input: Write a function to create an AI-based system in Python for predicting stock market movements using deep learning. 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, compile and train the model, and finally evaluate the model's performance.
  
  
  def predict_stock_movement(data_path):
      # 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_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
      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
  
  # Test cases
  assert predict_stock_movement('data.csv') is not None
  assert isinstance(predict_stock_movement('data.csv'), float)
  
output: def predict_stock_movement(data_path):
      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_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