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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 default global base price
global_parm_base_price = 10000000

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']

# create default average error between 46~50 seconds:
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
plt.figure(figsize=(6,3))
plt.plot(df_parm_si[(df_parm_si['ccyy-mm'] == global_parm_ccyy_mm)]['si'])



    


















    






=================================================
  Global Parameters for Month : 2017-06
-------------------------------------------------
global_parm_dynamic_increment : 300
global_parm_alpha             : 0.777107313458705
global_parm_beta              : 0.179154416550987
global_parm_gamma             : 0.120598828573260
global_parm_sec57_weight      : 0.500000
global_parm_month_weight      : 0.900000
global_parm_short_weight      : 0.125000
=================================================










    
Out[11]:








[<matplotlib.lines.Line2D at 0x7f624d4bb048>]










    
























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.:  1769 >>>>
2017-06
11:29:00
88400
#### global_parm_base_price : 88399 ####
---- Pre-Process ---
#### f_actual_datetime   : 2017-06 11:29:00 ####
#### f_actual_price4pm   : 1 ####
#### f_actual_si         : 0.0023822130 ####
#### f_1_step_si         : 0.0148610890 ####
#### f_actual_price4pmsi : 419.7777444754 ####
---- call predicitno function shl_pm ---- 11:29:00

<<<< Record No.:  1770 >>>>
2017-06
11:29:01
88500
---- Pre-Process ---
#### f_actual_datetime   : 2017-06 11:29:01 ####
#### f_actual_price4pm   : 101 ####
#### f_actual_si         : 0.0148610890 ####
#### f_1_step_si         : 0.0237690550 ####
#### f_actual_price4pmsi : 6796.2717940792 ####
     previous_pred_les_level : 419.777744
     previous_pred_les_trend : 0.000000
---- call predicitno function shl_pm ---- 11:29:01
     f_1_step_pred_les_level  : 5374.997905
     f_1_step_pred_les_trend  : 887.749577

<<<< Record No.:  1771 >>>>
2017-06
11:29:02
88500
---- Pre-Process ---
#### f_actual_datetime   : 2017-06 11:29:02 ####
#### f_actual_price4pm   : 101 ####
#### f_actual_si         : 0.0237690550 ####
#### f_1_step_si         : 0.0309433440 ####
#### f_actual_price4pmsi : 4249.2223607544 ####
     previous_pred_les_level : 5374.997905
     previous_pred_les_trend : 887.749577
---- call predicitno function shl_pm ---- 11:29:02
     f_1_step_pred_les_level  : 4698.022384
     f_1_step_pred_les_trend  : 607.422165

<<<< Record No.:  1772 >>>>
2017-06
11:29:03
88500
---- Pre-Process ---
#### f_actual_datetime   : 2017-06 11:29:03 ####
#### f_actual_price4pm   : 101 ####
#### f_actual_si         : 0.0309433440 ####
#### f_1_step_si         : 0.0392121080 ####
#### f_actual_price4pmsi : 3264.0298992895 ####
     previous_pred_les_level : 4698.022384
     previous_pred_les_trend : 607.422165
---- call predicitno function shl_pm ---- 11:29:03
     f_1_step_pred_les_level  : 3719.046295
     f_1_step_pred_les_trend  : 323.211911

<<<< Record No.:  1773 >>>>
2017-06
11:29:04
88500
---- Pre-Process ---
#### f_actual_datetime   : 2017-06 11:29:04 ####
#### f_actual_price4pm   : 101 ####
#### f_actual_si         : 0.0392121080 ####
#### f_1_step_si         : 0.0387689300 ####
#### f_actual_price4pmsi : 2575.7350255181 ####
     previous_pred_les_level : 3719.046295
     previous_pred_les_trend : 323.211911
---- call predicitno function shl_pm ---- 11:29:04
     f_1_step_pred_les_level  : 2902.612317
     f_1_step_pred_les_trend  : 119.039317

<<<< Record No.:  1774 >>>>
2017-06
11:29:05
88500
---- Pre-Process ---
#### f_actual_datetime   : 2017-06 11:29:05 ####
#### f_actual_price4pm   : 101 ####
#### f_actual_si         : 0.0387689300 ####
#### f_1_step_si         : 0.0757339540 ####
#### f_actual_price4pmsi : 2605.1789409715 ####
     previous_pred_les_level : 2902.612317
     previous_pred_les_trend : 119.039317
---- call predicitno function shl_pm ---- 11:29:05
     f_1_step_pred_les_level  : 2698.007658
     f_1_step_pred_les_trend  : 61.057069

<<<< Record No.:  1775 >>>>
2017-06
11:29:06
88500
---- Pre-Process ---
#### f_actual_datetime   : 2017-06 11:29:06 ####
#### f_actual_price4pm   : 101 ####
#### f_actual_si         : 0.0757339540 ####
#### f_1_step_si         : 0.0941974490 ####
#### f_actual_price4pmsi : 1333.6158310181 ####
     previous_pred_les_level : 2698.007658
     previous_pred_les_trend : 61.057069
---- call predicitno function shl_pm ---- 11:29:06
     f_1_step_pred_les_level  : 1651.337965
     f_1_step_pred_les_trend  : -137.397073

<<<< Record No.:  1776 >>>>
2017-06
11:29:07
88500
---- Pre-Process ---
#### f_actual_datetime   : 2017-06 11:29:07 ####
#### f_actual_price4pm   : 101 ####
#### f_actual_si         : 0.0941974490 ####
#### f_1_step_si         : 0.1333751000 ####
#### f_actual_price4pmsi : 1072.2158728524 ####
     previous_pred_les_level : 1651.337965
     previous_pred_les_trend : -137.397073
---- call predicitno function shl_pm ---- 11:29:07
     f_1_step_pred_les_level  : 1170.673149
     f_1_step_pred_les_trend  : -198.895005

