In [1]:
import pandas as pd
In [2]:
import shl_pm
+-----------------------------------------------+
| Loaded SHL Prediction Module |
| Version 0.0.0.1 |
+-----------------------------------------------+
+-----------------------------------------------+
| SHL Prediction Module User Guide |
+-----------------------------------------------+
+-----------------------------------------------+
| Key Function I:
| shl_initialize(in_ccyy_mm='2017-07')
+-----------------------------------------------+
This function takes one input. Run this funciton once, before calling shl_predict_price_k_step()
Inputs:
(1) in_ccyy_mm: the (current) year month for predicting bidding price
string, i.e. '2017-07'
Outputs: N.A.
+-----------------------------------------------+
| Key Function II:
| shl_predict_price_k_step(in_current_time, in_current_price, in_k_seconds=1, return_value='f_1_step_pred_set_price_rounded')
+-----------------------------------------------+
This function takes four inputs then returns prediciton values in a python list.
Ensure this function is called 'once and only once' for EVERY second with price, starting from '11:29:00'!
This is to ensure prediction module could capture all actual prices for internal prediction calculation.
Inputs:
(1) in_current_time: current time/second of bidding price
string, i.e. '11:29:50'
(2) in_current_price : current bidding price
number/integer/float, i.e. 89400
(3) in_k_seconds : forecast price in the next k seconds
integer, default value = 1, i.e. 7
(4) return_value : return result of predicted price, or predicted set price = predicted price + dynamic increment
string, i.e. 89600 predicted price (return_value = 'f_1_step_pred_price_rounded')
string, i.e. 89800 predicted set price (return_value = 'f_1_step_pred_set_price_rounded')
Outputs:
(1) Returned restuls in python list
list of integer , i.e. [89800] (in_k_seconds = 1)
list of integers, i.e. [89800, 89900, 89900, 90000, 90100, 90100, 90200] (in_k_seconds = 7)
In [ ]:
In [3]:
# which month to predictsimulate?
# shl_sm_parm_ccyy_mm = '2017-04'
# shl_sm_parm_ccyy_mm_offset = 1647
# shl_sm_parm_ccyy_mm = '2017-05'
# shl_sm_parm_ccyy_mm_offset = 1708
# shl_sm_parm_ccyy_mm = '2017-06'
# shl_sm_parm_ccyy_mm_offset = 1769
shl_sm_parm_ccyy_mm = '2017-07'
shl_sm_parm_ccyy_mm_offset = 1830
#----------------------------------
shl_sm_data = pd.read_csv('shl_sm_data/history_ts.csv')
shl_sm_data
Out[3]:
ccyy-mm
time
bid-price
ref-price
0
2015-01
11:29:00
74000
74000
1
2015-01
11:29:01
74000
74000
2
2015-01
11:29:02
74000
74000
3
2015-01
11:29:03
74000
74000
4
2015-01
11:29:04
74000
74000
5
2015-01
11:29:05
74000
74000
6
2015-01
11:29:06
74000
74000
7
2015-01
11:29:07
74000
74000
8
2015-01
11:29:08
74000
74000
9
2015-01
11:29:09
74000
74000
10
2015-01
11:29:10
74000
74000
11
2015-01
11:29:11
74000
74000
12
2015-01
11:29:12
74000
74000
13
2015-01
11:29:13
74000
74000
14
2015-01
11:29:14
74000
74000
15
2015-01
11:29:15
74000
74000
16
2015-01
11:29:16
74000
74000
17
2015-01
11:29:17
74000
74000
18
2015-01
11:29:18
74000
74000
19
2015-01
11:29:19
74000
74000
20
2015-01
11:29:20
74000
74000
21
2015-01
11:29:21
74000
74000
22
2015-01
11:29:22
74000
74000
23
2015-01
11:29:23
74000
74000
24
2015-01
11:29:24
74000
74000
25
2015-01
11:29:25
74000
74000
26
2015-01
11:29:26
74000
74000
27
2015-01
11:29:27
74000
74000
28
2015-01
11:29:28
74000
74000
29
2015-01
11:29:29
74000
74000
...
...
...
...
...
1861
2017-07
11:29:31
90700
89800
1862
2017-07
11:29:32
90700
89800
1863
2017-07
11:29:33
90700
89800
1864
2017-07
11:29:34
90700
89800
1865
2017-07
11:29:35
90800
89800
1866
2017-07
11:29:36
90800
89800
1867
2017-07
11:29:37
90900
89800
1868
2017-07
11:29:38
91000
89800
1869
2017-07
11:29:39
91000
89800
1870
2017-07
11:29:40
91000
89800
1871
2017-07
11:29:41
91000
89800
1872
2017-07
11:29:42
91000
89800
1873
2017-07
11:29:43
91000
89800
1874
2017-07
11:29:44
91100
89800
1875
2017-07
11:29:45
91100
89800
1876
2017-07
11:29:46
91200
89800
1877
2017-07
11:29:47
91300
89800
1878
2017-07
11:29:48
91400
89800
1879
2017-07
11:29:49
91400
89800
1880
2017-07
11:29:50
91500
89800
1881
2017-07
11:29:51
91600
89800
1882
2017-07
11:29:52
91700
89800
1883
2017-07
11:29:53
91800
89800
1884
2017-07
11:29:54
91900
89800
1885
2017-07
11:29:55
92000
89800
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
1891 rows × 4 columns
In [4]:
shl_pm.shl_initialize(shl_sm_parm_ccyy_mm)
+-----------------------------------------------+
| shl_initialize() |
+-----------------------------------------------+
shl_global_parm_ccyy_mm : 2017-07
-------------------------------------------------
shl_global_parm_alpha : 0.636279780099081
shl_global_parm_beta : 0.237518711616408
shl_global_parm_gamma : 0.223562510966253
shl_global_parm_short_weight : 0.1250000000
shl_global_parm_short_weight_ratio: 0.0000000000
shl_global_parm_sec57_weight : 0.5000000000
shl_global_parm_month_weight : 0.9000000000
shl_global_parm_dynamic_increment : 300
-------------------------------------------------
prediction results dataframe: shl_data_pm_1_step
Empty DataFrame
Columns: []
Index: []
prediction results dataframe: shl_data_pm_k_step
Empty DataFrame
Columns: []
Index: []
In [5]:
# Upon receiving 11:29:00 second price, to predict till 11:29:49 <- one-step forward price forecasting
for i in range(shl_sm_parm_ccyy_mm_offset, shl_sm_parm_ccyy_mm_offset+50): # use csv data as simulatino
# for i in range(shl_sm_parm_ccyy_mm_offset, shl_sm_parm_ccyy_mm_offset+55): # use csv data as simulatino
print('\n<<<< Record No.: %5d >>>>' % i)
print(shl_sm_data['ccyy-mm'][i]) # format: ccyy-mm
print(shl_sm_data['time'][i]) # format: hh:mm:ss
print(shl_sm_data['bid-price'][i]) # format: integer
######################################################################################################################
# call prediction function, returned result is in 'list' format, i.e. [89400]
shl_sm_prediction_list_local_1 = shl_pm.shl_predict_price_k_step(shl_sm_data['time'][i], shl_sm_data['bid-price'][i],1) # <- one-step forward price forecasting
print(shl_sm_prediction_list_local_1)
######################################################################################################################
<<<< Record No.: 1830 >>>>
2017-07
11:29:00
90400
==>> Forecasting 1 out of next 1 seconds/steps...
+-----------------------------------------------+
| shl_predict_price_1_step() |
+-----------------------------------------------+
current_ccyy_mm : 2017-07
in_current_time : 11:29:00
in_current_price : 90400.000000
*INFO* At time [ 11:29:00 ] Set shl_global_parm_base_price : 90399
*INFO* f_current_datetime : 2017-07 11:29:00
*INFO* f_current_si : 0.0023669570
*INFO* f_current_price4pm : 1
*INFO* f_current_price4pmsi : 422.4833826724
*INFO* f_1_step_time : 11:29:01
*INFO* f_1_step_si : 0.0223882810
-------------------------------------------------
==>> Prediction Restuls in Python List : [90700]
[90700]
<<<< Record No.: 1831 >>>>
2017-07
11:29:01
90400
==>> Forecasting 1 out of next 1 seconds/steps...
+-----------------------------------------------+
| shl_predict_price_1_step() |
+-----------------------------------------------+
current_ccyy_mm : 2017-07
in_current_time : 11:29:01
in_current_price : 90400.000000
*INFO* f_current_datetime : 2017-07 11:29:01
*INFO* f_current_si : 0.0223882810
*INFO* f_current_price4pm : 1
*INFO* f_current_price4pmsi : 44.6662251559
*INFO* f_1_step_time : 11:29:02
*INFO* f_1_step_si : 0.0309107700
previous_pred_les_level : 422.4833826724
previous_pred_les_trend : 0.0000000000
f_1_step_pred_les_level : 182.0859647701
f_1_step_pred_les_trend : -57.0988849760
f_1_step_pred_les : 124.9870797941
f_1_step_pred_adj_misc : 0.0000000000
pred_les + pred_adj_misc : 124.9870797941
f_1_step_pred_price_inc : 3.8634468765
f_1_step_pred_price : 90402.8634468765
f_1_step_pred_price_rounded : 90400
f_1_step_pred_set_price_rounded : 90700
-------------------------------------------------
==>> Prediction Restuls in Python List : [90700]
[90700]
<<<< Record No.: 1832 >>>>
2017-07
11:29:02
90400
==>> Forecasting 1 out of next 1 seconds/steps...
+-----------------------------------------------+
| shl_predict_price_1_step() |
+-----------------------------------------------+
current_ccyy_mm : 2017-07
in_current_time : 11:29:02
in_current_price : 90400.000000
*INFO* f_current_datetime : 2017-07 11:29:02
*INFO* f_current_si : 0.0309107700
*INFO* f_current_price4pm : 1
*INFO* f_current_price4pmsi : 32.3511837460
*INFO* f_1_step_time : 11:29:03
*INFO* f_1_step_si : 0.0377696020
previous_pred_les_level : 182.0859647701
previous_pred_les_trend : -57.0988849760
f_1_step_pred_les_level : 66.0447322273
f_1_step_pred_les_trend : -71.0987954298
f_1_step_pred_les : -5.0540632024
f_1_step_pred_adj_misc : 0.0000000000
pred_les + pred_adj_misc : -5.0540632024
f_1_step_pred_price_inc : -0.1908899556
f_1_step_pred_price : 90398.8091100444
f_1_step_pred_price_rounded : 90400
f_1_step_pred_set_price_rounded : 90700
-------------------------------------------------
==>> Prediction Restuls in Python List : [90700]
[90700]
<<<< Record No.: 1833 >>>>
2017-07
11:29:03
90400
==>> Forecasting 1 out of next 1 seconds/steps...
+-----------------------------------------------+
| shl_predict_price_1_step() |
+-----------------------------------------------+
current_ccyy_mm : 2017-07
in_current_time : 11:29:03
in_current_price : 90400.000000
*INFO* f_current_datetime : 2017-07 11:29:03
*INFO* f_current_si : 0.0377696020
*INFO* f_current_price4pm : 1
*INFO* f_current_price4pmsi : 26.4763181778
*INFO* f_1_step_time : 11:29:04
*INFO* f_1_step_si : 0.0457052300
previous_pred_les_level : 66.0447322273
previous_pred_les_trend : -71.0987954298
f_1_step_pred_les_level : 15.0080809286
f_1_step_pred_les_trend : -66.3336608035
f_1_step_pred_les : -51.3255798749
f_1_step_pred_adj_misc : 0.0000000000
pred_les + pred_adj_misc : -51.3255798749
f_1_step_pred_price_inc : -2.3458474331
f_1_step_pred_price : 90396.6541525669
f_1_step_pred_price_rounded : 90400
f_1_step_pred_set_price_rounded : 90700
-------------------------------------------------
==>> Prediction Restuls in Python List : [90700]
[90700]
<<<< Record No.: 1834 >>>>
2017-07
11:29:04
90400
==>> Forecasting 1 out of next 1 seconds/steps...
+-----------------------------------------------+
| shl_predict_price_1_step() |
+-----------------------------------------------+
current_ccyy_mm : 2017-07
in_current_time : 11:29:04
in_current_price : 90400.000000
*INFO* f_current_datetime : 2017-07 11:29:04
*INFO* f_current_si : 0.0457052300
*INFO* f_current_price4pm : 1
*INFO* f_current_price4pmsi : 21.8793341594
*INFO* f_1_step_time : 11:29:05
*INFO* f_1_step_si : 0.0452799070
previous_pred_les_level : 15.0080809286
previous_pred_les_trend : -66.3336608035
f_1_step_pred_les_level : -4.7467732710
f_1_step_pred_les_trend : -55.2703226703
f_1_step_pred_les : -60.0170959413
f_1_step_pred_adj_misc : 0.0000000000
pred_les + pred_adj_misc : -60.0170959413
f_1_step_pred_price_inc : -2.7175685226
f_1_step_pred_price : 90396.2824314774
f_1_step_pred_price_rounded : 90400
f_1_step_pred_set_price_rounded : 90700
-------------------------------------------------
==>> Prediction Restuls in Python List : [90700]
[90700]
<<<< Record No.: 1835 >>>>
2017-07
11:29:05
90400
==>> Forecasting 1 out of next 1 seconds/steps...
+-----------------------------------------------+
| shl_predict_price_1_step() |
+-----------------------------------------------+
current_ccyy_mm : 2017-07
in_current_time : 11:29:05
in_current_price : 90400.000000
*INFO* f_current_datetime : 2017-07 11:29:05
*INFO* f_current_si : 0.0452799070
*INFO* f_current_price4pm : 1
*INFO* f_current_price4pmsi : 22.0848510135
*INFO* f_1_step_time : 11:29:06
*INFO* f_1_step_si : 0.0807556680
previous_pred_les_level : -4.7467732710
previous_pred_les_trend : -55.2703226703
f_1_step_pred_les_level : -7.7772871872
f_1_step_pred_les_trend : -42.8623905999
f_1_step_pred_les : -50.6396777871
f_1_step_pred_adj_misc : 0.0000000000
pred_les + pred_adj_misc : -50.6396777871
f_1_step_pred_price_inc : -4.0894410070
f_1_step_pred_price : 90394.9105589930
f_1_step_pred_price_rounded : 90400
f_1_step_pred_set_price_rounded : 90700
-------------------------------------------------
==>> Prediction Restuls in Python List : [90700]
[90700]
<<<< Record No.: 1836 >>>>
2017-07
11:29:06
90400
==>> Forecasting 1 out of next 1 seconds/steps...
+-----------------------------------------------+
| shl_predict_price_1_step() |
+-----------------------------------------------+
current_ccyy_mm : 2017-07
in_current_time : 11:29:06
in_current_price : 90400.000000
*INFO* f_current_datetime : 2017-07 11:29:06
*INFO* f_current_si : 0.0807556680
*INFO* f_current_price4pm : 1
*INFO* f_current_price4pmsi : 12.3830317396
*INFO* f_1_step_time : 11:29:07
*INFO* f_1_step_si : 0.0985017130
previous_pred_les_level : -7.7772871872
previous_pred_les_trend : -42.8623905999
f_1_step_pred_les_level : -10.5396020282
f_1_step_pred_les_trend : -33.3378722700
f_1_step_pred_les : -43.8774742981
f_1_step_pred_adj_misc : 0.0000000000
pred_les + pred_adj_misc : -43.8774742981
f_1_step_pred_price_inc : -4.3220063805
f_1_step_pred_price : 90394.6779936195
f_1_step_pred_price_rounded : 90400
f_1_step_pred_set_price_rounded : 90700
-------------------------------------------------
==>> Prediction Restuls in Python List : [90700]
[90700]
<<<< Record No.: 1837 >>>>
2017-07
11:29:07
90400
==>> Forecasting 1 out of next 1 seconds/steps...
