall = recall_score(y_test, y_pred_class)
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f1 = f1_score(y_test, y_pred_class)
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metrics = {
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'accuracy': accuracy,
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'precision': precision,
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'recall': recall,
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'f1_score': f1
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}
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metrics = {
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'accuracy': accuracy_score(y_test, y_pred_class),
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'precision': precision_score(y_test, y_pred_class, average='weighted', zero_division=0),
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'recall': recall_score(y_test, y_pred_class, average='weighted', zero_division=0),
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'f1_score': f1_score(y_test, y_pred_class, average='weighted', zero_division=0)
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}
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logger.info(f"Evaluation metrics: {metrics}")
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return metrics
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def predict(self, X):
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"""
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Make predictions with the model.
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Args:
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X: Input data
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Returns:
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Predictions
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"""
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# Convert to PyTorch tensor
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X_tensor = torch.tensor(X, dtype=torch.float32).to(self.device)
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# Get predictions
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self.model.eval()
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with torch.no_grad():
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predictions = self.model(X_tensor)
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if self.output_size > 1:
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# Multi-class classification
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probs = predictions.cpu().numpy()
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_, class_preds = torch.max(predictions, 1)
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class_preds = class_preds.cpu().numpy()
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return class_preds, probs
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else:
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# Binary classification or regression
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preds = predictions.cpu().numpy()
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if self.output_size == 1:
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# Binary classification
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class_preds = (preds > 0.5).astype(int)
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return class_preds.flatten(), preds.flatten()
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else:
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# Regression
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return preds.flatten(), None
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def save(self, filepath):
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"""
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Save the model to a file.
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@ -151,35 +151,59 @@ def main():
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logger.info("Neural Network Trading System finished successfully")
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def train(data_interface, model, args):
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"""Train the model using the data interface"""
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"""Enhanced training with performance tracking"""
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from torch.utils.tensorboard import SummaryWriter
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logger.info("Starting training mode...")
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writer = SummaryWriter(log_dir=f"runs/{args.model_type}_{datetime.now().strftime('%Y%m%d_%H%M%S')}")
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try:
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# Prepare training data
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logger.info("Preparing training data...")
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X_train, y_train, X_val, y_val = data_interface.prepare_training_data()
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best_val_acc = 0
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# Train the model
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logger.info("Training model...")
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model.train(
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X_train, y_train,
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X_val, y_val,
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batch_size=args.batch_size,
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epochs=args.epochs
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)
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# Save the model
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for epoch in range(args.epochs):
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# Refresh data every few epochs
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if epoch % 3 == 0:
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X_train, y_train, X_val, y_val = data_interface.prepare_training_data(refresh=True)
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else:
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X_train, y_train, X_val, y_val = data_interface.prepare_training_data()
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# Train for one epoch
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train_loss, train_acc = model.train_epoch(
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X_train, y_train,
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batch_size=args.batch_size
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)
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# Validate
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val_loss, val_acc = model.evaluate(X_val, y_val)
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# Log metrics
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writer.add_scalar('Loss/Train', train_loss, epoch)
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writer.add_scalar('Accuracy/Train', train_acc, epoch)
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writer.add_scalar('Loss/Validation', val_loss, epoch)
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writer.add_scalar('Accuracy/Validation', val_acc, epoch)
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# Save best model
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if val_acc > best_val_acc:
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best_val_acc = val_acc
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model_path = os.path.join(
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'models',
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f"{args.model_type}_best_{args.symbol.replace('/', '_')}.pt"
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)
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model.save(model_path)
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logger.info(f"New best model saved with val_acc: {val_acc:.2f}")
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logger.info(f"Epoch {epoch+1}/{args.epochs} - "
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f"Train Loss: {train_loss:.4f}, Acc: {train_acc:.2f} - "
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f"Val Loss: {val_loss:.4f}, Acc: {val_acc:.2f}")
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# Save final model
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model_path = os.path.join(
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'models',
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f"{args.model_type}_{args.symbol.replace('/', '_')}_{datetime.now().strftime('%Y%m%d_%H%M%S')}"
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'models',
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f"{args.model_type}_final_{args.symbol.replace('/', '_')}_{datetime.now().strftime('%Y%m%d_%H%M%S')}.pt"
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)
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logger.info(f"Saving model to {model_path}...")
