Why does my LSTM always predict a straight line?

I'm trying to predict some stock data with LSTM in Pytorch.

The shape of my original dataset is (2577,6). I want to use 60 time steps to predict the next data, so I reshape my data to

(number of batches, time steps, number of features)

by doing:

look_back = 60
train_percent = 0.8

x_data = []
y_data = []

for i in range(data.shape[0] - look_back):

  x_data.append(data[i : i + look_back, :])
  y_data.append(data[i + look_back, 3])

where the fourth column is the response data. The shape after reshaping is (2013, 60, 6).

Here is my code

class model(nn.Module):

  def __init__(self, input_dim, hidden_dim, num_layers, output_dim):

    super(model, self).__init__()
    
    self.hidden_dim = hidden_dim
    self.num_layers = num_layers

    self.lstm = nn.LSTM(input_dim, hidden_dim, num_layers, batch_first = False)
    self.fc = nn.Linear(hidden_dim, output_dim)

  def forward(self, x):
        
    out, (hn, cn) = self.lstm(x)
    out = self.fc(out[:, -1, :]) 

    return out

lstm = model(input_dim = 6, hidden_dim = 32, output_dim = 1, num_layers = 5)

loss_fn = torch.nn.MSELoss()

optimiser = torch.optim.Adam(lstm.parameters(), lr=0.01)

num_epochs = 10000

train_loss = []
test_loss = []

for i in range(num_epochs):
    
    lstm.train()

    optimiser.zero_grad()

    y_train_pred = lstm(x_train)

    loss = loss_fn(y_train_pred, y_train)

    loss.backward()

    optimiser.step()

    train_loss.append(loss.item())

    with torch.no_grad():

      lstm.eval()

      y_test_pred = lstm(x_test)

      loss = loss_fn(y_test_pred, y_test)

      test_loss.append(loss.item())

    if i % 100 == 0:

      clear_output()

      plt.plot(train_loss, label = 'training_loss')
      plt.plot(test_loss, label = 'validation_loss')
      plt.legend()
      plt.show()

      plt.plot(y_train.clone().cpu(), label = 'true value')
      plt.plot(y_train_pred.clone().detach().cpu(), label = 'predicted value')
      plt.legend()
      plt.show()

After 400 iterations, the loss stabilizes, but when I look at the prediction on the training set, it looks a straight line at the mid of the plot. (I cannot post any image yet).