In [1]:
!pip install keras
!pip install ccs
!pip install cfscrape
!pip install retrying
!pip install boto
!pip install pymongo
!pip install xgboost
Requirement already satisfied (use --upgrade to upgrade): keras in /opt/conda/lib/python3.5/site-packages
Requirement already satisfied (use --upgrade to upgrade): six in /opt/conda/lib/python3.5/site-packages (from keras)
Requirement already satisfied (use --upgrade to upgrade): pyyaml in /opt/conda/lib/python3.5/site-packages (from keras)
Requirement already satisfied (use --upgrade to upgrade): theano in /opt/conda/lib/python3.5/site-packages (from keras)
Requirement already satisfied (use --upgrade to upgrade): scipy>=0.14 in /opt/conda/lib/python3.5/site-packages (from theano->keras)
Requirement already satisfied (use --upgrade to upgrade): numpy>=1.9.1 in /opt/conda/lib/python3.5/site-packages (from theano->keras)
You are using pip version 8.1.2, however version 9.0.1 is available.
You should consider upgrading via the 'pip install --upgrade pip' command.
Requirement already satisfied (use --upgrade to upgrade): ccs in /opt/conda/lib/python3.5/site-packages
Requirement already satisfied (use --upgrade to upgrade): jsonschema in /opt/conda/lib/python3.5/site-packages (from ccs)
You are using pip version 8.1.2, however version 9.0.1 is available.
You should consider upgrading via the 'pip install --upgrade pip' command.
Requirement already satisfied (use --upgrade to upgrade): cfscrape in /opt/conda/lib/python3.5/site-packages
Requirement already satisfied (use --upgrade to upgrade): requests>=2.0.0 in /opt/conda/lib/python3.5/site-packages (from cfscrape)
Requirement already satisfied (use --upgrade to upgrade): PyExecJS>=1.4.0 in /opt/conda/lib/python3.5/site-packages (from cfscrape)
Requirement already satisfied (use --upgrade to upgrade): six==1.10.0 in /opt/conda/lib/python3.5/site-packages (from PyExecJS>=1.4.0->cfscrape)
You are using pip version 8.1.2, however version 9.0.1 is available.
You should consider upgrading via the 'pip install --upgrade pip' command.
Requirement already satisfied (use --upgrade to upgrade): retrying in /opt/conda/lib/python3.5/site-packages
Requirement already satisfied (use --upgrade to upgrade): six>=1.7.0 in /opt/conda/lib/python3.5/site-packages (from retrying)
You are using pip version 8.1.2, however version 9.0.1 is available.
You should consider upgrading via the 'pip install --upgrade pip' command.
Collecting boto
Downloading boto-2.48.0-py2.py3-none-any.whl (1.4MB)
100% |████████████████████████████████| 1.4MB 779kB/s ta 0:00:01 89% |████████████████████████████▋ | 1.2MB 1.5MB/s eta 0:00:01
Installing collected packages: boto
Successfully installed boto-2.48.0
You are using pip version 8.1.2, however version 9.0.1 is available.
You should consider upgrading via the 'pip install --upgrade pip' command.
Collecting pymongo
Downloading pymongo-3.4.0-cp35-cp35m-manylinux1_x86_64.whl (359kB)
100% |████████████████████████████████| 368kB 1.2MB/s ta 0:00:01
Installing collected packages: pymongo
Successfully installed pymongo-3.4.0
You are using pip version 8.1.2, however version 9.0.1 is available.
You should consider upgrading via the 'pip install --upgrade pip' command.
Requirement already satisfied (use --upgrade to upgrade): xgboost in /opt/conda/lib/python3.5/site-packages
Requirement already satisfied (use --upgrade to upgrade): numpy in /opt/conda/lib/python3.5/site-packages (from xgboost)
Requirement already satisfied (use --upgrade to upgrade): scipy in /opt/conda/lib/python3.5/site-packages (from xgboost)
Requirement already satisfied (use --upgrade to upgrade): scikit-learn in /opt/conda/lib/python3.5/site-packages (from xgboost)
You are using pip version 8.1.2, however version 9.0.1 is available.
You should consider upgrading via the 'pip install --upgrade pip' command.
In [2]:
import pandas as pd
import glob
import os
import shutil
from datetime import datetime, date, timedelta
import json
import copy
import numpy as np
import sys
import ccs
from retrying import retry
In [3]:
from sklearn.cross_validation import cross_val_score
from sklearn.cross_validation import KFold
from sklearn.preprocessing import StandardScaler
from sklearn.pipeline import Pipeline
import sklearn.metrics
from sklearn.cross_validation import StratifiedKFold
from sklearn.model_selection import GridSearchCV
from sklearn.cross_validation import train_test_split
from sklearn.externals import joblib
from sklearn.metrics import mean_squared_error
/opt/conda/lib/python3.5/site-packages/sklearn/cross_validation.py:44: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.
"This module will be removed in 0.20.", DeprecationWarning)
In [4]:
import matplotlib.pyplot as plt
%matplotlib inline
In [5]:
days_back_file = -1*21
In [6]:
pd.set_option('display.max_rows', 300)
pd.set_option('display.max_columns', 50)
In [7]:
first_available = date(2017, 6, 27) # start date
yesterday = date.today() - timedelta(1) # end date
# yesterday = date(2017, 7, 21) # end date
delta = yesterday - first_available # timedelta
candidate_dates = [(first_available + timedelta(days=i)).strftime('%Y%m%d') for i in range(delta.days + 1)]
# We use only the last 2 weeks to calculate our model
dates_list = candidate_dates[days_back_file:]
print('Retrieving data for dates: {}'.format(dates_list))
Retrieving data for dates: ['20170711', '20170712', '20170713', '20170714', '20170715', '20170716', '20170717', '20170718', '20170719', '20170720', '20170721', '20170722', '20170723', '20170724', '20170725', '20170726', '20170727', '20170728', '20170729', '20170730', '20170731']
In [8]:
pair = 'BTC-ETH'
x_features = 50
y_select = [1, 2, 3, 4, 5, 6]
#y_select_2 = 3
n_hops_back = 5
# feature_selector = 'f_regression'
feature_selector = 'mutual_info_regression'
n_columns_original_data = 166
n_columns_f_selection = 50
datapoints_back = 15
datapoints_forward = 6
In [9]:
df=pd.read_csv('../data/allticker_bittrex_fe_{}_{}_{}_fileslb_{}_datalb_{}.csv.gz'.format(
pair, n_columns_original_data, yesterday.strftime('%Y%m%d'), days_back_file*-1, datapoints_back), compression='gzip')
In [10]:
df
Out[10]:
ask
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high
last
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open_buy_orders
open_sell_orders
prev_day
volume
ask_prev1
ask_prev2
ask_prev3
base_volume_prev1
base_volume_prev2
base_volume_prev3
bid_prev1
bid_prev2
bid_prev3
high_prev1
high_prev2
high_prev3
last_prev1
last_prev2
last_prev3
...
high_prev15
last_prev13
last_prev14
last_prev15
low_prev13
low_prev14
low_prev15
open_buy_orders_prev13
open_buy_orders_prev14
open_buy_orders_prev15
open_sell_orders_prev13
open_sell_orders_prev14
open_sell_orders_prev15
prev_day_prev13
prev_day_prev14
prev_day_prev15
volume_prev13
volume_prev14
volume_prev15
last_next1
last_next2
last_next3
last_next4
last_next5
last_next6
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13171
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0.070169
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13174
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...
0.072700
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0.067500
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13206.0
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43904.507206
43865.216581
43846.783908
0.070300
0.069920
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0.070000
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0.070000
315609
0.070169
3151.905264
0.069998
0.072700
0.070169
0.067500
2647
13173
0.070775
44885.538584
0.070169
0.069999
0.069999
3151.904690
3150.217978
3144.310073
0.069998
0.069913
0.069998
0.072700
0.072700
0.072700
0.069998
0.069999
0.069998
...
0.072700
0.069999
0.069992
0.069887
0.067500
0.067500
0.067500
2669.0
2668.0
2665.0
13206.0
13205.0
13204.0
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0.071476
43904.507206
43870.255468
43847.673425
0.070300
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315610
0.070169
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0.069998
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13173
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...
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0.071476
44199.101885
43881.014520
43848.791961
0.069920
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0.070000
0.069920
315611
0.070300
3155.395062
0.069998
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0.070300
0.067500
2646
13156
0.070775
44935.123283
0.070169
0.069998
0.069999
3151.905264
3152.332624
3143.689687
0.069998
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0.069998
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0.069998
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...
0.072700
0.069315
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0.067500
2640.0
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13197.0
0.071463
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44199.101885
43886.800275
43853.659400
0.069920
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0.069920
315612
0.070300
3155.395062
0.069998
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0.070300
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2646
13156
0.070775
44935.123283
0.070300
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3155.395062
3152.332624
3142.983965
0.069998
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0.070000
...
0.072700
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44199.101885
43887.943265
43853.659400
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0.067500
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44198.448454
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43846.661265
0.069920
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0.070000
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0.070000
0.069920
315616 rows × 166 columns
In [11]:
# TODO: Draw 3 lines with last in Y and timestamp in x
# TODO: Draw more lines with bid, ask, volumes, to try to find correlations
# TODO: Basic regression and accuracy. / graphs
# TODO: Get Orderbook/historical trades/chart... to try to find proxies for price.
plt.figure(figsize=(15,10))
plt.style.use('ggplot')
plt.plot(df.index, df['last'])
#Change font size of x-axis
plt.tick_params(axis='x', which='major', labelsize=6)
In [12]:
n=2
plt.figure(figsize=(15,10))
line1,=plt.plot(df['last_prev{}'.format(n)],linewidth=2,color='r',label='ASK_PREV{}'.format(n))
# line2,=plt.plot(df['last_prev{}'.format(n)],linewidth=2,color='b',label='LAST_PREV{}'.format(n))
line3,=plt.plot(df['last'],linewidth=2,color='y',label='LAST')
line4,=plt.plot(df['last_next{}'.format(n)],linewidth=2,color='g',label='BID_PREV{}'.format(n))
plt.title("TIME SERIES")
plt.legend([line1, line3, line4])
plt.show()
# print('Accuracy Train:',1-np.mean(abs(trainPredict-trainY)))
# print('Accuracy Test:',1-np.mean(abs(testPredict-testY)))
In [13]:
from sklearn.preprocessing import MinMaxScaler
In [14]:
# We now need it to be in the same order as the original model.
dataset = df
In [15]:
dataset.shape
Out[15]:
(315616, 166)
In [16]:
dataset_np = dataset.values
dataset_np = dataset_np.astype('float32')
dataset_np[:10,102]
Out[16]:
array([ 0.09035811, 0.09035811, 0.09035811, 0.09035811, 0.09035811,
0.09035811, 0.09035811, 0.09035811, 0.09035811, 0.09035811], dtype=float32)
In [17]:
def create_dataset(_dataset, y_position, _datapoints_forward):
dataX, dataY = _dataset[:,0:_dataset.shape[1]-_datapoints_forward], _dataset[:,y_position].reshape(_dataset.shape[0],1)
return dataX, dataY
In [18]:
n_predictions = len(y_select)
y_position = [None] * n_predictions
datapoints_forward_prediction = [None] * n_predictions
X_pre_scale = [None] * n_predictions
Y_pre_scale = [None] * n_predictions
In [19]:
for i in range(n_predictions):
y_position[i] = dataset_np.shape[1]-(1+datapoints_forward-y_select[i])
datapoints_forward_prediction[i] = y_position[i] - (dataset_np.shape[1]-1+datapoints_forward)
X_pre_scale[i], Y_pre_scale[i] = create_dataset(dataset_np, y_position[i], datapoints_forward)
print(Y_pre_scale[i].shape)
(315616, 1)
(315616, 1)
(315616, 1)
(315616, 1)
(315616, 1)
(315616, 1)
In [20]:
columns = list(dataset.columns.values)
current_price_ds_ilocation = columns.index('ask')
In [21]:
X = [None] * n_predictions
y = [None] * n_predictions
f_select_f_regression = [None] * n_predictions
f_select_mutual_info = [None] * n_predictions
list_f_select_f_regression = [None] * n_predictions
list_f_select_mutual_info = [None] * n_predictions
f_selector_mdl = [None] * n_predictions
current_price_ilocation = [None] * n_predictions
X_new = [None] * n_predictions
In [22]:
from sklearn.feature_selection import f_regression, mutual_info_regression
from sklearn.feature_selection import SelectKBest
for i in range(n_predictions):
X[i], y[i] = X_pre_scale[i], Y_pre_scale[i].reshape(Y_pre_scale[i].shape[0],)
print(X[i].shape)
# It's to expensive to calculate, so we won't do it on a daily basis
# f_select_f_regression[i] = SelectKBest(f_regression, k=x_features).fit(X[i], y[i])
# f_select_mutual_info[i] = SelectKBest(mutual_info_regression, k=x_features).fit(X[i], y[i])
f_select_f_regression[i] = joblib.load('../models/latest/fe/f_select_f_regression_{}_{}_{}_{}_fileslb_{}_datalb_{}_predictlf_{}.pkl'.format(
pair, n_columns_f_selection, y_select[i], yesterday.strftime('%Y%m%d'), days_back_file*-1, datapoints_back, y_select[i]))
f_select_mutual_info[i] = joblib.load('../models/latest/fe/f_select_mutual_info_{}_{}_{}_{}_fileslb_{}_datalb_{}_predictlf_{}.pkl'.format(
pair, n_columns_f_selection, y_select[i], yesterday.strftime('%Y%m%d'), days_back_file*-1, datapoints_back, y_select[i]))
list_f_select_f_regression[i] = list(f_select_f_regression[i].get_support(indices=True))
list_f_select_mutual_info[i] = list(f_select_mutual_info[i].get_support(indices=True))
if feature_selector == 'f_regression':
f_selector_mdl[i] = f_select_f_regression[i]
current_price_ilocation[i] = list_f_select_f_regression[i].index(current_price_ds_ilocation)
if feature_selector == 'mutual_info_regression':
f_selector_mdl[i] = f_select_mutual_info[i]
current_price_ilocation[i] = list_f_select_mutual_info[i].index(current_price_ds_ilocation)
X_new[i] = f_selector_mdl[i].transform(X[i])
print(X_new[i].shape)
joblib.dump(f_select_f_regression[i],
'../models/latest/fe/f_select_f_regression_{}_{}_{}_{}_fileslb_{}_datalb_{}_predictlf_{}.pkl'.format(
pair, n_columns_f_selection, y_select[i], yesterday.strftime('%Y%m%d'), days_back_file*-1, datapoints_back, y_select[i]))