<<<< Record No.:  1777 >>>>
2017-06
11:29:08
88500
---- Pre-Process ---
#### f_actual_datetime   : 2017-06 11:29:08 ####
#### f_actual_price4pm   : 101 ####
#### f_actual_si         : 0.1333751000 ####
#### f_1_step_si         : 0.2041837360 ####
#### f_actual_price4pmsi : 757.2627874318 ####
     previous_pred_les_level : 1170.673149
     previous_pred_les_trend : -198.895005
---- call predicitno function shl_pm ---- 11:29:08
     f_1_step_pred_les_level  : 805.076692
     f_1_step_pred_les_trend  : -228.760306

<<<< Record No.:  1778 >>>>
2017-06
11:29:09
88500
---- Pre-Process ---
#### f_actual_datetime   : 2017-06 11:29:09 ####
#### f_actual_price4pm   : 101 ####
#### f_actual_si         : 0.2041837360 ####
#### f_1_step_si         : 0.2321693820 ####
#### f_actual_price4pmsi : 494.6525221774 ####
     previous_pred_les_level : 805.076692
     previous_pred_les_trend : -228.760306
---- call predicitno function shl_pm ---- 11:29:09
     f_1_step_pred_les_level  : 512.854800
     f_1_step_pred_les_trend  : -240.129730

<<<< Record No.:  1779 >>>>
2017-06
11:29:10
88500
---- Pre-Process ---
#### f_actual_datetime   : 2017-06 11:29:10 ####
#### f_actual_price4pm   : 101 ####
#### f_actual_si         : 0.2321693820 ####
#### f_1_step_si         : 0.2946054880 ####
#### f_actual_price4pmsi : 435.0272164656 ####
     previous_pred_les_level : 512.854800
     previous_pred_les_trend : -240.129730
---- call predicitno function shl_pm ---- 11:29:10
     f_1_step_pred_les_level  : 398.851255
     f_1_step_pred_les_trend  : -217.533667

<<<< Record No.:  1780 >>>>
2017-06
11:29:11
88500
---- Pre-Process ---
#### f_actual_datetime   : 2017-06 11:29:11 ####
#### f_actual_price4pm   : 101 ####
#### f_actual_si         : 0.2946054880 ####
#### f_1_step_si         : 0.3488655530 ####
#### f_actual_price4pmsi : 342.8313596113 ####
     previous_pred_les_level : 398.851255
     previous_pred_les_trend : -217.533667
---- call predicitno function shl_pm ---- 11:29:11
     f_1_step_pred_les_level  : 306.831121
     f_1_step_pred_les_trend  : -195.047363

<<<< Record No.:  1781 >>>>
2017-06
11:29:12
88500
---- Pre-Process ---
#### f_actual_datetime   : 2017-06 11:29:12 ####
#### f_actual_price4pm   : 101 ####
#### f_actual_si         : 0.3488655530 ####
#### f_1_step_si         : 0.3571459030 ####
#### f_actual_price4pmsi : 289.5098101015 ####
     previous_pred_les_level : 306.831121
     previous_pred_les_trend : -195.047363
---- call predicitno function shl_pm ---- 11:29:12
     f_1_step_pred_les_level  : 249.895973
     f_1_step_pred_les_trend  : -170.303950

<<<< Record No.:  1782 >>>>
2017-06
11:29:13
88500
---- Pre-Process ---
#### f_actual_datetime   : 2017-06 11:29:13 ####
#### f_actual_price4pm   : 101 ####
#### f_actual_si         : 0.3571459030 ####
#### f_1_step_si         : 0.3775201620 ####
#### f_actual_price4pmsi : 282.7975881890 ####
     previous_pred_les_level : 249.895973
     previous_pred_les_trend : -170.303950
---- call predicitno function shl_pm ---- 11:29:13
     f_1_step_pred_les_level  : 237.504554
     f_1_step_pred_les_trend  : -142.013222

<<<< Record No.:  1783 >>>>
2017-06
11:29:14
88500
---- Pre-Process ---
#### f_actual_datetime   : 2017-06 11:29:14 ####
#### f_actual_price4pm   : 101 ####
#### f_actual_si         : 0.3775201620 ####
#### f_1_step_si         : 0.4092913790 ####
#### f_actual_price4pmsi : 267.5353800044 ####
     previous_pred_les_level : 237.504554
     previous_pred_les_trend : -142.013222
---- call predicitno function shl_pm ---- 11:29:14
     f_1_step_pred_les_level  : 229.188020
     f_1_step_pred_les_trend  : -118.060870

<<<< Record No.:  1784 >>>>
2017-06
11:29:15
88500
---- Pre-Process ---
#### f_actual_datetime   : 2017-06 11:29:15 ####
#### f_actual_price4pm   : 101 ####
#### f_actual_si         : 0.4092913790 ####
#### f_1_step_si         : 0.4206648770 ####
#### f_actual_price4pmsi : 246.7679633194 ####
     previous_pred_les_level : 229.188020
     previous_pred_les_trend : -118.060870
---- call predicitno function shl_pm ---- 11:29:15
     f_1_step_pred_les_level  : 216.534618
     f_1_step_pred_les_trend  : -99.176657