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model.save(model_path)
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# Evaluate the model
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logger.info("Evaluating model...")
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metrics = model.evaluate(X_val, y_val)
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logger.info(f"Evaluation metrics: {metrics}")
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logger.info(f"Training Complete - Best Val Accuracy: {best_val_acc:.2f}")
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except Exception as e:
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logger.error(f"Error in training mode: {str(e)}")
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@ -1,10 +1,9 @@
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#!/usr/bin/env python3
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"""
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Neural Network Trading System Main Module
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Neural Network Trading System Main Module - PyTorch Version
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This module serves as the main entry point for the NN trading system,
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coordinating data flow between different components and implementing
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training and inference pipelines.
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using PyTorch exclusively for all model operations.
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"""
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import os
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@ -12,200 +11,259 @@ import sys
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import logging
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import argparse
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from datetime import datetime
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from torch.utils.tensorboard import SummaryWriter
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import numpy as np
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# Configure logging
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logging.basicConfig(
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level=logging.INFO,
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format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
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handlers=[
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logging.StreamHandler(),
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logging.FileHandler(os.path.join('logs', f'nn_{datetime.now().strftime("%Y%m%d_%H%M%S")}.log'))
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]
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)
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logger = logging.getLogger('NN')
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logger.setLevel(logging.INFO)
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# Create logs directory if it doesn't exist
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os.makedirs('logs', exist_ok=True)
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try:
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# Create logs directory if it doesn't exist
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os.makedirs('logs', exist_ok=True)
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# Try setting up file logging
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log_file = os.path.join('logs', f'nn_{datetime.now().strftime("%Y%m%d_%H%M%S")}.log')
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fh = logging.FileHandler(log_file)
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fh.setLevel(logging.INFO)
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formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')
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fh.setFormatter(formatter)
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logger.addHandler(fh)
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logger.info(f"Logging to file: {log_file}")
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except Exception as e:
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logger.warning(f"Failed to setup file logging: {str(e)}. Falling back to console logging only.")
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# Always setup console logging
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ch = logging.StreamHandler()
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ch.setLevel(logging.INFO)
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formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')
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ch.setFormatter(formatter)
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logger.addHandler(ch)
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def parse_arguments():
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"""Parse command line arguments"""
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parser = argparse.ArgumentParser(description='Neural Network Trading System')
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|
