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SHL github project: uat_shl

  • training module: shl_tm

  • prediction module: shl_pm

  • simulation module: shl_sm

  • misc module: shl_mm

data feeds:

  • historical bidding price, per second, time series

  • live bidding price, per second, time series

parameter lookup table: python dictionary

  • parm_si (seasonality index per second)

  • parm_month (parameter like alpha, beta, gamma, etc. per month)

[1] Import useful reference packages





In [1]:



    


%matplotlib inline
import matplotlib.pyplot as plt



    











In [2]:



    


import pandas as pd

Read raw data





In [3]:



    


df_history_ts_process = pd.read_csv('data/history_ts.csv') 
df_history_ts_process.tail()



    


















    
Out[3]:











  
    

      
      ccyy-mm
      time
      bid-price
      ref-price
    

  
  
    

      1886
      2017-07
      11:29:56
      92100
      89800
    

    

      1887
      2017-07
      11:29:57
      92100
      89800
    

    

      1888
      2017-07
      11:29:58
      92100
      89800
    

    

      1889
      2017-07
      11:29:59
      92200
      89800
    

    

      1890
      2017-07
      11:30:00
      92200
      89800
    

  




















In [4]:



    


df_history_table_process = pd.read_csv('data/history_table.csv') 
df_history_table_process.tail()



    


















    
Out[4]:











  
    

      
      ccyy-mm
      volume-plate
      deal-price-low
      deal-price-avg
      deal-early-second
      volume-bidder
    

  
  
    

      26
      2017-03
      10356
      87800
      87916
      55
      262010
    

    

      27
      2017-04
      12196
      89800
      89850
      59
      252273
    

    

      28
      2017-05
      10316
      90100
      90209
      55
      270197
    

    

      29
      2017-06
      10312
      89400
      89532
      45
      244349
    

    

      30
      2017-07
      10325
      92200
      92250
      57
      269189
    

  




















In [5]:



    


df_parm_si = pd.read_csv('data/parm_si.csv') 
# print(df_parm_si[(df_parm_si['ccyy-mm'] == '2017-07') & (df_parm_si['time'] == '11:29:00')].iloc[0]['si'])
df_parm_si.tail()



    


















    
Out[5]:











  
    

      
      ccyy-mm
      time
      si
    

  
  
    

      2191
      2017-12
      11:29:56
      1.0
    

    

      2192
      2017-12
      11:29:57
      1.0
    

    

      2193
      2017-12
      11:29:58
      1.0
    

    

      2194
      2017-12
      11:29:59
      1.0
    

    

      2195
      2017-12
      11:30:00
      1.0
    

  




















In [6]:



    


df_parm_month = pd.read_csv('data/parm_month.csv') 
# print(df_parm_month[(df_parm_month['ccyy-mm'] == '2017-07') & (df_parm_month['time'] == '11:29:00')].iloc[0]['di'])
df_parm_month.tail()



    


















    
Out[6]:











  
    

      
      ccyy-mm
      time
      alpha
      beta
      gamma
      sec57-weight
      month-weight
      short-weight
      direction
      di
    

  
  
    

      67
      2020-08
      11:29:00
      0.5
      0.5
      0.5
      0.5
      0.9
      0.125
      -1
      300
    

    

      68
      2020-09
      11:29:00
      0.5
      0.5
      0.5
      0.5
      0.9
      0.125
      -1
      300
    

    

      69
      2020-10
      11:29:00
      0.5
      0.5
      0.5
      0.5
      0.9
      0.125
      -1
      300
    

    

      70
      2020-11
      11:29:00
      0.5
      0.5
      0.5
      0.5
      0.9
      0.125
      -1
      300
    

    

      71
      2020-12
      11:29:00
      0.5
      0.5
      0.5
      0.5
      0.9
      0.125
      -1
      300

Initialization





In [7]:



    


# function to fetch Seasonality-Index
def fetech_si(ccyy_mm, time, df_parm_si):
#     return df_parm_si[(df_parm_si['ccyy-mm'] == '2017-09') & (df_parm_si['time'] == '11:29:00')]
    return df_parm_si[(df_parm_si['ccyy-mm'] == ccyy_mm) & (df_parm_si['time'] == time)].iloc[0]['si']



    











In [8]:



    


# function to fetch Dynamic-Increment
def fetech_di(ccyy_mm, df_parm_month):
#     print(df_parm_month[df_parm_month['ccyy-mm'] == '2017-07'].iloc[0]['di'])
    return df_parm_month[df_parm_month['ccyy-mm'] == ccyy_mm].iloc[0]['di']



    











In [9]:



    


def get_previous_n_sec_time_as_str(df_time_field, n):
    return str((pd.to_datetime(df_time_field, format='%H:%M:%S') - pd.Timedelta(seconds=n)).time())
# print(get_previous_n_sec_time_as_str('11:29:57',3))

def get_future_n_sec_time_as_str(df_time_field, n):
    return str((pd.to_datetime(df_time_field, format='%H:%M:%S') - pd.Timedelta(seconds=-n)).time())
# print(get_future_n_sec_time_as_str('11:29:57',3))

shl_sm Simulation Module Parm:





In [10]:



    


# which month to predict?
global_parm_ccyy_mm = '2017-04'
global_parm_ccyy_mm_offset = 1647

# global_parm_ccyy_mm = '2017-05'
# global_parm_ccyy_mm_offset = 1708

# global_parm_ccyy_mm = '2017-06'
# global_parm_ccyy_mm_offset = 1769

# global_parm_ccyy_mm = '2017-07'
# global_parm_ccyy_mm_offset = 1830

shl_pm Prediction Module Parm:





In [11]:



    


# create global base price
global_parm_base_price = 10000000

# create predictino results dataframe: shl_pm
# df_shl_pm = pd.DataFrame()

global_parm_dynamic_increment = fetech_di(global_parm_ccyy_mm, df_parm_month)

global_parm_alpha = df_parm_month[df_parm_month['ccyy-mm'] == global_parm_ccyy_mm].iloc[0]['alpha']
global_parm_beta  = df_parm_month[df_parm_month['ccyy-mm'] == global_parm_ccyy_mm].iloc[0]['beta']
global_parm_gamma = df_parm_month[df_parm_month['ccyy-mm'] == global_parm_ccyy_mm].iloc[0]['gamma']
global_parm_sec57_weight = df_parm_month[df_parm_month['ccyy-mm'] == global_parm_ccyy_mm].iloc[0]['sec57-weight']
global_parm_month_weight = df_parm_month[df_parm_month['ccyy-mm'] == global_parm_ccyy_mm].iloc[0]['month-weight']
global_parm_short_weight = df_parm_month[df_parm_month['ccyy-mm'] == global_parm_ccyy_mm].iloc[0]['short-weight']

global_parm_short_weight_misc = 0

print('=================================================')
print('  Global Parameters for Month : %s' % global_parm_ccyy_mm)
print('-------------------------------------------------')

print('global_parm_dynamic_increment : %d' % global_parm_dynamic_increment)

print('global_parm_alpha             : %0.15f' % global_parm_alpha) # used in forecasting
print('global_parm_beta              : %0.15f' % global_parm_beta)  # used in forecasting
print('global_parm_gamma             : %0.15f' % global_parm_gamma) # used in forecasting
print('global_parm_sec57_weight      : %f' % global_parm_sec57_weight) # used in training a model
print('global_parm_month_weight      : %f' % global_parm_month_weight) # used in training a model
print('global_parm_short_weight      : %f' % global_parm_short_weight) # used in training a model
print('=================================================')

# plot seasonality index
# print(df_parm_si[(df_parm_si['ccyy-mm'] == '2017-07')]['si'])
plt.figure(figsize=(6,3))
plt.plot(df_parm_si[(df_parm_si['ccyy-mm'] == '2017-07')]['si'])



    


















    






=================================================
  Global Parameters for Month : 2017-04
-------------------------------------------------
global_parm_dynamic_increment : 300
global_parm_alpha             : 0.794138757302472
global_parm_beta              : 0.198876393122000
global_parm_gamma             : 0.143886052000000
global_parm_sec57_weight      : 0.500000
global_parm_month_weight      : 0.900000
global_parm_short_weight      : 0.125000
=================================================










    
Out[11]:








[<matplotlib.lines.Line2D at 0x7fb82a8f31d0>]










    
























In [ ]:

Start of shl_sm





In [12]:



    


# 11:29:00~11:29:50

global_parm_short_weight_misc = 0

# for i in range(1830, 1830+51): # use July 2015 data as simulatino
for i in range(global_parm_ccyy_mm_offset, global_parm_ccyy_mm_offset+51): # use July 2015 data as simulatino
    print('\n<<<< Record No.: %5d >>>>' % i)
    print(df_history_ts_process['ccyy-mm'][i]) # format: ccyy-mm
    print(df_history_ts_process['time'][i]) # format: hh:mm:ss
    print(df_history_ts_process['bid-price'][i]) # format: integer
#     print(df_history_ts_process['ref-price'][i])
    
    # capture & calculate 11:29:00 bid price - 1 = base price
    if df_history_ts_process['time'][i] == '11:29:00':
        global_parm_base_price = df_history_ts_process['bid-price'][i] -1 
        print('#### global_parm_base_price : %d ####' % global_parm_base_price)

        
    print('---- Pre-Process ---')
    # pre-process: ccyy-mm-hh:mm:ss
    f_actual_datetime = df_history_ts_process['ccyy-mm'][i] + ' ' + df_history_ts_process['time'][i]
    f_actual_price4pm = df_history_ts_process['bid-price'][i] -  global_parm_base_price
    print('#### f_actual_datetime   : %s ####' %  f_actual_datetime)
    print('#### f_actual_price4pm   : %d ####' % f_actual_price4pm)
    
