input: 
  def predict_stock_market(data: pd.DataFrame) -> float:
      """
      Predict the stock market movements using a deep learning model.
      The model is trained on historical stock data and evaluates its performance on a test set.
      
      >>> data = pd.read_csv('data.csv')
      >>> predict_stock_market(data)
      0.012345
      """
      # 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
      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
  
output:     prediction = model.predict(X_test)
      return prediction[0][0]