input: Write a function to predict stock market movements by implementing a deep learning model in Python. The function should import necessary libraries, load the dataset, split it into training and testing data, build a sequential model with specified layers, compile and train the model, and finally evaluate the model's performance. If the given dataset is empty then return -1.
  assert predict_stock_movements(pd.DataFrame({'X': [], 'y': []})) == -1
  assert isinstance(predict_stock_movements(pd.DataFrame({'X': [1,2,3], 'y': [4,5,6]})), float)
output: 
  def predict_stock_movements(df):
      if df.empty:
          return -1
      
      import numpy as np
      from sklearn.model_selection import train_test_split
      from keras.models import Sequential
      from keras.layers import Dense
      
      X = df['X'].values
      y = df['y'].values
      
      X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
      
      model = Sequential()
      model.add(Dense(10, input_dim=1, activation='relu'))
      model.add(Dense(1))
      
      model.compile(optimizer='adam', loss='mean_squared_error')
      model.fit(X_train, y_train, epochs=10, batch_size=10, verbose=0)
      
      loss = model.evaluate(X_test, y_test, verbose=0)
      return loss
  