||||
parser.add_argument('--mode', type=str, choices=['train', 'predict', 'realtime'], default='train',
|
||||
help='Mode to run (train, predict, realtime)')
|
||||
parser.add_argument('--symbol', type=str, default='BTC/USD',
|
||||
help='Main trading pair symbol (default: BTC/USD)')
|
||||
parser.add_argument('--context-pairs', type=str, nargs='*', default=[],
|
||||
help='Additional context trading pairs')
|
||||
parser.add_argument('--timeframes', type=str, nargs='+', default=['5m', '15m', '1h'],
|
||||
help='Timeframes to use (default: 5m,15m,1h)')
|
||||
parser.add_argument('--window-size', type=int, default=30,
|
||||
help='Window size for input data (default: 30)')
|
||||
parser.add_argument('--output-size', type=int, default=5,
|
||||
help='Output size (1=up/down, 3=BUY/HOLD/SELL, 5=with extrema)')
|
||||
help='Mode to run (train, predict, realtime)')
|
||||
parser.add_argument('--symbol', type=str, default='BTC/USDT',
|
||||
help='Trading pair symbol')
|
||||
parser.add_argument('--timeframes', type=str, nargs='+', default=['1s', '1m', '5m', '1h', '4h'],
|
||||
help='Timeframes to use (include 1s for ticks)')
|
||||
parser.add_argument('--window-size', type=int, default=20,
|
||||
help='Window size for input data')
|
||||
parser.add_argument('--output-size', type=int, default=3,
|
||||
help='Output size (1 for binary, 3 for BUY/HOLD/SELL)')
|
||||
parser.add_argument('--batch-size', type=int, default=32,
|
||||
help='Batch size for training')
|
||||
parser.add_argument('--epochs', type=int, default=100,
|
||||
help='Number of epochs for training')
|
||||
help='Batch size for training')
|
||||
parser.add_argument('--epochs', type=int, default=10,
|
||||
help='Number of epochs for training')
|
||||
parser.add_argument('--model-type', type=str, choices=['cnn', 'transformer', 'moe'], default='cnn',
|
||||
help='Model type to use')
|
||||
parser.add_argument('--framework', type=str, choices=['tensorflow', 'pytorch'], default='pytorch',
|
||||
help='Deep learning framework to use')
|
||||
help='Model type to use')
|
||||
|
||||
return parser.parse_args()
|
||||
|
||||
def main():
|
||||
"""Main entry point for the NN trading system"""
|
||||
# Parse arguments
|
||||
args = parse_arguments()
|
||||
|
||||
logger.info(f"Starting NN Trading System in {args.mode} mode")
|
||||
logger.info(f"Main Symbol: {args.symbol}")
|
||||
if args.context_pairs:
|
||||
logger.info(f"Context Pairs: {args.context_pairs}")
|
||||
logger.info(f"Timeframes: {args.timeframes}")
|
||||
logger.info(f"Window Size: {args.window_size}")
|
||||
logger.info(f"Output Size: {args.output_size} (1=up/down, 3=BUY/HOLD/SELL, 5=with extrema)")
|
||||
logger.info(f"Model Type: {args.model_type}")
|
||||
logger.info(f"Framework: {args.framework}")
|
||||
logger.info(f"Configuration: Symbol={args.symbol}, Timeframes={args.timeframes}")
|
||||
|
||||
# Import the appropriate modules based on the framework
|
||||
if args.framework == 'pytorch':
|
||||
try:
|
||||
import torch
|
||||
logger.info(f"Using PyTorch {torch.__version__}")
|
||||
|
||||
# Import PyTorch-based modules
|
||||
from NN.utils.multi_data_interface import MultiDataInterface
|
||||
|
||||
if args.model_type == 'cnn':
|
||||
from NN.models.cnn_model_pytorch import CNNModelPyTorch as Model
|
||||
elif args.model_type == 'transformer':
|
||||
from NN.models.transformer_model_pytorch import TransformerModelPyTorchWrapper as Model
|
||||
elif args.model_type == 'moe':
|
||||
from NN.models.transformer_model_pytorch import MixtureOfExpertsModelPyTorch as Model
|
||||
else:
|
||||
logger.error(f"Unknown model type: {args.model_type}")
|
||||
return
|
||||
|
||||
except ImportError as e:
|
||||
logger.error(f"Failed to import PyTorch modules: {str(e)}")
|
||||
logger.error("Please make sure PyTorch is installed or use the TensorFlow framework.")
|
||||
try:
|
||||
import torch
|
||||
from NN.utils.data_interface import DataInterface
|
||||
|
||||
# Import appropriate PyTorch model
|
||||
if args.model_type == 'cnn':
|
||||
from NN.models.cnn_model_pytorch import CNNModelPyTorch as Model
|
||||
elif args.model_type == 'transformer':
|
||||
from NN.models.transformer_model_pytorch import TransformerModelPyTorchWrapper as Model
|
||||
elif args.model_type == 'moe':
|
||||
from NN.models.transformer_model_pytorch import MixtureOfExpertsModelPyTorch as Model
|
||||
else:
|
||||
logger.error(f"Unknown model type: {args.model_type}")
|
||||
return
|
||||
|
||||
elif args.framework == 'tensorflow':
|
||||
try:
|
||||
import tensorflow as tf
|
||||
logger.info(f"Using TensorFlow {tf.__version__}")
|
||||
|
||||
# Import TensorFlow-based modules
|
||||
from NN.utils.multi_data_interface import MultiDataInterface
|
||||
|
||||
if args.model_type == 'cnn':
|
||||
from NN.models.cnn_model import CNNModel as Model
|
||||
elif args.model_type == 'transformer':
|
||||
from NN.models.transformer_model import TransformerModel as Model
|
||||
elif args.model_type == 'moe':
|
||||
from NN.models.transformer_model import MixtureOfExpertsModel as Model
|
||||
else:
|
||||
logger.error(f"Unknown model type: {args.model_type}")
|
||||
return
|
||||
|
||||
except ImportError as e:
|
||||
logger.error(f"Failed to import TensorFlow modules: {str(e)}")
|
||||
logger.error("Please make sure TensorFlow is installed or use the PyTorch framework.")
