input: Write a python function to predict stock prices based on given features using a pre-trained linear regression model.
  assert predict_price([10, 20, 30]) == expected_value_1
  assert predict_price([15, 25, 35]) == expected_value_2
  assert predict_price([20, 30, 40]) == expected_value_3
output: import pickle
  import numpy as np
  
  # Load the pre-trained linear regression model
  with open('linear_regression_model.pkl', 'rb') as file:
      model = pickle.load(file)
  
  def predict_price(features):
      features = np.array(features).reshape(1, -1)
      return model.predict(features)[0]