input: from typing import Tuple
  
  def predict_stock_movements(data: pd.DataFrame) -> Tuple[float, 'Sequential']:
      """
      Create an AI-based system in Python for predicting stock market movements.
  
      Parameters:
      data (pd.DataFrame): A DataFrame containing stock market data with at least two columns:
                           one for the independent variable and one for the dependent variable.
  
      Returns:
      Tuple[float, 'Sequential']: A tuple containing the mean squared error (MSE) score
                                  of the model evaluation and the trained Sequential model.
  
      Example:
      data = pd.DataFrame({
          'IndependentVar': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10],
          'DependentVar': [10, 20, 30, 40, 50, 60, 70, 80, 90, 100]
      })
      >>> predict_stock_movements(data)
      (500.0, Sequential)
      """
      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
  
      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[0], model
output:     X_train = X_train.values.reshape(-1, 1)
      y_train = y_train.values.reshape(-1, 1)
      X_test = X_test.values.reshape(-1, 1)
      y_test = y_test.values.reshape(-1, 1)