    # get Seasonality-Index
    f_actual_si = fetech_si(df_history_ts_process['ccyy-mm'][i]
                                         ,df_history_ts_process['time'][i]
                                         ,df_parm_si)
    print('#### f_actual_si         : %0.10f ####' %  f_actual_si)
    f_1_step_si = fetech_si(df_history_ts_process['ccyy-mm'][i]
                                         ,df_history_ts_process['time'][i+1]
                                         ,df_parm_si)
    print('#### f_1_step_si         : %0.10f ####' %  f_1_step_si)
    # get de-seasoned price: price4pmsi
    f_actual_price4pmsi = f_actual_price4pm / f_actual_si
    print('#### f_actual_price4pmsi : %0.10f ####' % f_actual_price4pmsi)
    


    if df_history_ts_process['time'][i] == '11:29:00':
        df_shl_pm = pd.DataFrame() # initialize prediction dataframe at 11:29:00
        print('---- call predicitno function shl_pm ---- %s' % df_history_ts_process['time'][i])
        f_1_step_pred_les_level = f_actual_price4pmsi
        f_1_step_pred_les_trend = 0
        f_1_step_pred_les = f_1_step_pred_les_level + f_1_step_pred_les_trend
        f_1_step_pred_les_misc = 0
#         f_1_step_pred_price_inc = (f_1_step_pred_les + f_1_step_pred_les_misc) * f_actual_si
        f_1_step_pred_price_inc = (f_1_step_pred_les + f_1_step_pred_les_misc) * f_1_step_si
        f_1_step_pred_price = f_1_step_pred_price_inc + global_parm_base_price
        f_1_step_pred_price_rounded = round(f_1_step_pred_price/100, 0) * 100
        f_1_step_pred_dynamic_increment = global_parm_dynamic_increment
        f_1_step_pred_set_price_rounded = f_1_step_pred_price_rounded + f_1_step_pred_dynamic_increment
        f_current_step_pred_les = f_1_step_pred_les
        f_current_step_pred_les_misc = f_1_step_pred_les_misc
        f_current_step_pred_price_inc = f_1_step_pred_price_inc
        f_current_step_pred_price = f_1_step_pred_price
        f_current_step_pred_price_rounded = f_1_step_pred_price_rounded
        f_current_step_pred_dynamic_increment = f_1_step_pred_dynamic_increment # +200 or + 300
        f_current_step_pred_set_price_rounded = f_1_step_pred_set_price_rounded
        f_current_step_error = f_current_step_pred_price_inc - f_actual_price4pm # current second forecast error
    else:
        previous_time = get_previous_n_sec_time_as_str(df_history_ts_process['time'][i], 1)
        previous_pred_les_level = df_shl_pm[(df_shl_pm['ccyy-mm'] == df_history_ts_process['ccyy-mm'][i]) \
                                            & (df_shl_pm['time'] ==previous_time)].iloc[0]['f_1_step_pred_les_level']
        print('     previous_pred_les_level : %f' % previous_pred_les_level)
        
        previous_pred_les_trend = df_shl_pm[(df_shl_pm['ccyy-mm'] == df_history_ts_process['ccyy-mm'][i]) \
                                            & (df_shl_pm['time'] ==previous_time)].iloc[0]['f_1_step_pred_les_trend']
        print('     previous_pred_les_trend : %f' % previous_pred_les_trend)
        
        f_current_step_pred_les = df_shl_pm[(df_shl_pm['ccyy-mm'] == df_history_ts_process['ccyy-mm'][i]) \
                                                    & (df_shl_pm['time'] ==previous_time)].iloc[0]['f_1_step_pred_les']
        f_current_step_pred_les_misc = df_shl_pm[(df_shl_pm['ccyy-mm'] == df_history_ts_process['ccyy-mm'][i]) \
                                                    & (df_shl_pm['time'] ==previous_time)].iloc[0]['f_1_step_pred_les_misc']
        f_current_step_pred_price_inc = df_shl_pm[(df_shl_pm['ccyy-mm'] == df_history_ts_process['ccyy-mm'][i]) \
                                                    & (df_shl_pm['time'] ==previous_time)].iloc[0]['f_1_step_pred_price_inc']
        f_current_step_pred_price = df_shl_pm[(df_shl_pm['ccyy-mm'] == df_history_ts_process['ccyy-mm'][i]) \
                                                    & (df_shl_pm['time'] ==previous_time)].iloc[0]['f_1_step_pred_price']
        f_current_step_pred_price_rounded = df_shl_pm[(df_shl_pm['ccyy-mm'] == df_history_ts_process['ccyy-mm'][i]) \
                                                    & (df_shl_pm['time'] ==previous_time)].iloc[0]['f_1_step_pred_price_rounded']
        f_current_step_pred_dynamic_increment = df_shl_pm[(df_shl_pm['ccyy-mm'] == df_history_ts_process['ccyy-mm'][i]) \
                                                    & (df_shl_pm['time'] ==previous_time)].iloc[0]['f_1_step_pred_dynamic_increment']
        f_current_step_pred_set_price_rounded = df_shl_pm[(df_shl_pm['ccyy-mm'] == df_history_ts_process['ccyy-mm'][i]) \
                                                    & (df_shl_pm['time'] ==previous_time)].iloc[0]['f_1_step_pred_set_price_rounded']

        f_current_step_error = f_current_step_pred_price_inc - f_actual_price4pm # current second forecast error
            
        if df_history_ts_process['time'][i] == '11:29:50':
            # function to get average forecast error between 46~50 seconds: mean(f_current_step_error)
            global_parm_short_weight_misc = (df_shl_pm.iloc[46:50]['f_current_step_error'].sum() \
                                             + f_current_step_error) / 5
            print('#### global_parm_short_weight_misc : %f' % global_parm_short_weight_misc)
            
#         call predicitno functino shl_pm, forcaste next k=1 step
        print('---- call predicitno function shl_pm ---- %s' % df_history_ts_process['time'][i])
        
        f_1_step_pred_les_level = global_parm_alpha * f_actual_price4pmsi \
                                    + (1 - global_parm_alpha) * (previous_pred_les_level + previous_pred_les_trend)
        print('     f_1_step_pred_les_level  : %f' % f_1_step_pred_les_level)
        f_1_step_pred_les_trend = global_parm_beta * (f_1_step_pred_les_level - previous_pred_les_level) \
                                    + (1 - global_parm_beta) * previous_pred_les_trend
        print('     f_1_step_pred_les_trend  : %f' % f_1_step_pred_les_trend)
        f_1_step_pred_les = f_1_step_pred_les_level + f_1_step_pred_les_trend
        
        f_1_step_pred_les_misc = global_parm_short_weight_misc * global_parm_short_weight * global_parm_gamma
        
#         f_1_step_pred_price_inc = (f_1_step_pred_les + f_1_step_pred_les_misc) * f_actual_si
        f_1_step_pred_price_inc = (f_1_step_pred_les + f_1_step_pred_les_misc) * f_1_step_si
        f_1_step_pred_price = f_1_step_pred_price_inc + global_parm_base_price
        f_1_step_pred_price_rounded = round(f_1_step_pred_price/100, 0) * 100
        f_1_step_pred_dynamic_increment = global_parm_dynamic_increment
        f_1_step_pred_set_price_rounded = f_1_step_pred_price_rounded + f_1_step_pred_dynamic_increment
   
        
    # write results to shl_pm dataframe
            
    df_shl_pm_current = {
                         'ccyy-mm' : df_history_ts_process['ccyy-mm'][i]
                        ,'time' : df_history_ts_process['time'][i]
                        ,'bid' : df_history_ts_process['bid-price'][i]
                        ,'datetime' : f_actual_datetime
                        ,'f_actual_price4pm' : f_actual_price4pm
                        ,'f_actual_si' : f_actual_si
                        ,'f_1_step_si' : f_1_step_si
                        ,'f_actual_price4pmsi' :  f_actual_price4pmsi
                        ,'f_1_step_pred_les_level' : f_1_step_pred_les_level
                        ,'f_1_step_pred_les_trend' : f_1_step_pred_les_trend
                        ,'f_1_step_pred_les' : f_1_step_pred_les
                        ,'f_1_step_pred_les_misc' : f_1_step_pred_les_misc
                        ,'f_1_step_pred_price_inc' : f_1_step_pred_price_inc
                        ,'f_1_step_pred_price' : f_1_step_pred_price
                        ,'f_1_step_pred_price_rounded' : f_1_step_pred_price_rounded
                        ,'f_1_step_pred_dynamic_increment' : f_1_step_pred_dynamic_increment # +200 or + 300
                        ,'f_1_step_pred_set_price_rounded' : f_1_step_pred_set_price_rounded
                        ,'f_current_step_pred_les' : f_current_step_pred_les
                        ,'f_current_step_pred_les_misc' : f_current_step_pred_les_misc
                        ,'f_current_step_pred_price_inc' : f_current_step_pred_price_inc
                        ,'f_current_step_pred_price' : f_current_step_pred_price
                        ,'f_current_step_pred_price_rounded' : f_current_step_pred_price_rounded
                        ,'f_current_step_pred_dynamic_increment' : f_current_step_pred_dynamic_increment # +200 or + 300
                        ,'f_current_step_pred_set_price_rounded' : f_current_step_pred_set_price_rounded
                        ,'f_current_step_error' : f_current_step_error
                        }
    df_shl_pm =  df_shl_pm.append(df_shl_pm_current, ignore_index=True)