<<<< Record No.:  1785 >>>>
2017-06
11:29:16
88500
---- Pre-Process ---
#### f_actual_datetime   : 2017-06 11:29:16 ####
#### f_actual_price4pm   : 101 ####
#### f_actual_si         : 0.4206648770 ####
#### f_1_step_si         : 0.4638547580 ####
#### f_actual_price4pmsi : 240.0961086181 ####
     previous_pred_les_level : 216.534618
     previous_pred_les_trend : -99.176657
---- call predicitno function shl_pm ---- 11:29:16
     f_1_step_pred_les_level  : 212.738673
     f_1_step_pred_les_trend  : -82.088781

<<<< Record No.:  1786 >>>>
2017-06
11:29:17
88500
---- Pre-Process ---
#### f_actual_datetime   : 2017-06 11:29:17 ####
#### f_actual_price4pm   : 101 ####
#### f_actual_si         : 0.4638547580 ####
#### f_1_step_si         : 0.4951657820 ####
#### f_actual_price4pmsi : 217.7405712846 ####
     previous_pred_les_level : 212.738673
     previous_pred_les_trend : -82.088781
---- call predicitno function shl_pm ---- 11:29:17
     f_1_step_pred_les_level  : 198.328696
     f_1_step_pred_les_trend  : -69.963824

<<<< Record No.:  1787 >>>>
2017-06
11:29:18
88500
---- Pre-Process ---
#### f_actual_datetime   : 2017-06 11:29:18 ####
#### f_actual_price4pm   : 101 ####
#### f_actual_si         : 0.4951657820 ####
#### f_1_step_si         : 0.5080609120 ####
#### f_actual_price4pmsi : 203.9720911087 ####
     previous_pred_les_level : 198.328696
     previous_pred_les_trend : -69.963824
---- call predicitno function shl_pm ---- 11:29:18
     f_1_step_pred_les_level  : 187.119795
     f_1_step_pred_les_trend  : -59.437620

<<<< Record No.:  1788 >>>>
2017-06
11:29:19
88600
---- Pre-Process ---
#### f_actual_datetime   : 2017-06 11:29:19 ####
#### f_actual_price4pm   : 201 ####
#### f_actual_si         : 0.5080609120 ####
#### f_1_step_si         : 0.5316053510 ####
#### f_actual_price4pmsi : 395.6218540977 ####
     previous_pred_les_level : 187.119795
     previous_pred_les_trend : -59.437620
---- call predicitno function shl_pm ---- 11:29:19
     f_1_step_pred_les_level  : 335.900059
     f_1_step_pred_les_trend  : -22.134467

<<<< Record No.:  1789 >>>>
2017-06
11:29:20
88600
---- Pre-Process ---
#### f_actual_datetime   : 2017-06 11:29:20 ####
#### f_actual_price4pm   : 201 ####
#### f_actual_si         : 0.5316053510 ####
#### f_1_step_si         : 0.5724520020 ####
#### f_actual_price4pmsi : 378.1000315777 ####
     previous_pred_les_level : 335.900059
     previous_pred_les_trend : -22.134467
---- call predicitno function shl_pm ---- 11:29:20
     f_1_step_pred_les_level  : 363.760356
     f_1_step_pred_les_trend  : -13.177684

<<<< Record No.:  1790 >>>>
2017-06
11:29:21
88600
---- Pre-Process ---
#### f_actual_datetime   : 2017-06 11:29:21 ####
#### f_actual_price4pm   : 201 ####
#### f_actual_si         : 0.5724520020 ####
#### f_1_step_si         : 0.5843722490 ####
#### f_actual_price4pmsi : 351.1211408079 ####
     previous_pred_les_level : 363.760356
     previous_pred_les_trend : -13.177684
---- call predicitno function shl_pm ---- 11:29:21
     f_1_step_pred_les_level  : 351.001120
     f_1_step_pred_les_trend  : -13.102717

<<<< Record No.:  1791 >>>>
2017-06
11:29:22
88600
---- Pre-Process ---
#### f_actual_datetime   : 2017-06 11:29:22 ####
#### f_actual_price4pm   : 201 ####
#### f_actual_si         : 0.5843722490 ####
#### f_1_step_si         : 0.5966521600 ####
#### f_actual_price4pmsi : 343.9588384698 ####
     previous_pred_les_level : 351.001120
     previous_pred_les_trend : -13.102717
---- call predicitno function shl_pm ---- 11:29:22
     f_1_step_pred_les_level  : 342.608012
     f_1_step_pred_les_trend  : -12.258970

<<<< Record No.:  1792 >>>>
2017-06
11:29:23
88600
---- Pre-Process ---
#### f_actual_datetime   : 2017-06 11:29:23 ####
#### f_actual_price4pm   : 201 ####
#### f_actual_si         : 0.5966521600 ####
#### f_1_step_si         : 0.6276205910 ####
#### f_actual_price4pmsi : 336.8796988852 ####
     previous_pred_les_level : 342.608012
     previous_pred_les_trend : -12.258970
---- call predicitno function shl_pm ---- 11:29:23
     f_1_step_pred_les_level  : 335.424063
     f_1_step_pred_les_trend  : -11.349757

<<<< Record No.:  1793 >>>>
2017-06
11:29:24
88600
---- Pre-Process ---
#### f_actual_datetime   : 2017-06 11:29:24 ####
#### f_actual_price4pm   : 201 ####
#### f_actual_si         : 0.6276205910 ####
#### f_1_step_si         : 0.6615943850 ####
#### f_actual_price4pmsi : 320.2571790701 ####
     previous_pred_les_level : 335.424063
     previous_pred_les_trend : -11.349757
---- call predicitno function shl_pm ---- 11:29:24
     f_1_step_pred_les_level  : 321.107989
     f_1_step_pred_les_trend  : -11.881186

