input: def predict_stock_market_movement(X_train: pd.Series, y_train: pd.Series, X_test: pd.Series, y_test: pd.Series) -> float:
      """
      Create an AI-based system for predicting stock market movements using a deep learning model.
      The model is trained on the given training data and then evaluated on the test data.
  
      >>> 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]
      >>> predict_stock_market_movement(X_train, y_train, X_test, y_test)
      0.03456789  # Example loss value
      """
      import tensorflow as tf
      from tensorflow.keras.layers import Dense
      from tensorflow.keras.models import Sequential
  
      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[0]
output:     import tensorflow as tf
      from tensorflow.keras.layers import Dense
      from tensorflow.keras.models import Sequential
  
      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[0]