+-----------------------------------------------+
| shl_predict_price_1_step() |
+-----------------------------------------------+
current_ccyy_mm : 2017-07
in_current_time : 11:29:07
in_current_price : 90400.000000
*INFO* f_current_datetime : 2017-07 11:29:07
*INFO* f_current_si : 0.0985017130
*INFO* f_current_price4pm : 1
*INFO* f_current_price4pmsi : 10.1521077100
*INFO* f_1_step_time : 11:29:08
*INFO* f_1_step_si : 0.1361543100
previous_pred_les_level : -10.5396020282
previous_pred_les_trend : -33.3378722700
f_1_step_pred_les_level : -9.4995437391
f_1_step_pred_les_trend : -25.1724704955
f_1_step_pred_les : -34.6720142347
f_1_step_pred_adj_misc : 0.0000000000
pred_les + pred_adj_misc : -34.6720142347
f_1_step_pred_price_inc : -4.7207441744
f_1_step_pred_price : 90394.2792558256
f_1_step_pred_price_rounded : 90400
f_1_step_pred_set_price_rounded : 90700
-------------------------------------------------
==>> Prediction Restuls in Python List : [90700]
[90700]
<<<< Record No.: 1838 >>>>
2017-07
11:29:08
90400
==>> Forecasting 1 out of next 1 seconds/steps...
+-----------------------------------------------+
| shl_predict_price_1_step() |
+-----------------------------------------------+
current_ccyy_mm : 2017-07
in_current_time : 11:29:08
in_current_price : 90400.000000
*INFO* f_current_datetime : 2017-07 11:29:08
*INFO* f_current_si : 0.1361543100
*INFO* f_current_price4pm : 1
*INFO* f_current_price4pmsi : 7.3446077469
*INFO* f_1_step_time : 11:29:09
*INFO* f_1_step_si : 0.2041642360
previous_pred_les_level : -9.4995437391
previous_pred_les_trend : -25.1724704955
f_1_step_pred_les_level : -7.9376872397
f_1_step_pred_les_trend : -18.8225675918
f_1_step_pred_les : -26.7602548315
f_1_step_pred_adj_misc : 0.0000000000
pred_les + pred_adj_misc : -26.7602548315
f_1_step_pred_price_inc : -5.4634869828
f_1_step_pred_price : 90393.5365130172
f_1_step_pred_price_rounded : 90400
f_1_step_pred_set_price_rounded : 90700
-------------------------------------------------
==>> Prediction Restuls in Python List : [90700]
[90700]
<<<< Record No.: 1839 >>>>
2017-07
11:29:09
90400
==>> Forecasting 1 out of next 1 seconds/steps...
+-----------------------------------------------+
| shl_predict_price_1_step() |
+-----------------------------------------------+
current_ccyy_mm : 2017-07
in_current_time : 11:29:09
in_current_price : 90400.000000
*INFO* f_current_datetime : 2017-07 11:29:09
*INFO* f_current_si : 0.2041642360
*INFO* f_current_price4pm : 1
*INFO* f_current_price4pmsi : 4.8980174961
*INFO* f_1_step_time : 11:29:10
*INFO* f_1_step_si : 0.2310771670
previous_pred_les_level : -7.9376872397
previous_pred_les_trend : -18.8225675918
f_1_step_pred_les_level : -6.6167362766
f_1_step_pred_les_trend : -14.0381050172
f_1_step_pred_les : -20.6548412938
f_1_step_pred_adj_misc : 0.0000000000
pred_les + pred_adj_misc : -20.6548412938
f_1_step_pred_price_inc : -4.7728622110
f_1_step_pred_price : 90394.2271377890
f_1_step_pred_price_rounded : 90400
f_1_step_pred_set_price_rounded : 90700
-------------------------------------------------
==>> Prediction Restuls in Python List : [90700]
[90700]
<<<< Record No.: 1840 >>>>
2017-07
11:29:10
90500
==>> Forecasting 1 out of next 1 seconds/steps...
+-----------------------------------------------+
| shl_predict_price_1_step() |
+-----------------------------------------------+
current_ccyy_mm : 2017-07
in_current_time : 11:29:10
in_current_price : 90500.000000
*INFO* f_current_datetime : 2017-07 11:29:10
*INFO* f_current_si : 0.2310771670
*INFO* f_current_price4pm : 101
*INFO* f_current_price4pmsi : 437.0834267671
*INFO* f_1_step_time : 11:29:11
*INFO* f_1_step_si : 0.2910254840
previous_pred_les_level : -6.6167362766
previous_pred_les_trend : -14.0381050172
f_1_step_pred_les_level : 270.5947632509
f_1_step_pred_les_trend : 55.1391258131
f_1_step_pred_les : 325.7338890640
f_1_step_pred_adj_misc : 0.0000000000
pred_les + pred_adj_misc : 325.7338890640
f_1_step_pred_price_inc : 94.7968627201
f_1_step_pred_price : 90493.7968627201
f_1_step_pred_price_rounded : 90500
f_1_step_pred_set_price_rounded : 90800
-------------------------------------------------
==>> Prediction Restuls in Python List : [90800]
[90800]
<<<< Record No.: 1841 >>>>
2017-07
11:29:11
90500
==>> Forecasting 1 out of next 1 seconds/steps...
+-----------------------------------------------+
| shl_predict_price_1_step() |
+-----------------------------------------------+
current_ccyy_mm : 2017-07
in_current_time : 11:29:11
in_current_price : 90500.000000
*INFO* f_current_datetime : 2017-07 11:29:11
*INFO* f_current_si : 0.2910254840
*INFO* f_current_price4pm : 101
*INFO* f_current_price4pmsi : 347.0486454032
*INFO* f_1_step_time : 11:29:12
*INFO* f_1_step_si : 0.3431273480
previous_pred_les_level : 270.5947632509
previous_pred_les_trend : 55.1391258131
f_1_step_pred_les_level : 339.2960375404
f_1_step_pred_les_trend : 58.3603898459
f_1_step_pred_les : 397.6564273863
f_1_step_pred_adj_misc : 0.0000000000
pred_les + pred_adj_misc : 397.6564273863
f_1_step_pred_price_inc : 136.4467953442
f_1_step_pred_price : 90535.4467953442
f_1_step_pred_price_rounded : 90500
f_1_step_pred_set_price_rounded : 90800
-------------------------------------------------
==>> Prediction Restuls in Python List : [90800]
[90800]
<<<< Record No.: 1842 >>>>
2017-07
11:29:12
90500
==>> Forecasting 1 out of next 1 seconds/steps...
+-----------------------------------------------+
| shl_predict_price_1_step() |
+-----------------------------------------------+
current_ccyy_mm : 2017-07
in_current_time : 11:29:12
in_current_price : 90500.000000
*INFO* f_current_datetime : 2017-07 11:29:12
*INFO* f_current_si : 0.3431273480
*INFO* f_current_price4pm : 101
*INFO* f_current_price4pmsi : 294.3513555206
*INFO* f_1_step_time : 11:29:13
*INFO* f_1_step_si : 0.3510740950
previous_pred_les_level : 339.2960375404
previous_pred_les_trend : 58.3603898459
f_1_step_pred_les_level : 331.9254989765
f_1_step_pred_les_trend : 42.7480644167
f_1_step_pred_les : 374.6735633932
f_1_step_pred_adj_misc : 0.0000000000
pred_les + pred_adj_misc : 374.6735633932
f_1_step_pred_price_inc : 131.5381821887
f_1_step_pred_price : 90530.5381821887
f_1_step_pred_price_rounded : 90500
f_1_step_pred_set_price_rounded : 90800
-------------------------------------------------
==>> Prediction Restuls in Python List : [90800]
[90800]
<<<< Record No.: 1843 >>>>
2017-07
11:29:13
90600
==>> Forecasting 1 out of next 1 seconds/steps...
+-----------------------------------------------+
| shl_predict_price_1_step() |
+-----------------------------------------------+
current_ccyy_mm : 2017-07
in_current_time : 11:29:13
in_current_price : 90600.000000
*INFO* f_current_datetime : 2017-07 11:29:13
*INFO* f_current_si : 0.3510740950
*INFO* f_current_price4pm : 201
*INFO* f_current_price4pmsi : 572.5287136324
*INFO* f_1_step_time : 11:29:14
*INFO* f_1_step_si : 0.3706555480
previous_pred_les_level : 331.9254989765
previous_pred_les_trend : 42.7480644167
f_1_step_pred_les_level : 500.5647948788
f_1_step_pred_les_trend : 72.6495875230
f_1_step_pred_les : 573.2143824018
f_1_step_pred_adj_misc : 0.0000000000
pred_les + pred_adj_misc : 573.2143824018
f_1_step_pred_price_inc : 212.4650910306
f_1_step_pred_price : 90611.4650910306
f_1_step_pred_price_rounded : 90600
f_1_step_pred_set_price_rounded : 90900
-------------------------------------------------
==>> Prediction Restuls in Python List : [90900]
[90900]
<<<< Record No.: 1844 >>>>
2017-07
11:29:14
90600
==>> Forecasting 1 out of next 1 seconds/steps...
+-----------------------------------------------+
| shl_predict_price_1_step() |
+-----------------------------------------------+
current_ccyy_mm : 2017-07
in_current_time : 11:29:14
in_current_price : 90600.000000
*INFO* f_current_datetime : 2017-07 11:29:14
*INFO* f_current_si : 0.3706555480
*INFO* f_current_price4pm : 201
*INFO* f_current_price4pmsi : 542.2824535733
*INFO* f_1_step_time : 11:29:15
*INFO* f_1_step_si : 0.4011467510
previous_pred_les_level : 500.5647948788
previous_pred_les_trend : 72.6495875230
f_1_step_pred_les_level : 553.5330215288
f_1_step_pred_les_trend : 67.9748960455
f_1_step_pred_les : 621.5079175743
f_1_step_pred_adj_misc : 0.0000000000
pred_les + pred_adj_misc : 621.5079175743
f_1_step_pred_price_inc : 249.3158818557
f_1_step_pred_price : 90648.3158818557
f_1_step_pred_price_rounded : 90600
f_1_step_pred_set_price_rounded : 90900
-------------------------------------------------
==>> Prediction Restuls in Python List : [90900]
[90900]
<<<< Record No.: 1845 >>>>
2017-07
11:29:15
90600
==>> Forecasting 1 out of next 1 seconds/steps...
+-----------------------------------------------+
| shl_predict_price_1_step() |
+-----------------------------------------------+
current_ccyy_mm : 2017-07
in_current_time : 11:29:15
in_current_price : 90600.000000
*INFO* f_current_datetime : 2017-07 11:29:15
*INFO* f_current_si : 0.4011467510
*INFO* f_current_price4pm : 201
*INFO* f_current_price4pmsi : 501.0635122905
*INFO* f_1_step_time : 11:29:16
*INFO* f_1_step_si : 0.4120902590
previous_pred_les_level : 553.5330215288
previous_pred_les_trend : 67.9748960455
f_1_step_pred_les_level : 544.8715778662
f_1_step_pred_les_trend : 49.7723313751
f_1_step_pred_les : 594.6439092413
f_1_step_pred_adj_misc : 0.0000000000
pred_les + pred_adj_misc : 594.6439092413
f_1_step_pred_price_inc : 245.0469625720
f_1_step_pred_price : 90644.0469625720
f_1_step_pred_price_rounded : 90600
f_1_step_pred_set_price_rounded : 90900
-------------------------------------------------
==>> Prediction Restuls in Python List : [90900]
[90900]
<<<< Record No.: 1846 >>>>
2017-07
11:29:16
90700
==>> Forecasting 1 out of next 1 seconds/steps...
+-----------------------------------------------+
| shl_predict_price_1_step() |
+-----------------------------------------------+
current_ccyy_mm : 2017-07
in_current_time : 11:29:16
in_current_price : 90700.000000
*INFO* f_current_datetime : 2017-07 11:29:16
*INFO* f_current_si : 0.4120902590
*INFO* f_current_price4pm : 301
*INFO* f_current_price4pmsi : 730.4225067839
*INFO* f_1_step_time : 11:29:17
*INFO* f_1_step_si : 0.4535685080
previous_pred_les_level : 544.8715778662
previous_pred_les_trend : 49.7723313751
f_1_step_pred_les_level : 681.0370854278
f_1_step_pred_les_trend : 70.2923272754
f_1_step_pred_les : 751.3294127032
f_1_step_pred_adj_misc : 0.0000000000
pred_les + pred_adj_misc : 751.3294127032
f_1_step_pred_price_inc : 340.7793607363
f_1_step_pred_price : 90739.7793607363
f_1_step_pred_price_rounded : 90700
f_1_step_pred_set_price_rounded : 91000
-------------------------------------------------
==>> Prediction Restuls in Python List : [91000]
[91000]
<<<< Record No.: 1847 >>>>
2017-07
11:29:17
90700
==>> Forecasting 1 out of next 1 seconds/steps...
+-----------------------------------------------+
| shl_predict_price_1_step() |
+-----------------------------------------------+
current_ccyy_mm : 2017-07
in_current_time : 11:29:17
in_current_price : 90700.000000
*INFO* f_current_datetime : 2017-07 11:29:17
*INFO* f_current_si : 0.4535685080
*INFO* f_current_price4pm : 301
*INFO* f_current_price4pmsi : 663.6263203705
*INFO* f_1_step_time : 11:29:18
*INFO* f_1_step_si : 0.4836754840
previous_pred_les_level : 681.0370854278
previous_pred_les_trend : 70.2923272754
f_1_step_pred_les_level : 695.5257083998
f_1_step_pred_les_trend : 57.0379033258
f_1_step_pred_les : 752.5636117256
f_1_step_pred_adj_misc : 0.0000000000
pred_les + pred_adj_misc : 752.5636117256
f_1_step_pred_price_inc : 363.9965691421
f_1_step_pred_price : 90762.9965691421
f_1_step_pred_price_rounded : 90800
f_1_step_pred_set_price_rounded : 91100
-------------------------------------------------
==>> Prediction Restuls in Python List : [91100]
[91100]
<<<< Record No.: 1848 >>>>
2017-07
11:29:18
90700
==>> Forecasting 1 out of next 1 seconds/steps...
+-----------------------------------------------+
| shl_predict_price_1_step() |
+-----------------------------------------------+
current_ccyy_mm : 2017-07
in_current_time : 11:29:18
in_current_price : 90700.000000
*INFO* f_current_datetime : 2017-07 11:29:18
*INFO* f_current_si : 0.4836754840
*INFO* f_current_price4pm : 301
*INFO* f_current_price4pmsi : 622.3180830062
*INFO* f_1_step_time : 11:29:19
*INFO* f_1_step_si : 0.5045423610
previous_pred_les_level : 695.5257083998
previous_pred_les_trend : 57.0379033258
f_1_step_pred_les_level : 669.6910153531
f_1_step_pred_les_trend : 37.3541110071
f_1_step_pred_les : 707.0451263602
f_1_step_pred_adj_misc : 0.0000000000
pred_les + pred_adj_misc : 707.0451263602
f_1_step_pred_price_inc : 356.7342173873
f_1_step_pred_price : 90755.7342173873
f_1_step_pred_price_rounded : 90800
f_1_step_pred_set_price_rounded : 91100
-------------------------------------------------
==>> Prediction Restuls in Python List : [91100]
[91100]
<<<< Record No.: 1849 >>>>
2017-07
11:29:19
90700
==>> Forecasting 1 out of next 1 seconds/steps...