    


















    






<<<< Record No.:  1647 >>>>
2017-04
11:29:00
88500
#### global_parm_base_price : 88499 ####
---- Pre-Process ---
#### f_actual_datetime   : 2017-04 11:29:00 ####
#### f_actual_price4pm   : 1 ####
#### f_actual_si         : 0.0023254760 ####
#### f_1_step_si         : 0.0159319520 ####
#### f_actual_price4pmsi : 430.0194884832 ####
---- call predicitno function shl_pm ---- 11:29:00

<<<< Record No.:  1648 >>>>
2017-04
11:29:01
88500
---- Pre-Process ---
#### f_actual_datetime   : 2017-04 11:29:01 ####
#### f_actual_price4pm   : 1 ####
#### f_actual_si         : 0.0159319520 ####
#### f_1_step_si         : 0.0256448490 ####
#### f_actual_price4pmsi : 62.7669478291 ####
     previous_pred_les_level : 430.019488
     previous_pred_les_trend : 0.000000
---- call predicitno function shl_pm ---- 11:29:01
     f_1_step_pred_les_level  : 138.370012
     f_1_step_pred_les_trend  : -58.002196

<<<< Record No.:  1649 >>>>
2017-04
11:29:02
88500
---- Pre-Process ---
#### f_actual_datetime   : 2017-04 11:29:02 ####
#### f_actual_price4pm   : 1 ####
#### f_actual_si         : 0.0256448490 ####
#### f_1_step_si         : 0.0334674110 ####
#### f_actual_price4pmsi : 38.9941855380 ####
     previous_pred_les_level : 138.370012
     previous_pred_les_trend : -58.002196
---- call predicitno function shl_pm ---- 11:29:02
     f_1_step_pred_les_level  : 47.511413
     f_1_step_pred_les_trend  : -64.536559

<<<< Record No.:  1650 >>>>
2017-04
11:29:03
88500
---- Pre-Process ---
#### f_actual_datetime   : 2017-04 11:29:03 ####
#### f_actual_price4pm   : 1 ####
#### f_actual_si         : 0.0334674110 ####
#### f_1_step_si         : 0.0424838500 ####
#### f_actual_price4pmsi : 29.8798135296 ####
     previous_pred_les_level : 47.511413
     previous_pred_les_trend : -64.536559
---- call predicitno function shl_pm ---- 11:29:03
     f_1_step_pred_les_level  : 20.223900
     f_1_step_pred_les_trend  : -57.128603

<<<< Record No.:  1651 >>>>
2017-04
11:29:04
88500
---- Pre-Process ---
#### f_actual_datetime   : 2017-04 11:29:04 ####
#### f_actual_price4pm   : 1 ####
#### f_actual_si         : 0.0424838500 ####
#### f_1_step_si         : 0.0420011250 ####
#### f_actual_price4pmsi : 23.5383563401 ####
     previous_pred_les_level : 20.223900
     previous_pred_les_trend : -57.128603
---- call predicitno function shl_pm ---- 11:29:04
     f_1_step_pred_les_level  : 11.095473
     f_1_step_pred_les_trend  : -47.582501

<<<< Record No.:  1652 >>>>
2017-04
11:29:05
88500
---- Pre-Process ---
#### f_actual_datetime   : 2017-04 11:29:05 ####
#### f_actual_price4pm   : 1 ####
#### f_actual_si         : 0.0420011250 ####
#### f_1_step_si         : 0.0823068310 ####
#### f_actual_price4pmsi : 23.8088860715 ####
     previous_pred_les_level : 11.095473
     previous_pred_les_trend : -47.582501
---- call predicitno function shl_pm ---- 11:29:05
     f_1_step_pred_les_level  : 11.396294
     f_1_step_pred_les_trend  : -38.059639

<<<< Record No.:  1653 >>>>
2017-04
11:29:06
88500
---- Pre-Process ---
#### f_actual_datetime   : 2017-04 11:29:06 ####
#### f_actual_price4pm   : 1 ####
#### f_actual_si         : 0.0823068310 ####
#### f_1_step_si         : 0.1024396780 ####
#### f_actual_price4pmsi : 12.1496598502 ####
     previous_pred_les_level : 11.396294
     previous_pred_les_trend : -38.059639
---- call predicitno function shl_pm ---- 11:29:06
     f_1_step_pred_les_level  : 4.159567
     f_1_step_pred_les_trend  : -31.929689

<<<< Record No.:  1654 >>>>
2017-04
11:29:07
88500
---- Pre-Process ---
#### f_actual_datetime   : 2017-04 11:29:07 ####
#### f_actual_price4pm   : 1 ####
#### f_actual_si         : 0.1024396780 ####
#### f_1_step_si         : 0.1336822070 ####
#### f_actual_price4pmsi : 9.7618424767 ####
     previous_pred_les_level : 4.159567
     previous_pred_les_trend : -31.929689
---- call predicitno function shl_pm ---- 11:29:07
     f_1_step_pred_les_level  : 2.035465
     f_1_step_pred_les_trend  : -26.002061

<<<< Record No.:  1655 >>>>
2017-04
11:29:08
88500
---- Pre-Process ---
#### f_actual_datetime   : 2017-04 11:29:08 ####
#### f_actual_price4pm   : 1 ####
#### f_actual_si         : 0.1336822070 ####
#### f_1_step_si         : 0.2110307450 ####
#### f_actual_price4pmsi : 7.4804270698 ####
     previous_pred_les_level : 2.035465
     previous_pred_les_trend : -26.002061
---- call predicitno function shl_pm ---- 11:29:08
     f_1_step_pred_les_level  : 1.006704
     f_1_step_pred_les_trend  : -21.035462

<<<< Record No.:  1656 >>>>
2017-04
11:29:09
88500
---- Pre-Process ---
#### f_actual_datetime   : 2017-04 11:29:09 ####
#### f_actual_price4pm   : 1 ####
#### f_actual_si         : 0.2110307450 ####
#### f_1_step_si         : 0.2416833600 ####
#### f_actual_price4pmsi : 4.7386460205 ####
     previous_pred_les_level : 1.006704
     previous_pred_les_trend : -21.035462
---- call predicitno function shl_pm ---- 11:29:09
     f_1_step_pred_les_level  : -0.360003
     f_1_step_pred_les_trend  : -17.123811

<<<< Record No.:  1657 >>>>
2017-04
11:29:10
88500
---- Pre-Process ---
#### f_actual_datetime   : 2017-04 11:29:10 ####
#### f_actual_price4pm   : 1 ####
#### f_actual_si         : 0.2416833600 ####
#### f_1_step_si         : 0.3098961080 ####
#### f_actual_price4pmsi : 4.1376452231 ####
     previous_pred_les_level : -0.360003
     previous_pred_les_trend : -17.123811
---- call predicitno function shl_pm ---- 11:29:10
     f_1_step_pred_les_level  : -0.313375
     f_1_step_pred_les_trend  : -13.709016

<<<< Record No.:  1658 >>>>
2017-04
11:29:11
88500
---- Pre-Process ---
#### f_actual_datetime   : 2017-04 11:29:11 ####
#### f_actual_price4pm   : 1 ####
#### f_actual_si         : 0.3098961080 ####
#### f_1_step_si         : 0.3691481330 ####
#### f_actual_price4pmsi : 3.2268878962 ####
     previous_pred_les_level : -0.313375
     previous_pred_les_trend : -13.709016
---- call predicitno function shl_pm ---- 11:29:11
     f_1_step_pred_les_level  : -0.324070
     f_1_step_pred_les_trend  : -10.984743

<<<< Record No.:  1659 >>>>
2017-04
11:29:12
88500
---- Pre-Process ---
#### f_actual_datetime   : 2017-04 11:29:12 ####
#### f_actual_price4pm   : 1 ####
#### f_actual_si         : 0.3691481330 ####
#### f_1_step_si         : 0.3673713040 ####
#### f_actual_price4pmsi : 2.7089396115 ####
     previous_pred_les_level : -0.324070
     previous_pred_les_trend : -10.984743
---- call predicitno function shl_pm ---- 11:29:12
     f_1_step_pred_les_level  : -0.176772
     f_1_step_pred_les_trend  : -8.770843

<<<< Record No.:  1660 >>>>
2017-04
11:29:13
88500
---- Pre-Process ---
#### f_actual_datetime   : 2017-04 11:29:13 ####
#### f_actual_price4pm   : 1 ####
#### f_actual_si         : 0.3673713040 ####
#### f_1_step_si         : 0.3897582150 ####
#### f_actual_price4pmsi : 2.7220416759 ####
     previous_pred_les_level : -0.176772
     previous_pred_les_trend : -8.770843
---- call predicitno function shl_pm ---- 11:29:13
     f_1_step_pred_les_level  : 0.319712
     f_1_step_pred_les_trend  : -6.927790

<<<< Record No.:  1661 >>>>
2017-04
11:29:14
88500
---- Pre-Process ---
#### f_actual_datetime   : 2017-04 11:29:14 ####
#### f_actual_price4pm   : 1 ####
#### f_actual_si         : 0.3897582150 ####
#### f_1_step_si         : 0.4137200670 ####
#### f_actual_price4pmsi : 2.5656931952 ####
     previous_pred_les_level : 0.319712
     previous_pred_les_trend : -6.927790
---- call predicitno function shl_pm ---- 11:29:14
     f_1_step_pred_les_level  : 0.677169
     f_1_step_pred_les_trend  : -5.478927