<<<< Record No.:  1794 >>>>
2017-06
11:29:25
88700
---- Pre-Process ---
#### f_actual_datetime   : 2017-06 11:29:25 ####
#### f_actual_price4pm   : 301 ####
#### f_actual_si         : 0.6615943850 ####
#### f_1_step_si         : 0.6796086800 ####
#### f_actual_price4pmsi : 454.9615396146 ####
     previous_pred_les_level : 321.107989
     previous_pred_les_trend : -11.881186
---- call predicitno function shl_pm ---- 11:29:25
     f_1_step_pred_les_level  : 422.478333
     f_1_step_pred_les_trend  : 8.408326

<<<< Record No.:  1795 >>>>
2017-06
11:29:26
88700
---- Pre-Process ---
#### f_actual_datetime   : 2017-06 11:29:26 ####
#### f_actual_price4pm   : 301 ####
#### f_actual_si         : 0.6796086800 ####
#### f_1_step_si         : 0.7008853550 ####
#### f_actual_price4pmsi : 442.9019358611 ####
     previous_pred_les_level : 422.478333
     previous_pred_les_trend : 8.408326
---- call predicitno function shl_pm ---- 11:29:26
     f_1_step_pred_les_level  : 440.223818
     f_1_step_pred_les_trend  : 10.081119

<<<< Record No.:  1796 >>>>
2017-06
11:29:27
88700
---- Pre-Process ---
#### f_actual_datetime   : 2017-06 11:29:27 ####
#### f_actual_price4pm   : 301 ####
#### f_actual_si         : 0.7008853550 ####
#### f_1_step_si         : 0.7258007710 ####
#### f_actual_price4pmsi : 429.4568260739 ####
     previous_pred_les_level : 440.223818
     previous_pred_les_trend : 10.081119
---- call predicitno function shl_pm ---- 11:29:27
     f_1_step_pred_les_level  : 434.103718
     f_1_step_pred_les_trend  : 7.178599

<<<< Record No.:  1797 >>>>
2017-06
11:29:28
88700
---- Pre-Process ---
#### f_actual_datetime   : 2017-06 11:29:28 ####
#### f_actual_price4pm   : 301 ####
#### f_actual_si         : 0.7258007710 ####
#### f_1_step_si         : 0.7406630210 ####
#### f_actual_price4pmsi : 414.7143569237 ####
     previous_pred_les_level : 434.103718
     previous_pred_les_trend : 7.178599
---- call predicitno function shl_pm ---- 11:29:28
     f_1_step_pred_les_level  : 420.636161
     f_1_step_pred_les_trend  : 3.479749

<<<< Record No.:  1798 >>>>
2017-06
11:29:29
88700
---- Pre-Process ---
#### f_actual_datetime   : 2017-06 11:29:29 ####
#### f_actual_price4pm   : 301 ####
#### f_actual_si         : 0.7406630210 ####
#### f_1_step_si         : 0.7749686950 ####
#### f_actual_price4pmsi : 406.3926393863 ####
     previous_pred_les_level : 420.636161
     previous_pred_les_trend : 3.479749
---- call predicitno function shl_pm ---- 11:29:29
     f_1_step_pred_les_level  : 410.343027
     f_1_step_pred_les_trend  : 1.012276

<<<< Record No.:  1799 >>>>
2017-06
11:29:30
88800
---- Pre-Process ---
#### f_actual_datetime   : 2017-06 11:29:30 ####
#### f_actual_price4pm   : 401 ####
#### f_actual_si         : 0.7749686950 ####
#### f_1_step_si         : 0.7967683450 ####
#### f_actual_price4pmsi : 517.4402560867 ####
     previous_pred_les_level : 410.343027
     previous_pred_les_trend : 1.012276
---- call predicitno function shl_pm ---- 11:29:30
     f_1_step_pred_les_level  : 493.794696
     f_1_step_pred_les_trend  : 15.781658

<<<< Record No.:  1800 >>>>
2017-06
11:29:31
88800
---- Pre-Process ---
#### f_actual_datetime   : 2017-06 11:29:31 ####
#### f_actual_price4pm   : 401 ####
#### f_actual_si         : 0.7967683450 ####
#### f_1_step_si         : 0.8239920950 ####
#### f_actual_price4pmsi : 503.2830464669 ####
     previous_pred_les_level : 493.794696
     previous_pred_les_trend : 15.781658
---- call predicitno function shl_pm ---- 11:29:31
     f_1_step_pred_les_level  : 504.685779
     f_1_step_pred_les_trend  : 14.905489

<<<< Record No.:  1801 >>>>
2017-06
11:29:32
88800
---- Pre-Process ---
#### f_actual_datetime   : 2017-06 11:29:32 ####
#### f_actual_price4pm   : 401 ####
#### f_actual_si         : 0.8239920950 ####
#### f_1_step_si         : 0.8456485560 ####
#### f_actual_price4pmsi : 486.6551541371 ####
     previous_pred_les_level : 504.685779
     previous_pred_les_trend : 14.905489
---- call predicitno function shl_pm ---- 11:29:32
     f_1_step_pred_les_level  : 493.996373
     f_1_step_pred_les_trend  : 10.320051

