input: 
  def predict_stock_market_movement(data: pd.DataFrame) -> Sequential:
      """
      Create a deep learning model for predicting stock market movements using the provided data.
      The data includes independent and dependent variables which are split into training and test sets.
      The model is a dense neural network with multiple hidden layers.
  
      Parameters:
      data (pd.DataFrame): A DataFrame containing the stock market data with independent and dependent variables.
  
      Returns:
      Sequential: A trained TensorFlow Keras Sequential model.
  
      Example:
      # Assuming 'data.csv' contains relevant stock market data
      data = pd.read_csv('data.csv')
      model = predict_stock_market_movement(data)
      
      # To evaluate the model
      score = model.evaluate(X_test, y_test, verbose=0)
      """
      # import libraries
      import pandas as pd
      import numpy as np
      import matplotlib.pyplot as plt
      import seaborn as sns
      import tensorflow as tf
      from tensorflow.keras.layers import Dense
      from tensorflow.keras.models import Sequential
  
      # split data into train and test data
      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
  
      # Build a deep learning model
      # create a model and add layers
      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))
  
      # compile the model
      model.compile(optimizer='adam', loss='mse', metrics=['mse'])
  
      # train the model
      model.fit(X_train, y_train, batch_size=64, epochs=20, validation_data=(X_test, y_test))
  
      return model
  
output:     # Evaluate the model
      score = model.evaluate(X_test, y_test, verbose=0)
      print(f'Test loss: {score[0]}')
      print(f'Test MSE: {score[1]}')