+-----------------------------------------------+
| shl_predict_price_1_step() |
+-----------------------------------------------+
current_ccyy_mm : 2017-07
in_current_time : 11:29:19
in_current_price : 90700.000000
*INFO* f_current_datetime : 2017-07 11:29:19
*INFO* f_current_si : 0.5045423610
*INFO* f_current_price4pm : 301
*INFO* f_current_price4pmsi : 596.5802344196
*INFO* f_1_step_time : 11:29:20
*INFO* f_1_step_si : 0.5273150370
previous_pred_les_level : 669.6910153531
previous_pred_les_trend : 37.3541110071
f_1_step_pred_les_level : 636.7585492076
f_1_step_pred_les_trend : 20.6597337579
f_1_step_pred_les : 657.4182829655
f_1_step_pred_adj_misc : 0.0000000000
pred_les + pred_adj_misc : 657.4182829655
f_1_step_pred_price_inc : 346.6665462064
f_1_step_pred_price : 90745.6665462064
f_1_step_pred_price_rounded : 90700
f_1_step_pred_set_price_rounded : 91000
-------------------------------------------------
==>> Prediction Restuls in Python List : [91000]
[91000]
<<<< Record No.: 1850 >>>>
2017-07
11:29:20
90700
==>> Forecasting 1 out of next 1 seconds/steps...
+-----------------------------------------------+
| shl_predict_price_1_step() |
+-----------------------------------------------+
current_ccyy_mm : 2017-07
in_current_time : 11:29:20
in_current_price : 90700.000000
*INFO* f_current_datetime : 2017-07 11:29:20
*INFO* f_current_si : 0.5273150370
*INFO* f_current_price4pm : 301
*INFO* f_current_price4pmsi : 570.8162651921
*INFO* f_1_step_time : 11:29:21
*INFO* f_1_step_si : 0.5666965740
previous_pred_les_level : 636.7585492076
previous_pred_les_trend : 20.6597337579
f_1_step_pred_les_level : 602.3151701405
f_1_step_pred_les_trend : 7.5717133936
f_1_step_pred_les : 609.8868835342
f_1_step_pred_adj_misc : 0.0000000000
pred_les + pred_adj_misc : 609.8868835342
f_1_step_pred_price_inc : 345.6208074263
f_1_step_pred_price : 90744.6208074263
f_1_step_pred_price_rounded : 90700
f_1_step_pred_set_price_rounded : 91000
-------------------------------------------------
==>> Prediction Restuls in Python List : [91000]
[91000]
<<<< Record No.: 1851 >>>>
2017-07
11:29:21
90700
==>> Forecasting 1 out of next 1 seconds/steps...
+-----------------------------------------------+
| shl_predict_price_1_step() |
+-----------------------------------------------+
current_ccyy_mm : 2017-07
in_current_time : 11:29:21
in_current_price : 90700.000000
*INFO* f_current_datetime : 2017-07 11:29:21
*INFO* f_current_si : 0.5666965740
*INFO* f_current_price4pm : 301
*INFO* f_current_price4pmsi : 531.1484378234
*INFO* f_1_step_time : 11:29:22
*INFO* f_1_step_si : 0.5783832890
previous_pred_les_level : 602.3151701405
previous_pred_les_trend : 7.5717133936
f_1_step_pred_les_level : 559.7872026120
f_1_step_pred_les_trend : -4.3278982714
f_1_step_pred_les : 555.4593043406
f_1_step_pred_adj_misc : 0.0000000000
pred_les + pred_adj_misc : 555.4593043406
f_1_step_pred_price_inc : 321.2683793502
f_1_step_pred_price : 90720.2683793502
f_1_step_pred_price_rounded : 90700
f_1_step_pred_set_price_rounded : 91000
-------------------------------------------------
==>> Prediction Restuls in Python List : [91000]
[91000]
<<<< Record No.: 1852 >>>>
2017-07
11:29:22
90700
==>> Forecasting 1 out of next 1 seconds/steps...
+-----------------------------------------------+
| shl_predict_price_1_step() |
+-----------------------------------------------+
current_ccyy_mm : 2017-07
in_current_time : 11:29:22
in_current_price : 90700.000000
*INFO* f_current_datetime : 2017-07 11:29:22
*INFO* f_current_si : 0.5783832890
*INFO* f_current_price4pm : 301
*INFO* f_current_price4pmsi : 520.4161422444
*INFO* f_1_step_time : 11:29:23
*INFO* f_1_step_si : 0.5903581650
previous_pred_les_level : 559.7872026120
previous_pred_les_trend : -4.3278982714
f_1_step_pred_les_level : 533.1620488681
f_1_step_pred_les_trend : -9.6239136638
f_1_step_pred_les : 523.5381352042
f_1_step_pred_adj_misc : 0.0000000000
pred_les + pred_adj_misc : 523.5381352042
f_1_step_pred_price_inc : 309.0750128067
f_1_step_pred_price : 90708.0750128067
f_1_step_pred_price_rounded : 90700
f_1_step_pred_set_price_rounded : 91000
-------------------------------------------------
==>> Prediction Restuls in Python List : [91000]
[91000]
<<<< Record No.: 1853 >>>>
2017-07
11:29:23
90700
==>> Forecasting 1 out of next 1 seconds/steps...
+-----------------------------------------------+
| shl_predict_price_1_step() |
+-----------------------------------------------+
current_ccyy_mm : 2017-07
in_current_time : 11:29:23
in_current_price : 90700.000000
*INFO* f_current_datetime : 2017-07 11:29:23
*INFO* f_current_si : 0.5903581650
*INFO* f_current_price4pm : 301
*INFO* f_current_price4pmsi : 509.8599762739
*INFO* f_1_step_time : 11:29:24
*INFO* f_1_step_si : 0.6203383340
previous_pred_les_level : 533.1620488681
previous_pred_les_trend : -9.6239136638
f_1_step_pred_les_level : 514.8349992479
f_1_step_pred_les_trend : -11.6910713032
f_1_step_pred_les : 503.1439279447
f_1_step_pred_adj_misc : 0.0000000000
pred_les + pred_adj_misc : 503.1439279447
f_1_step_pred_price_inc : 312.1194660234
f_1_step_pred_price : 90711.1194660234
f_1_step_pred_price_rounded : 90700
f_1_step_pred_set_price_rounded : 91000
-------------------------------------------------
==>> Prediction Restuls in Python List : [91000]
[91000]
<<<< Record No.: 1854 >>>>
2017-07
11:29:24
90700
==>> Forecasting 1 out of next 1 seconds/steps...
+-----------------------------------------------+
| shl_predict_price_1_step() |
+-----------------------------------------------+
current_ccyy_mm : 2017-07
in_current_time : 11:29:24
in_current_price : 90700.000000
*INFO* f_current_datetime : 2017-07 11:29:24
*INFO* f_current_si : 0.6203383340
*INFO* f_current_price4pm : 301
*INFO* f_current_price4pmsi : 485.2190869120
*INFO* f_1_step_time : 11:29:25
*INFO* f_1_step_si : 0.6624022500
previous_pred_les_level : 514.8349992479
previous_pred_les_trend : -11.6910713032
f_1_step_pred_les_level : 491.7387140341
f_1_step_pred_les_trend : -14.4000230169
f_1_step_pred_les : 477.3386910172
f_1_step_pred_adj_misc : 0.0000000000
pred_les + pred_adj_misc : 477.3386910172
f_1_step_pred_price_inc : 316.1902229418
f_1_step_pred_price : 90715.1902229418
f_1_step_pred_price_rounded : 90700
f_1_step_pred_set_price_rounded : 91000
-------------------------------------------------
==>> Prediction Restuls in Python List : [91000]
[91000]
<<<< Record No.: 1855 >>>>
2017-07
11:29:25
90700
==>> Forecasting 1 out of next 1 seconds/steps...
+-----------------------------------------------+
| shl_predict_price_1_step() |
+-----------------------------------------------+
current_ccyy_mm : 2017-07
in_current_time : 11:29:25
in_current_price : 90700.000000
*INFO* f_current_datetime : 2017-07 11:29:25
*INFO* f_current_si : 0.6624022500
*INFO* f_current_price4pm : 301
*INFO* f_current_price4pmsi : 454.4066690595
*INFO* f_1_step_time : 11:29:26
*INFO* f_1_step_si : 0.6803182270
previous_pred_les_level : 491.7387140341
previous_pred_les_trend : -14.4000230169
f_1_step_pred_les_level : 462.7475091287
f_1_step_pred_les_trend : -17.8657017400
f_1_step_pred_les : 444.8818073887
f_1_step_pred_adj_misc : 0.0000000000
pred_les + pred_adj_misc : 444.8818073887
f_1_step_pred_price_inc : 302.6612024272
f_1_step_pred_price : 90701.6612024272
f_1_step_pred_price_rounded : 90700
f_1_step_pred_set_price_rounded : 91000
-------------------------------------------------
==>> Prediction Restuls in Python List : [91000]
[91000]
<<<< Record No.: 1856 >>>>
2017-07
11:29:26
90700
==>> Forecasting 1 out of next 1 seconds/steps...
+-----------------------------------------------+
| shl_predict_price_1_step() |
+-----------------------------------------------+
current_ccyy_mm : 2017-07
in_current_time : 11:29:26
in_current_price : 90700.000000
*INFO* f_current_datetime : 2017-07 11:29:26
*INFO* f_current_si : 0.6803182270
*INFO* f_current_price4pm : 301
*INFO* f_current_price4pmsi : 442.4400053594
*INFO* f_1_step_time : 11:29:27
*INFO* f_1_step_si : 0.7013944910
previous_pred_les_level : 462.7475091287
previous_pred_les_trend : -17.8657017400
f_1_step_pred_les_level : 443.3281381305
f_1_step_pred_les_trend : -18.2347272605
f_1_step_pred_les : 425.0934108699
f_1_step_pred_adj_misc : 0.0000000000
pred_les + pred_adj_misc : 425.0934108699
f_1_step_pred_price_inc : 298.1581765446
f_1_step_pred_price : 90697.1581765446
f_1_step_pred_price_rounded : 90700
f_1_step_pred_set_price_rounded : 91000
-------------------------------------------------
==>> Prediction Restuls in Python List : [91000]
[91000]
<<<< Record No.: 1857 >>>>
2017-07
11:29:27
90700
==>> Forecasting 1 out of next 1 seconds/steps...
+-----------------------------------------------+
| shl_predict_price_1_step() |
+-----------------------------------------------+
current_ccyy_mm : 2017-07
in_current_time : 11:29:27
in_current_price : 90700.000000
*INFO* f_current_datetime : 2017-07 11:29:27
*INFO* f_current_si : 0.7013944910
*INFO* f_current_price4pm : 301
*INFO* f_current_price4pmsi : 429.1450871974
*INFO* f_1_step_time : 11:29:28
*INFO* f_1_step_si : 0.7261122680
previous_pred_les_level : 443.3281381305
previous_pred_les_trend : -18.2347272605
f_1_step_pred_les_level : 427.6714105926
f_1_step_pred_les_trend : -17.6224040878
f_1_step_pred_les : 410.0490065048
f_1_step_pred_adj_misc : 0.0000000000
pred_les + pred_adj_misc : 410.0490065048
f_1_step_pred_price_inc : 297.7416141043
f_1_step_pred_price : 90696.7416141043
f_1_step_pred_price_rounded : 90700
f_1_step_pred_set_price_rounded : 91000
-------------------------------------------------
==>> Prediction Restuls in Python List : [91000]
[91000]
<<<< Record No.: 1858 >>>>
2017-07
11:29:28
90700
==>> Forecasting 1 out of next 1 seconds/steps...
+-----------------------------------------------+
| shl_predict_price_1_step() |
+-----------------------------------------------+
current_ccyy_mm : 2017-07
in_current_time : 11:29:28
in_current_price : 90700.000000
*INFO* f_current_datetime : 2017-07 11:29:28
*INFO* f_current_si : 0.7261122680
*INFO* f_current_price4pm : 301
*INFO* f_current_price4pmsi : 414.5364474134
*INFO* f_1_step_time : 11:29:29
*INFO* f_1_step_si : 0.7412284280
previous_pred_les_level : 427.6714105926
previous_pred_les_trend : -17.6224040878
f_1_step_pred_les_level : 412.9042744193
f_1_step_pred_les_trend : -16.9442245315
f_1_step_pred_les : 395.9600498879
f_1_step_pred_adj_misc : 0.0000000000
pred_les + pred_adj_misc : 395.9600498879
f_1_step_pred_price_inc : 293.4968453292
f_1_step_pred_price : 90692.4968453292
f_1_step_pred_price_rounded : 90700
f_1_step_pred_set_price_rounded : 91000
-------------------------------------------------
==>> Prediction Restuls in Python List : [91000]
[91000]
<<<< Record No.: 1859 >>>>
2017-07
11:29:29
90700
==>> Forecasting 1 out of next 1 seconds/steps...
+-----------------------------------------------+
| shl_predict_price_1_step() |
+-----------------------------------------------+
current_ccyy_mm : 2017-07
in_current_time : 11:29:29
in_current_price : 90700.000000
*INFO* f_current_datetime : 2017-07 11:29:29
*INFO* f_current_si : 0.7412284280
*INFO* f_current_price4pm : 301
*INFO* f_current_price4pmsi : 406.0826442021
*INFO* f_1_step_time : 11:29:30
*INFO* f_1_step_si : 0.7848751150
previous_pred_les_level : 412.9042744193
previous_pred_les_trend : -16.9442245315
f_1_step_pred_les_level : 402.4008519721
f_1_step_pred_les_trend : -15.4144135186
f_1_step_pred_les : 386.9864384535
f_1_step_pred_adj_misc : 0.0000000000
pred_les + pred_adj_misc : 386.9864384535
f_1_step_pred_price_inc : 303.7360253846
f_1_step_pred_price : 90702.7360253846
f_1_step_pred_price_rounded : 90700
f_1_step_pred_set_price_rounded : 91000
-------------------------------------------------
==>> Prediction Restuls in Python List : [91000]
[91000]
<<<< Record No.: 1860 >>>>
2017-07
11:29:30
90700
==>> Forecasting 1 out of next 1 seconds/steps...
+-----------------------------------------------+
| shl_predict_price_1_step() |
+-----------------------------------------------+
current_ccyy_mm : 2017-07
in_current_time : 11:29:30
in_current_price : 90700.000000
*INFO* f_current_datetime : 2017-07 11:29:30
*INFO* f_current_si : 0.7848751150
*INFO* f_current_price4pm : 301
*INFO* f_current_price4pmsi : 383.5005012231
*INFO* f_1_step_time : 11:29:31
*INFO* f_1_step_si : 0.7883406290
previous_pred_les_level : 402.4008519721
previous_pred_les_trend : -15.4144135186
f_1_step_pred_les_level : 384.7684070791
f_1_step_pred_les_trend : -15.9412374730
f_1_step_pred_les : 368.8271696061
f_1_step_pred_adj_misc : 0.0000000000
pred_les + pred_adj_misc : 368.8271696061
f_1_step_pred_price_inc : 290.7614428795
f_1_step_pred_price : 90689.7614428795
f_1_step_pred_price_rounded : 90700
f_1_step_pred_set_price_rounded : 91000
-------------------------------------------------
==>> Prediction Restuls in Python List : [91000]
[91000]
<<<< Record No.: 1861 >>>>
2017-07
11:29:31
90700
==>> Forecasting 1 out of next 1 seconds/steps...
+-----------------------------------------------+
| shl_predict_price_1_step() |
+-----------------------------------------------+
current_ccyy_mm : 2017-07
in_current_time : 11:29:31
in_current_price : 90700.000000
*INFO* f_current_datetime : 2017-07 11:29:31
*INFO* f_current_si : 0.7883406290
*INFO* f_current_price4pm : 301
*INFO* f_current_price4pmsi : 381.8146482972
*INFO* f_1_step_time : 11:29:32
*INFO* f_1_step_si : 0.8143918490
previous_pred_les_level : 384.7684070791
previous_pred_les_trend : -15.9412374730
f_1_step_pred_les_level : 377.0908396917
f_1_step_pred_les_trend : -13.9784612011
f_1_step_pred_les : 363.1123784906
f_1_step_pred_adj_misc : 0.0000000000
pred_les + pred_adj_misc : 363.1123784906
f_1_step_pred_price_inc : 295.7157613138
f_1_step_pred_price : 90694.7157613138
f_1_step_pred_price_rounded : 90700
f_1_step_pred_set_price_rounded : 91000
-------------------------------------------------
==>> Prediction Restuls in Python List : [91000]
[91000]
<<<< Record No.: 1862 >>>>
2017-07
11:29:32
90700
==>> Forecasting 1 out of next 1 seconds/steps...