<<<< Record No.:  1802 >>>>
2017-06
11:29:33
88800
---- Pre-Process ---
#### f_actual_datetime   : 2017-06 11:29:33 ####
#### f_actual_price4pm   : 401 ####
#### f_actual_si         : 0.8456485560 ####
#### f_1_step_si         : 0.8708946860 ####
#### f_actual_price4pmsi : 474.1922600764 ####
     previous_pred_les_level : 493.996373
     previous_pred_les_trend : 10.320051
---- call predicitno function shl_pm ---- 11:29:33
     f_1_step_pred_les_level  : 480.906716
     f_1_step_pred_les_trend  : 6.126098

<<<< Record No.:  1803 >>>>
2017-06
11:29:34
88800
---- Pre-Process ---
#### f_actual_datetime   : 2017-06 11:29:34 ####
#### f_actual_price4pm   : 401 ####
#### f_actual_si         : 0.8708946860 ####
#### f_1_step_si         : 0.9278569940 ####
#### f_actual_price4pmsi : 460.4460291769 ####
     previous_pred_les_level : 480.906716
     previous_pred_les_trend : 6.126098
---- call predicitno function shl_pm ---- 11:29:34
     f_1_step_pred_les_level  : 466.372029
     f_1_step_pred_les_trend  : 2.424627

<<<< Record No.:  1804 >>>>
2017-06
11:29:35
88800
---- Pre-Process ---
#### f_actual_datetime   : 2017-06 11:29:35 ####
#### f_actual_price4pm   : 401 ####
#### f_actual_si         : 0.9278569940 ####
#### f_1_step_si         : 0.9616321100 ####
#### f_actual_price4pmsi : 432.1786682571 ####
     previous_pred_les_level : 466.372029
     previous_pred_les_trend : 2.424627
---- call predicitno function shl_pm ---- 11:29:35
     f_1_step_pred_les_level  : 440.340550
     f_1_step_pred_les_trend  : -2.673410

<<<< Record No.:  1805 >>>>
2017-06
11:29:36
88800
---- Pre-Process ---
#### f_actual_datetime   : 2017-06 11:29:36 ####
#### f_actual_price4pm   : 401 ####
#### f_actual_si         : 0.9616321100 ####
#### f_1_step_si         : 0.9787400280 ####
#### f_actual_price4pmsi : 416.9993865949 ####
     previous_pred_les_level : 440.340550
     previous_pred_les_trend : -2.673410
---- call predicitno function shl_pm ---- 11:29:36
     f_1_step_pred_les_level  : 421.606078
     f_1_step_pred_les_trend  : -5.550820

<<<< Record No.:  1806 >>>>
2017-06
11:29:37
88800
---- Pre-Process ---
#### f_actual_datetime   : 2017-06 11:29:37 ####
#### f_actual_price4pm   : 401 ####
#### f_actual_si         : 0.9787400280 ####
#### f_1_step_si         : 0.9870518520 ####
#### f_actual_price4pmsi : 409.7104323192 ####
     previous_pred_les_level : 421.606078
     previous_pred_les_trend : -5.550820
---- call predicitno function shl_pm ---- 11:29:37
     f_1_step_pred_les_level  : 411.124648
     f_1_step_pred_les_trend  : -6.434161

<<<< Record No.:  1807 >>>>
2017-06
11:29:38
88800
---- Pre-Process ---
#### f_actual_datetime   : 2017-06 11:29:38 ####
#### f_actual_price4pm   : 401 ####
#### f_actual_si         : 0.9870518520 ####
#### f_1_step_si         : 1.0277287330 ####
#### f_actual_price4pmsi : 406.2603187335 ####
     previous_pred_les_level : 411.124648
     previous_pred_les_trend : -6.434161
---- call predicitno function shl_pm ---- 11:29:38
     f_1_step_pred_les_level  : 405.910415
     f_1_step_pred_les_trend  : -6.215605

<<<< Record No.:  1808 >>>>
2017-06
11:29:39
88800
---- Pre-Process ---
#### f_actual_datetime   : 2017-06 11:29:39 ####
#### f_actual_price4pm   : 401 ####
#### f_actual_si         : 1.0277287330 ####
#### f_1_step_si         : 1.0732818240 ####
#### f_actual_price4pmsi : 390.1807812938 ####
     previous_pred_les_level : 405.910415
     previous_pred_les_trend : -6.215605
---- call predicitno function shl_pm ---- 11:29:39
     f_1_step_pred_les_level  : 392.301389
     f_1_step_pred_les_trend  : -7.540169

<<<< Record No.:  1809 >>>>
2017-06
11:29:40
88800
---- Pre-Process ---
#### f_actual_datetime   : 2017-06 11:29:40 ####
#### f_actual_price4pm   : 401 ####
#### f_actual_si         : 1.0732818240 ####
#### f_1_step_si         : 1.1014312080 ####
#### f_actual_price4pmsi : 373.6204145389 ####
     previous_pred_les_level : 392.301389
     previous_pred_les_trend : -7.540169
---- call predicitno function shl_pm ---- 11:29:40
     f_1_step_pred_les_level  : 376.103618
     f_1_step_pred_les_trend  : -9.091217

<<<< Record No.:  1810 >>>>
2017-06
11:29:41
88900
---- Pre-Process ---
#### f_actual_datetime   : 2017-06 11:29:41 ####
#### f_actual_price4pm   : 501 ####
#### f_actual_si         : 1.1014312080 ####
#### f_1_step_si         : 1.1636423890 ####
#### f_actual_price4pmsi : 454.8627243909 ####
     previous_pred_les_level : 376.103618
     previous_pred_les_trend : -9.091217
---- call predicitno function shl_pm ---- 11:29:41
     f_1_step_pred_les_level  : 435.281530
     f_1_step_pred_les_trend  : 3.139499