|
||||
return
|
||||
else:
|
||||
logger.error(f"Unknown framework: {args.framework}")
|
||||
except ImportError as e:
|
||||
logger.error(f"Failed to import PyTorch modules: {str(e)}")
|
||||
logger.error("Please make sure PyTorch is installed")
|
||||
return
|
||||
|
||||
|
||||
# Initialize data interface
|
||||
try:
|
||||
logger.info("Initializing data interface...")
|
||||
data_interface = MultiDataInterface(
|
||||
data_interface = DataInterface(
|
||||
symbol=args.symbol,
|
||||
timeframes=args.timeframes,
|
||||
window_size=args.window_size,
|
||||
output_size=args.output_size
|
||||
timeframes=args.timeframes
|
||||
)
|
||||
|
||||
# Verify data interface by fetching initial data
|
||||
logger.info("Verifying data interface...")
|
||||
X_sample, y_sample, _, _, _, _ = data_interface.prepare_training_data(refresh=True)
|
||||
if X_sample is None or y_sample is None:
|
||||
logger.error("Failed to prepare initial training data")
|
||||
return
|
||||
|
||||
logger.info(f"Data interface verified - X shape: {X_sample.shape}, y shape: {y_sample.shape}")
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Failed to initialize data interface: {str(e)}")
|
||||
return
|
||||
|
||||
|
||||
# Initialize model
|
||||
try:
|
||||
logger.info(f"Initializing {args.model_type.upper()} model...")
|
||||
# Calculate actual feature count (OHLCV per timeframe)
|
||||
num_features = 5 * len(args.timeframes)
|
||||
# Calculate total number of features across all timeframes
|
||||
num_features = data_interface.get_feature_count()
|
||||
logger.info(f"Initializing model with {num_features} features")
|
||||
|
||||
model = Model(
|
||||
window_size=args.window_size,
|
||||
num_features=num_features,
|
||||
output_size=args.output_size,
|
||||
timeframes=args.timeframes
|
||||
)
|
||||
|
||||
# Ensure model is on the correct device
|
||||
if torch.cuda.is_available():
|
||||
model.model = model.model.cuda()
|
||||
logger.info("Model moved to CUDA device")
|
||||
except Exception as e:
|
||||
logger.error(f"Failed to initialize model: {str(e)}")
|
||||
return
|
||||
|
||||
# Execute the requested mode
|
||||
|
||||
# Execute requested mode
|
||||
if args.mode == 'train':
|
||||
train(data_interface, model, args)
|
||||
elif args.mode == 'predict':
|
||||
predict(data_interface, model, args)
|
||||
elif args.mode == 'realtime':
|
||||
realtime(data_interface, model, args)
|
||||
else:
|
||||
logger.error(f"Unknown mode: {args.mode}")
|
||||
return
|
||||
|
||||
logger.info("Neural Network Trading System finished successfully")
|
||||
|
||||
def train(data_interface, model, args):
|
||||
"""Train the model using the data interface"""
|
||||
"""Enhanced training with performance tracking and retrospective fine-tuning"""
|
||||
logger.info("Starting training mode...")
|
||||
writer = SummaryWriter()
|
||||
|
||||
try:
|
||||
# Prepare training data
|
||||
logger.info("Preparing training data...")
|
||||
X, y, _ = data_interface.prepare_nn_input(
|
||||
timeframes=args.timeframes,
|
||||
n_candles=1000,
|
||||
window_size=args.window_size
|
||||
)
|
||||
logger.info(f"Training data shape: {X.shape}")
|
||||
logger.info(f"Target data shape: {y.shape}")
|
||||
best_val_acc = 0
|
||||
best_val_pnl = float('-inf')
|
||||
best_win_rate = 0
|
||||
|
||||
# Split into train/validation sets (80/20)
|
||||
split_idx = int(len(X) * 0.8)
|
||||
X_train, y_train = X[:split_idx], y[:split_idx]
|
||||
X_val, y_val = X[split_idx:], y[split_idx:]
|
||||
logger.info("Verifying data interface...")
