input: Write a function that implements an AI-based system in Python for predicting stock market movements by utilizing a deep learning model. The function will involve loading data, splitting it into training and testing sets, and training a neural network to make predictions on stock market data.
  
  
  def predict_stock_movements():
      #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
  
  
  assert predict_stock_movements()==0.0
output: def predict_stock_movements():
      import pandas as pd
      import numpy as np
      import tensorflow as tf
      from tensorflow.keras.layers import Dense
      from tensorflow.keras.models import Sequential
      
      data = pd.read_csv('data.csv') 
      
      X_train = data.iloc[:, 0]
      y_train = data.iloc[:, 1]
      X_test = data.iloc[:, 0]
      y_test = data.iloc[:, 1]
      
      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