<<<< Record No.:  1811 >>>>
2017-06
11:29:42
88900
---- Pre-Process ---
#### f_actual_datetime   : 2017-06 11:29:42 ####
#### f_actual_price4pm   : 501 ####
#### f_actual_si         : 1.1636423890 ####
#### f_1_step_si         : 1.2770115610 ####
#### f_actual_price4pmsi : 430.5446456196 ####
     previous_pred_les_level : 435.281530
     previous_pred_les_trend : 3.139499
---- call predicitno function shl_pm ---- 11:29:42
     f_1_step_pred_les_level  : 432.300234
     f_1_step_pred_les_trend  : 2.042932

<<<< Record No.:  1812 >>>>
2017-06
11:29:43
89000
---- Pre-Process ---
#### f_actual_datetime   : 2017-06 11:29:43 ####
#### f_actual_price4pm   : 601 ####
#### f_actual_si         : 1.2770115610 ####
#### f_1_step_si         : 1.3885120710 ####
#### f_actual_price4pmsi : 470.6300384073 ####
     previous_pred_les_level : 432.300234
     previous_pred_les_trend : 2.042932
---- call predicitno function shl_pm ---- 11:29:43
     f_1_step_pred_les_level  : 462.541960
     f_1_step_pred_les_trend  : 7.094870

<<<< Record No.:  1813 >>>>
2017-06
11:29:44
89000
---- Pre-Process ---
#### f_actual_datetime   : 2017-06 11:29:44 ####
#### f_actual_price4pm   : 601 ####
#### f_actual_si         : 1.3885120710 ####
#### f_1_step_si         : 1.4408790780 ####
#### f_actual_price4pmsi : 432.8374326391 ####
     previous_pred_les_level : 462.541960
     previous_pred_les_trend : 7.094870
---- call predicitno function shl_pm ---- 11:29:44
     f_1_step_pred_les_level  : 441.039749
     f_1_step_pred_les_trend  : 1.971577

<<<< Record No.:  1814 >>>>
2017-06
11:29:45
89100
---- Pre-Process ---
#### f_actual_datetime   : 2017-06 11:29:45 ####
#### f_actual_price4pm   : 701 ####
#### f_actual_si         : 1.4408790780 ####
#### f_1_step_si         : 1.5694557710 ####
#### f_actual_price4pmsi : 486.5085562718 ####
     previous_pred_les_level : 441.039749
     previous_pred_les_trend : 1.971577
---- call predicitno function shl_pm ---- 11:29:45
     f_1_step_pred_les_level  : 476.813342
     f_1_step_pred_les_trend  : 8.027357

<<<< Record No.:  1815 >>>>
2017-06
11:29:46
89100
---- Pre-Process ---
#### f_actual_datetime   : 2017-06 11:29:46 ####
#### f_actual_price4pm   : 701 ####
#### f_actual_si         : 1.5694557710 ####
#### f_1_step_si         : 1.6448651010 ####
#### f_actual_price4pmsi : 446.6516438073 ####
     previous_pred_les_level : 476.813342
     previous_pred_les_trend : 8.027357
---- call predicitno function shl_pm ---- 11:29:46
     f_1_step_pred_les_level  : 455.163705
     f_1_step_pred_les_trend  : 2.710593

<<<< Record No.:  1816 >>>>
2017-06
11:29:47
89100
---- Pre-Process ---
#### f_actual_datetime   : 2017-06 11:29:47 ####
#### f_actual_price4pm   : 701 ####
#### f_actual_si         : 1.6448651010 ####
#### f_1_step_si         : 1.7484231600 ####
#### f_actual_price4pmsi : 426.1747662917 ####
     previous_pred_les_level : 455.163705
     previous_pred_les_trend : 2.710593
---- call predicitno function shl_pm ---- 11:29:47
     f_1_step_pred_les_level  : 433.240360
     f_1_step_pred_les_trend  : -1.702686

<<<< Record No.:  1817 >>>>
2017-06
11:29:48
89100
---- Pre-Process ---
#### f_actual_datetime   : 2017-06 11:29:48 ####
#### f_actual_price4pm   : 701 ####
#### f_actual_si         : 1.7484231600 ####
#### f_1_step_si         : 1.7903136450 ####
#### f_actual_price4pmsi : 400.9326895441 ####
     previous_pred_les_level : 433.240360
     previous_pred_les_trend : -1.702686
---- call predicitno function shl_pm ---- 11:29:48
     f_1_step_pred_les_level  : 407.754317
     f_1_step_pred_les_trend  : -5.963580

<<<< Record No.:  1818 >>>>
2017-06
11:29:49
89100
---- Pre-Process ---
#### f_actual_datetime   : 2017-06 11:29:49 ####
#### f_actual_price4pm   : 701 ####
#### f_actual_si         : 1.7903136450 ####
#### f_1_step_si         : 1.9309782790 ####
#### f_actual_price4pmsi : 391.5515038149 ####
     previous_pred_les_level : 407.754317
     previous_pred_les_trend : -5.963580
---- call predicitno function shl_pm ---- 11:29:49
     f_1_step_pred_les_level  : 393.833754
     f_1_step_pred_les_trend  : -7.389108