+-----------------------------------------------+
| shl_predict_price_1_step() |
+-----------------------------------------------+
current_ccyy_mm : 2017-07
in_current_time : 11:29:32
in_current_price : 90700.000000
*INFO* f_current_datetime : 2017-07 11:29:32
*INFO* f_current_si : 0.8143918490
*INFO* f_current_price4pm : 301
*INFO* f_current_price4pmsi : 369.6009486952
*INFO* f_1_step_time : 11:29:33
*INFO* f_1_step_si : 0.8351005610
previous_pred_les_level : 377.0908396917
previous_pred_les_trend : -13.9784612011
f_1_step_pred_les_level : 367.2409245135
f_1_step_pred_les_trend : -12.9978542688
f_1_step_pred_les : 354.2430702447
f_1_step_pred_adj_misc : 0.0000000000
pred_les + pred_adj_misc : 354.2430702447
f_1_step_pred_price_inc : 295.8285866917
f_1_step_pred_price : 90694.8285866917
f_1_step_pred_price_rounded : 90700
f_1_step_pred_set_price_rounded : 91000
-------------------------------------------------
==>> Prediction Restuls in Python List : [91000]
[91000]
<<<< Record No.: 1863 >>>>
2017-07
11:29:33
90700
==>> Forecasting 1 out of next 1 seconds/steps...
+-----------------------------------------------+
| shl_predict_price_1_step() |
+-----------------------------------------------+
current_ccyy_mm : 2017-07
in_current_time : 11:29:33
in_current_price : 90700.000000
*INFO* f_current_datetime : 2017-07 11:29:33
*INFO* f_current_si : 0.8351005610
*INFO* f_current_price4pm : 301
*INFO* f_current_price4pmsi : 360.4356338111
*INFO* f_1_step_time : 11:29:34
*INFO* f_1_step_si : 0.8670445380
previous_pred_les_level : 367.2409245135
previous_pred_les_trend : -12.9978542688
f_1_step_pred_les_level : 358.1832732289
f_1_step_pred_les_trend : -12.0619823325
f_1_step_pred_les : 346.1212908964
f_1_step_pred_adj_misc : 0.0000000000
pred_les + pred_adj_misc : 346.1212908964
f_1_step_pred_price_inc : 300.1025747573
f_1_step_pred_price : 90699.1025747573
f_1_step_pred_price_rounded : 90700
f_1_step_pred_set_price_rounded : 91000
-------------------------------------------------
==>> Prediction Restuls in Python List : [91000]
[91000]
<<<< Record No.: 1864 >>>>
2017-07
11:29:34
90700
==>> Forecasting 1 out of next 1 seconds/steps...
+-----------------------------------------------+
| shl_predict_price_1_step() |
+-----------------------------------------------+
current_ccyy_mm : 2017-07
in_current_time : 11:29:34
in_current_price : 90700.000000
*INFO* f_current_datetime : 2017-07 11:29:34
*INFO* f_current_si : 0.8670445380
*INFO* f_current_price4pm : 301
*INFO* f_current_price4pmsi : 347.1563302784
*INFO* f_1_step_time : 11:29:35
*INFO* f_1_step_si : 0.9216129500
previous_pred_les_level : 358.1832732289
previous_pred_les_trend : -12.0619823325
f_1_step_pred_les_level : 346.7798655268
f_1_step_pred_les_trend : -11.9055585348
f_1_step_pred_les : 334.8743069920
f_1_step_pred_adj_misc : 0.0000000000
pred_les + pred_adj_misc : 334.8743069920
f_1_step_pred_price_inc : 308.6244979461
f_1_step_pred_price : 90707.6244979461
f_1_step_pred_price_rounded : 90700
f_1_step_pred_set_price_rounded : 91000
-------------------------------------------------
==>> Prediction Restuls in Python List : [91000]
[91000]
<<<< Record No.: 1865 >>>>
2017-07
11:29:35
90800
==>> Forecasting 1 out of next 1 seconds/steps...
+-----------------------------------------------+
| shl_predict_price_1_step() |
+-----------------------------------------------+
current_ccyy_mm : 2017-07
in_current_time : 11:29:35
in_current_price : 90800.000000
*INFO* f_current_datetime : 2017-07 11:29:35
*INFO* f_current_si : 0.9216129500
*INFO* f_current_price4pm : 401
*INFO* f_current_price4pmsi : 435.1067332550
*INFO* f_1_step_time : 11:29:36
*INFO* f_1_step_si : 0.9539289700
previous_pred_les_level : 346.7798655268
previous_pred_les_trend : -11.9055585348
f_1_step_pred_les_level : 398.6501731334
f_1_step_pred_les_trend : 3.2424030233
f_1_step_pred_les : 401.8925761567
f_1_step_pred_adj_misc : 0.0000000000
pred_les + pred_adj_misc : 401.8925761567
f_1_step_pred_price_inc : 383.3769712238
f_1_step_pred_price : 90782.3769712238
f_1_step_pred_price_rounded : 90800
f_1_step_pred_set_price_rounded : 91100
-------------------------------------------------
==>> Prediction Restuls in Python List : [91100]
[91100]
<<<< Record No.: 1866 >>>>
2017-07
11:29:36
90800
==>> Forecasting 1 out of next 1 seconds/steps...
+-----------------------------------------------+
| shl_predict_price_1_step() |
+-----------------------------------------------+
current_ccyy_mm : 2017-07
in_current_time : 11:29:36
in_current_price : 90800.000000
*INFO* f_current_datetime : 2017-07 11:29:36
*INFO* f_current_si : 0.9539289700
*INFO* f_current_price4pm : 401
*INFO* f_current_price4pmsi : 420.3667281433
*INFO* f_1_step_time : 11:29:37
*INFO* f_1_step_si : 0.9779660700
previous_pred_les_level : 398.6501731334
previous_pred_les_trend : 3.2424030233
f_1_step_pred_les_level : 413.6473055203
f_1_step_pred_les_trend : 6.0343711971
f_1_step_pred_les : 419.6816767174
f_1_step_pred_adj_misc : 0.0000000000
pred_les + pred_adj_misc : 419.6816767174
f_1_step_pred_price_inc : 410.4344400303
f_1_step_pred_price : 90809.4344400303
f_1_step_pred_price_rounded : 90800
f_1_step_pred_set_price_rounded : 91100
-------------------------------------------------
==>> Prediction Restuls in Python List : [91100]
[91100]
<<<< Record No.: 1867 >>>>
2017-07
11:29:37
90900
==>> Forecasting 1 out of next 1 seconds/steps...
+-----------------------------------------------+
| shl_predict_price_1_step() |
+-----------------------------------------------+
current_ccyy_mm : 2017-07
in_current_time : 11:29:37
in_current_price : 90900.000000
*INFO* f_current_datetime : 2017-07 11:29:37
*INFO* f_current_si : 0.9779660700
*INFO* f_current_price4pm : 501
*INFO* f_current_price4pmsi : 512.2877115767
*INFO* f_1_step_time : 11:29:38
*INFO* f_1_step_si : 0.9935136330
previous_pred_les_level : 413.6473055203
previous_pred_les_trend : 6.0343711971
f_1_step_pred_les_level : 478.6050242136
f_1_step_pred_les_trend : 20.0297687786
f_1_step_pred_les : 498.6347929921
f_1_step_pred_adj_misc : 0.0000000000
pred_les + pred_adj_misc : 498.6347929921
f_1_step_pred_price_inc : 495.4004647258
f_1_step_pred_price : 90894.4004647258
f_1_step_pred_price_rounded : 90900
f_1_step_pred_set_price_rounded : 91200
-------------------------------------------------
==>> Prediction Restuls in Python List : [91200]
[91200]
<<<< Record No.: 1868 >>>>
2017-07
11:29:38
91000
==>> Forecasting 1 out of next 1 seconds/steps...
+-----------------------------------------------+
| shl_predict_price_1_step() |
+-----------------------------------------------+
current_ccyy_mm : 2017-07
in_current_time : 11:29:38
in_current_price : 91000.000000
*INFO* f_current_datetime : 2017-07 11:29:38
*INFO* f_current_si : 0.9935136330
*INFO* f_current_price4pm : 601
*INFO* f_current_price4pmsi : 604.9237574982
*INFO* f_1_step_time : 11:29:39
*INFO* f_1_step_si : 1.0325517050
previous_pred_les_level : 478.6050242136
previous_pred_les_trend : 20.0297687786
f_1_step_pred_les_level : 566.2643119550
f_1_step_pred_les_trend : 36.0930449899
f_1_step_pred_les : 602.3573569448
f_1_step_pred_adj_misc : 0.0000000000
pred_les + pred_adj_misc : 602.3573569448
f_1_step_pred_price_inc : 621.9651159327
f_1_step_pred_price : 91020.9651159327
f_1_step_pred_price_rounded : 91000
f_1_step_pred_set_price_rounded : 91300
-------------------------------------------------
==>> Prediction Restuls in Python List : [91300]
[91300]
<<<< Record No.: 1869 >>>>
2017-07
11:29:39
91000
==>> Forecasting 1 out of next 1 seconds/steps...
+-----------------------------------------------+
| shl_predict_price_1_step() |
+-----------------------------------------------+
current_ccyy_mm : 2017-07
in_current_time : 11:29:39
in_current_price : 91000.000000
*INFO* f_current_datetime : 2017-07 11:29:39
*INFO* f_current_si : 1.0325517050
*INFO* f_current_price4pm : 601
*INFO* f_current_price4pmsi : 582.0531766978
*INFO* f_1_step_time : 11:29:40
*INFO* f_1_step_si : 1.0762695320
previous_pred_les_level : 566.2643119550
previous_pred_les_trend : 36.0930449899
f_1_step_pred_les_level : 589.4382176022
f_1_step_pred_les_trend : 33.0245076580
f_1_step_pred_les : 622.4627252602
f_1_step_pred_adj_misc : 0.0000000000
pred_les + pred_adj_misc : 622.4627252602
f_1_step_pred_price_inc : 669.9376660032
f_1_step_pred_price : 91068.9376660032
f_1_step_pred_price_rounded : 91100
f_1_step_pred_set_price_rounded : 91400
-------------------------------------------------
==>> Prediction Restuls in Python List : [91400]
[91400]
<<<< Record No.: 1870 >>>>
2017-07
11:29:40
91000
==>> Forecasting 1 out of next 1 seconds/steps...
+-----------------------------------------------+
| shl_predict_price_1_step() |
+-----------------------------------------------+
current_ccyy_mm : 2017-07
in_current_time : 11:29:40
in_current_price : 91000.000000
*INFO* f_current_datetime : 2017-07 11:29:40
*INFO* f_current_si : 1.0762695320
*INFO* f_current_price4pm : 601
*INFO* f_current_price4pmsi : 558.4103072055
*INFO* f_1_step_time : 11:29:41
*INFO* f_1_step_si : 1.1032848210
previous_pred_les_level : 589.4382176022
previous_pred_les_trend : 33.0245076580
f_1_step_pred_les_level : 581.7074667855
f_1_step_pred_les_trend : 23.3443711735
f_1_step_pred_les : 605.0518379590
f_1_step_pred_adj_misc : 0.0000000000
pred_les + pred_adj_misc : 605.0518379590
f_1_step_pred_price_inc : 667.5445087383
f_1_step_pred_price : 91066.5445087383
f_1_step_pred_price_rounded : 91100
f_1_step_pred_set_price_rounded : 91400
-------------------------------------------------
==>> Prediction Restuls in Python List : [91400]
[91400]
<<<< Record No.: 1871 >>>>
2017-07
11:29:41
91000
==>> Forecasting 1 out of next 1 seconds/steps...
+-----------------------------------------------+
| shl_predict_price_1_step() |
+-----------------------------------------------+
current_ccyy_mm : 2017-07
in_current_time : 11:29:41
in_current_price : 91000.000000
*INFO* f_current_datetime : 2017-07 11:29:41
*INFO* f_current_si : 1.1032848210
*INFO* f_current_price4pm : 601
*INFO* f_current_price4pmsi : 544.7369424110
*INFO* f_1_step_time : 11:29:42
*INFO* f_1_step_si : 1.1629896100
previous_pred_les_level : 581.7074667855
previous_pred_les_trend : 23.3443711735
f_1_step_pred_les_level : 566.6746894830
f_1_step_pred_les_trend : 14.2290803120
f_1_step_pred_les : 580.9037697950
f_1_step_pred_adj_misc : 0.0000000000
pred_les + pred_adj_misc : 580.9037697950
f_1_step_pred_price_inc : 675.5850486814
f_1_step_pred_price : 91074.5850486814
f_1_step_pred_price_rounded : 91100
f_1_step_pred_set_price_rounded : 91400
-------------------------------------------------
==>> Prediction Restuls in Python List : [91400]
[91400]
<<<< Record No.: 1872 >>>>
2017-07
11:29:42
91000
==>> Forecasting 1 out of next 1 seconds/steps...
+-----------------------------------------------+
| shl_predict_price_1_step() |
+-----------------------------------------------+
current_ccyy_mm : 2017-07
in_current_time : 11:29:42
in_current_price : 91000.000000
*INFO* f_current_datetime : 2017-07 11:29:42
*INFO* f_current_si : 1.1629896100
*INFO* f_current_price4pm : 601
*INFO* f_current_price4pmsi : 516.7715986732
*INFO* f_1_step_time : 11:29:43
*INFO* f_1_step_si : 1.2717913130
previous_pred_les_level : 566.6746894830
previous_pred_les_trend : 14.2290803120
f_1_step_pred_les_level : 540.0977660563
f_1_step_pred_les_trend : 4.5368908778
f_1_step_pred_les : 544.6346569341
f_1_step_pred_adj_misc : 0.0000000000
pred_les + pred_adj_misc : 544.6346569341
f_1_step_pred_price_inc : 692.6616254475
f_1_step_pred_price : 91091.6616254475
f_1_step_pred_price_rounded : 91100
f_1_step_pred_set_price_rounded : 91400
-------------------------------------------------
==>> Prediction Restuls in Python List : [91400]
[91400]
<<<< Record No.: 1873 >>>>
2017-07
11:29:43
91000
==>> Forecasting 1 out of next 1 seconds/steps...
+-----------------------------------------------+
| shl_predict_price_1_step() |
+-----------------------------------------------+
current_ccyy_mm : 2017-07
in_current_time : 11:29:43
in_current_price : 91000.000000
*INFO* f_current_datetime : 2017-07 11:29:43
*INFO* f_current_si : 1.2717913130
*INFO* f_current_price4pm : 601
*INFO* f_current_price4pmsi : 472.5618062151
*INFO* f_1_step_time : 11:29:44
*INFO* f_1_step_si : 1.3866613510
previous_pred_les_level : 540.0977660563
previous_pred_les_trend : 4.5368908778
f_1_step_pred_les_level : 498.7761593275
f_1_step_pred_les_trend : -6.3553603904
f_1_step_pred_les : 492.4207989371
f_1_step_pred_adj_misc : 0.0000000000
pred_les + pred_adj_misc : 492.4207989371
f_1_step_pred_price_inc : 682.8208903146
f_1_step_pred_price : 91081.8208903146
f_1_step_pred_price_rounded : 91100
f_1_step_pred_set_price_rounded : 91400
-------------------------------------------------
==>> Prediction Restuls in Python List : [91400]
[91400]
<<<< Record No.: 1874 >>>>
2017-07
11:29:44
91100
==>> Forecasting 1 out of next 1 seconds/steps...