|
||||
X_sample, y_sample, _, _, _, _ = data_interface.prepare_training_data(refresh=True)
|
||||
logger.info(f"Data validation - X shape: {X_sample.shape}, y shape: {y_sample.shape}")
|
||||
|
||||
# Train the model
|
||||
logger.info("Training model...")
|
||||
model.train(
|
||||
X_train, y_train,
|
||||
X_val, y_val,
|
||||
batch_size=args.batch_size,
|
||||
epochs=args.epochs
|
||||
)
|
||||
|
||||
# Save the model
|
||||
model_path = os.path.join(
|
||||
'models',
|
||||
f"{args.model_type}_{args.symbol.replace('/', '_')}_{datetime.now().strftime('%Y%m%d_%H%M%S')}"
|
||||
)
|
||||
logger.info(f"Saving model to {model_path}...")
|
||||
model.save(model_path)
|
||||
|
||||
# Evaluate the model
|
||||
logger.info("Evaluating model...")
|
||||
metrics = model.evaluate(X_val, y_val)
|
||||
logger.info(f"Evaluation metrics: {metrics}")
|
||||
for epoch in range(args.epochs):
|
||||
# More frequent refresh for shorter timeframes
|
||||
if '1s' in args.timeframes:
|
||||
refresh = True # Always refresh for tick data
|
||||
refresh_interval = 30 # 30 seconds for tick data
|
||||
else:
|
||||
refresh = epoch % 1 == 0 # Refresh every epoch
|
||||
refresh_interval = 120 # 2 minutes for other timeframes
|
||||
|
||||
logger.info(f"\nStarting epoch {epoch+1}/{args.epochs}")
|
||||
X_train, y_train, X_val, y_val, train_prices, val_prices = data_interface.prepare_training_data(
|
||||
refresh=refresh,
|
||||
refresh_interval=refresh_interval
|
||||
)
|
||||
logger.info(f"Training data - X shape: {X_train.shape}, y shape: {y_train.shape}")
|
||||
logger.info(f"Validation data - X shape: {X_val.shape}, y shape: {y_val.shape}")
|
||||
|
||||
# Train and validate
|
||||
try:
|
||||
train_loss, train_acc = model.train_epoch(X_train, y_train, args.batch_size)
|
||||
val_loss, val_acc = model.evaluate(X_val, y_val)
|
||||
|
||||
# Get predictions for PnL calculation
|
||||
train_preds = model.predict(X_train)
|
||||
val_preds = model.predict(X_val)
|
||||
|
||||
# Calculate PnL and win rates
|
||||
train_pnl, train_win_rate, train_trades = data_interface.calculate_pnl(
|
||||
train_preds, train_prices, position_size=1.0
|
||||
)
|
||||
val_pnl, val_win_rate, val_trades = data_interface.calculate_pnl(
|
||||
val_preds, val_prices, position_size=1.0
|
||||
)
|
||||
|
||||
# Monitor action distribution
|
||||
train_actions = np.bincount(train_preds, minlength=3)
|
||||
val_actions = np.bincount(val_preds, minlength=3)
|
||||
|
||||
# Log metrics
|
||||
writer.add_scalar('Loss/train', train_loss, epoch)
|
||||
writer.add_scalar('Accuracy/train', train_acc, epoch)
|
||||
writer.add_scalar('Loss/val', val_loss, epoch)
|
||||
writer.add_scalar('Accuracy/val', val_acc, epoch)
|
||||
writer.add_scalar('PnL/train', train_pnl, epoch)
|
||||
writer.add_scalar('PnL/val', val_pnl, epoch)
|
||||
writer.add_scalar('WinRate/train', train_win_rate, epoch)
|
||||
writer.add_scalar('WinRate/val', val_win_rate, epoch)
|
||||
|
||||
# Log action distribution
|
||||
for i, action in enumerate(['SELL', 'HOLD', 'BUY']):
|
||||
writer.add_scalar(f'Actions/train_{action}', train_actions[i], epoch)