<<<< Record No.:  1819 >>>>
2017-06
11:29:50
89100
---- Pre-Process ---
#### f_actual_datetime   : 2017-06 11:29:50 ####
#### f_actual_price4pm   : 701 ####
#### f_actual_si         : 1.9309782790 ####
#### f_1_step_si         : 2.0018259430 ####
#### f_actual_price4pmsi : 363.0284232731 ####
     previous_pred_les_level : 393.833754
     previous_pred_les_trend : -7.389108
#### global_parm_short_weight_misc : 45.827121
---- call predicitno function shl_pm ---- 11:29:50
     f_1_step_pred_les_level  : 368.247728
     f_1_step_pred_les_trend  : -10.649166

















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
      88400.0
      2017-06
      2017-06 11:29:00
      300.0
      419.777744
      419.777744
      0.0
      0.000000
      88405.238354
      6.238354
      ...
      0.002382
      5.238354
      300.0
      419.777744
      0.0
      88405.238354
      6.238354
      88400.0
      88700.0
      11:29:00
    

    

      1
      88500.0
      2017-06
      2017-06 11:29:01
      300.0
      6262.747481
      5374.997905
      0.0
      887.749577
      88547.859589
      148.859589
      ...
      0.014861
      -94.761646
      300.0
      419.777744
      0.0
      88405.238354
      6.238354
      88400.0
      88700.0
      11:29:01
    

    

      2
      88500.0
      2017-06
      2017-06 11:29:02
      300.0
      5305.444549
      4698.022384
      0.0
      607.422165
      88563.168196
      164.168196
      ...
      0.023769
      47.859589
      300.0
      6262.747481
      0.0
      88547.859589
      148.859589
      88500.0
      88800.0
      11:29:02
    

    

      3
      88500.0
      2017-06
      2017-06 11:29:03
      300.0
      4042.258206
      3719.046295
      0.0
      323.211911
      88557.505465
      158.505465
      ...
      0.030943
      63.168196
      300.0
      5305.444549
      0.0
      88563.168196
      164.168196
      88600.0
      88900.0
      11:29:03
    

    

      4
      88500.0
      2017-06
      2017-06 11:29:04
      300.0
      3021.651634
      2902.612317
      0.0
      119.039317
      88516.146201
      117.146201
      ...
      0.039212
      57.505465
      300.0
      4042.258206
      0.0
      88557.505465
      158.505465
      88600.0
      88900.0
      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
      2017-06
      2017-06 11:29:46
      300.0
      457.874298
      455.163705
      0.000000
      2.710593
      89152.141453
      753.141453
      ...
      1.569456
      59.936033
      300.0
      484.840699
      0.0
      89159.936033
      760.936033
      89200.0
      89500.0
      11:29:46
    

    

      47
      89100.0
      2017-06
      2017-06 11:29:47
      300.0
      431.537674
      433.240360
      0.000000
      -1.702686
      89153.510464
      754.510464
      ...
      1.644865
      52.141453
      300.0
      457.874298
      0.0
      89152.141453
      753.141453
      89200.0
      89500.0
      11:29:47
    

    

      48
      89100.0
      2017-06
      2017-06 11:29:48
      300.0
      401.790737
      407.754317
      0.000000
      -5.963580
      89118.331439
      719.331439
      ...
      1.748423
      53.510464
      300.0
      431.537674
      0.0
      89153.510464
      754.510464
      89200.0
      89500.0
      11:29:48
    

    

      49
      89100.0
      2017-06
      2017-06 11:29:49
      300.0
      386.444646
      393.833754
      0.000000
      -7.389108
      89145.216217
      746.216217
      ...
      1.790314
      18.331439
      300.0
      401.790737
      0.0
      89118.331439
      719.331439
      89100.0
      89400.0
      11:29:49
    

    

      50
      89100.0
      2017-06
      2017-06 11:29:50
      300.0
      357.598562
      368.247728
      0.690837
      -10.649166
      89116.233014
      717.233014
      ...
      1.930978
      45.216217
      300.0
      386.444646
      0.0
      89145.216217
      746.216217
      89100.0
      89400.0
      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 0x7f624d3644e0>]










    

















    
