<<<< Record No.:  1662 >>>>
2017-04
11:29:15
88500
---- Pre-Process ---
#### f_actual_datetime   : 2017-04 11:29:15 ####
#### f_actual_price4pm   : 1 ####
#### f_actual_si         : 0.4137200670 ####
#### f_1_step_si         : 0.4263723000 ####
#### f_actual_price4pmsi : 2.4170932951 ####
     previous_pred_les_level : 0.677169
     previous_pred_les_trend : -5.478927
---- call predicitno function shl_pm ---- 11:29:15
     f_1_step_pred_les_level  : 0.931012
     f_1_step_pred_les_trend  : -4.338814

<<<< Record No.:  1663 >>>>
2017-04
11:29:16
88500
---- Pre-Process ---
#### f_actual_datetime   : 2017-04 11:29:16 ####
#### f_actual_price4pm   : 1 ####
#### f_actual_si         : 0.4263723000 ####
#### f_1_step_si         : 0.4737120060 ####
#### f_actual_price4pmsi : 2.3453681208 ####
     previous_pred_les_level : 0.931012
     previous_pred_les_trend : -4.338814
---- call predicitno function shl_pm ---- 11:29:16
     f_1_step_pred_les_level  : 1.161013
     f_1_step_pred_les_trend  : -3.430185

<<<< Record No.:  1664 >>>>
2017-04
11:29:17
88500
---- Pre-Process ---
#### f_actual_datetime   : 2017-04 11:29:17 ####
#### f_actual_price4pm   : 1 ####
#### f_actual_si         : 0.4737120060 ####
#### f_1_step_si         : 0.4940771930 ####
#### f_actual_price4pmsi : 2.1109872398 ####
     previous_pred_les_level : 1.161013
     previous_pred_les_trend : -3.430185
---- call predicitno function shl_pm ---- 11:29:17
     f_1_step_pred_les_level  : 1.209282
     f_1_step_pred_les_trend  : -2.738402

<<<< Record No.:  1665 >>>>
2017-04
11:29:18
88600
---- Pre-Process ---
#### f_actual_datetime   : 2017-04 11:29:18 ####
#### f_actual_price4pm   : 101 ####
#### f_actual_si         : 0.4940771930 ####
#### f_1_step_si         : 0.5085858270 ####
#### f_actual_price4pmsi : 204.4214981605 ####
     previous_pred_les_level : 1.209282
     previous_pred_les_trend : -2.738402
---- call predicitno function shl_pm ---- 11:29:18
     f_1_step_pred_les_level  : 162.024248
     f_1_step_pred_les_trend  : 29.788502

<<<< Record No.:  1666 >>>>
2017-04
11:29:19
88600
---- Pre-Process ---
#### f_actual_datetime   : 2017-04 11:29:19 ####
#### f_actual_price4pm   : 101 ####
#### f_actual_si         : 0.5085858270 ####
#### f_1_step_si         : 0.5347445570 ####
#### f_actual_price4pmsi : 198.5898832372 ####
     previous_pred_les_level : 162.024248
     previous_pred_les_trend : 29.788502
---- call predicitno function shl_pm ---- 11:29:19
     f_1_step_pred_les_level  : 197.194734
     f_1_step_pred_les_trend  : 30.858851

<<<< Record No.:  1667 >>>>
2017-04
11:29:20
88600
---- Pre-Process ---
#### f_actual_datetime   : 2017-04 11:29:20 ####
#### f_actual_price4pm   : 101 ####
#### f_actual_si         : 0.5347445570 ####
#### f_1_step_si         : 0.5796917280 ####
#### f_actual_price4pmsi : 188.8752277660 ####
     previous_pred_les_level : 197.194734
     previous_pred_les_trend : 30.858851
---- call predicitno function shl_pm ---- 11:29:20
     f_1_step_pred_les_level  : 196.940533
     f_1_step_pred_les_trend  : 24.671200

<<<< Record No.:  1668 >>>>
2017-04
11:29:21
88600
---- Pre-Process ---
#### f_actual_datetime   : 2017-04 11:29:21 ####
#### f_actual_price4pm   : 101 ####
#### f_actual_si         : 0.5796917280 ####
#### f_1_step_si         : 0.5932005150 ####
#### f_actual_price4pmsi : 174.2305351647 ####
     previous_pred_les_level : 196.940533
     previous_pred_les_trend : 24.671200
---- call predicitno function shl_pm ---- 11:29:21
     f_1_step_pred_les_level  : 183.984487
     f_1_step_pred_les_trend  : 17.188029

<<<< Record No.:  1669 >>>>
2017-04
11:29:22
88600
---- Pre-Process ---
#### f_actual_datetime   : 2017-04 11:29:22 ####
#### f_actual_price4pm   : 101 ####
#### f_actual_si         : 0.5932005150 ####
#### f_1_step_si         : 0.5935049370 ####
#### f_actual_price4pmsi : 170.2628326275 ####
     previous_pred_les_level : 183.984487
     previous_pred_les_trend : 17.188029
---- call predicitno function shl_pm ---- 11:29:22
     f_1_step_pred_les_level  : 176.625939
     f_1_step_pred_les_trend  : 12.306294

<<<< Record No.:  1670 >>>>
2017-04
11:29:23
88700
---- Pre-Process ---
#### f_actual_datetime   : 2017-04 11:29:23 ####
#### f_actual_price4pm   : 201 ####
#### f_actual_si         : 0.5935049370 ####
#### f_1_step_si         : 0.6276944400 ####
#### f_actual_price4pmsi : 338.6660960497 ####
     previous_pred_les_level : 176.625939
     previous_pred_les_trend : 12.306294
---- call predicitno function shl_pm ---- 11:29:23
     f_1_step_pred_les_level  : 307.841697
     f_1_step_pred_les_trend  : 35.954579

<<<< Record No.:  1671 >>>>
2017-04
11:29:24
88700
---- Pre-Process ---
#### f_actual_datetime   : 2017-04 11:29:24 ####
#### f_actual_price4pm   : 201 ####
#### f_actual_si         : 0.6276944400 ####
#### f_1_step_si         : 0.6651491160 ####
#### f_actual_price4pmsi : 320.2195004308 ####
     previous_pred_les_level : 307.841697
     previous_pred_les_trend : 35.954579
---- call predicitno function shl_pm ---- 11:29:24
     f_1_step_pred_les_level  : 325.073045
     f_1_step_pred_les_trend  : 32.230971

<<<< Record No.:  1672 >>>>
2017-04
11:29:25
88700
---- Pre-Process ---
#### f_actual_datetime   : 2017-04 11:29:25 ####
#### f_actual_price4pm   : 201 ####
#### f_actual_si         : 0.6651491160 ####
#### f_1_step_si         : 0.6716154480 ####
#### f_actual_price4pmsi : 302.1878781239 ####
     previous_pred_les_level : 325.073045
     previous_pred_les_trend : 32.230971
---- call predicitno function shl_pm ---- 11:29:25
     f_1_step_pred_les_level  : 313.534155
     f_1_step_pred_les_trend  : 23.526179

<<<< Record No.:  1673 >>>>
2017-04
11:29:26
88800
---- Pre-Process ---
#### f_actual_datetime   : 2017-04 11:29:26 ####
#### f_actual_price4pm   : 301 ####
#### f_actual_si         : 0.6716154480 ####
#### f_1_step_si         : 0.6950966840 ####
#### f_actual_price4pmsi : 448.1731337425 ####
     previous_pred_les_level : 313.534155
     previous_pred_les_trend : 23.526179
---- call predicitno function shl_pm ---- 11:29:26
     f_1_step_pred_les_level  : 425.299315
     f_1_step_pred_les_trend  : 41.074829

<<<< Record No.:  1674 >>>>
2017-04
11:29:27
88800
---- Pre-Process ---
#### f_actual_datetime   : 2017-04 11:29:27 ####
#### f_actual_price4pm   : 301 ####
#### f_actual_si         : 0.6950966840 ####
#### f_1_step_si         : 0.7226188200 ####
#### f_actual_price4pmsi : 433.0332843308 ####
     previous_pred_les_level : 425.299315
     previous_pred_les_trend : 41.074829
---- call predicitno function shl_pm ---- 11:29:27
     f_1_step_pred_les_level  : 439.896875
     f_1_step_pred_les_trend  : 35.809125

<<<< Record No.:  1675 >>>>
2017-04
11:29:28
88800
---- Pre-Process ---
#### f_actual_datetime   : 2017-04 11:29:28 ####
#### f_actual_price4pm   : 301 ####
#### f_actual_si         : 0.7226188200 ####
#### f_1_step_si         : 0.7390681120 ####
#### f_actual_price4pmsi : 416.5404936450 ####
     previous_pred_les_level : 439.896875
     previous_pred_les_trend : 35.809125
---- call predicitno function shl_pm ---- 11:29:28
     f_1_step_pred_les_level  : 428.720378
     f_1_step_pred_les_trend  : 26.464794

<<<< Record No.:  1676 >>>>
2017-04
11:29:29
88800
---- Pre-Process ---
#### f_actual_datetime   : 2017-04 11:29:29 ####
#### f_actual_price4pm   : 301 ####
#### f_actual_si         : 0.7390681120 ####
#### f_1_step_si         : 0.7768357070 ####
#### f_actual_price4pmsi : 407.2696347099 ####
     previous_pred_les_level : 428.720378
     previous_pred_les_trend : 26.464794
---- call predicitno function shl_pm ---- 11:29:29
     f_1_step_pred_les_level  : 417.133587
     f_1_step_pred_les_trend  : 18.897232