+-----------------------------------------------+
| shl_predict_price_1_step() |
+-----------------------------------------------+
current_ccyy_mm : 2017-07
in_current_time : 11:29:44
in_current_price : 91100.000000
*INFO* f_current_datetime : 2017-07 11:29:44
*INFO* f_current_si : 1.3866613510
*INFO* f_current_price4pm : 701
*INFO* f_current_price4pmsi : 505.5307840624
*INFO* f_1_step_time : 11:29:45
*INFO* f_1_step_si : 1.4370894140
previous_pred_les_level : 498.7761593275
previous_pred_les_trend : -6.3553603904
f_1_step_pred_les_level : 500.7624173897
f_1_step_pred_les_trend : -4.3740699228
f_1_step_pred_les : 496.3883474669
f_1_step_pred_adj_misc : 0.0000000000
pred_les + pred_adj_misc : 496.3883474669
f_1_step_pred_price_inc : 713.3544393777
f_1_step_pred_price : 91112.3544393777
f_1_step_pred_price_rounded : 91100
f_1_step_pred_set_price_rounded : 91400
-------------------------------------------------
==>> Prediction Restuls in Python List : [91400]
[91400]
<<<< Record No.: 1875 >>>>
2017-07
11:29:45
91100
==>> Forecasting 1 out of next 1 seconds/steps...
+-----------------------------------------------+
| shl_predict_price_1_step() |
+-----------------------------------------------+
current_ccyy_mm : 2017-07
in_current_time : 11:29:45
in_current_price : 91100.000000
*INFO* f_current_datetime : 2017-07 11:29:45
*INFO* f_current_si : 1.4370894140
*INFO* f_current_price4pm : 701
*INFO* f_current_price4pmsi : 487.7914993813
*INFO* f_1_step_time : 11:29:46
*INFO* f_1_step_si : 1.5686206330
previous_pred_les_level : 500.7624173897
previous_pred_les_trend : -4.3740699228
f_1_step_pred_les_level : 490.9183468574
f_1_step_pred_les_trend : -5.6732974201
f_1_step_pred_les : 485.2450494374
f_1_step_pred_adj_misc : 0.0000000000
pred_les + pred_adj_misc : 485.2450494374
f_1_step_pred_price_inc : 761.1653966086
f_1_step_pred_price : 91160.1653966086
f_1_step_pred_price_rounded : 91200
f_1_step_pred_set_price_rounded : 91500
-------------------------------------------------
==>> Prediction Restuls in Python List : [91500]
[91500]
<<<< Record No.: 1876 >>>>
2017-07
11:29:46
91200
==>> Forecasting 1 out of next 1 seconds/steps...
+-----------------------------------------------+
| shl_predict_price_1_step() |
+-----------------------------------------------+
current_ccyy_mm : 2017-07
in_current_time : 11:29:46
in_current_price : 91200.000000
*INFO* f_current_datetime : 2017-07 11:29:46
*INFO* f_current_si : 1.5686206330
*INFO* f_current_price4pm : 801
*INFO* f_current_price4pmsi : 510.6397194764
*INFO* f_1_step_time : 11:29:47
*INFO* f_1_step_si : 1.6413910300
previous_pred_les_level : 490.9183468574
previous_pred_les_trend : -5.6732974201
f_1_step_pred_les_level : 501.4031645055
f_1_step_pred_les_trend : -1.8354427469
f_1_step_pred_les : 499.5677217585
f_1_step_pred_adj_misc : 0.0000000000
pred_les + pred_adj_misc : 499.5677217585
f_1_step_pred_price_inc : 819.9859773720
f_1_step_pred_price : 91218.9859773720
f_1_step_pred_price_rounded : 91200
f_1_step_pred_set_price_rounded : 91500
-------------------------------------------------
==>> Prediction Restuls in Python List : [91500]
[91500]
<<<< Record No.: 1877 >>>>
2017-07
11:29:47
91300
==>> Forecasting 1 out of next 1 seconds/steps...
+-----------------------------------------------+
| shl_predict_price_1_step() |
+-----------------------------------------------+
current_ccyy_mm : 2017-07
in_current_time : 11:29:47
in_current_price : 91300.000000
*INFO* f_current_datetime : 2017-07 11:29:47
*INFO* f_current_si : 1.6413910300
*INFO* f_current_price4pm : 901
*INFO* f_current_price4pmsi : 548.9246520374
*INFO* f_1_step_time : 11:29:48
*INFO* f_1_step_si : 1.7490712830
previous_pred_les_level : 501.4031645055
previous_pred_les_trend : -1.8354427469
f_1_step_pred_les_level : 530.9725385027
f_1_step_pred_les_trend : 5.6237888647
f_1_step_pred_les : 536.5963273674
f_1_step_pred_adj_misc : 0.0000000000
pred_les + pred_adj_misc : 536.5963273674
f_1_step_pred_price_inc : 938.5452267616
f_1_step_pred_price : 91337.5452267616
f_1_step_pred_price_rounded : 91300
f_1_step_pred_set_price_rounded : 91600
-------------------------------------------------
==>> Prediction Restuls in Python List : [91600]
[91600]
<<<< Record No.: 1878 >>>>
2017-07
11:29:48
91400
==>> Forecasting 1 out of next 1 seconds/steps...
+-----------------------------------------------+
| shl_predict_price_1_step() |
+-----------------------------------------------+
current_ccyy_mm : 2017-07
in_current_time : 11:29:48
in_current_price : 91400.000000
*INFO* f_current_datetime : 2017-07 11:29:48
*INFO* f_current_si : 1.7490712830
*INFO* f_current_price4pm : 1001
*INFO* f_current_price4pmsi : 572.3037189674
*INFO* f_1_step_time : 11:29:49
*INFO* f_1_step_si : 1.7897347710
previous_pred_les_level : 530.9725385027
previous_pred_les_trend : 5.6237888647
f_1_step_pred_les_level : 559.3162186426
f_1_step_pred_les_trend : 11.0201881684
f_1_step_pred_les : 570.3364068110
f_1_step_pred_adj_misc : 0.0000000000
pred_les + pred_adj_misc : 570.3364068110
f_1_step_pred_price_inc : 1020.7508984368
f_1_step_pred_price : 91419.7508984368
f_1_step_pred_price_rounded : 91400
f_1_step_pred_set_price_rounded : 91700
-------------------------------------------------
==>> Prediction Restuls in Python List : [91700]
[91700]
<<<< Record No.: 1879 >>>>
2017-07
11:29:49
91400
==>> Forecasting 1 out of next 1 seconds/steps...
+-----------------------------------------------+
| shl_predict_price_1_step() |
+-----------------------------------------------+
current_ccyy_mm : 2017-07
in_current_time : 11:29:49
in_current_price : 91400.000000
*INFO* f_current_datetime : 2017-07 11:29:49
*INFO* f_current_si : 1.7897347710
*INFO* f_current_price4pm : 1001
*INFO* f_current_price4pmsi : 559.3007501557
*INFO* f_1_step_time : 11:29:50
*INFO* f_1_step_si : 1.9329318490
previous_pred_les_level : 559.3162186426
previous_pred_les_trend : 11.0201881684
f_1_step_pred_les_level : 563.3146416211
f_1_step_pred_les_trend : 9.3523875472
f_1_step_pred_les : 572.6670291683
f_1_step_pred_adj_misc : 0.0000000000
pred_les + pred_adj_misc : 572.6670291683
f_1_step_pred_price_inc : 1106.9263395517
f_1_step_pred_price : 91505.9263395517
f_1_step_pred_price_rounded : 91500
f_1_step_pred_set_price_rounded : 91800
-------------------------------------------------
==>> Prediction Restuls in Python List : [91800]
[91800]
In [6]:
# Upon receiving 11:29:50 second price, to predict till 11:30:00 <- ten-step forward price forecasting
for i in range(shl_sm_parm_ccyy_mm_offset+50, shl_sm_parm_ccyy_mm_offset+51): # use csv data as simulation
print('\n<<<< Record No.: %5d >>>>' % i)
print(shl_sm_data['ccyy-mm'][i]) # format: ccyy-mm
print(shl_sm_data['time'][i]) # format: hh:mm:ss
print(shl_sm_data['bid-price'][i]) # format: integer/boost-trap-float
######################################################################################################################
# call prediction function, returned result is in 'list' format, i.e. [89400, 89400, 89400, 89500, 89500, 89500, 89500, 89600, 89600, 89600]
shl_sm_prediction_list_local_k = shl_pm.shl_predict_price_k_step(shl_sm_data['time'][i], shl_sm_data['bid-price'][i],10) # <- ten-step forward price forecasting
print(shl_sm_prediction_list_local_k)
######################################################################################################################
<<<< Record No.: 1880 >>>>
2017-07
11:29:50
91500
==>> Forecasting 1 out of next 10 seconds/steps...
+-----------------------------------------------+
| shl_predict_price_1_step() |
+-----------------------------------------------+
current_ccyy_mm : 2017-07
in_current_time : 11:29:50
in_current_price : 91500.000000
*INFO* f_current_datetime : 2017-07 11:29:50
*INFO* f_current_si : 1.9329318490
*INFO* f_current_price4pm : 1101
*INFO* f_current_price4pmsi : 569.6010444288
*INFO* f_1_step_time : 11:29:51
*INFO* f_1_step_si : 2.0011852710
*INFO* sec50_error : 5.9263395517
*INFO* sec46_49_error : -163.5525008210
*INFO* shl_global_parm_short_weight_misc : -31.5252322539
*INFO* shl_global_parm_short_weight_ratio : 1
previous_pred_les_level : 563.3146416211
previous_pred_les_trend : 9.3523875472
f_1_step_pred_les_level : 570.7162050725
f_1_step_pred_les_trend : 8.8890303214
f_1_step_pred_les : 579.6052353939
f_1_step_pred_adj_misc : -0.8809825102
pred_les + pred_adj_misc : 578.7242528837
f_1_step_pred_price_inc : 1158.1344508413
f_1_step_pred_price : 91557.1344508413
f_1_step_pred_price_rounded : 91600
f_1_step_pred_set_price_rounded : 91900
-------------------------------------------------
==>> Forecasting 2 out of next 10 seconds/steps...
+-----------------------------------------------+
| shl_predict_price_1_step() |
+-----------------------------------------------+
current_ccyy_mm : 2017-07
in_current_time : 11:29:51
in_current_price : 91557.134451
*INFO* f_current_datetime : 2017-07 11:29:51
*INFO* f_current_si : 2.0011852710
*INFO* f_current_price4pm : 1158
*INFO* f_current_price4pmsi : 578.7242528837
*INFO* f_1_step_time : 11:29:52
*INFO* f_1_step_si : 2.0661036070
*INFO* shl_global_parm_short_weight_ratio : 2
previous_pred_les_level : 570.7162050725
previous_pred_les_trend : 8.8890303214
f_1_step_pred_les_level : 579.0446840360
f_1_step_pred_les_trend : 8.7558888851
f_1_step_pred_les : 587.8005729211
f_1_step_pred_adj_misc : -1.7619650204
pred_les + pred_adj_misc : 586.0386079007
f_1_step_pred_price_inc : 1210.8164816249
f_1_step_pred_price : 91609.8164816249
f_1_step_pred_price_rounded : 91600
f_1_step_pred_set_price_rounded : 91900
-------------------------------------------------
==>> Forecasting 3 out of next 10 seconds/steps...
+-----------------------------------------------+
| shl_predict_price_1_step() |
+-----------------------------------------------+
current_ccyy_mm : 2017-07
in_current_time : 11:29:52
in_current_price : 91609.816482
*INFO* f_current_datetime : 2017-07 11:29:52
*INFO* f_current_si : 2.0661036070
*INFO* f_current_price4pm : 1210
*INFO* f_current_price4pmsi : 586.0386079007
*INFO* f_1_step_time : 11:29:53
*INFO* f_1_step_si : 2.1682095660
*INFO* shl_global_parm_short_weight_ratio : 3
previous_pred_les_level : 579.0446840360
previous_pred_les_trend : 8.7558888851
f_1_step_pred_les_level : 586.6794702054
f_1_step_pred_les_trend : 8.4896060125
f_1_step_pred_les : 595.1690762179
f_1_step_pred_adj_misc : -2.6429475306
pred_les + pred_adj_misc : 592.5261286873
f_1_step_pred_price_inc : 1284.7208203248
f_1_step_pred_price : 91683.7208203248
f_1_step_pred_price_rounded : 91700
f_1_step_pred_set_price_rounded : 92000
-------------------------------------------------
==>> Forecasting 4 out of next 10 seconds/steps...
+-----------------------------------------------+
| shl_predict_price_1_step() |
+-----------------------------------------------+
current_ccyy_mm : 2017-07
in_current_time : 11:29:53
in_current_price : 91683.720820
*INFO* f_current_datetime : 2017-07 11:29:53
*INFO* f_current_si : 2.1682095660
*INFO* f_current_price4pm : 1284
*INFO* f_current_price4pmsi : 592.5261286873
*INFO* f_1_step_time : 11:29:54
*INFO* f_1_step_si : 2.2903489060
*INFO* shl_global_parm_short_weight_ratio : 4
previous_pred_les_level : 586.6794702054
previous_pred_les_trend : 8.4896060125
f_1_step_pred_les_level : 593.4874221443
f_1_step_pred_les_trend : 8.0901817035
f_1_step_pred_les : 601.5776038478
f_1_step_pred_adj_misc : -3.5239300407
pred_les + pred_adj_misc : 598.0536738071
f_1_step_pred_price_inc : 1369.7515775334
f_1_step_pred_price : 91768.7515775334
f_1_step_pred_price_rounded : 91800
f_1_step_pred_set_price_rounded : 92100
-------------------------------------------------
==>> Forecasting 5 out of next 10 seconds/steps...
+-----------------------------------------------+
| shl_predict_price_1_step() |
+-----------------------------------------------+
current_ccyy_mm : 2017-07
in_current_time : 11:29:54
in_current_price : 91768.751578
*INFO* f_current_datetime : 2017-07 11:29:54
*INFO* f_current_si : 2.2903489060
*INFO* f_current_price4pm : 1369
*INFO* f_current_price4pmsi : 598.0536738071
*INFO* f_1_step_time : 11:29:55
*INFO* f_1_step_si : 2.4136021070
*INFO* shl_global_parm_short_weight_ratio : 5
previous_pred_les_level : 593.4874221443
previous_pred_les_trend : 8.0901817035
f_1_step_pred_les_level : 599.3353984164
f_1_step_pred_les_trend : 7.5576159583
f_1_step_pred_les : 606.8930143747
f_1_step_pred_adj_misc : -4.4049125509
pred_les + pred_adj_misc : 602.4881018238
f_1_step_pred_price_inc : 1454.1665520044
f_1_step_pred_price : 91853.1665520044
f_1_step_pred_price_rounded : 91900
f_1_step_pred_set_price_rounded : 92200
-------------------------------------------------
==>> Forecasting 6 out of next 10 seconds/steps...
+-----------------------------------------------+
| shl_predict_price_1_step() |
+-----------------------------------------------+
current_ccyy_mm : 2017-07
in_current_time : 11:29:55
in_current_price : 91853.166552
*INFO* f_current_datetime : 2017-07 11:29:55
*INFO* f_current_si : 2.4136021070
*INFO* f_current_price4pm : 1454
*INFO* f_current_price4pmsi : 602.4881018238
*INFO* f_1_step_time : 11:29:56
*INFO* f_1_step_si : 2.5506970550
*INFO* shl_global_parm_short_weight_ratio : 6
previous_pred_les_level : 599.3353984164
previous_pred_les_trend : 7.5576159583
f_1_step_pred_les_level : 604.0902575855
f_1_step_pred_les_trend : 6.8919087767
f_1_step_pred_les : 610.9821663622
f_1_step_pred_adj_misc : -5.2858950611
pred_les + pred_adj_misc : 605.6962713011
f_1_step_pred_price_inc : 1544.9476954322
f_1_step_pred_price : 91943.9476954322
f_1_step_pred_price_rounded : 91900
f_1_step_pred_set_price_rounded : 92200
-------------------------------------------------
==>> Forecasting 7 out of next 10 seconds/steps...
