input: def predict_stock_market_movements():
      """ A comprehensive AI-based system in Python for predicting stock market movements. 
      This function reads stock market data, splits it into training and testing sets, 
      builds, compiles, and trains a deep learning model using TensorFlow and Keras, 
      and finally evaluates the model's performance.
      >>> predict_stock_market_movements()
      [XXX, XXX]  # Placeholder for actual evaluation scores, exact values depend on the data
      """
      
      #import libraries
      import pandas as pd
      import numpy as np
      import tensorflow as tf
      from tensorflow.keras.layers import Dense
      from tensorflow.keras.models import Sequential
  
      #import data
      data = pd.read_csv('data.csv') 
  
      #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))
  
      #Evaluate the model
      score = model.evaluate(X_test, y_test, verbose=0)
      return score
output:     #Evaluate the model
      score = model.evaluate(X_test, y_test, verbose=0)
      return score