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  :  2017-06 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 : 368.247728
     previous_pred_les_trend : -10.649166
---- Pre-Process ---
#### f_actual_datetime   : 2017-06 11:29:51 ####
#### previous_pred_price: 89116.2330136 ####
#### f_actual_price4pm   : 717 ####
#### f_actual_si         : 2.0018259430 ####
#### f_actual_price4pmsi : 358.2893988020 ####
     f_1_step_pred_les_level  : 358.135416
     f_1_step_pred_les_trend  : -10.552987
---------------------------
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 : 358.135416
     previous_pred_les_trend : -10.552987
---- Pre-Process ---
#### f_actual_datetime   : 2017-06 11:29:52 ####
#### previous_pred_price: 89118.1595185 ####
#### f_actual_price4pm   : 719 ####
#### f_actual_si         : 2.0608409580 ####
#### f_actual_price4pmsi : 348.9641040323 ####
     f_1_step_pred_les_level  : 348.656139
     f_1_step_pred_les_trend  : -10.360627
---------------------------
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 : 348.656139
     previous_pred_les_trend : -10.360627
---- Pre-Process ---
#### f_actual_datetime   : 2017-06 11:29:53 ####
#### previous_pred_price: 89136.5541184 ####
#### f_actual_price4pm   : 737 ####
#### f_actual_si         : 2.1669312840 ####
#### f_actual_price4pmsi : 340.3680236019 ####
     f_1_step_pred_les_level  : 339.906076
     f_1_step_pred_les_trend  : -10.072087
---------------------------
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 : 339.906076
     previous_pred_les_trend : -10.072087
---- Pre-Process ---
#### f_actual_datetime   : 2017-06 11:29:54 ####
#### previous_pred_price: 89159.1456176 ####
#### f_actual_price4pm   : 760 ####
#### f_actual_si         : 2.2854831720 ####
#### f_actual_price4pmsi : 332.5973373828 ####
     f_1_step_pred_les_level  : 331.981407
     f_1_step_pred_les_trend  : -9.687368
---------------------------
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 : 331.981407
     previous_pred_les_trend : -9.687368
---- Pre-Process ---
#### f_actual_datetime   : 2017-06 11:29:55 ####
#### previous_pred_price: 89182.469462 ####
#### f_actual_price4pm   : 783 ####
#### f_actual_si         : 2.4051380830 ####
#### f_actual_price4pmsi : 325.7482252467 ####
     f_1_step_pred_les_level  : 324.978313
     f_1_step_pred_les_trend  : -9.206468
---------------------------
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 : 324.978313
     previous_pred_les_trend : -9.206468
---- Pre-Process ---
#### f_actual_datetime   : 2017-06 11:29:56 ####
#### previous_pred_price: 89211.4447588 ####
#### f_actual_price4pm   : 812 ####
#### f_actual_si         : 2.5395496220 ####
#### f_actual_price4pmsi : 319.9168670655 ####
     f_1_step_pred_les_level  : 318.992972
     f_1_step_pred_les_trend  : -8.629389
---------------------------
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 : 318.992972
     previous_pred_les_trend : -8.629389
---- Pre-Process ---
#### f_actual_datetime   : 2017-06 11:29:57 ####
#### previous_pred_price: 89247.6110952 ####
#### f_actual_price4pm   : 848 ####
#### f_actual_si         : 2.6922988440 ####
#### f_actual_price4pmsi : 315.1994427111 ####
     f_1_step_pred_les_level  : 314.121565
     f_1_step_pred_les_trend  : -7.956130
---------------------------
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 : 314.121565
     previous_pred_les_trend : -7.956130
---- Pre-Process ---
#### f_actual_datetime   : 2017-06 11:29:58 ####
#### previous_pred_price: 89257.9989936 ####
#### f_actual_price4pm   : 858 ####
#### f_actual_si         : 2.7559213250 ####
#### f_actual_price4pmsi : 311.6921320554 ####
     f_1_step_pred_les_level  : 310.460272
     f_1_step_pred_les_trend  : -7.186691
---------------------------
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 : 310.460272
     previous_pred_les_trend : -7.186691
---- Pre-Process ---
#### f_actual_datetime   : 2017-06 11:29:59 ####
#### previous_pred_price: 89301.8001579 ####
#### f_actual_price4pm   : 902 ####
#### f_actual_si         : 2.9170470950 ####
#### f_actual_price4pmsi : 309.4911149701 ####
     f_1_step_pred_les_level  : 308.105272
     f_1_step_pred_les_trend  : -6.321072
---------------------------
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 : 308.105272
     previous_pred_les_trend : -6.321072
---- Pre-Process ---
#### f_actual_datetime   : 2017-06 11:30:00 ####
#### previous_pred_price: 89345.1000544 ####
#### f_actual_price4pm   : 946 ####
#### f_actual_si         : 3.0648617500 ####
#### f_actual_price4pmsi : 308.6925713271 ####
     f_1_step_pred_les_level  : 307.152746
     f_1_step_pred_les_trend  : -5.359273
---------------------------

















In [18]:



    


df_shl_pm_k_step['f_current_step_pred_les_misc'].tail(11)



    


















    
Out[18]:








50    0.000000
51    0.690837
52    1.381674
53    2.072511
54    2.763349
55    3.454186
56    4.145023
57    4.835860
58    5.526697
59    6.217534
60    6.908371
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 0x7f624d312a58>]

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
      89100
      89145.216217
      45.216217
    

    

      51
      89100
      89116.233014
      16.233014
    

    

      52
      89100
      89118.159518
      18.159518
    

    

      53
      89200
      89136.554118
      -63.445882
    

    

      54
      89200
      89159.145618
      -40.854382
    

    

      55
      89200
      89182.469462
      -17.530538
    

    

      56
      89200
      89211.444759
      11.444759
    

    

      57
      89300
      89247.611095
      -52.388905
    

    

      58
      89300
      89257.998994
      -42.001006
    

    

      59
      89300
      89301.800158
      1.800158
    

    

      60
      89400
      89345.100054
      -54.899946
    

  




















In [21]:



    


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



    


















    
Out[21]:








bid                                               89247.6
ccyy-mm                                           2017-06
datetime                                 2017-06 11:29:57
f_1_step_pred_dynamic_increment                       300
f_1_step_pred_les                                 306.165
f_1_step_pred_les_level                           314.122
f_1_step_pred_les_misc                             5.5267
f_1_step_pred_les_trend                          -7.95613
f_1_step_pred_price                                 89258
f_1_step_pred_price_inc                           858.999
f_1_step_pred_price_rounded                         89300
f_1_step_pred_set_price_rounded                     89600
f_1_step_si                                       2.75592
f_actual_price4pm                                 848.611
f_actual_price4pmsi                               315.199
f_actual_si                                        2.6923
f_current_step_error                          4.77485e-12
f_current_step_pred_dynamic_increment                 300
f_current_step_pred_les                           310.364
f_current_step_pred_les_misc                      4.83586
f_current_step_pred_price                         89247.6
f_current_step_pred_price_inc                     848.611
f_current_step_pred_price_rounded                   89200
f_current_step_pred_set_price_rounded               89500
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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