+-----------------------------------------------+
| shl_predict_price_1_step() |
+-----------------------------------------------+
current_ccyy_mm : 2017-07
in_current_time : 11:29:56
in_current_price : 91943.947695
*INFO* f_current_datetime : 2017-07 11:29:56
*INFO* f_current_si : 2.5506970550
*INFO* f_current_price4pm : 1544
*INFO* f_current_price4pmsi : 605.6962713011
*INFO* f_1_step_time : 11:29:57
*INFO* f_1_step_si : 2.7053908880
*INFO* shl_global_parm_short_weight_ratio : 7
previous_pred_les_level : 604.0902575855
previous_pred_les_trend : 6.8919087767
f_1_step_pred_les_level : 607.6188582151
f_1_step_pred_les_trend : 6.0930601589
f_1_step_pred_les : 613.7119183739
f_1_step_pred_adj_misc : -6.1668775713
pred_les + pred_adj_misc : 607.5450408027
f_1_step_pred_price_inc : 1643.6468174371
f_1_step_pred_price : 92042.6468174371
f_1_step_pred_price_rounded : 92000
f_1_step_pred_set_price_rounded : 92300
-------------------------------------------------
==>> Forecasting 8 out of next 10 seconds/steps...
+-----------------------------------------------+
| shl_predict_price_1_step() |
+-----------------------------------------------+
current_ccyy_mm : 2017-07
in_current_time : 11:29:57
in_current_price : 92042.646817
*INFO* f_current_datetime : 2017-07 11:29:57
*INFO* f_current_si : 2.7053908880
*INFO* f_current_price4pm : 1643
*INFO* f_current_price4pmsi : 607.5450408027
*INFO* f_1_step_time : 11:29:58
*INFO* f_1_step_si : 2.7745487590
*INFO* shl_global_parm_short_weight_ratio : 8
previous_pred_les_level : 607.6188582151
previous_pred_les_trend : 6.0930601589
f_1_step_pred_les_level : 609.7880588690
f_1_step_pred_les_trend : 5.1610701047
f_1_step_pred_les : 614.9491289737
f_1_step_pred_adj_misc : -7.0478600815
pred_les + pred_adj_misc : 607.9012688922
f_1_step_pred_price_inc : 1686.6517111994
f_1_step_pred_price : 92085.6517111994
f_1_step_pred_price_rounded : 92100
f_1_step_pred_set_price_rounded : 92400
-------------------------------------------------
==>> Forecasting 9 out of next 10 seconds/steps...
+-----------------------------------------------+
| shl_predict_price_1_step() |
+-----------------------------------------------+
current_ccyy_mm : 2017-07
in_current_time : 11:29:58
in_current_price : 92085.651711
*INFO* f_current_datetime : 2017-07 11:29:58
*INFO* f_current_si : 2.7745487590
*INFO* f_current_price4pm : 1686
*INFO* f_current_price4pmsi : 607.9012688922
*INFO* f_1_step_time : 11:29:59
*INFO* f_1_step_si : 2.9291830210
*INFO* shl_global_parm_short_weight_ratio : 9
previous_pred_les_level : 609.7880588690
previous_pred_les_trend : 5.1610701047
f_1_step_pred_les_level : 610.4647181109
f_1_step_pred_les_trend : 4.0959386142
f_1_step_pred_les : 614.5606567251
f_1_step_pred_adj_misc : -7.9288425917
pred_les + pred_adj_misc : 606.6318141334
f_1_step_pred_price_inc : 1776.9356099580
f_1_step_pred_price : 92175.9356099580
f_1_step_pred_price_rounded : 92200
f_1_step_pred_set_price_rounded : 92500
-------------------------------------------------
==>> Forecasting 10 out of next 10 seconds/steps...
+-----------------------------------------------+
| shl_predict_price_1_step() |
+-----------------------------------------------+
current_ccyy_mm : 2017-07
in_current_time : 11:29:59
in_current_price : 92175.935610
*INFO* f_current_datetime : 2017-07 11:29:59
*INFO* f_current_si : 2.9291830210
*INFO* f_current_price4pm : 1776
*INFO* f_current_price4pmsi : 606.6318141334
*INFO* f_1_step_time : 11:30:00
*INFO* f_1_step_si : 3.0710424510
*INFO* shl_global_parm_short_weight_ratio : 10
previous_pred_les_level : 610.4647181109
previous_pred_les_trend : 4.0959386142
f_1_step_pred_les_level : 609.5156945044
f_1_step_pred_les_trend : 2.8976656874
f_1_step_pred_les : 612.4133601918
f_1_step_pred_adj_misc : -8.8098251018
pred_les + pred_adj_misc : 603.6035350899
f_1_step_pred_price_inc : 1853.6920798349
f_1_step_pred_price : 92252.6920798349
f_1_step_pred_price_rounded : 92300
f_1_step_pred_set_price_rounded : 92600
-------------------------------------------------
==>> Prediction Restuls in Python List : [91900, 91900, 92000, 92100, 92200, 92200, 92300, 92400, 92500, 92600]
[91900, 91900, 92000, 92100, 92200, 92200, 92300, 92400, 92500, 92600]
In [7]:
shl_pm.shl_data_pm_1_step
Out[7]:
ccyy-mm
f_1_step_pred_adj_misc
f_1_step_pred_les
f_1_step_pred_les_level
f_1_step_pred_les_trend
f_1_step_pred_price
f_1_step_pred_price_inc
f_1_step_pred_price_rounded
f_1_step_pred_set_price_rounded
f_1_step_si
f_1_step_time
f_current_bid
f_current_datetime
f_current_price4pm
f_current_price4pmsi
f_current_si
0
2017-07
0.000000
422.483383
422.483383
0.000000
90408.458677
9.458677
90400.0
90700.0
0.022388
11:29:01
90400.0
2017-07 11:29:00
1.0
422.483383
0.002367
1
2017-07
0.000000
124.987080
182.085965
-57.098885
90402.863447
3.863447
90400.0
90700.0
0.030911
11:29:02
90400.0
2017-07 11:29:01
1.0
44.666225
0.022388
2
2017-07
0.000000
-5.054063
66.044732
-71.098795
90398.809110
-0.190890
90400.0
90700.0
0.037770
11:29:03
90400.0
2017-07 11:29:02
1.0
32.351184
0.030911
3
2017-07
0.000000
-51.325580
15.008081
-66.333661
90396.654153
-2.345847
90400.0
90700.0
0.045705
11:29:04
90400.0
2017-07 11:29:03
1.0
26.476318
0.037770
4
2017-07
0.000000
-60.017096
-4.746773
-55.270323
90396.282431
-2.717569
90400.0
90700.0
0.045280
11:29:05
90400.0
2017-07 11:29:04
1.0
21.879334
0.045705
5
2017-07
0.000000
-50.639678
-7.777287
-42.862391
90394.910559
-4.089441
90400.0
90700.0
0.080756
11:29:06
90400.0
2017-07 11:29:05
1.0
22.084851
0.045280
6
2017-07
0.000000
-43.877474
-10.539602
-33.337872
90394.677994
-4.322006
90400.0
90700.0
0.098502
11:29:07
90400.0
2017-07 11:29:06
1.0
12.383032
0.080756
7
2017-07
0.000000
-34.672014
-9.499544
-25.172470
90394.279256
-4.720744
90400.0
90700.0
0.136154
11:29:08
90400.0
2017-07 11:29:07
1.0
10.152108
0.098502
8
2017-07
0.000000
-26.760255
-7.937687
-18.822568
90393.536513
-5.463487
90400.0
90700.0
0.204164
11:29:09
90400.0
2017-07 11:29:08
1.0
7.344608
0.136154
9
2017-07
0.000000
-20.654841
-6.616736
-14.038105
90394.227138
-4.772862
90400.0
90700.0
0.231077
11:29:10
90400.0
2017-07 11:29:09
1.0
4.898017
0.204164
10
2017-07
0.000000
325.733889
270.594763
55.139126
90493.796863
94.796863
90500.0
90800.0
0.291025
11:29:11
90500.0
2017-07 11:29:10
101.0
437.083427
0.231077
11
2017-07
0.000000
397.656427
339.296038
58.360390
90535.446795
136.446795
90500.0
90800.0
0.343127
11:29:12
90500.0
2017-07 11:29:11
101.0
347.048645
0.291025
12
2017-07
0.000000
374.673563
331.925499
42.748064
90530.538182
131.538182
90500.0
90800.0
0.351074
11:29:13
90500.0
2017-07 11:29:12
101.0
294.351356
0.343127
13
2017-07
0.000000
573.214382
500.564795
72.649588
90611.465091
212.465091
90600.0
90900.0
0.370656
11:29:14
90600.0
2017-07 11:29:13
201.0
572.528714
0.351074
14
2017-07
0.000000
621.507918
553.533022
67.974896
90648.315882
249.315882
90600.0
90900.0
0.401147
11:29:15
90600.0
2017-07 11:29:14
201.0
542.282454
0.370656
15
2017-07
0.000000
594.643909
544.871578
49.772331
90644.046963
245.046963
90600.0
90900.0
0.412090
11:29:16
90600.0
2017-07 11:29:15
201.0
501.063512
0.401147
16
2017-07
0.000000
751.329413
681.037085
70.292327
90739.779361
340.779361
90700.0
91000.0
0.453569
11:29:17
90700.0
2017-07 11:29:16
301.0
730.422507
0.412090
17
2017-07
0.000000
752.563612
695.525708
57.037903
90762.996569
363.996569
90800.0
91100.0
0.483675
11:29:18
90700.0
2017-07 11:29:17
301.0
663.626320
0.453569
18
2017-07
0.000000
707.045126
669.691015
37.354111
90755.734217
356.734217
90800.0
91100.0
0.504542
11:29:19
90700.0
2017-07 11:29:18
301.0
622.318083
0.483675
19
2017-07
0.000000
657.418283
636.758549
20.659734
90745.666546
346.666546
90700.0
91000.0
0.527315
11:29:20
90700.0
2017-07 11:29:19
301.0
596.580234
0.504542
20
2017-07
0.000000
609.886884
602.315170
7.571713
90744.620807
345.620807
90700.0
91000.0
0.566697
11:29:21
90700.0
2017-07 11:29:20
301.0
570.816265
0.527315
21
2017-07
0.000000
555.459304
559.787203
-4.327898
90720.268379
321.268379
90700.0
91000.0
0.578383
11:29:22
90700.0
2017-07 11:29:21
301.0
531.148438
0.566697
22
2017-07
0.000000
523.538135
533.162049
-9.623914
90708.075013
309.075013
90700.0
91000.0
0.590358
11:29:23
90700.0
2017-07 11:29:22
301.0
520.416142
0.578383
23
2017-07
0.000000
503.143928
514.834999
-11.691071
90711.119466
312.119466
90700.0
91000.0
0.620338
11:29:24
90700.0
2017-07 11:29:23
301.0
509.859976
0.590358
24
2017-07
0.000000
477.338691
491.738714
-14.400023
90715.190223
316.190223
90700.0
91000.0
0.662402
11:29:25
90700.0
2017-07 11:29:24
301.0
485.219087
0.620338
25
2017-07
0.000000
444.881807
462.747509
-17.865702
90701.661202
302.661202
90700.0
91000.0
0.680318
11:29:26
90700.0
2017-07 11:29:25
301.0
454.406669
0.662402
26
2017-07
0.000000
425.093411
443.328138
-18.234727
90697.158177
298.158177
90700.0
91000.0
0.701394
11:29:27
90700.0
2017-07 11:29:26
301.0
442.440005
0.680318
27
2017-07
0.000000
410.049007
427.671411
-17.622404
90696.741614
297.741614
90700.0
91000.0
0.726112
11:29:28
90700.0
2017-07 11:29:27
301.0
429.145087
0.701394
28
2017-07
0.000000
395.960050
412.904274
-16.944225
90692.496845
293.496845
90700.0
91000.0
0.741228
11:29:29
90700.0
2017-07 11:29:28
301.0
414.536447
0.726112
29
2017-07
0.000000
386.986438
402.400852
-15.414414
90702.736025
303.736025
90700.0
91000.0
0.784875
11:29:30
90700.0
2017-07 11:29:29
301.0
406.082644
0.741228
30
2017-07
0.000000
368.827170
384.768407
-15.941237
90689.761443
290.761443
90700.0
91000.0
0.788341
11:29:31
90700.0
2017-07 11:29:30
301.0
383.500501
0.784875
31
2017-07
0.000000
363.112378
377.090840
-13.978461
90694.715761
295.715761
90700.0
91000.0
0.814392
11:29:32
90700.0
2017-07 11:29:31
301.0
381.814648
0.788341
32
2017-07
0.000000
354.243070
367.240925
-12.997854
90694.828587
295.828587
90700.0
91000.0
0.835101
11:29:33
90700.0
2017-07 11:29:32
301.0
369.600949
0.814392
33
2017-07
0.000000
346.121291
358.183273
-12.061982
90699.102575
300.102575
90700.0
91000.0
0.867045
11:29:34
90700.0
2017-07 11:29:33
301.0
360.435634
0.835101
34
2017-07
0.000000
334.874307
346.779866
-11.905559
90707.624498
308.624498
90700.0
91000.0
0.921613
11:29:35
90700.0
2017-07 11:29:34
301.0
347.156330
0.867045
35
2017-07
0.000000
401.892576
398.650173
3.242403
90782.376971
383.376971
90800.0
91100.0
0.953929
11:29:36
90800.0
2017-07 11:29:35
401.0
435.106733
0.921613
36
2017-07
0.000000
419.681677
413.647306
6.034371
90809.434440
410.434440
90800.0
91100.0
0.977966
11:29:37
90800.0
2017-07 11:29:36
401.0
420.366728
0.953929
37
2017-07
0.000000
498.634793
478.605024
20.029769
90894.400465
495.400465
90900.0
91200.0
0.993514
11:29:38
90900.0
2017-07 11:29:37
501.0
512.287712
0.977966
38
2017-07
0.000000
602.357357
566.264312
36.093045
91020.965116
621.965116
91000.0
91300.0
1.032552
11:29:39
91000.0
2017-07 11:29:38
601.0
604.923757
0.993514
39
2017-07
0.000000
622.462725
589.438218
33.024508
91068.937666
669.937666
91100.0
91400.0
1.076270
11:29:40
91000.0
2017-07 11:29:39
601.0
582.053177
1.032552
40
2017-07
0.000000
605.051838
581.707467
23.344371
91066.544509
667.544509
91100.0
91400.0
1.103285
11:29:41
91000.0
2017-07 11:29:40
601.0
558.410307
1.076270
41
2017-07
0.000000
580.903770
566.674689
14.229080
91074.585049
675.585049
91100.0
91400.0
1.162990
11:29:42
91000.0
2017-07 11:29:41
601.0
544.736942
1.103285
42
2017-07
0.000000
544.634657
540.097766
4.536891
91091.661625
692.661625
91100.0
91400.0
1.271791
11:29:43
91000.0
2017-07 11:29:42
601.0
516.771599
1.162990
43
2017-07
0.000000
492.420799
498.776159
-6.355360
91081.820890
682.820890
91100.0
91400.0
1.386661
11:29:44
91000.0
2017-07 11:29:43
601.0
472.561806
1.271791
44
2017-07
0.000000
496.388347
500.762417
-4.374070
91112.354439
713.354439
91100.0
91400.0
1.437089
11:29:45
91100.0
2017-07 11:29:44
701.0
505.530784
1.386661
45
2017-07
0.000000
485.245049
490.918347
-5.673297
91160.165397
761.165397
91200.0
91500.0
1.568621
11:29:46
91100.0
2017-07 11:29:45
701.0
487.791499
1.437089
46
2017-07
0.000000
499.567722
501.403165
-1.835443
91218.985977
819.985977
91200.0
91500.0
1.641391
11:29:47
91200.0
2017-07 11:29:46
801.0
510.639719
1.568621
47
2017-07
0.000000
536.596327
530.972539
5.623789
91337.545227
938.545227
91300.0
91600.0
1.749071
11:29:48
91300.0
2017-07 11:29:47
901.0
548.924652
1.641391
48
2017-07
0.000000
570.336407
559.316219
11.020188
91419.750898
1020.750898
91400.0
91700.0
1.789735
11:29:49
91400.0
2017-07 11:29:48
1001.0
572.303719
1.749071
49
2017-07
0.000000
572.667029
563.314642
9.352388
91505.926340
1106.926340
91500.0
91800.0
1.932932
11:29:50
91400.0
2017-07 11:29:49
1001.0
559.300750
1.789735
50
2017-07
-0.880983
579.605235
570.716205
8.889030
91557.134451
1158.134451
91600.0
91900.0
2.001185
11:29:51
91500.0
2017-07 11:29:50
1101.0
569.601044
1.932932
In [8]:
shl_pm.shl_data_pm_k_step
Out[8]:
ccyy-mm
f_1_step_pred_adj_misc