joblib.dump(f_select_mutual_info[i],
'../models/latest/fe/f_select_mutual_info_{}_{}_{}_{}_fileslb_{}_datalb_{}_predictlf_{}.pkl'.format(
pair, n_columns_f_selection, y_select[i], yesterday.strftime('%Y%m%d'), days_back_file*-1, datapoints_back, y_select[i]))
for param_pair in zip(list_f_select_f_regression[i], list_f_select_mutual_info[i]):
print("f_regression:{}:{}........mutual_info:{}:{}".format(param_pair[0], columns[param_pair[0]],
param_pair[1], columns[param_pair[1]]))
(315616, 160)
(315616, 50)
f_regression:0:ask........mutual_info:0:ask
f_regression:2:bid........mutual_info:1:base_volume
f_regression:4:last........mutual_info:2:bid
f_regression:7:open_sell_orders........mutual_info:4:last
f_regression:10:ask_prev1........mutual_info:9:volume
f_regression:11:ask_prev2........mutual_info:10:ask_prev1
f_regression:12:ask_prev3........mutual_info:11:ask_prev2
f_regression:16:bid_prev1........mutual_info:12:ask_prev3
f_regression:17:bid_prev2........mutual_info:13:base_volume_prev1
f_regression:18:bid_prev3........mutual_info:14:base_volume_prev2
f_regression:22:last_prev1........mutual_info:15:base_volume_prev3
f_regression:23:last_prev2........mutual_info:16:bid_prev1
f_regression:24:last_prev3........mutual_info:17:bid_prev2
f_regression:31:open_sell_orders_prev1........mutual_info:18:bid_prev3
f_regression:40:ask_prev4........mutual_info:37:volume_prev1
f_regression:41:ask_prev5........mutual_info:38:volume_prev2
f_regression:42:ask_prev6........mutual_info:39:volume_prev3
f_regression:46:bid_prev4........mutual_info:40:ask_prev4
f_regression:47:bid_prev5........mutual_info:42:ask_prev6
f_regression:48:bid_prev6........mutual_info:43:base_volume_prev4
f_regression:52:last_prev4........mutual_info:45:base_volume_prev6
f_regression:53:last_prev5........mutual_info:46:bid_prev4
f_regression:54:last_prev6........mutual_info:48:bid_prev6
f_regression:70:ask_prev7........mutual_info:67:volume_prev4
f_regression:71:ask_prev8........mutual_info:69:volume_prev6
f_regression:72:ask_prev9........mutual_info:70:ask_prev7
f_regression:76:bid_prev7........mutual_info:71:ask_prev8
f_regression:77:bid_prev8........mutual_info:72:ask_prev9
f_regression:78:bid_prev9........mutual_info:73:base_volume_prev7
f_regression:82:last_prev7........mutual_info:74:base_volume_prev8
f_regression:83:last_prev8........mutual_info:75:base_volume_prev9
f_regression:84:last_prev9........mutual_info:77:bid_prev8
f_regression:100:ask_prev10........mutual_info:97:volume_prev7
f_regression:101:ask_prev11........mutual_info:98:volume_prev8
f_regression:102:ask_prev12........mutual_info:99:volume_prev9
f_regression:106:bid_prev10........mutual_info:100:ask_prev10
f_regression:107:bid_prev11........mutual_info:102:ask_prev12
f_regression:108:bid_prev12........mutual_info:103:base_volume_prev10
f_regression:112:last_prev10........mutual_info:105:base_volume_prev12
f_regression:113:last_prev11........mutual_info:106:bid_prev10
f_regression:114:last_prev12........mutual_info:108:bid_prev12
f_regression:130:ask_prev13........mutual_info:127:volume_prev10
f_regression:131:ask_prev14........mutual_info:129:volume_prev12
f_regression:132:ask_prev15........mutual_info:131:ask_prev14
f_regression:136:bid_prev13........mutual_info:133:base_volume_prev13
f_regression:137:bid_prev14........mutual_info:134:base_volume_prev14
f_regression:138:bid_prev15........mutual_info:135:base_volume_prev15
f_regression:142:last_prev13........mutual_info:157:volume_prev13
f_regression:143:last_prev14........mutual_info:158:volume_prev14
f_regression:144:last_prev15........mutual_info:159:volume_prev15
(315616, 160)
(315616, 50)
f_regression:0:ask........mutual_info:0:ask
f_regression:2:bid........mutual_info:1:base_volume
f_regression:4:last........mutual_info:2:bid
f_regression:7:open_sell_orders........mutual_info:9:volume
f_regression:10:ask_prev1........mutual_info:10:ask_prev1
f_regression:11:ask_prev2........mutual_info:11:ask_prev2
f_regression:12:ask_prev3........mutual_info:13:base_volume_prev1
f_regression:16:bid_prev1........mutual_info:14:base_volume_prev2
f_regression:17:bid_prev2........mutual_info:16:bid_prev1
f_regression:18:bid_prev3........mutual_info:17:bid_prev2
f_regression:22:last_prev1........mutual_info:37:volume_prev1
f_regression:23:last_prev2........mutual_info:38:volume_prev2
f_regression:24:last_prev3........mutual_info:40:ask_prev4
f_regression:31:open_sell_orders_prev1........mutual_info:41:ask_prev5
f_regression:40:ask_prev4........mutual_info:42:ask_prev6
f_regression:41:ask_prev5........mutual_info:43:base_volume_prev4
f_regression:42:ask_prev6........mutual_info:44:base_volume_prev5
f_regression:46:bid_prev4........mutual_info:45:base_volume_prev6
f_regression:47:bid_prev5........mutual_info:46:bid_prev4
f_regression:48:bid_prev6........mutual_info:47:bid_prev5
f_regression:52:last_prev4........mutual_info:48:bid_prev6
f_regression:53:last_prev5........mutual_info:67:volume_prev4
f_regression:54:last_prev6........mutual_info:68:volume_prev5
f_regression:70:ask_prev7........mutual_info:69:volume_prev6
f_regression:71:ask_prev8........mutual_info:70:ask_prev7
f_regression:72:ask_prev9........mutual_info:71:ask_prev8
f_regression:76:bid_prev7........mutual_info:73:base_volume_prev7
f_regression:77:bid_prev8........mutual_info:74:base_volume_prev8
f_regression:78:bid_prev9........mutual_info:76:bid_prev7
f_regression:82:last_prev7........mutual_info:77:bid_prev8
f_regression:83:last_prev8........mutual_info:97:volume_prev7
f_regression:84:last_prev9........mutual_info:98:volume_prev8
f_regression:100:ask_prev10........mutual_info:100:ask_prev10
f_regression:101:ask_prev11........mutual_info:101:ask_prev11
f_regression:102:ask_prev12........mutual_info:102:ask_prev12
f_regression:106:bid_prev10........mutual_info:103:base_volume_prev10
f_regression:107:bid_prev11........mutual_info:104:base_volume_prev11
f_regression:108:bid_prev12........mutual_info:105:base_volume_prev12
f_regression:112:last_prev10........mutual_info:106:bid_prev10
f_regression:113:last_prev11........mutual_info:108:bid_prev12
f_regression:114:last_prev12........mutual_info:127:volume_prev10
f_regression:130:ask_prev13........mutual_info:128:volume_prev11
f_regression:131:ask_prev14........mutual_info:129:volume_prev12
f_regression:132:ask_prev15........mutual_info:130:ask_prev13
f_regression:136:bid_prev13........mutual_info:131:ask_prev14
f_regression:137:bid_prev14........mutual_info:133:base_volume_prev13
f_regression:138:bid_prev15........mutual_info:134:base_volume_prev14
f_regression:142:last_prev13........mutual_info:137:bid_prev14
f_regression:143:last_prev14........mutual_info:157:volume_prev13
f_regression:144:last_prev15........mutual_info:158:volume_prev14
(315616, 160)
(315616, 50)
f_regression:0:ask........mutual_info:0:ask
f_regression:2:bid........mutual_info:1:base_volume
f_regression:4:last........mutual_info:2:bid
f_regression:7:open_sell_orders........mutual_info:9:volume
f_regression:10:ask_prev1........mutual_info:10:ask_prev1
f_regression:11:ask_prev2........mutual_info:11:ask_prev2
f_regression:12:ask_prev3........mutual_info:13:base_volume_prev1
f_regression:16:bid_prev1........mutual_info:14:base_volume_prev2
f_regression:17:bid_prev2........mutual_info:16:bid_prev1
f_regression:18:bid_prev3........mutual_info:17:bid_prev2
f_regression:22:last_prev1........mutual_info:37:volume_prev1
f_regression:23:last_prev2........mutual_info:38:volume_prev2
f_regression:24:last_prev3........mutual_info:40:ask_prev4
f_regression:31:open_sell_orders_prev1........mutual_info:41:ask_prev5
f_regression:40:ask_prev4........mutual_info:42:ask_prev6
f_regression:41:ask_prev5........mutual_info:43:base_volume_prev4
f_regression:42:ask_prev6........mutual_info:44:base_volume_prev5
f_regression:46:bid_prev4........mutual_info:45:base_volume_prev6
f_regression:47:bid_prev5........mutual_info:46:bid_prev4
f_regression:48:bid_prev6........mutual_info:47:bid_prev5
f_regression:52:last_prev4........mutual_info:48:bid_prev6
f_regression:53:last_prev5........mutual_info:67:volume_prev4
f_regression:54:last_prev6........mutual_info:68:volume_prev5
f_regression:70:ask_prev7........mutual_info:69:volume_prev6
f_regression:71:ask_prev8........mutual_info:70:ask_prev7
f_regression:72:ask_prev9........mutual_info:71:ask_prev8
f_regression:76:bid_prev7........mutual_info:73:base_volume_prev7
f_regression:77:bid_prev8........mutual_info:74:base_volume_prev8
f_regression:78:bid_prev9........mutual_info:76:bid_prev7
f_regression:82:last_prev7........mutual_info:77:bid_prev8
f_regression:83:last_prev8........mutual_info:97:volume_prev7
f_regression:84:last_prev9........mutual_info:98:volume_prev8
f_regression:100:ask_prev10........mutual_info:100:ask_prev10
f_regression:101:ask_prev11........mutual_info:101:ask_prev11
f_regression:102:ask_prev12........mutual_info:102:ask_prev12
f_regression:106:bid_prev10........mutual_info:103:base_volume_prev10
f_regression:107:bid_prev11........mutual_info:104:base_volume_prev11
f_regression:108:bid_prev12........mutual_info:105:base_volume_prev12
f_regression:112:last_prev10........mutual_info:106:bid_prev10
f_regression:113:last_prev11........mutual_info:108:bid_prev12
f_regression:114:last_prev12........mutual_info:127:volume_prev10
f_regression:130:ask_prev13........mutual_info:128:volume_prev11
f_regression:131:ask_prev14........mutual_info:129:volume_prev12
f_regression:132:ask_prev15........mutual_info:130:ask_prev13
f_regression:136:bid_prev13........mutual_info:131:ask_prev14
f_regression:137:bid_prev14........mutual_info:133:base_volume_prev13
f_regression:138:bid_prev15........mutual_info:134:base_volume_prev14
f_regression:142:last_prev13........mutual_info:137:bid_prev14
f_regression:143:last_prev14........mutual_info:157:volume_prev13
f_regression:144:last_prev15........mutual_info:158:volume_prev14
(315616, 160)
(315616, 50)
f_regression:0:ask........mutual_info:0:ask
f_regression:2:bid........mutual_info:1:base_volume
f_regression:4:last........mutual_info:2:bid
f_regression:7:open_sell_orders........mutual_info:9:volume
f_regression:10:ask_prev1........mutual_info:10:ask_prev1
f_regression:11:ask_prev2........mutual_info:11:ask_prev2
f_regression:12:ask_prev3........mutual_info:13:base_volume_prev1
f_regression:16:bid_prev1........mutual_info:14:base_volume_prev2
f_regression:17:bid_prev2........mutual_info:16:bid_prev1
f_regression:18:bid_prev3........mutual_info:17:bid_prev2
f_regression:22:last_prev1........mutual_info:37:volume_prev1
f_regression:23:last_prev2........mutual_info:38:volume_prev2
f_regression:24:last_prev3........mutual_info:40:ask_prev4
f_regression:31:open_sell_orders_prev1........mutual_info:41:ask_prev5
f_regression:40:ask_prev4........mutual_info:42:ask_prev6
f_regression:41:ask_prev5........mutual_info:43:base_volume_prev4
f_regression:42:ask_prev6........mutual_info:44:base_volume_prev5
f_regression:46:bid_prev4........mutual_info:45:base_volume_prev6
f_regression:47:bid_prev5........mutual_info:46:bid_prev4
f_regression:48:bid_prev6........mutual_info:47:bid_prev5
f_regression:52:last_prev4........mutual_info:48:bid_prev6
f_regression:53:last_prev5........mutual_info:67:volume_prev4
f_regression:54:last_prev6........mutual_info:68:volume_prev5
f_regression:70:ask_prev7........mutual_info:69:volume_prev6
f_regression:71:ask_prev8........mutual_info:70:ask_prev7
f_regression:72:ask_prev9........mutual_info:71:ask_prev8
f_regression:76:bid_prev7........mutual_info:73:base_volume_prev7
f_regression:77:bid_prev8........mutual_info:74:base_volume_prev8
f_regression:78:bid_prev9........mutual_info:76:bid_prev7
f_regression:82:last_prev7........mutual_info:77:bid_prev8
f_regression:83:last_prev8........mutual_info:97:volume_prev7
f_regression:84:last_prev9........mutual_info:98:volume_prev8
f_regression:100:ask_prev10........mutual_info:100:ask_prev10
f_regression:101:ask_prev11........mutual_info:101:ask_prev11
f_regression:102:ask_prev12........mutual_info:102:ask_prev12
f_regression:106:bid_prev10........mutual_info:103:base_volume_prev10
f_regression:107:bid_prev11........mutual_info:104:base_volume_prev11
f_regression:108:bid_prev12........mutual_info:105:base_volume_prev12
f_regression:112:last_prev10........mutual_info:106:bid_prev10
f_regression:113:last_prev11........mutual_info:127:volume_prev10
f_regression:114:last_prev12........mutual_info:128:volume_prev11
f_regression:130:ask_prev13........mutual_info:129:volume_prev12
f_regression:131:ask_prev14........mutual_info:130:ask_prev13
f_regression:132:ask_prev15........mutual_info:131:ask_prev14
f_regression:136:bid_prev13........mutual_info:133:base_volume_prev13
f_regression:137:bid_prev14........mutual_info:134:base_volume_prev14
f_regression:138:bid_prev15........mutual_info:135:base_volume_prev15
f_regression:142:last_prev13........mutual_info:137:bid_prev14
f_regression:143:last_prev14........mutual_info:157:volume_prev13
f_regression:144:last_prev15........mutual_info:158:volume_prev14
(315616, 160)
(315616, 50)
f_regression:0:ask........mutual_info:0:ask
f_regression:2:bid........mutual_info:1:base_volume
f_regression:4:last........mutual_info:2:bid
f_regression:7:open_sell_orders........mutual_info:9:volume
f_regression:10:ask_prev1........mutual_info:10:ask_prev1
f_regression:11:ask_prev2........mutual_info:11:ask_prev2
f_regression:12:ask_prev3........mutual_info:13:base_volume_prev1
f_regression:16:bid_prev1........mutual_info:14:base_volume_prev2
f_regression:17:bid_prev2........mutual_info:16:bid_prev1
f_regression:18:bid_prev3........mutual_info:17:bid_prev2
f_regression:22:last_prev1........mutual_info:37:volume_prev1
f_regression:23:last_prev2........mutual_info:38:volume_prev2
f_regression:24:last_prev3........mutual_info:40:ask_prev4
f_regression:31:open_sell_orders_prev1........mutual_info:41:ask_prev5
f_regression:40:ask_prev4........mutual_info:42:ask_prev6
f_regression:41:ask_prev5........mutual_info:43:base_volume_prev4
f_regression:42:ask_prev6........mutual_info:44:base_volume_prev5
f_regression:46:bid_prev4........mutual_info:45:base_volume_prev6
f_regression:47:bid_prev5........mutual_info:46:bid_prev4
f_regression:48:bid_prev6........mutual_info:48:bid_prev6
f_regression:52:last_prev4........mutual_info:67:volume_prev4
f_regression:53:last_prev5........mutual_info:68:volume_prev5
f_regression:54:last_prev6........mutual_info:69:volume_prev6
f_regression:70:ask_prev7........mutual_info:70:ask_prev7
