input: 
  def predict_stock_market(data: pd.DataFrame) -> float:
      """ Predicts stock market movements using a deep learning model trained on the input data.
      The function first splits the data into training and testing sets, builds a deep learning model,
      trains the model, and then evaluates the model's performance by predicting the stock market movements
      for the test data.
      
      >>> data = pd.DataFrame({'X': [1, 2, 3, 4, 5], 'y': [2, 4, 6, 8, 10]})
      >>> predict_stock_market(data)
      0.0
      """
  
output:     from sklearn.model_selection import train_test_split
      from keras.models import Sequential
      from keras.layers import Dense
      from sklearn.metrics import mean_absolute_error
  
      X = data[['X']]
      y = data['y']
  
      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=100, verbose=0)
  
      y_pred = model.predict(X_test)
      mae = mean_absolute_error(y_test, y_pred)
  
      return mae