f_1_step_pred_les
f_1_step_pred_les_level
f_1_step_pred_les_trend
f_1_step_pred_price
f_1_step_pred_price_inc
f_1_step_pred_price_rounded
f_1_step_pred_set_price_rounded
f_1_step_si
f_1_step_time
f_current_bid
f_current_datetime
f_current_price4pm
f_current_price4pmsi
f_current_si
0
2017-07
0.000000
422.483383
422.483383
0.000000
90408.458677
9.458677
90400.0
90700.0
0.022388
11:29:01
90400.000000
2017-07 11:29:00
1.000000
422.483383
0.002367
1
2017-07
0.000000
124.987080
182.085965
-57.098885
90402.863447
3.863447
90400.0
90700.0
0.030911
11:29:02
90400.000000
2017-07 11:29:01
1.000000
44.666225
0.022388
2
2017-07
0.000000
-5.054063
66.044732
-71.098795
90398.809110
-0.190890
90400.0
90700.0
0.037770
11:29:03
90400.000000
2017-07 11:29:02
1.000000
32.351184
0.030911
3
2017-07
0.000000
-51.325580
15.008081
-66.333661
90396.654153
-2.345847
90400.0
90700.0
0.045705
11:29:04
90400.000000
2017-07 11:29:03
1.000000
26.476318
0.037770
4
2017-07
0.000000
-60.017096
-4.746773
-55.270323
90396.282431
-2.717569
90400.0
90700.0
0.045280
11:29:05
90400.000000
2017-07 11:29:04
1.000000
21.879334
0.045705
5
2017-07
0.000000
-50.639678
-7.777287
-42.862391
90394.910559
-4.089441
90400.0
90700.0
0.080756
11:29:06
90400.000000
2017-07 11:29:05
1.000000
22.084851
0.045280
6
2017-07
0.000000
-43.877474
-10.539602
-33.337872
90394.677994
-4.322006
90400.0
90700.0
0.098502
11:29:07
90400.000000
2017-07 11:29:06
1.000000
12.383032
0.080756
7
2017-07
0.000000
-34.672014
-9.499544
-25.172470
90394.279256
-4.720744
90400.0
90700.0
0.136154
11:29:08
90400.000000
2017-07 11:29:07
1.000000
10.152108
0.098502
8
2017-07
0.000000
-26.760255
-7.937687
-18.822568
90393.536513
-5.463487
90400.0
90700.0
0.204164
11:29:09
90400.000000
2017-07 11:29:08
1.000000
7.344608
0.136154
9
2017-07
0.000000
-20.654841
-6.616736
-14.038105
90394.227138
-4.772862
90400.0
90700.0
0.231077
11:29:10
90400.000000
2017-07 11:29:09
1.000000
4.898017
0.204164
10
2017-07
0.000000
325.733889
270.594763
55.139126
90493.796863
94.796863
90500.0
90800.0
0.291025
11:29:11
90500.000000
2017-07 11:29:10
101.000000
437.083427
0.231077
11
2017-07
0.000000
397.656427
339.296038
58.360390
90535.446795
136.446795
90500.0
90800.0
0.343127
11:29:12
90500.000000
2017-07 11:29:11
101.000000
347.048645
0.291025
12
2017-07
0.000000
374.673563
331.925499
42.748064
90530.538182
131.538182
90500.0
90800.0
0.351074
11:29:13
90500.000000
2017-07 11:29:12
101.000000
294.351356
0.343127
13
2017-07
0.000000
573.214382
500.564795
72.649588
90611.465091
212.465091
90600.0
90900.0
0.370656
11:29:14
90600.000000
2017-07 11:29:13
201.000000
572.528714
0.351074
14
2017-07
0.000000
621.507918
553.533022
67.974896
90648.315882
249.315882
90600.0
90900.0
0.401147
11:29:15
90600.000000
2017-07 11:29:14
201.000000
542.282454
0.370656
15
2017-07
0.000000
594.643909
544.871578
49.772331
90644.046963
245.046963
90600.0
90900.0
0.412090
11:29:16
90600.000000
2017-07 11:29:15
201.000000
501.063512
0.401147
16
2017-07
0.000000
751.329413
681.037085
70.292327
90739.779361
340.779361
90700.0
91000.0
0.453569
11:29:17
90700.000000
2017-07 11:29:16
301.000000
730.422507
0.412090
17
2017-07
0.000000
752.563612
695.525708
57.037903
90762.996569
363.996569
90800.0
91100.0
0.483675
11:29:18
90700.000000
2017-07 11:29:17
301.000000
663.626320
0.453569
18
2017-07
0.000000
707.045126
669.691015
37.354111
90755.734217
356.734217
90800.0
91100.0
0.504542
11:29:19
90700.000000
2017-07 11:29:18
301.000000
622.318083
0.483675
19
2017-07
0.000000
657.418283
636.758549
20.659734
90745.666546
346.666546
90700.0
91000.0
0.527315
11:29:20
90700.000000
2017-07 11:29:19
301.000000
596.580234
0.504542
20
2017-07
0.000000
609.886884
602.315170
7.571713
90744.620807
345.620807
90700.0
91000.0
0.566697
11:29:21
90700.000000
2017-07 11:29:20
301.000000
570.816265
0.527315
21
2017-07
0.000000
555.459304
559.787203
-4.327898
90720.268379
321.268379
90700.0
91000.0
0.578383
11:29:22
90700.000000
2017-07 11:29:21
301.000000
531.148438
0.566697
22
2017-07
0.000000
523.538135
533.162049
-9.623914
90708.075013
309.075013
90700.0
91000.0
0.590358
11:29:23
90700.000000
2017-07 11:29:22
301.000000
520.416142
0.578383
23
2017-07
0.000000
503.143928
514.834999
-11.691071
90711.119466
312.119466
90700.0
91000.0
0.620338
11:29:24
90700.000000
2017-07 11:29:23
301.000000
509.859976
0.590358
24
2017-07
0.000000
477.338691
491.738714
-14.400023
90715.190223
316.190223
90700.0
91000.0
0.662402
11:29:25
90700.000000
2017-07 11:29:24
301.000000
485.219087
0.620338
25
2017-07
0.000000
444.881807
462.747509
-17.865702
90701.661202
302.661202
90700.0
91000.0
0.680318
11:29:26
90700.000000
2017-07 11:29:25
301.000000
454.406669
0.662402
26
2017-07
0.000000
425.093411
443.328138
-18.234727
90697.158177
298.158177
90700.0
91000.0
0.701394
11:29:27
90700.000000
2017-07 11:29:26
301.000000
442.440005
0.680318
27
2017-07
0.000000
410.049007
427.671411
-17.622404
90696.741614
297.741614
90700.0
91000.0
0.726112
11:29:28
90700.000000
2017-07 11:29:27
301.000000
429.145087
0.701394
28
2017-07
0.000000
395.960050
412.904274
-16.944225
90692.496845
293.496845
90700.0
91000.0
0.741228
11:29:29
90700.000000
2017-07 11:29:28
301.000000
414.536447
0.726112
29
2017-07
0.000000
386.986438
402.400852
-15.414414
90702.736025
303.736025
90700.0
91000.0
0.784875
11:29:30
90700.000000
2017-07 11:29:29
301.000000
406.082644
0.741228
30
2017-07
0.000000
368.827170
384.768407
-15.941237
90689.761443
290.761443
90700.0
91000.0
0.788341
11:29:31
90700.000000
2017-07 11:29:30
301.000000
383.500501
0.784875
31
2017-07
0.000000
363.112378
377.090840
-13.978461
90694.715761
295.715761
90700.0
91000.0
0.814392
11:29:32
90700.000000
2017-07 11:29:31
301.000000
381.814648
0.788341
32
2017-07
0.000000
354.243070
367.240925
-12.997854
90694.828587
295.828587
90700.0
91000.0
0.835101
11:29:33
90700.000000
2017-07 11:29:32
301.000000
369.600949
0.814392
33
2017-07
0.000000
346.121291
358.183273
-12.061982
90699.102575
300.102575
90700.0
91000.0
0.867045
11:29:34
90700.000000
2017-07 11:29:33
301.000000
360.435634
0.835101
34
2017-07
0.000000
334.874307
346.779866
-11.905559
90707.624498
308.624498
90700.0
91000.0
0.921613
11:29:35
90700.000000
2017-07 11:29:34
301.000000
347.156330
0.867045
35
2017-07
0.000000
401.892576
398.650173
3.242403
90782.376971
383.376971
90800.0
91100.0
0.953929
11:29:36
90800.000000
2017-07 11:29:35
401.000000
435.106733
0.921613
36
2017-07
0.000000
419.681677
413.647306
6.034371
90809.434440
410.434440
90800.0
91100.0
0.977966
11:29:37
90800.000000
2017-07 11:29:36
401.000000
420.366728
0.953929
37
2017-07
0.000000
498.634793
478.605024
20.029769
90894.400465
495.400465
90900.0
91200.0
0.993514
11:29:38
90900.000000
2017-07 11:29:37
501.000000
512.287712
0.977966
38
2017-07
0.000000
602.357357
566.264312
36.093045
91020.965116
621.965116
91000.0
91300.0
1.032552
11:29:39
91000.000000
2017-07 11:29:38
601.000000
604.923757
0.993514
39
2017-07
0.000000
622.462725
589.438218
33.024508
91068.937666
669.937666
91100.0
91400.0
1.076270
11:29:40
91000.000000
2017-07 11:29:39
601.000000
582.053177
1.032552
40
2017-07
0.000000
605.051838
581.707467
23.344371
91066.544509
667.544509
91100.0
91400.0
1.103285
11:29:41
91000.000000
2017-07 11:29:40
601.000000
558.410307
1.076270
41
2017-07
0.000000
580.903770
566.674689
14.229080
91074.585049
675.585049
91100.0
91400.0
1.162990
11:29:42
91000.000000
2017-07 11:29:41
601.000000
544.736942
1.103285
42
2017-07
0.000000
544.634657
540.097766
4.536891
91091.661625
692.661625
91100.0
91400.0
1.271791
11:29:43
91000.000000
2017-07 11:29:42
601.000000
516.771599
1.162990
43
2017-07
0.000000
492.420799
498.776159
-6.355360
91081.820890
682.820890
91100.0
91400.0
1.386661
11:29:44
91000.000000
2017-07 11:29:43
601.000000
472.561806
1.271791
44
2017-07
0.000000
496.388347
500.762417
-4.374070
91112.354439
713.354439
91100.0
91400.0
1.437089
11:29:45
91100.000000
2017-07 11:29:44
701.000000
505.530784
1.386661
45
2017-07
0.000000
485.245049
490.918347
-5.673297
91160.165397
761.165397
91200.0
91500.0
1.568621
11:29:46
91100.000000
2017-07 11:29:45
701.000000
487.791499
1.437089
46
2017-07
0.000000
499.567722
501.403165
-1.835443
91218.985977
819.985977
91200.0
91500.0
1.641391
11:29:47
91200.000000
2017-07 11:29:46
801.000000
510.639719
1.568621
47
2017-07
0.000000
536.596327
530.972539
5.623789
91337.545227
938.545227
91300.0
91600.0
1.749071
11:29:48
91300.000000
2017-07 11:29:47
901.000000
548.924652
1.641391
48
2017-07
0.000000
570.336407
559.316219
11.020188
91419.750898
1020.750898
91400.0
91700.0
1.789735
11:29:49
91400.000000
2017-07 11:29:48
1001.000000
572.303719
1.749071
49
2017-07
0.000000
572.667029
563.314642
9.352388
91505.926340
1106.926340
91500.0
91800.0
1.932932
11:29:50
91400.000000
2017-07 11:29:49
1001.000000
559.300750
1.789735
50
2017-07
-0.880983
579.605235
570.716205
8.889030
91557.134451
1158.134451
91600.0
91900.0
2.001185
11:29:51
91500.000000
2017-07 11:29:50
1101.000000
569.601044
1.932932
51
2017-07
-1.761965
587.800573
579.044684
8.755889
91609.816482
1210.816482
91600.0
91900.0
2.066104
11:29:52
91557.134451
2017-07 11:29:51
1158.134451
578.724253
2.001185
52
2017-07
-2.642948
595.169076
586.679470
8.489606
91683.720820
1284.720820
91700.0
92000.0
2.168210
11:29:53
91609.816482
2017-07 11:29:52
1210.816482
586.038608
2.066104
53
2017-07
-3.523930
601.577604
593.487422
8.090182
91768.751578
1369.751578
91800.0
92100.0
2.290349
11:29:54
91683.720820
2017-07 11:29:53
1284.720820
592.526129
2.168210
54
2017-07
-4.404913
606.893014
599.335398
7.557616
91853.166552
1454.166552
91900.0
92200.0
2.413602
11:29:55
91768.751578
2017-07 11:29:54
1369.751578
598.053674
2.290349
55
2017-07
-5.285895
610.982166
604.090258
6.891909
91943.947695
1544.947695
91900.0
92200.0
2.550697
11:29:56
91853.166552
2017-07 11:29:55
1454.166552
602.488102
2.413602
56
2017-07
-6.166878
613.711918
607.618858
6.093060
92042.646817
1643.646817
92000.0
92300.0
2.705391
11:29:57
91943.947695
2017-07 11:29:56
1544.947695
605.696271
2.550697
57
2017-07
-7.047860
614.949129
609.788059
5.161070
92085.651711
1686.651711
92100.0
92400.0
2.774549
11:29:58
92042.646817
2017-07 11:29:57
1643.646817
607.545041
2.705391
58
2017-07
-7.928843
614.560657
610.464718
4.095939
92175.935610
1776.935610
92200.0
92500.0
2.929183
11:29:59
92085.651711
2017-07 11:29:58
1686.651711
607.901269
2.774549
59
2017-07
-8.809825
612.413360
609.515695
2.897666
92252.692080
1853.692080
92300.0
92600.0
3.071042
11:30:00
92175.935610
2017-07 11:29:59
1776.935610
606.631814
2.929183
In [9]:
print(shl_sm_prediction_list_local_1)
[91800]
In [10]:
print(shl_sm_prediction_list_local_k)
[91900, 91900, 92000, 92100, 92200, 92200, 92300, 92400, 92500, 92600]
In [11]:
shl_pm.shl_data_pm_1_step.tail(11)
Out[11]:
ccyy-mm
f_1_step_pred_adj_misc
f_1_step_pred_les
f_1_step_pred_les_level
f_1_step_pred_les_trend
f_1_step_pred_price
f_1_step_pred_price_inc
f_1_step_pred_price_rounded
f_1_step_pred_set_price_rounded
f_1_step_si
f_1_step_time
f_current_bid
f_current_datetime
f_current_price4pm
f_current_price4pmsi
f_current_si
40
2017-07
0.000000
605.051838
581.707467
23.344371
91066.544509
667.544509
91100.0
91400.0
1.103285
11:29:41
91000.0
2017-07 11:29:40
601.0
558.410307
1.076270
41
2017-07
0.000000
580.903770
566.674689
14.229080
91074.585049
675.585049
91100.0
91400.0
1.162990
11:29:42
91000.0
2017-07 11:29:41
601.0
544.736942
1.103285
42
2017-07
0.000000
544.634657
540.097766
4.536891
91091.661625
692.661625
91100.0
91400.0
1.271791
11:29:43
91000.0
2017-07 11:29:42
601.0
516.771599
1.162990
43
2017-07
0.000000
492.420799
498.776159
-6.355360
91081.820890
682.820890
91100.0
91400.0
1.386661
11:29:44
91000.0
2017-07 11:29:43
601.0
472.561806
1.271791
44
2017-07
0.000000
496.388347
500.762417
-4.374070
91112.354439
713.354439
91100.0
91400.0
1.437089
11:29:45
91100.0
2017-07 11:29:44
701.0
505.530784
1.386661
45
2017-07
0.000000
485.245049
490.918347
-5.673297
91160.165397
761.165397
91200.0
91500.0
1.568621
11:29:46
91100.0
2017-07 11:29:45
701.0
487.791499
1.437089
46
2017-07
0.000000
499.567722
501.403165
-1.835443
91218.985977
819.985977
91200.0
91500.0
1.641391
11:29:47
91200.0
2017-07 11:29:46
801.0
510.639719
1.568621
47
2017-07
0.000000
536.596327
530.972539
5.623789
91337.545227
938.545227
91300.0
91600.0
1.749071
11:29:48
91300.0
2017-07 11:29:47
901.0
548.924652
1.641391
48
2017-07
0.000000
570.336407
559.316219
11.020188
91419.750898
1020.750898
91400.0
91700.0
1.789735
11:29:49
91400.0
2017-07 11:29:48
1001.0
572.303719
1.749071
49
2017-07
0.000000
572.667029
563.314642
9.352388
91505.926340
1106.926340
91500.0
91800.0
1.932932
11:29:50
91400.0
2017-07 11:29:49
1001.0
559.300750
1.789735
50
2017-07
-0.880983
579.605235
570.716205
8.889030
91557.134451
1158.134451
91600.0
91900.0
2.001185
11:29:51
91500.0
2017-07 11:29:50