f_regression:71:ask_prev8........mutual_info:71:ask_prev8
f_regression:72:ask_prev9........mutual_info:73:base_volume_prev7
f_regression:76:bid_prev7........mutual_info:74:base_volume_prev8
f_regression:77:bid_prev8........mutual_info:75:base_volume_prev9
f_regression:78:bid_prev9........mutual_info:76:bid_prev7
f_regression:82:last_prev7........mutual_info:77:bid_prev8
f_regression:83:last_prev8........mutual_info:97:volume_prev7
f_regression:84:last_prev9........mutual_info:98:volume_prev8
f_regression:100:ask_prev10........mutual_info:100:ask_prev10
f_regression:101:ask_prev11........mutual_info:101:ask_prev11
f_regression:102:ask_prev12........mutual_info:102:ask_prev12
f_regression:106:bid_prev10........mutual_info:103:base_volume_prev10
f_regression:107:bid_prev11........mutual_info:104:base_volume_prev11
f_regression:108:bid_prev12........mutual_info:105:base_volume_prev12
f_regression:112:last_prev10........mutual_info:106:bid_prev10
f_regression:113:last_prev11........mutual_info:127:volume_prev10
f_regression:114:last_prev12........mutual_info:128:volume_prev11
f_regression:130:ask_prev13........mutual_info:129:volume_prev12
f_regression:131:ask_prev14........mutual_info:130:ask_prev13
f_regression:132:ask_prev15........mutual_info:131:ask_prev14
f_regression:136:bid_prev13........mutual_info:133:base_volume_prev13
f_regression:137:bid_prev14........mutual_info:134:base_volume_prev14
f_regression:138:bid_prev15........mutual_info:135:base_volume_prev15
f_regression:142:last_prev13........mutual_info:137:bid_prev14
f_regression:143:last_prev14........mutual_info:157:volume_prev13
f_regression:144:last_prev15........mutual_info:158:volume_prev14
(315616, 160)
(315616, 50)
f_regression:0:ask........mutual_info:0:ask
f_regression:2:bid........mutual_info:1:base_volume
f_regression:4:last........mutual_info:2:bid
f_regression:7:open_sell_orders........mutual_info:9:volume
f_regression:10:ask_prev1........mutual_info:10:ask_prev1
f_regression:11:ask_prev2........mutual_info:11:ask_prev2
f_regression:12:ask_prev3........mutual_info:13:base_volume_prev1
f_regression:16:bid_prev1........mutual_info:14:base_volume_prev2
f_regression:17:bid_prev2........mutual_info:15:base_volume_prev3
f_regression:18:bid_prev3........mutual_info:17:bid_prev2
f_regression:22:last_prev1........mutual_info:37:volume_prev1
f_regression:23:last_prev2........mutual_info:38:volume_prev2
f_regression:24:last_prev3........mutual_info:40:ask_prev4
f_regression:31:open_sell_orders_prev1........mutual_info:41:ask_prev5
f_regression:40:ask_prev4........mutual_info:42:ask_prev6
f_regression:41:ask_prev5........mutual_info:43:base_volume_prev4
f_regression:42:ask_prev6........mutual_info:44:base_volume_prev5
f_regression:46:bid_prev4........mutual_info:45:base_volume_prev6
f_regression:47:bid_prev5........mutual_info:46:bid_prev4
f_regression:48:bid_prev6........mutual_info:67:volume_prev4
f_regression:52:last_prev4........mutual_info:68:volume_prev5
f_regression:53:last_prev5........mutual_info:69:volume_prev6
f_regression:54:last_prev6........mutual_info:70:ask_prev7
f_regression:70:ask_prev7........mutual_info:71:ask_prev8
f_regression:71:ask_prev8........mutual_info:73:base_volume_prev7
f_regression:72:ask_prev9........mutual_info:74:base_volume_prev8
f_regression:76:bid_prev7........mutual_info:75:base_volume_prev9
f_regression:77:bid_prev8........mutual_info:77:bid_prev8
f_regression:78:bid_prev9........mutual_info:97:volume_prev7
f_regression:82:last_prev7........mutual_info:98:volume_prev8
f_regression:83:last_prev8........mutual_info:99:volume_prev9
f_regression:84:last_prev9........mutual_info:100:ask_prev10
f_regression:100:ask_prev10........mutual_info:101:ask_prev11
f_regression:101:ask_prev11........mutual_info:102:ask_prev12
f_regression:102:ask_prev12........mutual_info:103:base_volume_prev10
f_regression:106:bid_prev10........mutual_info:104:base_volume_prev11
f_regression:107:bid_prev11........mutual_info:105:base_volume_prev12
f_regression:108:bid_prev12........mutual_info:106:bid_prev10
f_regression:112:last_prev10........mutual_info:127:volume_prev10
f_regression:113:last_prev11........mutual_info:128:volume_prev11
f_regression:114:last_prev12........mutual_info:129:volume_prev12
f_regression:130:ask_prev13........mutual_info:130:ask_prev13
f_regression:131:ask_prev14........mutual_info:131:ask_prev14
f_regression:132:ask_prev15........mutual_info:133:base_volume_prev13
f_regression:136:bid_prev13........mutual_info:134:base_volume_prev14
f_regression:137:bid_prev14........mutual_info:135:base_volume_prev15
f_regression:138:bid_prev15........mutual_info:137:bid_prev14
f_regression:142:last_prev13........mutual_info:157:volume_prev13
f_regression:143:last_prev14........mutual_info:158:volume_prev14
f_regression:144:last_prev15........mutual_info:159:volume_prev15
In [23]:
def get_scalers_and_datasets(_X_pre_scale, _Y_pre_scale, _scalers_base_path, i):
# _scaler_X = joblib.load(_scalers_base_path.format('scalerX'))
# _scaler_Y = joblib.load(_scalers_base_path.format('scalerY'))
_scaler_X = MinMaxScaler(feature_range=(0, 1))
_scaler_Y = MinMaxScaler(feature_range=(0, 1))
_scaled_X_dataset_np = _scaler_X.fit(_X_pre_scale)
_scaled_Y_dataset_np = _scaler_Y.fit(_Y_pre_scale)
joblib.dump(_scaler_X, _scalers_base_path.format('scalerX'))
joblib.dump(_scaler_Y, _scalers_base_path.format('scalerY'))
print(_scaler_Y)
_scaled_X_dataset_np = _scaler_X.transform(_X_pre_scale)
_scaled_Y_dataset_np = _scaler_Y.transform(_Y_pre_scale)
return (_scaler_X, _scaler_Y, _scaled_X_dataset_np, _scaled_Y_dataset_np)
In [24]:
scaler_base_path = [None] * n_predictions
scaler_X = [None] * n_predictions
scaler_Y = [None] * n_predictions
scaled_X_dataset_np = [None] * n_predictions
scaled_Y_dataset_np = [None] * n_predictions
In [25]:
for i in range(n_predictions):
scaler_base_path[i] = '../models/latest/fe/minmax_{{}}_{}_{}_{}_{}_{}_fileslb_{}_datalb_{}_predictlf_{}.pkl'.format(
pair, feature_selector, n_columns_f_selection, y_select[i], yesterday.strftime('%Y%m%d'), days_back_file*-1, \
datapoints_back, y_select[i])
print(Y_pre_scale[i].shape)
scaler_X[i], scaler_Y[i], scaled_X_dataset_np[i], scaled_Y_dataset_np[i] = get_scalers_and_datasets(X_new[i], Y_pre_scale[i],
scaler_base_path[i], i)
print(scaled_X_dataset_np[i].shape)
(315616, 1)
MinMaxScaler(copy=True, feature_range=(0, 1))
(315616, 50)
(315616, 1)
MinMaxScaler(copy=True, feature_range=(0, 1))
(315616, 50)
(315616, 1)
MinMaxScaler(copy=True, feature_range=(0, 1))
(315616, 50)
(315616, 1)
MinMaxScaler(copy=True, feature_range=(0, 1))
(315616, 50)
(315616, 1)
MinMaxScaler(copy=True, feature_range=(0, 1))
(315616, 50)
(315616, 1)
MinMaxScaler(copy=True, feature_range=(0, 1))
(315616, 50)
In [26]:
def get_train_test_scaled_datasets(_scaled_X_dataset_np, _scaled_Y_dataset_np):
np.random.seed(7)
# We temporarily use almost all the data for training
train_size = int(len(_scaled_X_dataset_np) * 0.99)
test_size = len(_scaled_X_dataset_np) - train_size
_trainX, _testX = _scaled_X_dataset_np[0:train_size,:], _scaled_X_dataset_np[train_size:len(_scaled_X_dataset_np),:]
_trainY, _testY = _scaled_Y_dataset_np[0:train_size,:], _scaled_Y_dataset_np[train_size:len(_scaled_Y_dataset_np),:]
return _trainX, _testX, _trainY, _testY
In [27]:
from sklearn.ensemble import RandomForestRegressor
import math
trainX = [None] * n_predictions
testX = [None] * n_predictions
trainY = [None] * n_predictions
testY = [None] * n_predictions
forest_reg = [None] * n_predictions
trainPredict = [None] * n_predictions
testPredict = [None] * n_predictions
trainScore = [None] * n_predictions
testScore = [None] * n_predictions
pd_validation_random_forest = [None] * n_predictions
In [28]:
for i in range(n_predictions):
trainX[i], testX[i], trainY[i], testY[i] = get_train_test_scaled_datasets(scaled_X_dataset_np[i], scaled_Y_dataset_np[i])
print(testX[i][:,current_price_ilocation])
print(testY[i].shape)
forest_reg[i] = RandomForestRegressor(n_estimators=20)
forest_reg[i].fit(trainX[i], trainY[i])
# forest_reg[i] = joblib.load(
# '../models/latest/random_forest_regressor/random_forest_regressor_{}_{}_{}_{}_{}_fileslb_{}_datalb_{}_predictlf_{}.pkl'.format(
# pair, feature_selector, n_columns_f_selection, y_select[i], yesterday.strftime('%Y%m%d'), days_back_file*-1, datapoints_back, y_select[i]))
joblib.dump(forest_reg[i],
'../models/latest/random_forest_regressor/random_forest_regressor_{}_{}_{}_{}_{}_fileslb_{}_datalb_{}_predictlf_{}.pkl'.format(
pair, feature_selector, n_columns_f_selection, y_select[i], yesterday.strftime('%Y%m%d'), days_back_file*-1, datapoints_back, y_select[i]))
trainPredict[i] = forest_reg[i].predict(trainX[i])
testPredict[i] = forest_reg[i].predict(testX[i])
trainPredict[i] = scaler_Y[i].inverse_transform(trainPredict[i])
trainX[i] = scaler_X[i].inverse_transform(trainX[i])
trainY[i] = scaler_Y[i].inverse_transform(trainY[i])
testPredict[i] = scaler_Y[i].inverse_transform(testPredict[i])
testX[i] = scaler_X[i].inverse_transform(testX[i])
testY[i] = scaler_Y[i].inverse_transform(testY[i])
print (testPredict[i].shape)
# calculate root mean squared error
trainScore[i] = math.sqrt(mean_squared_error(trainY[i][:,0], trainPredict[i]))
print('Train Score: %.5f RMSE' % (trainScore[i]))
testScore[i] = math.sqrt(mean_squared_error(testY[i][:,0], testPredict[i]))
print('Test Score: %.5f RMSE' % (testScore[i]))
pd_validation_random_forest[i] = pd.DataFrame({'predicted': testPredict[i], \
'actual': testY[i][:,0], 'last': testX[i][:, current_price_ilocation[i]]})
print(pd_validation_random_forest[i].head(10))
plt.figure(figsize=(15,10))
line1,=plt.plot(pd_validation_random_forest[i]['predicted'],linewidth=2,color='r',label='PREDICTION')
line2,=plt.plot(pd_validation_random_forest[i]['actual'],linewidth=2,color='b',label='TIME SERIES')
line3,=plt.plot(pd_validation_random_forest[i]['last'],linewidth=2,color='g',label='LAST')
plt.title("TIME SERIES PREDICTION")
plt.legend([line1, line2, line3])
plt.show()
[[ 0.07684577 0.07684577 0.07684577 0.07684577 0.07684577 0.07684577]
[ 0.07684577 0.07684577 0.07684577 0.07684577 0.07684577 0.07684577]
[ 0.07077003 0.07077003 0.07077003 0.07077003 0.07077003 0.07077003]
...,
[ 0.0905894 0.0905894 0.0905894 0.0905894 0.0905894 0.0905894 ]
[ 0.09059 0.09059 0.09059 0.09059 0.09059 0.09059 ]
[ 0.09059 0.09059 0.09059 0.09059 0.09059 0.09059 ]]
(3157, 1)
/opt/conda/lib/python3.5/site-packages/ipykernel/__main__.py:7: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
/opt/conda/lib/python3.5/site-packages/sklearn/preprocessing/data.py:374: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.
warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)
/opt/conda/lib/python3.5/site-packages/sklearn/preprocessing/data.py:374: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.
warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)
(3157,)
Train Score: 0.00004 RMSE
Test Score: 0.00018 RMSE
actual last predicted
0 0.069435 0.069700 0.069533
1 0.069400 0.069700 0.069538
2 0.069400 0.069435 0.069580
3 0.069400 0.069700 0.069436
4 0.069680 0.069700 0.069511
5 0.069680 0.069680 0.069511
6 0.069323 0.069680 0.069548
7 0.069680 0.069680 0.069532
8 0.069680 0.069680 0.069532
9 0.069680 0.069680 0.069527
[[ 0.07684577 0.07684577 0.07684577 0.07684577 0.07684577 0.07684577]
[ 0.07684577 0.07684577 0.07684577 0.07684577 0.07684577 0.07684577]
[ 0.07077003 0.07077003 0.07077003 0.07077003 0.07077003 0.07077003]
...,
[ 0.0905894 0.0905894 0.0905894 0.0905894 0.0905894 0.0905894 ]
[ 0.09059 0.09059 0.09059 0.09059 0.09059 0.09059 ]
[ 0.09059 0.09059 0.09059 0.09059 0.09059 0.09059 ]]
(3157, 1)
/opt/conda/lib/python3.5/site-packages/ipykernel/__main__.py:7: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
/opt/conda/lib/python3.5/site-packages/sklearn/preprocessing/data.py:374: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.
warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)
/opt/conda/lib/python3.5/site-packages/sklearn/preprocessing/data.py:374: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.
warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)
(3157,)
Train Score: 0.00004 RMSE
Test Score: 0.00021 RMSE
actual last predicted
0 0.069680 0.069700 0.069602
1 0.069323 0.069700 0.069610
2 0.069680 0.069435 0.069575
3 0.069680 0.069700 0.069554
4 0.069680 0.069700 0.069584
5 0.069680 0.069680 0.069584
6 0.069400 0.069680 0.069559
7 0.069680 0.069680 0.069551
8 0.069680 0.069680 0.069558
9 0.069680 0.069680 0.069490
[[ 0.07684577 0.07684577 0.07684577 0.07684577 0.07684577 0.07684577]
[ 0.07684577 0.07684577 0.07684577 0.07684577 0.07684577 0.07684577]
[ 0.07077003 0.07077003 0.07077003 0.07077003 0.07077003 0.07077003]
...,
[ 0.0905894 0.0905894 0.0905894 0.0905894 0.0905894 0.0905894 ]
[ 0.09059 0.09059 0.09059 0.09059 0.09059 0.09059 ]
[ 0.09059 0.09059 0.09059 0.09059 0.09059 0.09059 ]]
(3157, 1)
/opt/conda/lib/python3.5/site-packages/ipykernel/__main__.py:7: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
/opt/conda/lib/python3.5/site-packages/sklearn/preprocessing/data.py:374: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.
warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)
/opt/conda/lib/python3.5/site-packages/sklearn/preprocessing/data.py:374: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.
warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)
(3157,)
Train Score: 0.00005 RMSE
Test Score: 0.00028 RMSE
actual last predicted
0 0.06968 0.069700 0.069535
1 0.06968 0.069700 0.069577
2 0.06968 0.069435 0.069483
3 0.06968 0.069700 0.069505
4 0.06940 0.069700 0.069519
5 0.06940 0.069680 0.069470
6 0.06940 0.069680 0.069464
7 0.06940 0.069680 0.069464
8 0.06940 0.069680 0.069480
9 0.06940 0.069680 0.069488
[[ 0.07684577 0.07684577 0.07684577 0.07684577 0.07684577 0.07684577]
[ 0.07684577 0.07684577 0.07684577 0.07684577 0.07684577 0.07684577]
[ 0.07077003 0.07077003 0.07077003 0.07077003 0.07077003 0.07077003]
...,
[ 0.0905894 0.0905894 0.0905894 0.0905894 0.0905894 0.0905894 ]
[ 0.09059 0.09059 0.09059 0.09059 0.09059 0.09059 ]
[ 0.09059 0.09059 0.09059 0.09059 0.09059 0.09059 ]]
(3157, 1)
/opt/conda/lib/python3.5/site-packages/ipykernel/__main__.py:7: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
/opt/conda/lib/python3.5/site-packages/sklearn/preprocessing/data.py:374: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.
warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)
/opt/conda/lib/python3.5/site-packages/sklearn/preprocessing/data.py:374: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.
warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)
(3157,)
Train Score: 0.00004 RMSE
Test Score: 0.00030 RMSE
actual last predicted
0 0.069400 0.069700 0.069483
1 0.069400 0.069700 0.069464
2 0.069400 0.069435 0.069387
3 0.069400 0.069700 0.069359
4 0.069669 0.069700 0.069419
5 0.069669 0.069680 0.069423
6 0.069669 0.069680 0.069408
7 0.069680 0.069680 0.069438
8 0.069669 0.069680 0.069443
9 0.069669 0.069680 0.069394
[[ 0.07684577 0.07684577 0.07684577 0.07684577 0.07684577 0.07684577]
[ 0.07684577 0.07684577 0.07684577 0.07684577 0.07684577 0.07684577]
[ 0.07077003 0.07077003 0.07077003 0.07077003 0.07077003 0.07077003]
...,
[ 0.0905894 0.0905894 0.0905894 0.0905894 0.0905894 0.0905894 ]
[ 0.09059 0.09059 0.09059 0.09059 0.09059 0.09059 ]
[ 0.09059 0.09059 0.09059 0.09059 0.09059 0.09059 ]]
(3157, 1)
/opt/conda/lib/python3.5/site-packages/ipykernel/__main__.py:7: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
/opt/conda/lib/python3.5/site-packages/sklearn/preprocessing/data.py:374: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.
warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)
/opt/conda/lib/python3.5/site-packages/sklearn/preprocessing/data.py:374: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.
warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)
(3157,)
Train Score: 0.00004 RMSE
Test Score: 0.00034 RMSE
actual last predicted
0 0.069700 0.069700 0.069651
1 0.069700 0.069700 0.069575
2 0.069700 0.069435 0.069587
3 0.069669 0.069700 0.069513
4 0.069669 0.069700 0.069546
5 0.069739 0.069680 0.069532
6 0.069739 0.069680 0.069523
7 0.069440 0.069680 0.069523
8 0.069739 0.069680 0.069523
9 0.069739 0.069680 0.069503
[[ 0.07684577 0.07684577 0.07684577 0.07684577 0.07684577 0.07684577]
[ 0.07684577 0.07684577 0.07684577 0.07684577 0.07684577 0.07684577]
[ 0.07077003 0.07077003 0.07077003 0.07077003 0.07077003 0.07077003]
...,
[ 0.0905894 0.0905894 0.0905894 0.0905894 0.0905894 0.0905894 ]
[ 0.09059 0.09059 0.09059 0.09059 0.09059 0.09059 ]
[ 0.09059 0.09059 0.09059 0.09059 0.09059 0.09059 ]]
(3157, 1)
/opt/conda/lib/python3.5/site-packages/ipykernel/__main__.py:7: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
/opt/conda/lib/python3.5/site-packages/sklearn/preprocessing/data.py:374: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.
warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)
/opt/conda/lib/python3.5/site-packages/sklearn/preprocessing/data.py:374: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.
warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)
(3157,)
Train Score: 0.00004 RMSE
Test Score: 0.00041 RMSE
actual last predicted
0 0.069440 0.069700 0.069711
1 0.069440 0.069700 0.069719
2 0.069440 0.069435 0.069687
3 0.069739 0.069700 0.069673
4 0.069739 0.069700 0.069694
5 0.069739 0.069680 0.069654
6 0.069739 0.069680 0.069671
7 0.069739 0.069680 0.069678
8 0.069739 0.069680 0.069678
9 0.069739 0.069680 0.069535
In [29]:
import xgboost as xgb
import math
trainX = [None] * n_predictions
testX = [None] * n_predictions
trainY = [None] * n_predictions
testY = [None] * n_predictions
clf = [None] * n_predictions
xgb_reg = [None] * n_predictions
trainPredict = [None] * n_predictions
testPredict = [None] * n_predictions
trainScore = [None] * n_predictions
testScore = [None] * n_predictions
pd_validation_xgb = [None] * n_predictions
In [30]:
for i in range(n_predictions):
trainX[i], testX[i], trainY[i], testY[i] = get_train_test_scaled_datasets(scaled_X_dataset_np[i], scaled_Y_dataset_np[i])
# We train only with the latest days to avoid overfitting
trainX[i], trainY[i] = trainX[i][:], trainY[i][:]
print(testX[i][:,current_price_ilocation])
print(testY[i].shape)
# xgb_reg[i] = xgb.XGBRegressor(max_depth=5, learning_rate=0.1, n_estimators=100)
# xgb_reg[i].fit(trainX[i], trainY[i])
xgb_reg[i] = xgb.XGBRegressor()
clf[i] = GridSearchCV(xgb_reg[i],
{'max_depth': [2,4,6],
'n_estimators': [50,100,200]}, verbose=1)
clf[i].fit(trainX[i], trainY[i])
print(clf[i].best_score_)
print(clf[i].best_params_)
joblib.dump(clf[i],
'../models/latest/random_forest_regressor/xgboost_cv_clf_regressor_{}_{}_{}_{}_{}_fileslb_{}_datalb_{}_predictlf_{}.pkl'.format(
pair, feature_selector, n_columns_f_selection, y_select[i], yesterday.strftime('%Y%m%d'), days_back_file*-1, datapoints_back, y_select[i]))
# trainPredict[i] = xgb_reg[i].predict(trainX[i])
# testPredict[i] = xgb_reg[i].predict(testX[i])
trainPredict[i] = clf[i].predict(trainX[i])
testPredict[i] = clf[i].predict(testX[i])
trainPredict[i] = scaler_Y[i].inverse_transform(trainPredict[i])
trainX[i] = scaler_X[i].inverse_transform(trainX[i])
trainY[i] = scaler_Y[i].inverse_transform(trainY[i])
testPredict[i] = scaler_Y[i].inverse_transform(testPredict[i])
testX[i] = scaler_X[i].inverse_transform(testX[i])
testY[i] = scaler_Y[i].inverse_transform(testY[i])
print (testPredict[i].shape)
# calculate root mean squared error
trainScore[i] = math.sqrt(mean_squared_error(trainY[i][:,0], trainPredict[i]))
print('Train Score: %.5f RMSE' % (trainScore[i]))
testScore[i] = math.sqrt(mean_squared_error(testY[i][:,0], testPredict[i]))
print('Test Score: %.5f RMSE' % (testScore[i]))
pd_validation_xgb[i] = pd.DataFrame({'predicted': testPredict[i], \
'actual': testY[i][:,0], 'last': testX[i][:, current_price_ilocation[i]]})
print(pd_validation_xgb[i].head(10))
plt.figure(figsize=(15,10))
line1,=plt.plot(pd_validation_xgb[i]['predicted'],linewidth=2,color='r',label='PREDICTION')
line2,=plt.plot(pd_validation_xgb[i]['actual'],linewidth=2,color='b',label='TIME SERIES')
line3,=plt.plot(pd_validation_xgb[i]['last'],linewidth=2,color='g',label='LAST')
plt.title("TIME SERIES PREDICTION")
plt.legend([line1, line2, line3])
plt.show()
[[ 0.07684577 0.07684577 0.07684577 0.07684577 0.07684577 0.07684577]
[ 0.07684577 0.07684577 0.07684577 0.07684577 0.07684577 0.07684577]
[ 0.07077003 0.07077003 0.07077003 0.07077003 0.07077003 0.07077003]
...,
[ 0.0905894 0.0905894 0.0905894 0.0905894 0.0905894 0.0905894 ]
[ 0.09059 0.09059 0.09059 0.09059 0.09059 0.09059 ]
[ 0.09059 0.09059 0.09059 0.09059 0.09059 0.09059 ]]
(3157, 1)
Fitting 3 folds for each of 9 candidates, totalling 27 fits
[Parallel(n_jobs=1)]: Done 27 out of 27 | elapsed: 7.6min finished
0.964539612902
{'n_estimators': 100, 'max_depth': 4}
/opt/conda/lib/python3.5/site-packages/sklearn/preprocessing/data.py:374: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.
warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)
/opt/conda/lib/python3.5/site-packages/sklearn/preprocessing/data.py:374: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.
warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)
(3157,)
Train Score: 0.00014 RMSE
Test Score: 0.00018 RMSE
actual last predicted
0 0.069435 0.069700 0.069670
1 0.069400 0.069700 0.069670
2 0.069400 0.069435 0.069421
3 0.069400 0.069700 0.069401
4 0.069680 0.069700 0.069447
5 0.069680 0.069680 0.069447
6 0.069323 0.069680 0.069433
7 0.069680 0.069680 0.069433
8 0.069680 0.069680 0.069433
9 0.069680 0.069680 0.069433
[[ 0.07684577 0.07684577 0.07684577 0.07684577 0.07684577 0.07684577]
[ 0.07684577 0.07684577 0.07684577 0.07684577 0.07684577 0.07684577]
[ 0.07077003 0.07077003 0.07077003 0.07077003 0.07077003 0.07077003]
...,
[ 0.0905894 0.0905894 0.0905894 0.0905894 0.0905894 0.0905894 ]
[ 0.09059 0.09059 0.09059 0.09059 0.09059 0.09059 ]
[ 0.09059 0.09059 0.09059 0.09059 0.09059 0.09059 ]]
(3157, 1)
Fitting 3 folds for each of 9 candidates, totalling 27 fits
[Parallel(n_jobs=1)]: Done 27 out of 27 | elapsed: 7.4min finished
0.961790398675
{'n_estimators': 50, 'max_depth': 6}
/opt/conda/lib/python3.5/site-packages/sklearn/preprocessing/data.py:374: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.
warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)
/opt/conda/lib/python3.5/site-packages/sklearn/preprocessing/data.py:374: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.
warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)
(3157,)
Train Score: 0.00018 RMSE
Test Score: 0.00019 RMSE
actual last predicted
0 0.069680 0.069700 0.069666
1 0.069323 0.069700 0.069676
2 0.069680 0.069435 0.069618
3 0.069680 0.069700 0.069531
4 0.069680 0.069700 0.069620
5 0.069680 0.069680 0.069610
6 0.069400 0.069680 0.069610
7 0.069680 0.069680 0.069610
8 0.069680 0.069680 0.069610
9 0.069680 0.069680 0.069610
[[ 0.07684577 0.07684577 0.07684577 0.07684577 0.07684577 0.07684577]
[ 0.07684577 0.07684577 0.07684577 0.07684577 0.07684577 0.07684577]
[ 0.07077003 0.07077003 0.07077003 0.07077003 0.07077003 0.07077003]
...,
[ 0.0905894 0.0905894 0.0905894 0.0905894 0.0905894 0.0905894 ]
[ 0.09059 0.09059 0.09059 0.09059 0.09059 0.09059 ]
[ 0.09059 0.09059 0.09059 0.09059 0.09059 0.09059 ]]
(3157, 1)
Fitting 3 folds for each of 9 candidates, totalling 27 fits
[Parallel(n_jobs=1)]: Done 27 out of 27 | elapsed: 7.3min finished
0.960077785646
{'n_estimators': 200, 'max_depth': 2}
/opt/conda/lib/python3.5/site-packages/sklearn/preprocessing/data.py:374: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.
warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)
/opt/conda/lib/python3.5/site-packages/sklearn/preprocessing/data.py:374: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.
warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)
(3157,)
Train Score: 0.00024 RMSE
Test Score: 0.00023 RMSE
actual last predicted
0 0.06968 0.069700 0.069607
1 0.06968 0.069700 0.069607
2 0.06968 0.069435 0.069360
3 0.06968 0.069700 0.069607
4 0.06940 0.069700 0.069607
5 0.06940 0.069680 0.069607
6 0.06940 0.069680 0.069607
7 0.06940 0.069680 0.069607
8 0.06940 0.069680 0.069607
9 0.06940 0.069680 0.069607
[[ 0.07684577 0.07684577 0.07684577 0.07684577 0.07684577 0.07684577]
[ 0.07684577 0.07684577 0.07684577 0.07684577 0.07684577 0.07684577]
[ 0.07077003 0.07077003 0.07077003 0.07077003 0.07077003 0.07077003]
...,
[ 0.0905894 0.0905894 0.0905894 0.0905894 0.0905894 0.0905894 ]
[ 0.09059 0.09059 0.09059 0.09059 0.09059 0.09059 ]
[ 0.09059 0.09059 0.09059 0.09059 0.09059 0.09059 ]]
(3157, 1)
Fitting 3 folds for each of 9 candidates, totalling 27 fits
[Parallel(n_jobs=1)]: Done 27 out of 27 | elapsed: 7.2min finished
0.956004898393
{'n_estimators': 200, 'max_depth': 4}
/opt/conda/lib/python3.5/site-packages/sklearn/preprocessing/data.py:374: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.
warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)
/opt/conda/lib/python3.5/site-packages/sklearn/preprocessing/data.py:374: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.
warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)
(3157,)
Train Score: 0.00024 RMSE
Test Score: 0.00026 RMSE
actual last predicted
0 0.069400 0.069700 0.069455
1 0.069400 0.069700 0.069455
2 0.069400 0.069435 0.069455
3 0.069400 0.069700 0.069455
4 0.069669 0.069700 0.069455
5 0.069669 0.069680 0.069455
6 0.069669 0.069680 0.069455
7 0.069680 0.069680 0.069455
8 0.069669 0.069680 0.069455
9 0.069669 0.069680 0.069455
[[ 0.07684577 0.07684577 0.07684577 0.07684577 0.07684577 0.07684577]
[ 0.07684577 0.07684577 0.07684577 0.07684577 0.07684577 0.07684577]
[ 0.07077003 0.07077003 0.07077003 0.07077003 0.07077003 0.07077003]
...,
[ 0.0905894 0.0905894 0.0905894 0.0905894 0.0905894 0.0905894 ]
[ 0.09059 0.09059 0.09059 0.09059 0.09059 0.09059 ]
[ 0.09059 0.09059 0.09059 0.09059 0.09059 0.09059 ]]
(3157, 1)
Fitting 3 folds for each of 9 candidates, totalling 27 fits
[Parallel(n_jobs=1)]: Done 27 out of 27 | elapsed: 7.4min finished
0.953831912787
{'n_estimators': 200, 'max_depth': 2}
/opt/conda/lib/python3.5/site-packages/sklearn/preprocessing/data.py:374: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.
warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)
/opt/conda/lib/python3.5/site-packages/sklearn/preprocessing/data.py:374: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.
warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)
(3157,)
Train Score: 0.00032 RMSE
Test Score: 0.00029 RMSE
actual last predicted
0 0.069700 0.069700 0.069458
1 0.069700 0.069700 0.069458
2 0.069700 0.069435 0.069458
3 0.069669 0.069700 0.069458
4 0.069669 0.069700 0.069458
5 0.069739 0.069680 0.069458
6 0.069739 0.069680 0.069458
7 0.069440 0.069680 0.069458
8 0.069739 0.069680 0.069458
9 0.069739 0.069680 0.069458
[[ 0.07684577 0.07684577 0.07684577 0.07684577 0.07684577 0.07684577]
[ 0.07684577 0.07684577 0.07684577 0.07684577 0.07684577 0.07684577]
[ 0.07077003 0.07077003 0.07077003 0.07077003 0.07077003 0.07077003]
...,
[ 0.0905894 0.0905894 0.0905894 0.0905894 0.0905894 0.0905894 ]
[ 0.09059 0.09059 0.09059 0.09059 0.09059 0.09059 ]
[ 0.09059 0.09059 0.09059 0.09059 0.09059 0.09059 ]]
(3157, 1)
Fitting 3 folds for each of 9 candidates, totalling 27 fits
[Parallel(n_jobs=1)]: Done 27 out of 27 | elapsed: 7.6min finished
0.949715860933
{'n_estimators': 200, 'max_depth': 2}
/opt/conda/lib/python3.5/site-packages/sklearn/preprocessing/data.py:374: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.
warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)
/opt/conda/lib/python3.5/site-packages/sklearn/preprocessing/data.py:374: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.
warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)
(3157,)
Train Score: 0.00036 RMSE
Test Score: 0.00033 RMSE
actual last predicted
0 0.069440 0.069700 0.069560
1 0.069440 0.069700 0.069560
2 0.069440 0.069435 0.069293
3 0.069739 0.069700 0.069560
4 0.069739 0.069700 0.069560
5 0.069739 0.069680 0.069560
6 0.069739 0.069680 0.069560
7 0.069739 0.069680 0.069560
8 0.069739 0.069680 0.069560
9 0.069739 0.069680 0.069560
In [ ]:
In [31]:
import os
os.environ["KERAS_BACKEND"] = "theano"
import keras; import keras.backend
if keras.backend.backend() != 'theano':
raise BaseException("This script uses other backend")
else:
keras.backend.set_image_dim_ordering('th')
print("Backend ok")
Using Theano backend.
