input: Write a function to create an AI-based system in Python for predicting stock market movements. The function should import necessary libraries, load data from a CSV file, split the data into training and testing sets, build a deep learning model using TensorFlow, train the model, and evaluate its performance. The function should return the evaluation score. If the provided data file does not exist, return None.
  def predict_stock_movements(data_file_path):
      try:
          #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_file_path)
  
          #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
      except FileNotFoundError:
          return None
  
  assert predict_stock_movements('data.csv') != None
  assert predict_stock_movements('non_existent_file.csv') == None
output: def predict_stock_movements(data_file_path):
      try:
          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_file_path)
  
          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
      except FileNotFoundError:
          return None