1101.0
569.601044
1.932932
In [12]:
shl_pm.shl_data_pm_k_step.tail(20)
Out[12]:
ccyy-mm
f_1_step_pred_adj_misc
f_1_step_pred_les
f_1_step_pred_les_level
f_1_step_pred_les_trend
f_1_step_pred_price
f_1_step_pred_price_inc
f_1_step_pred_price_rounded
f_1_step_pred_set_price_rounded
f_1_step_si
f_1_step_time
f_current_bid
f_current_datetime
f_current_price4pm
f_current_price4pmsi
f_current_si
40
2017-07
0.000000
605.051838
581.707467
23.344371
91066.544509
667.544509
91100.0
91400.0
1.103285
11:29:41
91000.000000
2017-07 11:29:40
601.000000
558.410307
1.076270
41
2017-07
0.000000
580.903770
566.674689
14.229080
91074.585049
675.585049
91100.0
91400.0
1.162990
11:29:42
91000.000000
2017-07 11:29:41
601.000000
544.736942
1.103285
42
2017-07
0.000000
544.634657
540.097766
4.536891
91091.661625
692.661625
91100.0
91400.0
1.271791
11:29:43
91000.000000
2017-07 11:29:42
601.000000
516.771599
1.162990
43
2017-07
0.000000
492.420799
498.776159
-6.355360
91081.820890
682.820890
91100.0
91400.0
1.386661
11:29:44
91000.000000
2017-07 11:29:43
601.000000
472.561806
1.271791
44
2017-07
0.000000
496.388347
500.762417
-4.374070
91112.354439
713.354439
91100.0
91400.0
1.437089
11:29:45
91100.000000
2017-07 11:29:44
701.000000
505.530784
1.386661
45
2017-07
0.000000
485.245049
490.918347
-5.673297
91160.165397
761.165397
91200.0
91500.0
1.568621
11:29:46
91100.000000
2017-07 11:29:45
701.000000
487.791499
1.437089
46
2017-07
0.000000
499.567722
501.403165
-1.835443
91218.985977
819.985977
91200.0
91500.0
1.641391
11:29:47
91200.000000
2017-07 11:29:46
801.000000
510.639719
1.568621
47
2017-07
0.000000
536.596327
530.972539
5.623789
91337.545227
938.545227
91300.0
91600.0
1.749071
11:29:48
91300.000000
2017-07 11:29:47
901.000000
548.924652
1.641391
48
2017-07
0.000000
570.336407
559.316219
11.020188
91419.750898
1020.750898
91400.0
91700.0
1.789735
11:29:49
91400.000000
2017-07 11:29:48
1001.000000
572.303719
1.749071
49
2017-07
0.000000
572.667029
563.314642
9.352388
91505.926340
1106.926340
91500.0
91800.0
1.932932
11:29:50
91400.000000
2017-07 11:29:49
1001.000000
559.300750
1.789735
50
2017-07
-0.880983
579.605235
570.716205
8.889030
91557.134451
1158.134451
91600.0
91900.0
2.001185
11:29:51
91500.000000
2017-07 11:29:50
1101.000000
569.601044
1.932932
51
2017-07
-1.761965
587.800573
579.044684
8.755889
91609.816482
1210.816482
91600.0
91900.0
2.066104
11:29:52
91557.134451
2017-07 11:29:51
1158.134451
578.724253
2.001185
52
2017-07
-2.642948
595.169076
586.679470
8.489606
91683.720820
1284.720820
91700.0
92000.0
2.168210
11:29:53
91609.816482
2017-07 11:29:52
1210.816482
586.038608
2.066104
53
2017-07
-3.523930
601.577604
593.487422
8.090182
91768.751578
1369.751578
91800.0
92100.0
2.290349
11:29:54
91683.720820
2017-07 11:29:53
1284.720820
592.526129
2.168210
54
2017-07
-4.404913
606.893014
599.335398
7.557616
91853.166552
1454.166552
91900.0
92200.0
2.413602
11:29:55
91768.751578
2017-07 11:29:54
1369.751578
598.053674
2.290349
55
2017-07
-5.285895
610.982166
604.090258
6.891909
91943.947695
1544.947695
91900.0
92200.0
2.550697
11:29:56
91853.166552
2017-07 11:29:55
1454.166552
602.488102
2.413602
56
2017-07
-6.166878
613.711918
607.618858
6.093060
92042.646817
1643.646817
92000.0
92300.0
2.705391
11:29:57
91943.947695
2017-07 11:29:56
1544.947695
605.696271
2.550697
57
2017-07
-7.047860
614.949129
609.788059
5.161070
92085.651711
1686.651711
92100.0
92400.0
2.774549
11:29:58
92042.646817
2017-07 11:29:57
1643.646817
607.545041
2.705391
58
2017-07
-7.928843
614.560657
610.464718
4.095939
92175.935610
1776.935610
92200.0
92500.0
2.929183
11:29:59
92085.651711
2017-07 11:29:58
1686.651711
607.901269
2.774549
59
2017-07
-8.809825
612.413360
609.515695
2.897666
92252.692080
1853.692080
92300.0
92600.0
3.071042
11:30:00
92175.935610
2017-07 11:29:59
1776.935610
606.631814
2.929183
In [ ]:
In [13]:
%matplotlib inline
import matplotlib.pyplot as plt
In [14]:
shl_data_pm_k_step_local = shl_pm.shl_data_pm_k_step.copy()
shl_data_pm_k_step_local.index = shl_data_pm_k_step_local.index + 1
shl_data_pm_k_step_local
Out[14]:
ccyy-mm
f_1_step_pred_adj_misc
f_1_step_pred_les
f_1_step_pred_les_level
f_1_step_pred_les_trend
f_1_step_pred_price
f_1_step_pred_price_inc
f_1_step_pred_price_rounded
f_1_step_pred_set_price_rounded
f_1_step_si
f_1_step_time
f_current_bid
f_current_datetime
f_current_price4pm
f_current_price4pmsi
f_current_si
1
2017-07
0.000000
422.483383
422.483383
0.000000
90408.458677
9.458677
90400.0
90700.0
0.022388
11:29:01
90400.000000
2017-07 11:29:00
1.000000
422.483383
0.002367
2
2017-07
0.000000
124.987080
182.085965
-57.098885
90402.863447
3.863447
90400.0
90700.0
0.030911
11:29:02
90400.000000
2017-07 11:29:01
1.000000
44.666225
0.022388
3
2017-07
0.000000
-5.054063
66.044732
-71.098795
90398.809110
-0.190890
90400.0
90700.0
0.037770
11:29:03
90400.000000
2017-07 11:29:02
1.000000
32.351184
0.030911
4
2017-07
0.000000
-51.325580
15.008081
-66.333661
90396.654153
-2.345847
90400.0
90700.0
0.045705
11:29:04
90400.000000
2017-07 11:29:03
1.000000
26.476318
0.037770
5
2017-07
0.000000
-60.017096
-4.746773
-55.270323
90396.282431
-2.717569
90400.0
90700.0
0.045280
11:29:05
90400.000000
2017-07 11:29:04
1.000000
21.879334
0.045705
6
2017-07
0.000000
-50.639678
-7.777287
-42.862391
90394.910559
-4.089441
90400.0
90700.0
0.080756
11:29:06
90400.000000
2017-07 11:29:05
1.000000
22.084851
0.045280
7
2017-07
0.000000
-43.877474
-10.539602
-33.337872
90394.677994
-4.322006
90400.0
90700.0
0.098502
11:29:07
90400.000000
2017-07 11:29:06
1.000000
12.383032
0.080756
8
2017-07
0.000000
-34.672014
-9.499544
-25.172470
90394.279256
-4.720744
90400.0
90700.0
0.136154
11:29:08
90400.000000
2017-07 11:29:07
1.000000
10.152108
0.098502
9
2017-07
0.000000
-26.760255
-7.937687
-18.822568
90393.536513
-5.463487
90400.0
90700.0
0.204164
11:29:09
90400.000000
2017-07 11:29:08
1.000000
7.344608
0.136154
10
2017-07
0.000000
-20.654841
-6.616736
-14.038105
90394.227138
-4.772862
90400.0
90700.0
0.231077
11:29:10
90400.000000
2017-07 11:29:09
1.000000
4.898017
0.204164
11
2017-07
0.000000
325.733889
270.594763
55.139126
90493.796863
94.796863
90500.0
90800.0
0.291025
11:29:11
90500.000000
2017-07 11:29:10
101.000000
437.083427
0.231077
12
2017-07
0.000000
397.656427
339.296038
58.360390
90535.446795
136.446795
90500.0
90800.0
0.343127
11:29:12
90500.000000
2017-07 11:29:11
101.000000
347.048645
0.291025
13
2017-07
0.000000
374.673563
331.925499
42.748064
90530.538182
131.538182
90500.0
90800.0
0.351074
11:29:13
90500.000000
2017-07 11:29:12
101.000000
294.351356
0.343127
14
2017-07
0.000000
573.214382
500.564795
72.649588
90611.465091
212.465091
90600.0
90900.0
0.370656
11:29:14
90600.000000
2017-07 11:29:13
201.000000
572.528714
0.351074
15
2017-07
0.000000
621.507918
553.533022
67.974896
90648.315882
249.315882
90600.0
90900.0
0.401147
11:29:15
90600.000000
2017-07 11:29:14
201.000000
542.282454
0.370656
16
2017-07
0.000000
594.643909
544.871578
49.772331
90644.046963
245.046963
90600.0
90900.0
0.412090
11:29:16
90600.000000
2017-07 11:29:15
201.000000
501.063512
0.401147
17
2017-07
0.000000
751.329413
681.037085
70.292327
90739.779361
340.779361
90700.0
91000.0
0.453569
11:29:17
90700.000000
2017-07 11:29:16
301.000000
730.422507
0.412090
18
2017-07
0.000000
752.563612
695.525708
57.037903
90762.996569
363.996569
90800.0
91100.0
0.483675
11:29:18
90700.000000
2017-07 11:29:17
301.000000
663.626320
0.453569
19
2017-07
0.000000
707.045126
669.691015
37.354111
90755.734217
356.734217
90800.0
91100.0
0.504542
11:29:19
90700.000000
2017-07 11:29:18
301.000000
622.318083
0.483675
20
2017-07
0.000000
657.418283
636.758549
20.659734
90745.666546
346.666546
90700.0
91000.0
0.527315
11:29:20
90700.000000
2017-07 11:29:19
301.000000
596.580234
0.504542
21
2017-07
0.000000
609.886884
602.315170
7.571713
90744.620807
345.620807
90700.0
91000.0
0.566697
11:29:21
90700.000000
2017-07 11:29:20
301.000000
570.816265
0.527315
22
2017-07
0.000000
555.459304
559.787203
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2017-07
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2017-07
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2017-07
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2017-07
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2017-07
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2017-07 11:29:26
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2017-07
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2017-07 11:29:27
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2017-07
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2017-07 11:29:28
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2017-07
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2017-07 11:29:29
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2017-07
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2017-07 11:29:30
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2017-07
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2017-07
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2017-07
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2017-07
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2017-07
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2017-07
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2017-07 11:29:36
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2017-07
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2017-07 11:29:37
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2017-07
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2017-07 11:29:38
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2017-07
0.000000
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2017-07
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2017-07
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2017-07
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2017-07
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2017-07
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2017-07
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2017-07
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2017-07
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2017-07
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2017-07
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2017-07
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2017-07
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2017-07
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2017-07
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2017-07
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2017-07
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2017-07
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2017-07
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2017-07
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2017-07 11:29:58
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2017-07
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2017-07 11:29:59
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In [15]:
# bid is predicted bid-price from shl_pm
plt.figure(figsize=(12,6))
plt.plot(shl_pm.shl_data_pm_k_step['f_current_bid'])
# plt.plot(shl_data_pm_1_step_k_step['f_1_step_pred_price'].shift(1))
plt.plot(shl_data_pm_k_step_local['f_1_step_pred_price'])
# bid is actual bid-price from raw dataset
shl_data_actual_bid_local = shl_sm_data[shl_sm_parm_ccyy_mm_offset:shl_sm_parm_ccyy_mm_offset+61].copy()
shl_data_actual_bid_local.reset_index(inplace=True)
plt.figure(figsize=(12,6))
plt.plot(shl_data_actual_bid_local['bid-price'])
plt.plot(shl_data_pm_k_step_local['f_1_step_pred_price'])
plt.figure(figsize=(12,6))
plt.plot(shl_data_actual_bid_local['bid-price'])
plt.plot(shl_data_pm_k_step_local['f_1_step_pred_price_rounded'])
Out[15]:
[<matplotlib.lines.Line2D at 0x7fad6f6a42b0>]
In [16]:
# pd.concat([shl_data_actual_bid_local['bid-price'], shl_data_pm_k_step_local['f_1_step_pred_price'], shl_data_pm_k_step_local['f_1_step_pred_price'] - shl_data_actual_bid_local['bid-price']], axis=1, join='inner')
pd.concat([shl_data_actual_bid_local['bid-price'].tail(11), shl_data_pm_k_step_local['f_1_step_pred_price'].tail(11), shl_data_pm_k_step_local['f_1_step_pred_price'].tail(11) - shl_data_actual_bid_local['bid-price'].tail(11)], axis=1, join='inner')
Out[16]:
bid-price
f_1_step_pred_price
0
50
91500
91505.926340
5.926340
51
91600
91557.134451
-42.865549
52
91700
91609.816482
-90.183518
53
91800
91683.720820
-116.279180
54
91900
91768.751578
-131.248422
55
92000
91853.166552
-146.833448
56
92100
91943.947695
-156.052305
57
92100
92042.646817
-57.353183
58
92100
92085.651711
-14.348289
59
92200
92175.935610
-24.064390
60
92200
92252.692080
52.692080
Content source: telescopeuser/uat_shl
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