Backend ok
In [32]:
from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation
from keras.layers.recurrent import LSTM
from keras.layers import LSTM
from sklearn.preprocessing import MinMaxScaler
from sklearn.metrics import mean_squared_error
from keras import backend as K
import keras.callbacks
from keras.optimizers import SGD
from keras.callbacks import LearningRateScheduler
#from keras.regularizers import l2#, activity_l2
from keras import regularizers
from keras.layers import deserialize as layer_from_config
from scipy.interpolate import spline
import math
In [33]:
def predict_plot_rnn_model(_scaler_X, _scaler_Y, _scaled_X_dataset_np, _scaled_Y_dataset_np, _days_back, _datapoints_back,
_n_layers, _n_epochs, _nunits, _datapoints_forward_prediction,
_pair, _feature_selector, x_n_columns, _y_select, _yesterday_str,
_current_price_ilocation):
_trainX, _testX, _trainY, _testY = get_train_test_scaled_datasets(_scaled_X_dataset_np, _scaled_Y_dataset_np)
_trainX[i], _trainY[i] = _trainX[i][:], _trainY[i][:]
_trainX = np.reshape(_trainX, (_trainX.shape[0], 1, _trainX.shape[1]))
_testX = np.reshape(_testX, (_testX.shape[0], 1, _testX.shape[1]))
batch_size = 1
_model = Sequential()
for _ in range(_n_layers):
_model.add(LSTM(_nunits, input_shape=_trainX.shape[1:], return_sequences=True))
_model.add(LSTM(_nunits, input_shape=_trainX.shape[1:]))
_model.add(Dense(1))
_model.compile(loss='mean_squared_error', optimizer='adam')
_model.fit(_trainX, _trainY, epochs=_n_epochs, batch_size=1, verbose=2)
# _model = keras.models.load_model('../models/latest/rnn_regressor/rnn_regressor_{}_{}_{}_{}_{}_fileslb_{}_datalb_{}_predictlf_{}_l{}_e{}.h5'.format(
# _pair, _feature_selector, x_n_columns, _y_select, _yesterday_str, _days_back*-1, _datapoints_back, _datapoints_forward_prediction, _n_layers, _n_epochs))
_trainPredict = _model.predict(_trainX)
_testPredict = _model.predict(_testX)
_trainX = np.reshape(_trainX, (_trainX.shape[0], _trainX.shape[2]))
_testX = np.reshape(_testX, (_testX.shape[0], _testX.shape[2]))
_trainPredict = _scaler_Y.inverse_transform(_trainPredict)
_trainX = _scaler_X.inverse_transform(_trainX)
_trainY = _scaler_Y.inverse_transform(_trainY)
_testPredict = _scaler_Y.inverse_transform(_testPredict)
_testX = _scaler_X.inverse_transform(_testX)
_testY = _scaler_Y.inverse_transform(_testY)
# calculate root mean squared error
_trainScore = math.sqrt(mean_squared_error(_trainY[:,0], _trainPredict))
print('Train Score: %.5f RMSE' % (_trainScore))
_testScore = math.sqrt(mean_squared_error(_testY[:,0], _testPredict))
print('Test Score: %.5f RMSE' % (_testScore))
_pd_validation = pd.DataFrame({'predicted': _testPredict[:,0], \
'actual': _testY[:,0], 'last': _testX[:,_current_price_ilocation]})
plt.figure(figsize=(15,10))
line1,=plt.plot(_pd_validation['predicted'],linewidth=2,color='r',label='PREDICTION')
line2,=plt.plot(_pd_validation['actual'],linewidth=2,color='b',label='TIME SERIES')
line3,=plt.plot(_pd_validation['last'],linewidth=2,color='g',label='LAST')
plt.title("TIME SERIES PREDICTION")
plt.legend([line1, line2, line3])
plt.show()
return _model, _pd_validation
In [34]:
model_l1_e5 = [None] * n_predictions
pd_validation_l1_e5 = [None] * n_predictions
In [35]:
for i in range(n_predictions):
print(scaled_X_dataset_np[i].shape)
model_l1_e5[i], pd_validation_l1_e5[i] = predict_plot_rnn_model(scaler_X[i], scaler_Y[i], scaled_X_dataset_np[i], scaled_Y_dataset_np[i],
days_back_file, datapoints_back, 1, 2, 50,
y_select[i], pair,
feature_selector, X_new[i].shape[1], y_select[i],
yesterday.strftime('%Y%m%d'), current_price_ilocation[i])
(315616, 50)
WARNING (theano.configdefaults): install mkl with `conda install mkl-service`: No module named 'mkl'
Epoch 1/2
529s - loss: 5.7416e-05
Epoch 2/2
683s - loss: 2.8491e-05
Train Score: 0.00019 RMSE
Test Score: 0.00016 RMSE
(315616, 50)
Epoch 1/2
577s - loss: 7.0955e-05
Epoch 2/2
765s - loss: 4.0015e-05
Train Score: 0.00022 RMSE
Test Score: 0.00019 RMSE
(315616, 50)
Epoch 1/2
590s - loss: 8.6983e-05
Epoch 2/2
715s - loss: 5.3453e-05
Train Score: 0.00025 RMSE
Test Score: 0.00020 RMSE
(315616, 50)
Epoch 1/2
587s - loss: 1.0717e-04
Epoch 2/2
774s - loss: 7.0873e-05
Train Score: 0.00031 RMSE
Test Score: 0.00023 RMSE
(315616, 50)
Epoch 1/2
610s - loss: 1.2978e-04
Epoch 2/2
778s - loss: 9.1054e-05
Train Score: 0.00037 RMSE
Test Score: 0.00031 RMSE
(315616, 50)
Epoch 1/2
560s - loss: 1.5822e-04
Epoch 2/2
745s - loss: 1.1283e-04
Train Score: 0.00039 RMSE
Test Score: 0.00031 RMSE
In [36]:
@retry(wait_exponential_multiplier=1000, wait_exponential_max=60000, stop_max_delay=1800000)
def returnTickerAll(exchange):
if exchange == 'POLONIEX':
return json.loads(ccs.poloniex.public.returnTicker())
elif exchange == 'BITTREX':
# We need to transform it in a dictionary:
initial_list = json.loads(ccs.bittrex.public.getMarketSummaries())['result']
markets = {}
for market in initial_list:
markets[str(market['MarketName']).replace('-', '_')] = market
return markets
else:
printEvent('Unknown exchange: {}'.format(exchange), "STOPPED_APPLICATION", 4, exchange)
sys.exit(-1)
In [37]:
def create_np_array(_dequeue, _datapoints_back, _datapoints_forward_predictionm):
current = _dequeue[0]
prev_list = []
current_list = [current['Ask'], current['BaseVolume'], current['Bid'], current['High'], current['Last'],
current['Low'], current['OpenBuyOrders'], current['OpenSellOrders'], current['PrevDay'],
current['Volume']]
for cohort in range(int(_datapoints_back/3)):
#p_1, p_2, p_3 = cohort*3 + 1, cohort*3 + 2, cohort*3 + 3
p_1, p_2, p_3 = 1 + cohort*63, 2 + 6 + cohort*63, 3 + 18 + cohort*63
# p_1_real_jump = p_1 + cohort*60
# p_2_real_jump = p_2 + 6 + cohort*60
# p_3_real_jump = p_3 + 18 + cohort*60
cohort_list = [_dequeue[p_1]['Ask'], _dequeue[p_2]['Ask'], _dequeue[p_3]['Ask'],\
_dequeue[p_1]['BaseVolume'], _dequeue[p_2]['BaseVolume'], _dequeue[p_3]['BaseVolume'],\
_dequeue[p_1]['Bid'], _dequeue[p_2]['Bid'], _dequeue[p_3]['Bid'],\
_dequeue[p_1]['High'], _dequeue[p_2]['High'], _dequeue[p_3]['High'],\
_dequeue[p_1]['Last'], _dequeue[p_2]['Last'], _dequeue[p_3]['Last'],\
_dequeue[p_1]['Low'], _dequeue[p_2]['Low'], _dequeue[p_3]['Low'],\
_dequeue[p_1]['OpenBuyOrders'], _dequeue[p_2]['OpenBuyOrders'], _dequeue[p_3]['OpenBuyOrders'],\
_dequeue[p_1]['OpenSellOrders'], _dequeue[p_2]['OpenSellOrders'], _dequeue[p_3]['OpenSellOrders'],\
_dequeue[p_1]['PrevDay'], _dequeue[p_2]['PrevDay'], _dequeue[p_3]['PrevDay'],\
_dequeue[p_1]['Volume'], _dequeue[p_2]['Volume'], _dequeue[p_3]['Volume']]
prev_list += cohort_list
return np.asarray(current_list + prev_list)
In [38]:
import warnings
warnings.filterwarnings('ignore')
In [39]:
def predict(_f_selector_mdl, _scaler_X, _scaler_Y, _model_rnn, _forest_reg, xgb_reg, _recent_data_points):
_X_new = _f_selector_mdl.transform(_recent_data_points)
scaled_recent_data_points = _scaler_X.transform(_X_new)
predicted_recent_rf_scaled = _forest_reg.predict(scaled_recent_data_points)
predicted_recent_xgb_scaled = xgb_reg.predict(scaled_recent_data_points)
scaled_recent_data_points_pres = scaled_recent_data_points #scaled_recent_data_points.reshape(1, scaled_recent_data_points.shape[0])
scaled_recent_data_points_rnn = np.reshape(scaled_recent_data_points_pres,
(scaled_recent_data_points_pres.shape[0], 1,
scaled_recent_data_points_pres.shape[1]))
predicted_recent_rnn_scaled = _model_rnn.predict(scaled_recent_data_points_rnn)
_predicted_recent_rf = _scaler_Y.inverse_transform(predicted_recent_rf_scaled)
_predicted_recent_rnn = _scaler_Y.inverse_transform(predicted_recent_rnn_scaled)
_predicted_recent_xgb = _scaler_Y.inverse_transform(predicted_recent_xgb_scaled)
return (_predicted_recent_rf, _predicted_recent_rnn, _predicted_recent_xgb)
In [58]:
import collections
import time
import pymongo
from bson import json_util
from bson.json_util import dumps, loads
from pymongo import MongoClient
real_data_points_back = (n_hops_back*3)*60
data_window = collections.deque(maxlen=real_data_points_back*18)
predictions = collections.deque(maxlen=real_data_points_back*18)
client = MongoClient('mongodb://172.17.0.3:27017/')
mydb = client.etherchainmldb
# 900 datapoints => 9000 seconds (+1800 seconds safe)-> 3 hours ago.
# results = mydb.predictions.find({"modelType": "dummy", "currencyPair": "BTC-NMR"})
delta_ts = datetime.utcnow() - datetime(1970, 1, 1)
beginning_of_window = int((delta_ts.days * 24 * 60 * 60 + delta_ts.seconds) * 1000 + delta_ts.microseconds / 1000.0) \
- 10800000
print(beginning_of_window)
results = mydb.bittrex_ticker.find({"MarketName": "BTC-ETH", "TimeStampEpoch": {"$gt": beginning_of_window}})
print(results.count())
print(results[0])
1501589370772
988
{'_id': ObjectId('59806f8e411bf4054e3108a4'), 'Low': 0.0675, 'TimeStamp': '2017-08-01T12:09:40.563', 'BaseVolume': 4641.77265665, 'Bid': 0.07173099, 'High': 0.07705, 'Created': '2015-08-14T09:02:24.817', 'OpenBuyOrders': 2802, 'Volume': 64457.57647019, 'PrevDay': 0.06894, 'TimeStampEpoch': 1501589380563, 'Ask': 0.071732, 'ClientTimeStampEpoch': 1501589390065, 'OpenSellOrders': 12853, 'MarketName': 'BTC-ETH', 'Last': 0.071732}
In [59]:
sorted_results = sorted(results, key=lambda x: x['TimeStampEpoch'], reverse=False)
In [60]:
#for _ in range(real_data_points_back):
for current_data in sorted_results[-real_data_points_back:]:
#current_data = returnTickerAll('BITTREX')[pair.replace('-', '_')]
data_window.appendleft(current_data)
print('Adding {} data for past window'.format(current_data))
Adding {'_id': ObjectId('5980731d411bf4054e3108fc'), 'Low': 0.0675, 'TimeStamp': '2017-08-01T12:24:52.76', 'BaseVolume': 4903.04791862, 'Bid': 0.07701821, 'High': 0.0777, 'Created': '2015-08-14T09:02:24.817', 'OpenBuyOrders': 2843, 'Volume': 67913.152401, 'PrevDay': 0.06894003, 'TimeStampEpoch': 1501590292760, 'Ask': 0.0777, 'ClientTimeStampEpoch': 1501590301265, 'OpenSellOrders': 12584, 'MarketName': 'BTC-ETH', 'Last': 0.0777} data for past window
Adding {'_id': ObjectId('59807327411bf4054e3108fd'), 'Low': 0.0675, 'TimeStamp': '2017-08-01T12:24:55.463', 'BaseVolume': 4903.06670354, 'Bid': 0.07701821, 'High': 0.0777, 'Created': '2015-08-14T09:02:24.817', 'OpenBuyOrders': 2842, 'Volume': 67913.39630343, 'PrevDay': 0.06894003, 'TimeStampEpoch': 1501590295463, 'Ask': 0.0777, 'ClientTimeStampEpoch': 1501590311682, 'OpenSellOrders': 12585, 'MarketName': 'BTC-ETH', 'Last': 0.07701821} data for past window
Adding {'_id': ObjectId('59807331411bf4054e3108fe'), 'Low': 0.0675, 'TimeStamp': '2017-08-01T12:25:13.63', 'BaseVolume': 4913.08712648, 'Bid': 0.077, 'High': 0.07799, 'Created': '2015-08-14T09:02:24.817', 'OpenBuyOrders': 2832, 'Volume': 68043.31116323, 'PrevDay': 0.06894004, 'TimeStampEpoch': 1501590313630, 'Ask': 0.07769998, 'ClientTimeStampEpoch': 1501590321989, 'OpenSellOrders': 12554, 'MarketName': 'BTC-ETH', 'Last': 0.07701822} data for past window
Adding {'_id': ObjectId('5980733d411bf4054e3108ff'), 'Low': 0.0675, 'TimeStamp': '2017-08-01T12:25:25.19', 'BaseVolume': 4913.43725502, 'Bid': 0.07700001, 'High': 0.07799, 'Created': '2015-08-14T09:02:24.817', 'OpenBuyOrders': 2834, 'Volume': 68047.82196981, 'PrevDay': 0.0689401, 'TimeStampEpoch': 1501590325190, 'Ask': 0.07769996, 'ClientTimeStampEpoch': 1501590333777, 'OpenSellOrders': 12553, 'MarketName': 'BTC-ETH', 'Last': 0.07769996} data for past window
Adding {'_id': ObjectId('59807348411bf4054e310900'), 'Low': 0.0675, 'TimeStamp': '2017-08-01T12:25:35.827', 'BaseVolume': 4916.1213141, 'Bid': 0.07700001, 'High': 0.07799, 'Created': '2015-08-14T09:02:24.817', 'OpenBuyOrders': 2835, 'Volume': 68082.66955208, 'PrevDay': 0.0689401, 'TimeStampEpoch': 1501590335827, 'Ask': 0.07768995, 'ClientTimeStampEpoch': 1501590344092, 'OpenSellOrders': 12560, 'MarketName': 'BTC-ETH', 'Last': 0.07700001} data for past window
Adding {'_id': ObjectId('59807352411bf4054e310901'), 'Low': 0.0675, 'TimeStamp': '2017-08-01T12:25:45.793', 'BaseVolume': 4921.89602496, 'Bid': 0.07649284, 'High': 0.07799, 'Created': '2015-08-14T09:02:24.817', 'OpenBuyOrders': 2833, 'Volume': 68156.88279729, 'PrevDay': 0.0689401, 'TimeStampEpoch': 1501590345793, 'Ask': 0.077, 'ClientTimeStampEpoch': 1501590354404, 'OpenSellOrders': 12565, 'MarketName': 'BTC-ETH', 'Last': 0.07700001} data for past window
