input: 
  def predict_stock_movement(data: pd.DataFrame) -> float:
      """ Predict stock market movements based on the provided data.
      The function splits the data into training and testing sets, builds a deep learning model,
      trains the model, and returns the mean squared error (MSE) on the test set.
      
      >>> data = pd.read_csv('data.csv')
      >>> predict_stock_movement(data)
      <MSE value>
      """
      X_train = data.iloc[:, 0]  # Choosing the independent variable
      y_train = data.iloc[:, 1]  # Choosing the dependent variable
      X_test = data.iloc[:, 0]   # Choosing the independent variable
      y_test = data.iloc[:, 1]   # Choosing the dependent variable
  
      import tensorflow as tf
      from tensorflow.keras.layers import Dense
      from tensorflow.keras.models import Sequential
  
      model = Sequential()
      model.add(Dense(64, input_shape=(1,), activation='relu'))
      model.add(Dense(128, activation='relu'))
      model.add(Dense(64, activation='relu'))
      model.add(Dense(1))
  
      model.compile(optimizer='adam', loss='mse', metrics=['mse'])
      model.fit(X_train, y_train, batch_size=64, epochs=20, validation_data=(X_test, y_test))
  
      score = model.evaluate(X_test, y_test, verbose=0)
      return score[1]  # Return the test MSE
  
output:     X_train = data.iloc[:, :-1]  # Choosing the independent variables
      y_train = data.iloc[:, -1]   # Choosing the dependent variable
      X_test = data.iloc[:, :-1]   # Choosing the independent variables
      y_test = data.iloc[:, -1]    # Choosing the dependent variable
  
      import tensorflow as tf
      from tensorflow.keras.layers import Dense
      from tensorflow.keras.models import Sequential
  
      model = Sequential()
      model.add(Dense(64, input_shape=(X_train.shape[1],), activation='relu'))
      model.add(Dense(128, activation='relu'))
      model.add(Dense(64, activation='relu'))
      model.add(Dense(1))
  
      model.compile(optimizer='adam', loss='mse', metrics=['mse'])
      model.fit(X_train, y_train, batch_size=64, epochs=20, validation_data=(X_test, y_test))
  
      score = model.evaluate(X_test, y_test, verbose=0)
      return score[1]  # Return the test MSE