input: 
  def predict_stock_movements(data: pd.DataFrame) -> float:
      """ Predict stock market movements using a deep learning model built with TensorFlow.
      This function takes a DataFrame containing stock data, splits it into training and test sets,
      builds and trains a simple deep learning model, and returns the model's mean squared error on the test set.
      >>> predict_stock_movements(pd.DataFrame({0: [x for x in range(100)], 1: [x*2 for x in range(100)]}))
      0.0
      """
      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[1]
  
output:     X_train = X_train.values.reshape(-1, 1)
      y_train = y_train.values.reshape(-1, 1)
      X_test = X_test.values.reshape(-1, 1)
      y_test = y_test.values.reshape(-1, 1)