Adding {'_id': ObjectId('5980735d411bf4054e310902'), 'Low': 0.0675, 'TimeStamp': '2017-08-01T12:25:56.557', 'BaseVolume': 4923.2433392, 'Bid': 0.07699999, 'High': 0.07799, 'Created': '2015-08-14T09:02:24.817', 'OpenBuyOrders': 2838, 'Volume': 68174.38066391, 'PrevDay': 0.0689401, 'TimeStampEpoch': 1501590356557, 'Ask': 0.0770001, 'ClientTimeStampEpoch': 1501590365103, 'OpenSellOrders': 12569, 'MarketName': 'BTC-ETH', 'Last': 0.07699999} data for past window
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Adding {'_id': ObjectId('5980991a411bf4054e310c71'), 'Low': 0.0684, 'TimeStamp': '2017-08-01T15:07:00.017', 'BaseVolume': 5821.65687803, 'Bid': 0.08060028, 'High': 0.08347598, 'Created': '2015-08-14T09:02:24.817', 'OpenBuyOrders': 2992, 'Volume': 78208.7685081, 'PrevDay': 0.06950005, 'TimeStampEpoch': 1501600020017, 'Ask': 0.08099998, 'ClientTimeStampEpoch': 1501600026795, 'OpenSellOrders': 11127, 'MarketName': 'BTC-ETH', 'Last': 0.08099998} data for past window
Adding {'_id': ObjectId('59809925411bf4054e310c72'), 'Low': 0.0684, 'TimeStamp': '2017-08-01T15:07:12.95', 'BaseVolume': 5821.72013769, 'Bid': 0.08060028, 'High': 0.08347598, 'Created': '2015-08-14T09:02:24.817', 'OpenBuyOrders': 2993, 'Volume': 78209.45930232, 'PrevDay': 0.06950011, 'TimeStampEpoch': 1501600032950, 'Ask': 0.08099998, 'ClientTimeStampEpoch': 1501600037108, 'OpenSellOrders': 11130, 'MarketName': 'BTC-ETH', 'Last': 0.08099998} data for past window
Adding {'_id': ObjectId('5980992f411bf4054e310c73'), 'Low': 0.0684, 'TimeStamp': '2017-08-01T15:07:23.07', 'BaseVolume': 5823.71471501, 'Bid': 0.08040002, 'High': 0.08347598, 'Created': '2015-08-14T09:02:24.817', 'OpenBuyOrders': 2981, 'Volume': 78234.07095273, 'PrevDay': 0.06971006, 'TimeStampEpoch': 1501600043070, 'Ask': 0.08060028, 'ClientTimeStampEpoch': 1501600047418, 'OpenSellOrders': 11132, 'MarketName': 'BTC-ETH', 'Last': 0.08099998} data for past window
Adding {'_id': ObjectId('59809939411bf4054e310c74'), 'Low': 0.0684, 'TimeStamp': '2017-08-01T15:07:34.41', 'BaseVolume': 5824.87194367, 'Bid': 0.0805, 'High': 0.08347598, 'Created': '2015-08-14T09:02:24.817', 'OpenBuyOrders': 3001, 'Volume': 78248.3616963, 'PrevDay': 0.06971006, 'TimeStampEpoch': 1501600054410, 'Ask': 0.08060028, 'ClientTimeStampEpoch': 1501600057766, 'OpenSellOrders': 11130, 'MarketName': 'BTC-ETH', 'Last': 0.08099997} data for past window
Adding {'_id': ObjectId('59809944411bf4054e310c75'), 'Low': 0.0684, 'TimeStamp': '2017-08-01T15:07:43.297', 'BaseVolume': 5824.96222804, 'Bid': 0.0805, 'High': 0.08347598, 'Created': '2015-08-14T09:02:24.817', 'OpenBuyOrders': 3005, 'Volume': 78249.47650089, 'PrevDay': 0.06971006, 'TimeStampEpoch': 1501600063297, 'Ask': 0.0809999, 'ClientTimeStampEpoch': 1501600068063, 'OpenSellOrders': 11133, 'MarketName': 'BTC-ETH', 'Last': 0.08099997} data for past window
Adding {'_id': ObjectId('5980994e411bf4054e310c76'), 'Low': 0.0684, 'TimeStamp': '2017-08-01T15:07:51.357', 'BaseVolume': 5825.12103688, 'Bid': 0.0805, 'High': 0.08347598, 'Created': '2015-08-14T09:02:24.817', 'OpenBuyOrders': 3006, 'Volume': 78251.43966094, 'PrevDay': 0.06971006, 'TimeStampEpoch': 1501600071357, 'Ask': 0.08099988, 'ClientTimeStampEpoch': 1501600078359, 'OpenSellOrders': 11134, 'MarketName': 'BTC-ETH', 'Last': 0.08099989} data for past window
Adding {'_id': ObjectId('59809958411bf4054e310c77'), 'Low': 0.0684, 'TimeStamp': '2017-08-01T15:08:04.68', 'BaseVolume': 5825.06261581, 'Bid': 0.0805, 'High': 0.08347598, 'Created': '2015-08-14T09:02:24.817', 'OpenBuyOrders': 3007, 'Volume': 78250.04968052, 'PrevDay': 0.06956, 'TimeStampEpoch': 1501600084680, 'Ask': 0.08099988, 'ClientTimeStampEpoch': 1501600088649, 'OpenSellOrders': 11136, 'MarketName': 'BTC-ETH', 'Last': 0.0805} data for past window
Adding {'_id': ObjectId('59809962411bf4054e310c78'), 'Low': 0.0684, 'TimeStamp': '2017-08-01T15:08:14.313', 'BaseVolume': 5823.39106161, 'Bid': 0.08050001, 'High': 0.08347598, 'Created': '2015-08-14T09:02:24.817', 'OpenBuyOrders': 3009, 'Volume': 78225.6698936, 'PrevDay': 0.06971006, 'TimeStampEpoch': 1501600094313, 'Ask': 0.08099988, 'ClientTimeStampEpoch': 1501600098800, 'OpenSellOrders': 11136, 'MarketName': 'BTC-ETH', 'Last': 0.08099988} data for past window
Adding {'_id': ObjectId('5980996c411bf4054e310c79'), 'Low': 0.0684, 'TimeStamp': '2017-08-01T15:08:19.873', 'BaseVolume': 5823.67616995, 'Bid': 0.08050001, 'High': 0.08347598, 'Created': '2015-08-14T09:02:24.817', 'OpenBuyOrders': 3011, 'Volume': 78229.20994538, 'PrevDay': 0.06971006, 'TimeStampEpoch': 1501600099873, 'Ask': 0.08098, 'ClientTimeStampEpoch': 1501600108981, 'OpenSellOrders': 11138, 'MarketName': 'BTC-ETH', 'Last': 0.08050001} data for past window
Adding {'_id': ObjectId('59809977411bf4054e310c7a'), 'Low': 0.0684, 'TimeStamp': '2017-08-01T15:08:35.62', 'BaseVolume': 5823.6839057, 'Bid': 0.08099953, 'High': 0.08347598, 'Created': '2015-08-14T09:02:24.817', 'OpenBuyOrders': 3013, 'Volume': 78228.88957369, 'PrevDay': 0.06968288, 'TimeStampEpoch': 1501600115620, 'Ask': 0.08099988, 'ClientTimeStampEpoch': 1501600119287, 'OpenSellOrders': 11136, 'MarketName': 'BTC-ETH', 'Last': 0.08099988} data for past window
Adding {'_id': ObjectId('59809981411bf4054e310c7b'), 'Low': 0.0684, 'TimeStamp': '2017-08-01T15:08:45.067', 'BaseVolume': 5824.87138139, 'Bid': 0.08055, 'High': 0.08347598, 'Created': '2015-08-14T09:02:24.817', 'OpenBuyOrders': 3010, 'Volume': 78243.33924209, 'PrevDay': 0.06971006, 'TimeStampEpoch': 1501600125067, 'Ask': 0.08099987, 'ClientTimeStampEpoch': 1501600129442, 'OpenSellOrders': 11138, 'MarketName': 'BTC-ETH', 'Last': 0.08055} data for past window
Adding {'_id': ObjectId('5980998b411bf4054e310c7c'), 'Low': 0.0684, 'TimeStamp': '2017-08-01T15:08:56.97', 'BaseVolume': 5825.48126366, 'Bid': 0.08055, 'High': 0.08347598, 'Created': '2015-08-14T09:02:24.817', 'OpenBuyOrders': 3014, 'Volume': 78250.51795625, 'PrevDay': 0.06971006, 'TimeStampEpoch': 1501600136970, 'Ask': 0.08097998, 'ClientTimeStampEpoch': 1501600139791, 'OpenSellOrders': 11138, 'MarketName': 'BTC-ETH', 'Last': 0.08097998} data for past window
Adding {'_id': ObjectId('59809996411bf4054e310c7d'), 'Low': 0.0684, 'TimeStamp': '2017-08-01T15:09:04.077', 'BaseVolume': 5825.56224364, 'Bid': 0.08055011, 'High': 0.08347598, 'Created': '2015-08-14T09:02:24.817', 'OpenBuyOrders': 3014, 'Volume': 78251.51795625, 'PrevDay': 0.06971006, 'TimeStampEpoch': 1501600144077, 'Ask': 0.08097997, 'ClientTimeStampEpoch': 1501600150081, 'OpenSellOrders': 11138, 'MarketName': 'BTC-ETH', 'Last': 0.08097998} data for past window
Adding {'_id': ObjectId('598099a0411bf4054e310c7e'), 'Low': 0.0684, 'TimeStamp': '2017-08-01T15:09:16.01', 'BaseVolume': 5826.56938076, 'Bid': 0.08055011, 'High': 0.08347598, 'Created': '2015-08-14T09:02:24.817', 'OpenBuyOrders': 3018, 'Volume': 78263.99641888, 'PrevDay': 0.06971006, 'TimeStampEpoch': 1501600156010, 'Ask': 0.08097995, 'ClientTimeStampEpoch': 1501600160285, 'OpenSellOrders': 11140, 'MarketName': 'BTC-ETH', 'Last': 0.08055011} data for past window
Adding {'_id': ObjectId('598099aa411bf4054e310c7f'), 'Low': 0.0684, 'TimeStamp': '2017-08-01T15:09:20.43', 'BaseVolume': 5826.57157525, 'Bid': 0.08055011, 'High': 0.08347598, 'Created': '2015-08-14T09:02:24.817', 'OpenBuyOrders': 3018, 'Volume': 78264.02351817, 'PrevDay': 0.06971006, 'TimeStampEpoch': 1501600160430, 'Ask': 0.08097994, 'ClientTimeStampEpoch': 1501600170488, 'OpenSellOrders': 11140, 'MarketName': 'BTC-ETH', 'Last': 0.08097995} data for past window
In [61]:
len(data_window)
Out[61]:
900
In [62]:
real_data_points_back
Out[62]:
900
In [63]:
from datetime import datetime
In [64]:
str(datetime.now())
Out[64]:
'2017-08-01 15:09:35.567409'
In [65]:
predicted_recent_rf = [None] * n_predictions
predicted_recent_rnn = [None] * n_predictions
predicted_recent_xgb = [None] * n_predictions
In [66]:
for _ in range(real_data_points_back*18):
current_data = returnTickerAll('BITTREX')[pair.replace('-', '_')]
data_window.appendleft(current_data)
recent_data_points = create_np_array(data_window, datapoints_back, datapoints_forward_prediction)
for i in range(n_predictions):
predicted_recent_rf[i], predicted_recent_rnn[i], predicted_recent_xgb[i] = predict(f_selector_mdl[i],
scaler_X[i], scaler_Y[i], model_l1_e5[i], forest_reg[i], clf[i], recent_data_points)
tuple = (str(datetime.now()), current_data['Last'], predicted_recent_rf[0][0], predicted_recent_rnn[0][0][0],
predicted_recent_xgb[0][0], predicted_recent_rf[1][0], predicted_recent_rnn[1][0][0],
predicted_recent_xgb[1][0], predicted_recent_rf[2][0], predicted_recent_rnn[2][0][0],
predicted_recent_xgb[2][0], predicted_recent_rf[3][0], predicted_recent_rnn[3][0][0],
predicted_recent_xgb[3][0], predicted_recent_rf[4][0], predicted_recent_rnn[4][0][0],
predicted_recent_xgb[4][0], predicted_recent_rf[5][0], predicted_recent_rnn[5][0][0],
predicted_recent_xgb[5][0])
for p in range(3, 20):
delta = (tuple[p]-tuple[2])/tuple[2]
if(math.fabs(delta)>=0.007):
print('Significant Delta: {}. Prediction position: {}. Current: {}. Prediction: {}'.format(delta, p, tuple[2], tuple[p]))
predictions.appendleft(tuple)
print(tuple)
time.sleep(10)
('2017-08-01 15:09:37.171431', 0.08055011, 0.08066117265467096, 0.080538213, 0.080668822, 0.080649962261343244, 0.080758907, 0.08075089, 0.080837243997556263, 0.080776349, 0.080612339, 0.080704873155487952, 0.080729328, 0.080694206, 0.080964137728362529, 0.080648035, 0.080729842, 0.081008923033615565, 0.080794685, 0.080743194)
('2017-08-01 15:09:47.369414', 0.08055011, 0.080655136987973414, 0.080535419, 0.080669306, 0.080627429172775281, 0.080757439, 0.08075089, 0.080837243997556263, 0.080775723, 0.080612339, 0.080704873155487952, 0.080730267, 0.080694206, 0.080901946678297157, 0.080649585, 0.080729842, 0.081007191396087391, 0.08079771, 0.080743194)
('2017-08-01 15:09:57.815381', 0.08055011, 0.080615375525682154, 0.080567569, 0.080669306, 0.080692635939213803, 0.08080817, 0.080765955, 0.080857225684301606, 0.080831006, 0.080612339, 0.080674652035238451, 0.080786161, 0.080694206, 0.080963631461630214, 0.080709331, 0.080729842, 0.081009665305655454, 0.080882654, 0.080743194)
('2017-08-01 15:10:08.441327', 0.08097994, 0.080832223695667393, 0.080644585, 0.080794819, 0.080700015534455119, 0.080774061, 0.08075089, 0.080824680459029644, 0.080795959, 0.080612339, 0.080674779293532589, 0.080751851, 0.080694206, 0.080872607333249399, 0.080672108, 0.080729842, 0.081002084193567539, 0.080828153, 0.080743194)
('2017-08-01 15:10:18.781949', 0.08097994, 0.080846588455027776, 0.080653869, 0.080794819, 0.080778757293432438, 0.080776662, 0.080762446, 0.080833529925833869, 0.080795482, 0.080612339, 0.08067193697181041, 0.080746539, 0.080694206, 0.080842106907774283, 0.080666043, 0.080729842, 0.080998803990858348, 0.080813147, 0.080743194)
('2017-08-01 15:10:29.195115', 0.08040003, 0.080594628784184552, 0.080509432, 0.080633737, 0.080767943157903685, 0.080768093, 0.080762446, 0.08083935702432328, 0.08078777, 0.080612339, 0.080704928620727021, 0.080734618, 0.080697894, 0.080776282434699601, 0.080652468, 0.08073274, 0.080959015381905822, 0.080794714, 0.080756545)
('2017-08-01 15:10:39.570665', 0.08055009, 0.080546995654817266, 0.080447219, 0.0805692, 0.08061773061257646, 0.080646187, 0.080601692, 0.08048922802658029, 0.080660678, 0.08043085, 0.080517127452888429, 0.080612786, 0.080493875, 0.080511467584361701, 0.080536529, 0.080391809, 0.080607443275183877, 0.080666296, 0.080362588)
('2017-08-01 15:10:49.946630', 0.08055007, 0.080477854280583058, 0.080366917, 0.0805692, 0.080574470002823403, 0.080538668, 0.080542073, 0.080420305751609772, 0.080544971, 0.08043085, 0.080516158552047523, 0.080501921, 0.080452748, 0.080467625517187683, 0.080426231, 0.080391809, 0.080606374579482998, 0.080577999, 0.080362588)
('2017-08-01 15:11:00.301027', 0.08055006, 0.080482465528169858, 0.080365218, 0.08056584, 0.080576315389814315, 0.080540791, 0.080542073, 0.080412805616663571, 0.080547608, 0.08043085, 0.080459768966723327, 0.080507264, 0.08044906, 0.080462400468079134, 0.080432475, 0.080388911, 0.080606374579482998, 0.080581538, 0.080349237)
('2017-08-01 15:11:10.658832', 0.08054899, 0.080539384574832687, 0.080364697, 0.08056584, 0.080447223616176122, 0.080540031, 0.080501154, 0.080334679082367008, 0.080548435, 0.08043085, 0.080519330425153704, 0.080505356, 0.080443189, 0.080447469490223758, 0.080429696, 0.080391809, 0.080586950556080575, 0.080577783, 0.080362588)