<<<< Record No.:  1677 >>>>
2017-04
11:29:30
88800
---- Pre-Process ---
#### f_actual_datetime   : 2017-04 11:29:30 ####
#### f_actual_price4pm   : 301 ####
#### f_actual_si         : 0.7768357070 ####
#### f_1_step_si         : 0.7824499290 ####
#### f_actual_price4pmsi : 387.4693159541 ####
     previous_pred_les_level : 417.133587
     previous_pred_les_trend : 18.897232
---- call predicitno function shl_pm ---- 11:29:30
     f_1_step_pred_les_level  : 397.466247
     f_1_step_pred_les_trend  : 11.227649

<<<< Record No.:  1678 >>>>
2017-04
11:29:31
88800
---- Pre-Process ---
#### f_actual_datetime   : 2017-04 11:29:31 ####
#### f_actual_price4pm   : 301 ####
#### f_actual_si         : 0.7824499290 ####
#### f_1_step_si         : 0.8121336350 ####
#### f_actual_price4pmsi : 384.6891524224 ####
     previous_pred_les_level : 397.466247
     previous_pred_les_trend : 11.227649
---- call predicitno function shl_pm ---- 11:29:31
     f_1_step_pred_les_level  : 389.630799
     f_1_step_pred_les_trend  : 7.436449

<<<< Record No.:  1679 >>>>
2017-04
11:29:32
88800
---- Pre-Process ---
#### f_actual_datetime   : 2017-04 11:29:32 ####
#### f_actual_price4pm   : 301 ####
#### f_actual_si         : 0.8121336350 ####
#### f_1_step_si         : 0.8357469890 ####
#### f_actual_price4pmsi : 370.6286589152 ####
     previous_pred_les_level : 389.630799
     previous_pred_les_trend : 7.436449
---- call predicitno function shl_pm ---- 11:29:32
     f_1_step_pred_les_level  : 376.071340
     f_1_step_pred_les_trend  : 3.260859

<<<< Record No.:  1680 >>>>
2017-04
11:29:33
88800
---- Pre-Process ---
#### f_actual_datetime   : 2017-04 11:29:33 ####
#### f_actual_price4pm   : 301 ####
#### f_actual_si         : 0.8357469890 ####
#### f_1_step_si         : 0.8632743780 ####
#### f_actual_price4pmsi : 360.1568464640 ####
     previous_pred_les_level : 376.071340
     previous_pred_les_trend : 3.260859
---- call predicitno function shl_pm ---- 11:29:33
     f_1_step_pred_les_level  : 364.104308
     f_1_step_pred_les_trend  : 0.232391

<<<< Record No.:  1681 >>>>
2017-04
11:29:34
88800
---- Pre-Process ---
#### f_actual_datetime   : 2017-04 11:29:34 ####
#### f_actual_price4pm   : 301 ####
#### f_actual_si         : 0.8632743780 ####
#### f_1_step_si         : 0.9253838400 ####
#### f_actual_price4pmsi : 348.6724588043 ####
     previous_pred_les_level : 364.104308
     previous_pred_les_trend : 0.232391
---- call predicitno function shl_pm ---- 11:29:34
     f_1_step_pred_les_level  : 351.897119
     f_1_step_pred_les_trend  : -2.241548

<<<< Record No.:  1682 >>>>
2017-04
11:29:35
88800
---- Pre-Process ---
#### f_actual_datetime   : 2017-04 11:29:35 ####
#### f_actual_price4pm   : 301 ####
#### f_actual_si         : 0.9253838400 ####
#### f_1_step_si         : 0.9622108990 ####
#### f_actual_price4pmsi : 325.2704304843 ####
     previous_pred_les_level : 351.897119
     previous_pred_les_trend : -2.241548
---- call predicitno function shl_pm ---- 11:29:35
     f_1_step_pred_les_level  : 330.290386
     f_1_step_pred_les_trend  : -6.092826

<<<< Record No.:  1683 >>>>
2017-04
11:29:36
88800
---- Pre-Process ---
#### f_actual_datetime   : 2017-04 11:29:36 ####
#### f_actual_price4pm   : 301 ####
#### f_actual_si         : 0.9622108990 ####
#### f_1_step_si         : 0.9808647010 ####
#### f_actual_price4pmsi : 312.8212331754 ####
     previous_pred_les_level : 330.290386
     previous_pred_les_trend : -6.092826
---- call predicitno function shl_pm ---- 11:29:36
     f_1_step_pred_les_level  : 315.163178
     f_1_step_pred_les_trend  : -7.889551

<<<< Record No.:  1684 >>>>
2017-04
11:29:37
88800
---- Pre-Process ---
#### f_actual_datetime   : 2017-04 11:29:37 ####
#### f_actual_price4pm   : 301 ####
#### f_actual_si         : 0.9808647010 ####
#### f_1_step_si         : 0.9899275870 ####
#### f_actual_price4pmsi : 306.8720891812 ####
     previous_pred_les_level : 315.163178
     previous_pred_les_trend : -7.889551
---- call predicitno function shl_pm ---- 11:29:37
     f_1_step_pred_les_level  : 306.954750
     f_1_step_pred_les_trend  : -7.952968

<<<< Record No.:  1685 >>>>
2017-04
11:29:38
88800
---- Pre-Process ---
#### f_actual_datetime   : 2017-04 11:29:38 ####
#### f_actual_price4pm   : 301 ####
#### f_actual_si         : 0.9899275870 ####
#### f_1_step_si         : 1.0344812310 ####
#### f_actual_price4pmsi : 304.0626445336 ####
     previous_pred_les_level : 306.954750
     previous_pred_les_trend : -7.952968
---- call predicitno function shl_pm ---- 11:29:38
     f_1_step_pred_les_level  : 303.020809
     f_1_step_pred_les_trend  : -7.153679

<<<< Record No.:  1686 >>>>
2017-04
11:29:39
88800
---- Pre-Process ---
#### f_actual_datetime   : 2017-04 11:29:39 ####
#### f_actual_price4pm   : 301 ####
#### f_actual_si         : 1.0344812310 ####
#### f_1_step_si         : 1.0843497460 ####
#### f_actual_price4pmsi : 290.9670963378 ####
     previous_pred_les_level : 303.020809
     previous_pred_les_trend : -7.153679
---- call predicitno function shl_pm ---- 11:29:39
     f_1_step_pred_les_level  : 291.975823
     f_1_step_pred_les_trend  : -7.927568

<<<< Record No.:  1687 >>>>
2017-04
11:29:40
88800
---- Pre-Process ---
#### f_actual_datetime   : 2017-04 11:29:40 ####
#### f_actual_price4pm   : 301 ####
#### f_actual_si         : 1.0843497460 ####
#### f_1_step_si         : 1.1152400140 ####
#### f_actual_price4pmsi : 277.5857154118 ####
     previous_pred_les_level : 291.975823
     previous_pred_les_trend : -7.927568
---- call predicitno function shl_pm ---- 11:29:40
     f_1_step_pred_les_level  : 278.916102
     f_1_step_pred_les_trend  : -8.948232

<<<< Record No.:  1688 >>>>
2017-04
11:29:41
88800
---- Pre-Process ---
#### f_actual_datetime   : 2017-04 11:29:41 ####
#### f_actual_price4pm   : 301 ####
#### f_actual_si         : 1.1152400140 ####
#### f_1_step_si         : 1.1698565470 ####
#### f_actual_price4pmsi : 269.8970591276 ####
     previous_pred_les_level : 278.916102
     previous_pred_les_trend : -8.948232
---- call predicitno function shl_pm ---- 11:29:41
     f_1_step_pred_les_level  : 269.911636
     f_1_step_pred_les_trend  : -8.959416

<<<< Record No.:  1689 >>>>
2017-04
11:29:42
88900
---- Pre-Process ---
#### f_actual_datetime   : 2017-04 11:29:42 ####
#### f_actual_price4pm   : 401 ####
#### f_actual_si         : 1.1698565470 ####
#### f_1_step_si         : 1.2937276400 ####
#### f_actual_price4pmsi : 342.7770704266 ####
     previous_pred_les_level : 269.911636
     previous_pred_les_trend : -8.959416
---- call predicitno function shl_pm ---- 11:29:42
     f_1_step_pred_les_level  : 325.932505
     f_1_step_pred_les_trend  : 3.963629

<<<< Record No.:  1690 >>>>
2017-04
11:29:43
88900
---- Pre-Process ---
#### f_actual_datetime   : 2017-04 11:29:43 ####
#### f_actual_price4pm   : 401 ####
#### f_actual_si         : 1.2937276400 ####
#### f_1_step_si         : 1.4025912320 ####
#### f_actual_price4pmsi : 309.9570478374 ####
     previous_pred_les_level : 325.932505
     previous_pred_les_trend : 3.963629
---- call predicitno function shl_pm ---- 11:29:43
     f_1_step_pred_les_level  : 314.061733
     f_1_step_pred_les_trend  : 0.814540

<<<< Record No.:  1691 >>>>
2017-04
11:29:44
89000
---- Pre-Process ---
#### f_actual_datetime   : 2017-04 11:29:44 ####
#### f_actual_price4pm   : 501 ####
#### f_actual_si         : 1.4025912320 ####
#### f_1_step_si         : 1.4472171400 ####
#### f_actual_price4pmsi : 357.1960158952 ####
     previous_pred_les_level : 314.061733
     previous_pred_les_trend : 0.814540
---- call predicitno function shl_pm ---- 11:29:44
     f_1_step_pred_les_level  : 348.484021
     f_1_step_pred_les_trend  : 7.498328