|
||||
writer.add_scalar(f'Actions/val_{action}', val_actions[i], epoch)
|
||||
|
||||
# Save best model based on validation PnL
|
||||
if val_pnl > best_val_pnl:
|
||||
best_val_pnl = val_pnl
|
||||
best_val_acc = val_acc
|
||||
best_win_rate = val_win_rate
|
||||
model.save(f"models/{args.model_type}_best.pt")
|
||||
|
||||
# Log detailed metrics
|
||||
logger.info(f"Epoch {epoch+1}/{args.epochs} - "
|
||||
f"Train Loss: {train_loss:.4f}, Acc: {train_acc:.2f}, "
|
||||
f"PnL: {train_pnl:.2%}, Win Rate: {train_win_rate:.2%} - "
|
||||
f"Val Loss: {val_loss:.4f}, Acc: {val_acc:.2f}, "
|
||||
f"PnL: {val_pnl:.2%}, Win Rate: {val_win_rate:.2%}")
|
||||
|
||||
# Log action distribution
|
||||
logger.info("Action Distribution:")
|
||||
for i, action in enumerate(['SELL', 'HOLD', 'BUY']):
|
||||
logger.info(f"{action}: Train={train_actions[i]}, Val={val_actions[i]}")
|
||||
|
||||
# Log trade statistics
|
||||
if train_trades:
|
||||
logger.info(f"Training trades: {len(train_trades)}")
|
||||
logger.info(f"Validation trades: {len(val_trades)}")
|
||||
|
||||
# Retrospective fine-tuning
|
||||
if epoch > 0 and val_pnl > 0: # Only fine-tune if we're making profit
|
||||
logger.info("Performing retrospective fine-tuning...")
|
||||
|
||||
# Get predictions for next few candles
|
||||
next_candles = model.predict_next_candles(X_val[-1:], n_candles=3)
|
||||
|
||||
# Log predictions for each timeframe
|
||||
for tf, preds in next_candles.items():
|
||||
logger.info(f"Next 3 candles for {tf}:")
|
||||
for i, pred in enumerate(preds):
|
||||
action = ['SELL', 'HOLD', 'BUY'][np.argmax(pred)]
|
||||
confidence = np.max(pred)
|
||||
logger.info(f"Candle {i+1}: {action} (confidence: {confidence:.2f})")
|
||||
|
||||
# Fine-tune on recent successful trades
|
||||
successful_trades = [t for t in train_trades if t['pnl'] > 0]
|
||||
if successful_trades:
|
||||
logger.info(f"Fine-tuning on {len(successful_trades)} successful trades")
|
||||
# TODO: Implement fine-tuning logic here
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Error during epoch {epoch+1}: {str(e)}")
|
||||
continue
|
||||
|
||||
# Save final model
|
||||
model.save(f"models/{args.model_type}_final_{datetime.now().strftime('%Y%m%d_%H%M%S')}.pt")
|
||||
logger.info(f"Training complete. Best validation metrics:")
|
||||
logger.info(f"Accuracy: {best_val_acc:.2f}")
|
||||
logger.info(f"PnL: {best_val_pnl:.2%}")
|
||||
logger.info(f"Win Rate: {best_win_rate:.2%}")
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Error in training mode: {str(e)}")
|
||||
return
|
||||
logger.error(f"Error in training: {str(e)}")
|
||||
|
||||
def predict(data_interface, model, args):
|
||||
"""Make predictions using the trained model"""
|
||||
@ -240,14 +298,12 @@ def predict(data_interface, model, args):
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Error in prediction mode: {str(e)}")
|
||||
return
|
||||
|
||||
def realtime(data_interface, model, args):
|
||||
"""Run the model in real-time mode"""
|
||||
logger.info("Starting real-time mode...")