('2017-08-01 15:11:21.014869', 0.08054897, 0.080531872855962944, 0.080364533, 0.0805692, 0.080447223616176122, 0.080542773, 0.080501154, 0.080334679082367008, 0.08055184, 0.08043085, 0.080514271504476764, 0.080510743, 0.080452748, 0.08044224444111521, 0.080434702, 0.080391809, 0.080586950556080575, 0.080580987, 0.080362588)
('2017-08-01 15:11:31.367647', 0.08054896, 0.080531872855962944, 0.080365755, 0.0805692, 0.080456436740085721, 0.08054442, 0.080501154, 0.080407622006112983, 0.080554232, 0.08043085, 0.080519330425153704, 0.08051233, 0.080452748, 0.080516662187519822, 0.080437884, 0.080391809, 0.080574374215681602, 0.08059109, 0.080362588)
('2017-08-01 15:11:41.741999', 0.08054889, 0.080534210837361883, 0.080366276, 0.0805692, 0.080454591353094823, 0.08054091, 0.080501154, 0.080434073625912469, 0.080544502, 0.080444224, 0.080569960019862358, 0.080505244, 0.080457292, 0.080459719294135257, 0.080429137, 0.080399752, 0.080596488429615587, 0.080580905, 0.080362588)
('2017-08-01 15:11:52.550512', 0.0805489, 0.080521659567698536, 0.080388449, 0.0805692, 0.080465601391752531, 0.080567263, 0.080536664, 0.08052535461092164, 0.080571726, 0.080444224, 0.080626805893290451, 0.080527149, 0.080469318, 0.080570106368616473, 0.08044897, 0.080608949, 0.080497483368230802, 0.080611534, 0.080569565)
('2017-08-01 15:12:02.896534', 0.08054896, 0.080566547143003306, 0.080342248, 0.080557719, 0.080440514125529855, 0.080511093, 0.080521725, 0.080443233112971163, 0.080511235, 0.080444224, 0.080562015839739667, 0.080467023, 0.080470212, 0.080603113336765897, 0.08038637, 0.080399752, 0.080574143396243289, 0.080491692, 0.080362588)
('2017-08-01 15:12:14.572550', 0.08055, 0.080470926227736744, 0.080330141, 0.080557719, 0.080488910356567328, 0.080485515, 0.080542073, 0.080491078540547156, 0.080485858, 0.08043085, 0.080518078372616556, 0.080442011, 0.080436669, 0.080459762576937724, 0.080361456, 0.080391809, 0.080611610490611663, 0.080496296, 0.080362588)
('2017-08-01 15:12:24.898126', 0.08055, 0.08049389066085029, 0.080328658, 0.080554299, 0.08050602911782534, 0.080486871, 0.080546439, 0.080468834813682638, 0.080484942, 0.080444224, 0.080586842860789029, 0.080442645, 0.080441214, 0.080458019206357673, 0.080361359, 0.080399752, 0.080647476623527642, 0.080497317, 0.080362588)
('2017-08-01 15:12:35.834046', 0.08054895, 0.080508882377107002, 0.080323167, 0.080554299, 0.080476177994767487, 0.080477878, 0.080505513, 0.080374299198101309, 0.080474734, 0.080444224, 0.080569128698876571, 0.080429152, 0.080441214, 0.080444129431248027, 0.080349244, 0.080399752, 0.080654024273614455, 0.080487914, 0.080362588)
('2017-08-01 15:12:47.270263', 0.08054894, 0.080503516610187328, 0.080323502, 0.080554299, 0.080477709220256433, 0.080480166, 0.080505513, 0.080399183587420597, 0.080478787, 0.080444224, 0.080585381205159151, 0.080435328, 0.080441214, 0.080518881400962763, 0.080352992, 0.080399752, 0.080698857421767342, 0.080486327, 0.080362588)
('2017-08-01 15:12:57.506131', 0.08041005, 0.080544694238774542, 0.080289982, 0.080522142, 0.080465708214216322, 0.080479294, 0.080505513, 0.080402959626110354, 0.08047647, 0.080444224, 0.080585381205159151, 0.080433212, 0.080441214, 0.080454997494346869, 0.080352031, 0.080399752, 0.080645417095860775, 0.080483943, 0.080362588)
('2017-08-01 15:13:07.852003', 0.08055, 0.080532518749579687, 0.080325365, 0.080557719, 0.08050602911782534, 0.080482647, 0.080546439, 0.080434471021900572, 0.080480248, 0.080444224, 0.080585381205159151, 0.080435216, 0.080441214, 0.08051585968895196, 0.080352932, 0.080399752, 0.080690250244013662, 0.080487058, 0.080362588)
('2017-08-01 15:13:18.184293', 0.080416, 0.080537387700817392, 0.080304913, 0.080522142, 0.080517376379764527, 0.080499843, 0.080546439, 0.080417999524301495, 0.080499046, 0.080444224, 0.080597735915164753, 0.080454633, 0.080457292, 0.080514810628010913, 0.080373451, 0.080399752, 0.080669516791000262, 0.080512479, 0.080362588)
('2017-08-01 15:13:28.955327', 0.080416, 0.080537387700817392, 0.080294408, 0.080518231, 0.08050602911782534, 0.08048296, 0.080546439, 0.080436979067165418, 0.080477528, 0.080444224, 0.08059657023799377, 0.080428571, 0.080441214, 0.080527808459601571, 0.080344871, 0.080399752, 0.080669775794314794, 0.080476761, 0.080362588)
('2017-08-01 15:13:39.336611', 0.08055011, 0.080721866542493512, 0.080412105, 0.080653921, 0.080702565502554446, 0.080590248, 0.080695637, 0.080789304936537673, 0.080588937, 0.080625713, 0.080655693894733052, 0.080534928, 0.080645226, 0.080646955424621414, 0.080446906, 0.080740683, 0.080557896112255695, 0.080598466, 0.080756545)
('2017-08-01 15:13:49.547380', 0.080411, 0.080573179617392521, 0.080368645, 0.080518231, 0.080528508504257565, 0.080579363, 0.080606058, 0.08056210233506321, 0.080578983, 0.080444224, 0.080570371595499485, 0.080524847, 0.080482341, 0.080488812170491711, 0.080438778, 0.080399752, 0.080495754424306523, 0.080558091, 0.080362588)
('2017-08-01 15:13:59.904140', 0.08055011, 0.080508575639772026, 0.080325194, 0.080553807, 0.080509659181056792, 0.080480561, 0.080546439, 0.080426150805239766, 0.080474257, 0.080444224, 0.080572550469651255, 0.080424145, 0.080441214, 0.080456175345089531, 0.080338128, 0.080399752, 0.080705463129463834, 0.080472089, 0.080362588)
('2017-08-01 15:14:10.141933', 0.08041999, 0.080537387700817392, 0.080295369, 0.080518231, 0.080508032746128846, 0.080480449, 0.080546439, 0.080426150805239766, 0.08047352, 0.080444224, 0.080586188648635329, 0.080423124, 0.080441214, 0.080456175345089531, 0.080337927, 0.080399752, 0.08071302807948981, 0.080471925, 0.080362588)
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('2017-08-01 15:38:54.657897', 0.08115, 0.081131104448938701, 0.080879934, 0.081088871, 0.081160065430164338, 0.081033081, 0.081091568, 0.081245832206293472, 0.081058964, 0.081008799, 0.081272455709560612, 0.080999933, 0.080959909, 0.081228572989050216, 0.080896996, 0.081032202, 0.08130176893031886, 0.080985822, 0.081115216)
('2017-08-01 15:39:05.017894', 0.08109999, 0.081176802351757199, 0.080911808, 0.08117298, 0.081097891136971814, 0.081092887, 0.081169076, 0.081171324331390182, 0.081121944, 0.081043042, 0.081245691149475135, 0.081063963, 0.081137583, 0.081143575703843493, 0.080960922, 0.081040144, 0.081335304726955743, 0.081094235, 0.081115216)
('2017-08-01 15:39:15.380895', 0.08109999, 0.081165204930695359, 0.080933161, 0.08117298, 0.081166692813732774, 0.081121057, 0.08117903, 0.081180509351170951, 0.081151046, 0.081043042, 0.081274332862860607, 0.081090845, 0.081137583, 0.081190116887960656, 0.08098796, 0.081040144, 0.081405160280652783, 0.081103951, 0.081115216)
('2017-08-01 15:39:25.773228', 0.08129997, 0.081227290273642325, 0.080985852, 0.081226625, 0.081185721807368424, 0.081130989, 0.08117903, 0.081178516230042214, 0.081161804, 0.081043042, 0.081251365500085354, 0.081102073, 0.081137583, 0.081162437818516342, 0.080999509, 0.081040144, 0.081405160280652783, 0.081120335, 0.081115216)
('2017-08-01 15:39:36.154395', 0.08129997, 0.081220468301889351, 0.080955744, 0.081225485, 0.081169133497459564, 0.081092842, 0.081172548, 0.081274180570946056, 0.081118025, 0.081043042, 0.081270397037908157, 0.081058413, 0.081137583, 0.081239187829854662, 0.08095064, 0.081040144, 0.081323303184791737, 0.081054315, 0.081115216)
('2017-08-01 15:39:46.547584', 0.08120149, 0.081291551026109493, 0.080976292, 0.08117184, 0.081287853456795914, 0.081152268, 0.081233904, 0.081241399995037392, 0.081181049, 0.081043042, 0.081240717010908028, 0.08111944, 0.081137583, 0.081201340927357007, 0.081013583, 0.081040144, 0.081434641904048366, 0.081139311, 0.081115216)
('2017-08-01 15:39:56.962824', 0.08120149, 0.081174094250768339, 0.080975309, 0.08117184, 0.081182427918208525, 0.08115045, 0.081189498, 0.081227017914397237, 0.081179701, 0.081043042, 0.081252533220318113, 0.081118539, 0.081137583, 0.081201340927357007, 0.081013173, 0.081040144, 0.081397781885367476, 0.081112847, 0.081115216)
('2017-08-01 15:40:07.327560', 0.08120149, 0.081152758777766662, 0.080956243, 0.08117184, 0.081185443666991097, 0.081131496, 0.081176318, 0.081236238533764646, 0.081159942, 0.081043042, 0.081286955265830407, 0.081096932, 0.081137583, 0.081150757719588101, 0.080992304, 0.081040144, 0.081223361184243989, 0.081108727, 0.081115216)
('2017-08-01 15:40:18.654467', 0.08120151, 0.081318189642195865, 0.080981202, 0.081267677, 0.081308968464468667, 0.081161156, 0.081228063, 0.081447887356539767, 0.081190169, 0.081392355, 0.081333713462587748, 0.081127517, 0.081201583, 0.081344288010875537, 0.081020012, 0.081394218, 0.081629295836004492, 0.081139103, 0.081161261)
('2017-08-01 15:40:28.855900', 0.08109999, 0.081295509516842848, 0.080992751, 0.081267677, 0.081242862545629513, 0.081206165, 0.081251405, 0.08138196205641815, 0.081237361, 0.081392355, 0.081362781900012365, 0.081174895, 0.081201583, 0.081398653316407069, 0.081066586, 0.081394218, 0.081501530444646683, 0.081196859, 0.081161261)
('2017-08-01 15:40:39.495123', 0.08137431, 0.081209455054698704, 0.08101362, 0.08112257, 0.081167687969659327, 0.081149615, 0.081153221, 0.081237824399755354, 0.081176125, 0.081043042, 0.081341465715769071, 0.081111938, 0.081031159, 0.081193409164625044, 0.081005611, 0.081040144, 0.081572717404823719, 0.081125893, 0.081115216)
('2017-08-01 15:40:50.014389', 0.08109999, 0.081133583693487846, 0.080909677, 0.081068918, 0.081206702316320073, 0.081101812, 0.081119932, 0.08126010148876682, 0.081126973, 0.081043042, 0.081182101309821092, 0.081064671, 0.081031159, 0.081195557490798029, 0.080960609, 0.081040144, 0.081259269889473504, 0.081072062, 0.081115216)
('2017-08-01 15:41:00.219731', 0.08111, 0.081285557949968584, 0.080955885, 0.081268817, 0.081283563333470976, 0.081154354, 0.081218109, 0.081334263205296645, 0.081181273, 0.081392355, 0.081355257940652126, 0.081121959, 0.081201583, 0.081515116000532586, 0.081014872, 0.081394218, 0.081498014415814626, 0.081137329, 0.081161261)
('2017-08-01 15:41:10.564760', 0.08137318, 0.081341792899527421, 0.081106678, 0.081349701, 0.081252447927169888, 0.081271894, 0.081360564, 0.081370053429208805, 0.081303872, 0.081392355, 0.081427994457280925, 0.081240423, 0.081274949, 0.081621009898212568, 0.081128113, 0.081394218, 0.081497715363635245, 0.081275813, 0.081161261)
('2017-08-01 15:41:20.784430', 0.0813732, 0.081205824997083925, 0.081053026, 0.081226625, 0.081207810426967314, 0.08120241, 0.081175484, 0.081252065069734619, 0.081230938, 0.081043042, 0.081223724587945995, 0.081168607, 0.081031159, 0.081210075505369461, 0.081061445, 0.081040144, 0.081494517833858235, 0.081134088, 0.081115216)
('2017-08-01 15:41:31.453166', 0.0811015, 0.081152975046067263, 0.080945313, 0.081268817, 0.081295413239875977, 0.0811399, 0.081218109, 0.081279207237138815, 0.081165411, 0.081043042, 0.08141142243655064, 0.081105307, 0.081201583, 0.081472924134267322, 0.080999084, 0.081394218, 0.081588266287884792, 0.081120975, 0.081161261)
('2017-08-01 15:41:41.676766', 0.08109999, 0.081191583508442952, 0.080983363, 0.081268817, 0.081232334285735938, 0.081191011, 0.081234083, 0.081259645068523165, 0.081219919, 0.081043042, 0.081440754098380946, 0.081159651, 0.081201583, 0.08140729023589241, 0.081052788, 0.081394218, 0.081581915857988199, 0.0811854, 0.081161261)
('2017-08-01 15:41:51.893289', 0.0813015, 0.081216659114876855, 0.081016503, 0.081238367, 0.081257964570793706, 0.081168883, 0.08117687, 0.081201348827453654, 0.081198305, 0.081022173, 0.081196551528857949, 0.08114022, 0.081134871, 0.081373848750189701, 0.08103703, 0.081394218, 0.081498885068239685, 0.081169903, 0.081161261)
('2017-08-01 15:42:02.219538', 0.0813015, 0.081248950262096206, 0.080982037, 0.081245482, 0.081269774938811698, 0.081124932, 0.08118505, 0.081267994938445387, 0.081148475, 0.081022173, 0.081343720023101959, 0.08108791, 0.081148922, 0.081359817191312703, 0.080981493, 0.081394218, 0.081548369515701893, 0.081124589, 0.081161261)
('2017-08-01 15:42:13.153848', 0.081, 0.081151953973040772, 0.080910757, 0.081184708, 0.081259583871648819, 0.081124149, 0.081173211, 0.081201183576636457, 0.081150271, 0.081022173, 0.081312798561395533, 0.081088737, 0.081134871, 0.081246763351209991, 0.080981486, 0.081394218, 0.081514584235150039, 0.08111918, 0.081161261)