<<<< Record No.:  1692 >>>>
2017-04
11:29:45
89100
---- Pre-Process ---
#### f_actual_datetime   : 2017-04 11:29:45 ####
#### f_actual_price4pm   : 601 ####
#### f_actual_si         : 1.4472171400 ####
#### f_1_step_si         : 1.5885072510 ####
#### f_actual_price4pmsi : 415.2797692819 ####
     previous_pred_les_level : 348.484021
     previous_pred_les_trend : 7.498328
---- call predicitno function shl_pm ---- 11:29:45
     f_1_step_pred_les_level  : 403.072729
     f_1_step_pred_les_trend  : 16.863493

<<<< Record No.:  1693 >>>>
2017-04
11:29:46
89100
---- Pre-Process ---
#### f_actual_datetime   : 2017-04 11:29:46 ####
#### f_actual_price4pm   : 601 ####
#### f_actual_si         : 1.5885072510 ####
#### f_1_step_si         : 1.6717957000 ####
#### f_actual_price4pmsi : 378.3426230013 ####
     previous_pred_les_level : 403.072729
     previous_pred_les_trend : 16.863493
---- call predicitno function shl_pm ---- 11:29:46
     f_1_step_pred_les_level  : 386.905133
     f_1_step_pred_les_trend  : 10.294389

<<<< Record No.:  1694 >>>>
2017-04
11:29:47
89100
---- Pre-Process ---
#### f_actual_datetime   : 2017-04 11:29:47 ####
#### f_actual_price4pm   : 601 ####
#### f_actual_si         : 1.6717957000 ####
#### f_1_step_si         : 1.7731833590 ####
#### f_actual_price4pmsi : 359.4936869379 ####
     previous_pred_les_level : 386.905133
     previous_pred_les_trend : 10.294389
---- call predicitno function shl_pm ---- 11:29:47
     f_1_step_pred_les_level  : 367.255857
     f_1_step_pred_les_trend  : 4.339301

<<<< Record No.:  1695 >>>>
2017-04
11:29:48
89200
---- Pre-Process ---
#### f_actual_datetime   : 2017-04 11:29:48 ####
#### f_actual_price4pm   : 701 ####
#### f_actual_si         : 1.7731833590 ####
#### f_1_step_si         : 1.8046457380 ####
#### f_actual_price4pmsi : 395.3341860795 ####
     previous_pred_les_level : 367.255857
     previous_pred_les_trend : 4.339301
---- call predicitno function shl_pm ---- 11:29:48
     f_1_step_pred_les_level  : 390.447240
     f_1_step_pred_les_trend  : 8.088535

<<<< Record No.:  1696 >>>>
2017-04
11:29:49
89200
---- Pre-Process ---
#### f_actual_datetime   : 2017-04 11:29:49 ####
#### f_actual_price4pm   : 701 ####
#### f_actual_si         : 1.8046457380 ####
#### f_1_step_si         : 1.9437936030 ####
#### f_actual_price4pmsi : 388.4418893078 ####
     previous_pred_les_level : 390.447240
     previous_pred_les_trend : 8.088535
---- call predicitno function shl_pm ---- 11:29:49
     f_1_step_pred_les_level  : 390.519829
     f_1_step_pred_les_trend  : 6.494353

<<<< Record No.:  1697 >>>>
2017-04
11:29:50
89200
---- Pre-Process ---
#### f_actual_datetime   : 2017-04 11:29:50 ####
#### f_actual_price4pm   : 701 ####
#### f_actual_si         : 1.9437936030 ####
#### f_1_step_si         : 2.0068021880 ####
#### f_actual_price4pmsi : 360.6349969040 ####
     previous_pred_les_level : 390.519829
     previous_pred_les_trend : 6.494353
#### global_parm_short_weight_misc : 35.188810
---- call predicitno function shl_pm ---- 11:29:50
     f_1_step_pred_les_level  : 368.124061
     f_1_step_pred_les_trend  : 0.748790

















In [13]:



    


# df_shl_pm.iloc[2]
df_shl_pm.head()



    


















    
Out[13]:











  
    

      
      bid
      ccyy-mm
      datetime
      f_1_step_pred_dynamic_increment
      f_1_step_pred_les
      f_1_step_pred_les_level
      f_1_step_pred_les_misc
      f_1_step_pred_les_trend
      f_1_step_pred_price
      f_1_step_pred_price_inc
      ...
      f_actual_si
      f_current_step_error
      f_current_step_pred_dynamic_increment
      f_current_step_pred_les
      f_current_step_pred_les_misc
      f_current_step_pred_price
      f_current_step_pred_price_inc
      f_current_step_pred_price_rounded
      f_current_step_pred_set_price_rounded
      time
    

  
  
    

      0
      88500.0
      2017-04
      2017-04 11:29:00
      300.0
      430.019488
      430.019488
      0.0
      0.000000
      88505.851050
      6.851050
      ...
      0.002325
      5.851050
      300.0
      430.019488
      0.0
      88505.851050
      6.851050
      88500.0
      88800.0
      11:29:00
    

    

      1
      88500.0
      2017-04
      2017-04 11:29:01
      300.0
      80.367816
      138.370012
      0.0
      -58.002196
      88501.061021
      2.061021
      ...
      0.015932
      5.851050
      300.0
      430.019488
      0.0
      88505.851050
      6.851050
      88500.0
      88800.0
      11:29:01
    

    

      2
      88500.0
      2017-04
      2017-04 11:29:02
      300.0
      -17.025146
      47.511413
      0.0
      -64.536559
      88498.430212
      -0.569788
      ...
      0.025645
      1.061021
      300.0
      80.367816
      0.0
      88501.061021
      2.061021
      88500.0
      88800.0
      11:29:02
    

    

      3
      88500.0
      2017-04
      2017-04 11:29:03
      300.0
      -36.904703
      20.223900
      0.0
      -57.128603
      88497.432146
      -1.567854
      ...
      0.033467
      -1.569788
      300.0
      -17.025146
      0.0
      88498.430212
      -0.569788
      88500.0
      88800.0
      11:29:03
    

    

      4
      88500.0
      2017-04
      2017-04 11:29:04
      300.0
      -36.487028
      11.095473
      0.0
      -47.582501
      88497.467504
      -1.532496
      ...
      0.042484
      -2.567854
      300.0
      -36.904703
      0.0
      88497.432146
      -1.567854
      88500.0
      88800.0
      11:29:04
    

  



5 rows × 25 columns



















In [14]:



    


df_shl_pm.tail()



    


















    
Out[14]:











  
    

      
      bid
      ccyy-mm
      datetime
      f_1_step_pred_dynamic_increment
      f_1_step_pred_les
      f_1_step_pred_les_level
      f_1_step_pred_les_misc
      f_1_step_pred_les_trend
      f_1_step_pred_price
      f_1_step_pred_price_inc
      ...
      f_actual_si
      f_current_step_error
      f_current_step_pred_dynamic_increment
      f_current_step_pred_les
      f_current_step_pred_les_misc
      f_current_step_pred_price
      f_current_step_pred_price_inc
      f_current_step_pred_price_rounded
      f_current_step_pred_set_price_rounded
      time
    

  
  
    

      46
      89100.0
      2017-04
      2017-04 11:29:46
      300.0
      397.199522
      386.905133
      0.000000
      10.294389
      89163.036453
      664.036453
      ...
      1.588507
      66.071733
      300.0
      419.936222
      0.0
      89166.071733
      667.071733
      89200.0
      89500.0
      11:29:46
    

    

      47
      89100.0
      2017-04
      2017-04 11:29:47
      300.0
      371.595158
      367.255857
      0.000000
      4.339301
      89157.906350
      658.906350
      ...
      1.671796
      63.036453
      300.0
      397.199522
      0.0
      89163.036453
      664.036453
      89200.0
      89500.0
      11:29:47
    

    

      48
      89200.0
      2017-04
      2017-04 11:29:48
      300.0
      398.535775
      390.447240
      0.000000
      8.088535
      89218.215889
      719.215889
      ...
      1.773183
      -42.093650
      300.0
      371.595158
      0.0
      89157.906350
      658.906350
      89200.0
      89500.0
      11:29:48
    

    

      49
      89200.0
      2017-04
      2017-04 11:29:49
      300.0
      397.014182
      390.519829
      0.000000
      6.494353
      89270.713627
      771.713627
      ...
      1.804646
      18.215889
      300.0
      398.535775
      0.0
      89218.215889
      719.215889
      89200.0
      89500.0
      11:29:49
    

    

      50
      89200.0
      2017-04
      2017-04 11:29:50
      300.0
      368.872851
      368.124061
      0.632897
      0.748790
      89240.524944
      741.524944
      ...
      1.943794
      70.713627
      300.0
      397.014182
      0.0
      89270.713627
      771.713627
      89300.0
      89600.0
      11:29:50
    

  



5 rows × 25 columns



















In [15]:



    


plt.figure(figsize=(12,6))
plt.plot(df_shl_pm['bid'])
plt.plot(df_shl_pm['f_current_step_pred_price'])
# plt.plot(df_shl_pm['f_1_step_pred_price'].shift(1))

plt.figure(figsize=(12,6))
plt.plot(df_shl_pm['bid'])
plt.plot(df_shl_pm['f_current_step_pred_price'])
plt.plot(df_shl_pm['f_1_step_pred_price'])



    


















    
Out[15]:








[<matplotlib.lines.Line2D at 0x7fb82a79e4e0>]










    

















    
























In [ ]:

Start of prediction module: shl_pm





In [16]:



    


# 11:29:51~
def predict_k_step_price(df_shl_pm, ccyy_mm, time, k):
    print('month & time  : ', ccyy_mm, time)
    print()
    
#     df_shl_pm_k = pd.DataFrame() # initialize prediction dataframe
    for sec in range(0, k):
        
        
        print('delta second(s) : ', sec)
        current_time  = get_future_n_sec_time_as_str(time, sec)
        print('current_time  : %s' % current_time)
        f_1_step_time  = get_future_n_sec_time_as_str(current_time, 1)
        print('f_1_step_time  : %s' % f_1_step_time)
        previous_time = get_previous_n_sec_time_as_str(current_time, 1)
        print('previous_time : %s' % previous_time)

        previous_pred_les_level = df_shl_pm[(df_shl_pm['ccyy-mm'] == global_parm_ccyy_mm) \
                                            & (df_shl_pm['time'] ==previous_time)].iloc[0]['f_1_step_pred_les_level']
        print('     previous_pred_les_level : %f' % previous_pred_les_level)
        
        previous_pred_les_trend = df_shl_pm[(df_shl_pm['ccyy-mm'] == global_parm_ccyy_mm) \
                                            & (df_shl_pm['time'] ==previous_time)].iloc[0]['f_1_step_pred_les_trend']
        print('     previous_pred_les_trend : %f' % previous_pred_les_trend)


        print('---- Pre-Process ---')
        ############ use predicted value for boost-trap
        previous_pred_price = df_shl_pm[(df_shl_pm['ccyy-mm'] == global_parm_ccyy_mm) \
                                            & (df_shl_pm['time'] == previous_time)].iloc[0]['f_1_step_pred_price']
        # pre-process: ccyy-mm-hh:mm:ss
        f_actual_datetime = global_parm_ccyy_mm + ' ' + current_time
#         f_actual_price4pm = df_history_ts_process['bid-price'][i] -  global_parm_base_price
        f_actual_price4pm = previous_pred_price -  global_parm_base_price
        print('#### f_actual_datetime   : %s ####' %  f_actual_datetime)
        print('#### previous_pred_price: %s ####' %  previous_pred_price)
        print('#### f_actual_price4pm   : %d ####' % f_actual_price4pm)

        # get Seasonality-Index
        f_actual_si = fetech_si(global_parm_ccyy_mm
                                             ,current_time
                                             ,df_parm_si)
        try:
            f_1_step_si = fetech_si(global_parm_ccyy_mm
                                                 ,f_1_step_time
                                                 ,df_parm_si)
        except:
            f_1_step_si = fetech_si(global_parm_ccyy_mm
                                                 ,current_time
                                                 ,df_parm_si)            

        print('#### f_actual_si         : %0.10f ####' %  f_actual_si)
        # get de-seasoned price: price4pmsi
        f_actual_price4pmsi = f_actual_price4pm / f_actual_si
        print('#### f_actual_price4pmsi : %0.10f ####' % f_actual_price4pmsi)

        f_current_step_pred_les = df_shl_pm[(df_shl_pm['ccyy-mm'] == df_history_ts_process['ccyy-mm'][i]) \
                                                    & (df_shl_pm['time'] ==previous_time)].iloc[0]['f_1_step_pred_les']
        f_current_step_pred_les_misc = df_shl_pm[(df_shl_pm['ccyy-mm'] == df_history_ts_process['ccyy-mm'][i]) \
                                                    & (df_shl_pm['time'] ==previous_time)].iloc[0]['f_1_step_pred_les_misc']
        f_current_step_pred_price_inc = df_shl_pm[(df_shl_pm['ccyy-mm'] == df_history_ts_process['ccyy-mm'][i]) \
                                                    & (df_shl_pm['time'] ==previous_time)].iloc[0]['f_1_step_pred_price_inc']
        f_current_step_pred_price = df_shl_pm[(df_shl_pm['ccyy-mm'] == df_history_ts_process['ccyy-mm'][i]) \
                                                    & (df_shl_pm['time'] ==previous_time)].iloc[0]['f_1_step_pred_price']
        f_current_step_pred_price_rounded = df_shl_pm[(df_shl_pm['ccyy-mm'] == df_history_ts_process['ccyy-mm'][i]) \
                                                    & (df_shl_pm['time'] ==previous_time)].iloc[0]['f_1_step_pred_price_rounded']
        f_current_step_pred_dynamic_increment = df_shl_pm[(df_shl_pm['ccyy-mm'] == df_history_ts_process['ccyy-mm'][i]) \
                                                    & (df_shl_pm['time'] ==previous_time)].iloc[0]['f_1_step_pred_dynamic_increment']
        f_current_step_pred_set_price_rounded = df_shl_pm[(df_shl_pm['ccyy-mm'] == df_history_ts_process['ccyy-mm'][i]) \
                                                    & (df_shl_pm['time'] ==previous_time)].iloc[0]['f_1_step_pred_set_price_rounded']
        
        f_current_step_error = f_current_step_pred_price_inc - f_actual_price4pm # current second forecast error
        

        f_1_step_pred_les_level = global_parm_alpha * f_actual_price4pmsi \
                                    + (1 - global_parm_alpha) * (previous_pred_les_level + previous_pred_les_trend)
        print('     f_1_step_pred_les_level  : %f' % f_1_step_pred_les_level)
        f_1_step_pred_les_trend = global_parm_beta * (f_1_step_pred_les_level - previous_pred_les_level) \
                                    + (1 - global_parm_beta) * previous_pred_les_trend
        print('     f_1_step_pred_les_trend  : %f' % f_1_step_pred_les_trend)
        f_1_step_pred_les = f_1_step_pred_les_level + f_1_step_pred_les_trend
        
#         f_1_step_pred_les_misc = 0
        f_1_step_pred_les_misc = global_parm_short_weight_misc * global_parm_short_weight * (sec+2) * global_parm_gamma
        
#         f_1_step_pred_price_inc = (f_1_step_pred_les + f_1_step_pred_les_misc) * f_actual_si
        f_1_step_pred_price_inc = (f_1_step_pred_les + f_1_step_pred_les_misc) * f_1_step_si
        f_1_step_pred_price = f_1_step_pred_price_inc + global_parm_base_price
        f_1_step_pred_price_rounded = round(f_1_step_pred_price/100, 0) * 100
        f_1_step_pred_dynamic_increment = global_parm_dynamic_increment
        f_1_step_pred_set_price_rounded = f_1_step_pred_price_rounded + f_1_step_pred_dynamic_increment 

#         write results to shl_pm dataframe
        df_shl_pm_current = {
                             'ccyy-mm' : global_parm_ccyy_mm
                            ,'time' : current_time
                            ,'bid' : previous_pred_price
                            ,'datetime' : f_actual_datetime
                            ,'f_actual_price4pm' : f_actual_price4pm
                            ,'f_actual_si' : f_actual_si
                            ,'f_1_step_si' : f_1_step_si
                            ,'f_actual_price4pmsi' :  f_actual_price4pmsi
                            ,'f_1_step_pred_les_level' : f_1_step_pred_les_level
                            ,'f_1_step_pred_les_trend' : f_1_step_pred_les_trend
                            ,'f_1_step_pred_les' : f_1_step_pred_les
                            ,'f_1_step_pred_les_misc' : f_1_step_pred_les_misc
                            ,'f_1_step_pred_price_inc' : f_1_step_pred_price_inc
                            ,'f_1_step_pred_price' : f_1_step_pred_price
                            ,'f_1_step_pred_price_rounded' : f_1_step_pred_price_rounded
                            ,'f_1_step_pred_dynamic_increment' : f_1_step_pred_dynamic_increment # +200 or + 300
                            ,'f_1_step_pred_set_price_rounded' : f_1_step_pred_set_price_rounded
                            ,'f_current_step_pred_les' : f_current_step_pred_les
                            ,'f_current_step_pred_les_misc' : f_current_step_pred_les_misc
                            ,'f_current_step_pred_price_inc' : f_current_step_pred_price_inc
                            ,'f_current_step_pred_price' : f_current_step_pred_price
                            ,'f_current_step_pred_price_rounded' : f_current_step_pred_price_rounded
                            ,'f_current_step_pred_dynamic_increment' : f_current_step_pred_dynamic_increment # +200 or + 300
                            ,'f_current_step_pred_set_price_rounded' : f_current_step_pred_set_price_rounded
                            ,'f_current_step_error' : f_current_step_error
                            }
        print('---------------------------')
        df_shl_pm =  df_shl_pm.append(df_shl_pm_current, ignore_index=True)
        
    return df_shl_pm



    











In [17]:



    


df_shl_pm_k_step = predict_k_step_price(df_shl_pm, global_parm_ccyy_mm, '11:29:51', 10)



    


















