code: import numpy as np
  import pandas as pd
  import trading_env
  
  from datetime import datetime
  st = datetime.now()
  ## need to refactor the testcase
  
  # df = pd.read_csv('trading_env/test/data/SGXTWsample.csv', index_col=0, parse_dates=['datetime'])
  df = pd.read_hdf('D:\[AIA]\TradingGym\dataset\SGXTWsample.h5', 'STW')
  
  env = trading_env.make(env_id='training_v1', obs_data_len=256, step_len=128,
                         df=df, fee=0.1, max_position=5, deal_col_name='Price', 
                         feature_names=['Price', 'Volume', 
                                        'Ask_price','Bid_price', 
                                        'Ask_deal_vol','Bid_deal_vol',
                                        'Bid/Ask_deal', 'Updown'], 
                         fluc_div=100.0)
  
  env.reset()
  env.render()
  print(env.df_sample['datetime'].iloc[0].date())
  for i in range(500):
      # print(i)
      state, reward, done, info = env.step(np.random.randint(3))
      # print(state, reward)
      # env.render()
      if done:
          break
  print(datetime.now() - st)
apis: pandas.read_hdf numpy.random.randint