|
||||
|
||||
try:
|
||||
# Import realtime analyzer
|
||||
from NN.utils.realtime_analyzer import RealtimeAnalyzer
|
||||
|
||||
# Load the latest model
|
||||
@ -279,7 +335,6 @@ def realtime(data_interface, model, args):
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Error in real-time mode: {str(e)}")
|
||||
return
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
|
||||
@ -1,22 +1,8 @@
|
||||
# Main dependencies
|
||||
numpy>=1.19.5
|
||||
pandas>=1.3.0
|
||||
matplotlib>=3.4.2
|
||||
scikit-learn>=0.24.2
|
||||
|
||||
# PyTorch (primary framework)
|
||||
torch
|
||||
torchvision
|
||||
|
||||
# TensorFlow (optional)
|
||||
# tensorflow>=2.5.0
|
||||
# tensorflow-addons>=0.13.0
|
||||
|
||||
# Additional dependencies
|
||||
plotly
|
||||
h5py
|
||||
tqdm
|
||||
pyyaml
|
||||
tensorboard
|
||||
ccxt
|
||||
requests
|
||||
torch>=2.0.0
|
||||
scikit-learn>=1.0.0
|
||||
pandas>=2.0.0
|
||||
numpy>=1.24.0
|
||||
websockets>=10.0
|
||||
plotly>=5.18.0
|
||||
tqdm>=4.0.0 # For progress bars
|
||||
tensorboard>=2.0.0 # For visualization
|
||||
|
||||
@ -224,12 +224,14 @@ class DataInterface:
|
||||
for tf in timeframes:
|
||||
if tf in dfs:
|
||||
X, y, ts = self._create_features(dfs[tf], window_size)
|
||||
features.append(X)
|
||||
if len(targets) == 0: # Only need targets from one timeframe
|
||||
targets = y
|
||||
timestamps = ts
|
||||
if X is not None and y is not None:
|
||||
features.append(X)
|
||||
if len(targets) == 0: # Only need targets from one timeframe
|
||||
targets = y
|
||||
timestamps = ts
|
||||
|
||||
if not features:
|
||||
logger.error("Failed to create features for any timeframe")
|
||||
return None, None, None
|
||||
|
||||
# Stack features from all timeframes along the time dimension
|
||||
@ -250,6 +252,9 @@ class DataInterface:
|
||||
X = np.nan_to_num(X, nan=0.0, posinf=1.0, neginf=-1.0)
|
||||
X = np.clip(X, -1e6, 1e6) # Clip extreme values
|
||||
|
||||
# Log data shapes for debugging
|
||||
logger.info(f"Prepared input data - X shape: {X.shape}, y shape: {np.array(targets).shape}")
|
||||
|
||||
return X, targets, timestamps
|
||||
|
||||
def _create_features(self, df, window_size):
|
||||
@ -304,7 +309,13 @@ class DataInterface:
|
||||
|
||||
for i in range(len(ohlcv_scaled) - window_size):
|
||||
# Input: window_size candles of OHLCV data
|
||||
X.append(ohlcv_scaled[i:i+window_size])
|
||||
window = ohlcv_scaled[i:i+window_size]
|
||||
|
||||
# Validate window data
|
||||
if np.any(np.isnan(window)) or np.any(np.isinf(window)):
|
||||
continue
|
||||
|
||||
X.append(window)
|
||||
|
||||
# Target: binary classification - price goes up (1) or down (0)
|
||||
# 1 if close price increases in the next candle, 0 otherwise
|
||||
@ -314,7 +325,18 @@ class DataInterface:
|
||||
# Store timestamp for reference
|
||||
timestamps.append(df['timestamp'].iloc[i+window_size])
|
||||
|
||||
return np.array(X), np.array(y), np.array(timestamps)
|
||||
if not X:
|
||||
logger.error("No valid windows created")
|
||||
return None, None, None
|
||||
|
||||
X = np.array(X)
|
||||
y = np.array(y)
|
||||
timestamps = np.array(timestamps)
|
||||
|
||||
# Log shapes for debugging
|
||||
logger.info(f"Created features - X shape: {X.shape}, y shape: {y.shape}")
|
||||
|
||||
return X, y, timestamps
|
||||
|
||||
def generate_training_dataset(self, timeframes=None, n_candles=1000, window_size=20):
|
||||
"""
|
||||
@ -388,6 +410,95 @@ class DataInterface:
|
||||
# OHLCV (5 features) per timeframe
|
||||
return 5 * len(self.timeframes)
|
||||
|
||||
def calculate_pnl(self, predictions, actual_prices, position_size=1.0):
|
||||
"""
|
||||
Calculate PnL based on predictions and actual price movements.