    






month & time  :  2017-04 11:29:51

delta second(s) :  0
current_time  : 11:29:51
f_1_step_time  : 11:29:52
previous_time : 11:29:50
     previous_pred_les_level : 368.124061
     previous_pred_les_trend : 0.748790
---- Pre-Process ---
#### f_actual_datetime   : 2017-04 11:29:51 ####
#### previous_pred_price: 89240.5249439 ####
#### f_actual_price4pm   : 741 ####
#### f_actual_si         : 2.0068021880 ####
#### f_actual_price4pmsi : 369.5057481776 ####
     f_1_step_pred_les_level  : 369.375459
     f_1_step_pred_les_trend  : 0.848747
---------------------------
delta second(s) :  1
current_time  : 11:29:52
f_1_step_time  : 11:29:53
previous_time : 11:29:51
     previous_pred_les_level : 369.375459
     previous_pred_les_trend : 0.848747
---- Pre-Process ---
#### f_actual_datetime   : 2017-04 11:29:52 ####
#### previous_pred_price: 89268.5075172 ####
#### f_actual_price4pm   : 769 ####
#### f_actual_si         : 2.0714084260 ####
#### f_actual_price4pmsi : 371.4900004829 ####
     f_1_step_pred_les_level  : 371.229422
     f_1_step_pred_les_trend  : 1.048660
---------------------------
delta second(s) :  2
current_time  : 11:29:53
f_1_step_time  : 11:29:54
previous_time : 11:29:52
     previous_pred_les_level : 371.229422
     previous_pred_les_trend : 1.048660
---- Pre-Process ---
#### f_actual_datetime   : 2017-04 11:29:53 ####
#### previous_pred_price: 89307.2739381 ####
#### f_actual_price4pm   : 808 ####
#### f_actual_si         : 2.1601392500 ####
#### f_actual_price4pmsi : 374.1767749891 ####
     f_1_step_pred_les_level  : 373.785908
     f_1_step_pred_les_trend  : 1.348531
---------------------------
delta second(s) :  3
current_time  : 11:29:54
f_1_step_time  : 11:29:55
previous_time : 11:29:53
     previous_pred_les_level : 373.785908
     previous_pred_les_trend : 1.348531
---- Pre-Process ---
#### f_actual_datetime   : 2017-04 11:29:54 ####
#### previous_pred_price: 89358.0591741 ####
#### f_actual_price4pm   : 859 ####
#### f_actual_si         : 2.2746530240 ####
#### f_actual_price4pmsi : 377.6660286291 ####
     f_1_step_pred_les_level  : 377.144872
     f_1_step_pred_les_trend  : 1.748359
---------------------------
delta second(s) :  4
current_time  : 11:29:55
f_1_step_time  : 11:29:56
previous_time : 11:29:54
     previous_pred_les_level : 377.144872
     previous_pred_les_trend : 1.748359
---- Pre-Process ---
#### f_actual_datetime   : 2017-04 11:29:55 ####
#### previous_pred_price: 89407.4182174 ####
#### f_actual_price4pm   : 908 ####
#### f_actual_si         : 2.3776989020 ####
#### f_actual_price4pmsi : 382.0577183357 ####
     f_1_step_pred_les_level  : 381.406273
     f_1_step_pred_les_trend  : 2.248144
---------------------------
delta second(s) :  5
current_time  : 11:29:56
f_1_step_time  : 11:29:57
previous_time : 11:29:55
     previous_pred_les_level : 381.406273
     previous_pred_les_trend : 2.248144
---- Pre-Process ---
#### f_actual_datetime   : 2017-04 11:29:56 ####
#### previous_pred_price: 89470.9715522 ####
#### f_actual_price4pm   : 971 ####
#### f_actual_si         : 2.5086257170 ####
#### f_actual_price4pmsi : 387.4518010419 ####
     f_1_step_pred_les_level  : 386.670067
     f_1_step_pred_les_trend  : 2.847885
---------------------------
delta second(s) :  6
current_time  : 11:29:57
f_1_step_time  : 11:29:58
previous_time : 11:29:56
     previous_pred_les_level : 386.670067
     previous_pred_les_trend : 2.847885
---- Pre-Process ---
#### f_actual_datetime   : 2017-04 11:29:57 ####
#### previous_pred_price: 89542.166226 ####
#### f_actual_price4pm   : 1043 ####
#### f_actual_si         : 2.6479779240 ####
#### f_actual_price4pmsi : 393.9482336804 ####
     f_1_step_pred_les_level  : 393.036210
     f_1_step_pred_les_trend  : 3.547584
---------------------------
delta second(s) :  7
current_time  : 11:29:58
f_1_step_time  : 11:29:59
previous_time : 11:29:57
     previous_pred_les_level : 393.036210
     previous_pred_les_trend : 3.547584
---- Pre-Process ---
#### f_actual_datetime   : 2017-04 11:29:58 ####
#### previous_pred_price: 89590.4151524 ####
#### f_actual_price4pm   : 1091 ####
#### f_actual_si         : 2.7173493770 ####
#### f_actual_price4pmsi : 401.6469731841 ####
     f_1_step_pred_les_level  : 400.604661
     f_1_step_pred_les_trend  : 4.347239
---------------------------
delta second(s) :  8
current_time  : 11:29:59
f_1_step_time  : 11:30:00
previous_time : 11:29:58
     previous_pred_les_level : 400.604661
     previous_pred_les_trend : 4.347239
---- Pre-Process ---
#### f_actual_datetime   : 2017-04 11:29:59 ####
#### previous_pred_price: 89675.8493614 ####
#### f_actual_price4pm   : 1176 ####
#### f_actual_si         : 2.8658350430 ####
#### f_actual_price4pmsi : 410.6479764859 ####
     f_1_step_pred_les_level  : 409.475375
     f_1_step_pred_les_trend  : 5.246852
---------------------------
delta second(s) :  9
current_time  : 11:30:00
f_1_step_time  : 11:30:01
previous_time : 11:29:59
     previous_pred_les_level : 409.475375
     previous_pred_les_trend : 5.246852
---- Pre-Process ---
#### f_actual_datetime   : 2017-04 11:30:00 ####
#### previous_pred_price: 89763.5305594 ####
#### f_actual_price4pm   : 1264 ####
#### f_actual_si         : 3.0032702860 ####
#### f_actual_price4pmsi : 421.0512005187 ####
     f_1_step_pred_les_level  : 419.748310
     f_1_step_pred_les_trend  : 6.246421
---------------------------

















In [18]:



    


df_shl_pm_k_step['f_current_step_pred_les_misc'].tail(11)



    


















    
Out[18]:








50    0.000000
51    0.632897
52    1.265795
53    1.898692
54    2.531590
55    3.164487
56    3.797384
57    4.430282
58    5.063179
59    5.696076
60    6.328974
Name: f_current_step_pred_les_misc, dtype: float64

















In [19]:



    


# bid is predicted bid-price from shl_pm
plt.figure(figsize=(12,6))
plt.plot(df_shl_pm_k_step['bid'])
# plt.plot(df_shl_pm_k_step['f_1_step_pred_price'].shift(1))
plt.plot(df_shl_pm_k_step['f_current_step_pred_price'])

# bid is actual bid-price from raw dataset
df_actual_bid = df_history_ts_process[global_parm_ccyy_mm_offset:global_parm_ccyy_mm_offset+61].copy()
df_actual_bid.reset_index(inplace=True)
plt.figure(figsize=(12,6))
plt.plot(df_actual_bid['bid-price'])
# plt.plot(df_shl_pm_k_step['f_1_step_pred_price'].shift(1))
plt.plot(df_shl_pm_k_step['f_current_step_pred_price'])
# plt.plot(df_shl_pm_k_step['bid'])



    


















    
Out[19]:








[<matplotlib.lines.Line2D at 0x7fb82a6d9518>]

End of prediction module: shl_pm





In [ ]:



    






    











In [20]:



    


# actual price & oredicted price & error
pd.concat([df_actual_bid['bid-price'].tail(11), df_shl_pm_k_step['f_current_step_pred_price'].tail(11), df_shl_pm_k_step['f_current_step_pred_price'].tail(11) - df_actual_bid['bid-price'].tail(11)], axis=1, join='inner')



    


















    
Out[20]:











  
    

      
      bid-price
      f_current_step_pred_price
      0
    

  
  
    

      50
      89200
      89270.713627
      70.713627
    

    

      51
      89200
      89240.524944
      40.524944
    

    

      52
      89200
      89268.507517
      68.507517
    

    

      53
      89300
      89307.273938
      7.273938
    

    

      54
      89300
      89358.059174
      58.059174
    

    

      55
      89400
      89407.418217
      7.418217
    

    

      56
      89400
      89470.971552
      70.971552
    

    

      57
      89500
      89542.166226
      42.166226
    

    

      58
      89500
      89590.415152
      90.415152
    

    

      59
      89600
      89675.849361
      75.849361
    

    

      60
      89800
      89763.530559
      -36.469441
    

  




















In [21]:



    


df_shl_pm_k_step.iloc[57]
# df_shl_pm_k_step.iloc[50:61]



    


















    
Out[21]:








bid                                               89542.2
ccyy-mm                                           2017-04
datetime                                 2017-04 11:29:57
f_1_step_pred_dynamic_increment                       300
f_1_step_pred_les                                 396.584
f_1_step_pred_les_level                           393.036
f_1_step_pred_les_misc                            5.06318
f_1_step_pred_les_trend                           3.54758
f_1_step_pred_price                               89590.4
f_1_step_pred_price_inc                           1091.42
f_1_step_pred_price_rounded                         89600
f_1_step_pred_set_price_rounded                     89900
f_1_step_si                                       2.71735
f_actual_price4pm                                 1043.17
f_actual_price4pmsi                               393.948
f_actual_si                                       2.64798
f_current_step_error                         -6.13909e-12
f_current_step_pred_dynamic_increment                 300
f_current_step_pred_les                           389.518
f_current_step_pred_les_misc                      4.43028
f_current_step_pred_price                         89542.2
f_current_step_pred_price_inc                     1043.17
f_current_step_pred_price_rounded                   89500
f_current_step_pred_set_price_rounded               89800
time                                             11:29:57
Name: 57, dtype: object

















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End of shl_sm





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The End

Content source: telescopeuser/uat_shl

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