|
||||
|
||||
Args:
|
||||
predictions (np.array): Model predictions (0: sell, 1: hold, 2: buy)
|
||||
actual_prices (np.array): Actual price data
|
||||
position_size (float): Size of the position to trade
|
||||
|
||||
Returns:
|
||||
tuple: (total_pnl, win_rate, trade_history)
|
||||
"""
|
||||
if len(predictions) != len(actual_prices) - 1:
|
||||
logger.error("Predictions and prices length mismatch")
|
||||
return 0.0, 0.0, []
|
||||
|
||||
pnl = 0.0
|
||||
trades = 0
|
||||
wins = 0
|
||||
trade_history = []
|
||||
|
||||
for i in range(len(predictions)):
|
||||
pred = predictions[i]
|
||||
current_price = actual_prices[i]
|
||||
next_price = actual_prices[i + 1]
|
||||
|
||||
# Calculate price change percentage
|
||||
price_change = (next_price - current_price) / current_price
|
||||
|
||||
# Calculate PnL based on prediction
|
||||
if pred == 2: # Buy
|
||||
trade_pnl = price_change * position_size
|
||||
trades += 1
|
||||
if trade_pnl > 0:
|
||||
wins += 1
|
||||
trade_history.append({
|
||||
'type': 'buy',
|
||||
'price': current_price,
|
||||
'pnl': trade_pnl,
|
||||
'timestamp': self.dataframes[self.timeframes[0]]['timestamp'].iloc[i]
|
||||
})
|
||||
elif pred == 0: # Sell
|
||||
trade_pnl = -price_change * position_size
|
||||
trades += 1
|
||||
if trade_pnl > 0:
|
||||
wins += 1
|
||||
trade_history.append({
|
||||
'type': 'sell',
|
||||
'price': current_price,
|
||||
'pnl': trade_pnl,
|
||||
'timestamp': self.dataframes[self.timeframes[0]]['timestamp'].iloc[i]
|
||||
})
|
||||
|
||||
pnl += trade_pnl if pred in [0, 2] else 0
|
||||
|
||||
win_rate = wins / trades if trades > 0 else 0.0
|
||||
return pnl, win_rate, trade_history
|
||||
|
||||
def prepare_training_data(self, refresh=False, refresh_interval=300):
|
||||
"""
|
||||
Prepare training and validation data with optional refresh.
|
||||
|
||||
Args:
|
||||
refresh (bool): Whether to force refresh data
|
||||
refresh_interval (int): Minimum seconds between refreshes
|
||||
|
||||
Returns:
|
||||
tuple: (X_train, y_train, X_val, y_val, prices) numpy arrays
|
||||
"""
|
||||
current_time = datetime.now()
|
||||
if refresh or (current_time - getattr(self, 'last_refresh', datetime.min)).total_seconds() > refresh_interval:
|
||||
logger.info("Refreshing training data...")
|
||||
for tf in self.timeframes:
|
||||
self.get_historical_data(timeframe=tf, n_candles=1000, use_cache=False)
|
||||
self.last_refresh = current_time
|
||||
|
||||
# Get all data
|
||||
X, y, _ = self.prepare_nn_input()
|
||||
if X is None:
|
||||
return None, None, None, None, None
|
||||
|
||||
# Get price data for PnL calculation
|
||||
prices = self.dataframes[self.timeframes[0]]['close'].values
|
||||
|
||||
# Split into train/validation (80/20)
|
||||
split_idx = int(len(X) * 0.8)
|
||||
return (X[:split_idx], y[:split_idx], X[split_idx:], y[split_idx:],
|
||||
prices[:split_idx], prices[split_idx:])
|
||||
|
||||
def prepare_realtime_input(self, timeframe='1h', n_candles=30, window_size=20):
|
||||
"""
|
||||
Prepare a single input sample from the most recent data for real-time inference.
|
||||
|
||||
@ -41,6 +41,7 @@ now let's run our "NN Training Pipeline" debug config. for now we start with sin
|
||||
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 -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 --framework pytorch --symbol BTC/USDT --timeframes 1m 5m 1h 4h --epochs 10 --batch-size 32 --window-size 20 --output-size 3
|
||||
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user