('2017-08-01 15:42:23.561368', 0.081, 0.081097344612897262, 0.080930769, 0.08118961, 0.081291770630994006, 0.081151001, 0.08117687, 0.081248121728157055, 0.081179678, 0.081022173, 0.081263724062403953, 0.081119634, 0.081134871, 0.081224320916206322, 0.081012852, 0.081394218, 0.081532420061221383, 0.081159048, 0.081161261)
('2017-08-01 15:42:33.945852', 0.081, 0.081155593091386827, 0.080947831, 0.08118961, 0.081275213227249343, 0.081177682, 0.081182465, 0.081141031044906664, 0.081208132, 0.081022173, 0.081273193399274149, 0.081148155, 0.081134871, 0.081273191450514426, 0.081043057, 0.081394218, 0.081534650584323826, 0.081201099, 0.081161261)
('2017-08-01 15:42:44.284400', 0.081, 0.081162413864941357, 0.080912516, 0.081190683, 0.081231750811016287, 0.081129044, 0.081165873, 0.081144326252212615, 0.081155539, 0.081022173, 0.081348836906782115, 0.081093945, 0.081148922, 0.081268974288116483, 0.080985233, 0.081394218, 0.081521117972959148, 0.081123628, 0.081161261)
('2017-08-01 15:42:54.636169', 0.08071001, 0.080882923665667322, 0.080762394, 0.080879554, 0.080942985033801387, 0.081026763, 0.080976576, 0.081066125742063802, 0.081050664, 0.081022173, 0.080994568022324895, 0.080993704, 0.080899209, 0.081024639970066356, 0.080892667, 0.081040144, 0.081124970869562246, 0.081012033, 0.080745853)
('2017-08-01 15:43:05.050483', 0.08129999, 0.081089323776423528, 0.080915891, 0.081244342, 0.08131744941121799, 0.081037171, 0.081155352, 0.081198549672701348, 0.081059612, 0.081022173, 0.081261403185423714, 0.081004009, 0.081148922, 0.081225799083096836, 0.080899559, 0.081394218, 0.08152735473289438, 0.08108861, 0.081161261)
('2017-08-01 15:43:15.313801', 0.081, 0.08084423527649269, 0.080841199, 0.080991931, 0.080974023359318234, 0.081039704, 0.081009649, 0.081007151375129913, 0.081063308, 0.081022173, 0.080993749081574057, 0.081005953, 0.080899209, 0.081070862342989547, 0.080904059, 0.081040144, 0.081330090837896413, 0.081024088, 0.081115216)
('2017-08-01 15:43:25.723957', 0.08120149, 0.081129040774499547, 0.080885299, 0.081094846, 0.08118534563701521, 0.081028812, 0.08111006, 0.081118179441499155, 0.081049003, 0.081022173, 0.081196523663408376, 0.08099398, 0.081122316, 0.081243936134392306, 0.080888838, 0.081040144, 0.081406328090974214, 0.081049927, 0.081115216)
('2017-08-01 15:43:36.712532', 0.08120149, 0.08114789881198417, 0.080933675, 0.081094846, 0.081209251127774246, 0.081097156, 0.081128433, 0.081144635429596823, 0.081120595, 0.081022173, 0.081210131144498196, 0.081064098, 0.081122316, 0.081197890751795523, 0.080957271, 0.081040144, 0.081319178367071546, 0.081090294, 0.081115216)
('2017-08-01 15:43:47.514703', 0.08120149, 0.08114789881198417, 0.080934241, 0.081095986, 0.081240814846455067, 0.081096016, 0.081132919, 0.081142605354743422, 0.081118926, 0.081022173, 0.081205684956975513, 0.081062749, 0.081122316, 0.081217821000714063, 0.080955967, 0.081040144, 0.0815042244911834, 0.081088893, 0.081115216)
('2017-08-01 15:43:57.699170', 0.08120149, 0.081161414983299485, 0.080933757, 0.081095986, 0.081224048277688962, 0.081095561, 0.081136309, 0.08111822132584294, 0.081116632, 0.081022173, 0.081202686152498899, 0.081056938, 0.081084922, 0.081265927902890653, 0.080949374, 0.081040144, 0.08150697454644841, 0.081079654, 0.081115216)
('2017-08-01 15:44:08.062906', 0.08120149, 0.081125973047279795, 0.080885544, 0.081094846, 0.081052538817504252, 0.08103741, 0.081053987, 0.081127819219829603, 0.081057683, 0.081022173, 0.081146385798785664, 0.081000715, 0.08100564, 0.081142346299840309, 0.080895789, 0.081040144, 0.081104695936343091, 0.081009597, 0.080745853)
('2017-08-01 15:44:18.440103', 0.0812, 0.081016373387161111, 0.080805168, 0.081095986, 0.081005607031404553, 0.080923289, 0.081010759, 0.081098518316431903, 0.080935858, 0.081022173, 0.080957841277339085, 0.080879919, 0.08100564, 0.080953575908262229, 0.080773473, 0.081040144, 0.081061966358144527, 0.080917709, 0.080745853)
('2017-08-01 15:44:28.794193', 0.0812, 0.081035613671354909, 0.080845341, 0.081095986, 0.081013102013139937, 0.080982484, 0.081041798, 0.081083427717555029, 0.08099968, 0.081022173, 0.081063416401960187, 0.080944143, 0.081043027, 0.081109215639559001, 0.080840446, 0.081040144, 0.081129174635701407, 0.081007406, 0.080745853)
('2017-08-01 15:44:38.977031', 0.0812, 0.081082900176099038, 0.080811702, 0.081095986, 0.081015381683683649, 0.080934621, 0.081041969, 0.081093719334966008, 0.080947876, 0.081022173, 0.081124008825641733, 0.080890581, 0.081043027, 0.081097605448079577, 0.08078596, 0.081040144, 0.081023196362726604, 0.08093407, 0.080745853)
('2017-08-01 15:44:49.456945', 0.08071004, 0.080864330248158908, 0.080720983, 0.080983609, 0.081068484391752252, 0.080968097, 0.081021555, 0.081136485375069387, 0.080984607, 0.081022173, 0.081091517201836147, 0.080927797, 0.08100564, 0.08107728592095681, 0.080825545, 0.081040144, 0.081050671208032721, 0.080988519, 0.080745853)
('2017-08-01 15:44:59.700557', 0.08119, 0.081210248291358506, 0.080912463, 0.081180096, 0.081280866050438508, 0.081071332, 0.081206717, 0.081278832571361578, 0.081091158, 0.081043042, 0.081386596990756926, 0.08103025, 0.081151634, 0.081067547835975695, 0.080921717, 0.081040144, 0.081252367370511169, 0.081110969, 0.081115216)
('2017-08-01 15:45:10.053023', 0.08119, 0.081226602852081262, 0.080982111, 0.081180096, 0.081271788852804477, 0.08116246, 0.081223398, 0.081229597759507555, 0.081184566, 0.081043042, 0.081370269599642636, 0.081119612, 0.081151634, 0.081131092058127907, 0.081008941, 0.081040144, 0.081063640838655207, 0.081115581, 0.081115216)
('2017-08-01 15:45:23.273778', 0.0812, 0.081195763797997839, 0.080985747, 0.081180096, 0.081248490156108716, 0.081159823, 0.081223398, 0.081201878586101853, 0.081181929, 0.081043042, 0.081334281815344328, 0.081113495, 0.081151634, 0.081404350900452907, 0.081003606, 0.081040144, 0.081615730647085702, 0.08111196, 0.081115216)
('2017-08-01 15:45:34.303136', 0.0812, 0.081125211918367399, 0.080962911, 0.081180096, 0.081256320107484764, 0.081130415, 0.081215307, 0.081206008199139645, 0.081148475, 0.081043042, 0.081352293012856444, 0.081081964, 0.081189021, 0.081325261018586628, 0.080970392, 0.081040144, 0.081326758698411508, 0.081066318, 0.081115216)
('2017-08-01 15:45:46.403171', 0.0812, 0.081141223568910209, 0.080965273, 0.081178956, 0.081272988719256145, 0.081134267, 0.081215307, 0.081158054319686673, 0.081153706, 0.081043042, 0.081367916062741033, 0.081087835, 0.081189021, 0.081373430382273682, 0.080976509, 0.081040144, 0.081583458434816, 0.081068367, 0.081115216)
('2017-08-01 15:45:56.794862', 0.08120149, 0.081144973808573162, 0.080884613, 0.081095986, 0.081001979348383241, 0.081031136, 0.081045181, 0.081093698939363967, 0.081046723, 0.081022173, 0.081130741488775582, 0.080984697, 0.081043027, 0.081097130418159927, 0.080878749, 0.081040144, 0.081032399724902879, 0.080943234, 0.080745853)
('2017-08-01 15:46:07.149709', 0.08120149, 0.08104005644194226, 0.080817595, 0.081095986, 0.081008681464212806, 0.080939442, 0.081030659, 0.081099423828882286, 0.080951594, 0.081022173, 0.081177816796940222, 0.080892429, 0.081043027, 0.080986930601153639, 0.080788493, 0.081040144, 0.081032399724902879, 0.080941565, 0.080745853)
('2017-08-01 15:46:18.301303', 0.0812, 0.081208459924358767, 0.080900416, 0.081180096, 0.081241864688432558, 0.081044719, 0.081200786, 0.08119829279380178, 0.08105991, 0.081043042, 0.081299153885981745, 0.080995873, 0.081189021, 0.081349722023165691, 0.080884829, 0.081040144, 0.081529376298264469, 0.081067011, 0.081115216)
('2017-08-01 15:46:28.661325', 0.08120149, 0.081286969624665528, 0.080997452, 0.081180096, 0.081236063712842838, 0.081180863, 0.081223398, 0.081197391198844365, 0.081202783, 0.081043042, 0.081371838234221061, 0.081134871, 0.081189021, 0.08114318235474037, 0.081025459, 0.081040144, 0.081323063561296441, 0.081140421, 0.081115216)
('2017-08-01 15:46:39.031117', 0.0812, 0.081259352026250481, 0.080994911, 0.081180096, 0.081260034604059811, 0.081182592, 0.081223398, 0.08126606151201167, 0.081204161, 0.081043042, 0.081447463697152278, 0.081139743, 0.081189021, 0.081298689200343832, 0.081029423, 0.081040144, 0.081686271450071318, 0.081143878, 0.081115216)
('2017-08-01 15:46:49.383675', 0.0812, 0.081269621072205117, 0.080997981, 0.081180096, 0.081260034604059811, 0.081183493, 0.081223398, 0.081256834981988296, 0.081206106, 0.081043042, 0.081447463697152278, 0.081141233, 0.081189021, 0.081316203138259477, 0.081030987, 0.081040144, 0.081546136725195909, 0.08114624, 0.081115216)
('2017-08-01 15:46:59.638001', 0.08071052, 0.081050010624274096, 0.080863461, 0.081067719, 0.081191101975452956, 0.081159063, 0.081193529, 0.08129206088130142, 0.081182741, 0.081043042, 0.081373999699464392, 0.081120163, 0.081151634, 0.081349087824446789, 0.081013501, 0.081040144, 0.081607830998574368, 0.081125468, 0.081115216)
Significant Delta: 0.007749229636344436. Prediction position: 17. Current: 0.08103453781876623. Prediction: 0.08166249306079888
('2017-08-01 15:47:09.845264', 0.08071052, 0.081034537818766225, 0.080849625, 0.081067719, 0.081227076502248308, 0.081136614, 0.081183642, 0.081333639587600703, 0.081157394, 0.081043042, 0.081343262213891973, 0.081092514, 0.081151634, 0.081183174523240795, 0.080985107, 0.081040144, 0.081662493060798882, 0.081115551, 0.081115216)
('2017-08-01 15:47:20.272660', 0.08071054, 0.080892630740513949, 0.080724299, 0.080880694, 0.080834098697314838, 0.080970511, 0.080964506, 0.080979671323070768, 0.080982544, 0.081022173, 0.080786166310941251, 0.080922216, 0.080899209, 0.080923907087107089, 0.080819018, 0.081040144, 0.080979681679330395, 0.080898486, 0.080745853)
('2017-08-01 15:47:31.979081', 0.081, 0.080829785627346371, 0.080713503, 0.080993079, 0.080978257600841291, 0.080859683, 0.080939345, 0.080904850014679605, 0.080866843, 0.081022173, 0.080866610240406936, 0.080811419, 0.080899209, 0.080887528063988925, 0.080711931, 0.081040144, 0.080985626276231334, 0.08085563, 0.080745853)
('2017-08-01 15:47:42.908784', 0.081, 0.080828144120795337, 0.080745362, 0.080993079, 0.081065300629721823, 0.080908082, 0.080952637, 0.080956508272316527, 0.080920771, 0.081022173, 0.080859627749472099, 0.08086744, 0.080899209, 0.080889267466063772, 0.080771267, 0.081040144, 0.080955992633517659, 0.080944069, 0.080745853)
('2017-08-01 15:47:53.264640', 0.081, 0.080821758580559885, 0.080743454, 0.080993079, 0.081065300629721823, 0.080909185, 0.080948152, 0.080890933893802086, 0.080923058, 0.081022173, 0.080901645083552118, 0.080869965, 0.080899209, 0.080891701912197911, 0.080774352, 0.081040144, 0.08087661375405733, 0.080946416, 0.080745853)
('2017-08-01 15:48:03.671685', 0.081, 0.080828752442822019, 0.080745384, 0.080993079, 0.080981604083635736, 0.080905452, 0.080945268, 0.080895990384892077, 0.080919869, 0.081022173, 0.080833020692148969, 0.080866501, 0.080899209, 0.080845168149779184, 0.080773093, 0.081040144, 0.08089091637456193, 0.080950148, 0.080745853)
('2017-08-01 15:48:14.156818', 0.081, 0.080839352413593385, 0.080771461, 0.080993079, 0.081022504551570479, 0.080948018, 0.080981515, 0.080863304524929416, 0.080964141, 0.081022173, 0.08090144105182738, 0.080909386, 0.080978498, 0.080837714490357898, 0.080810785, 0.081040144, 0.08081116189023059, 0.080987617, 0.081115216)
('2017-08-01 15:48:24.875877', 0.0809, 0.080826689343798983, 0.080766357, 0.080880694, 0.0809348325788775, 0.08097218, 0.080996044, 0.080875128570583821, 0.080986284, 0.081022173, 0.080851749184545005, 0.080929175, 0.080978498, 0.080864921352222313, 0.080829531, 0.081040144, 0.08081141264810493, 0.08097066, 0.081115216)
('2017-08-01 15:48:35.381032', 0.0809, 0.080832705324425561, 0.080766305, 0.080880694, 0.0809348325788775, 0.080971174, 0.080996044, 0.080939220467197717, 0.080985248, 0.081022173, 0.080851749184545005, 0.080927022, 0.080978498, 0.080876244845735384, 0.080827855, 0.081040144, 0.080827371285927985, 0.080970362, 0.081115216)
('2017-08-01 15:48:45.909961', 0.081, 0.080818671769853759, 0.080755837, 0.080993079, 0.080957786395787684, 0.08091668, 0.080980755, 0.08095215084469462, 0.080926538, 0.081022173, 0.080934563426211775, 0.080866426, 0.080978498, 0.080928430117218633, 0.080763362, 0.081040144, 0.080822412498942162, 0.08088053, 0.081115216)
('2017-08-01 15:48:56.491723', 0.081, 0.080793679905928631, 0.080726251, 0.080993079, 0.080832689072352887, 0.080886275, 0.080947384, 0.080852286531069198, 0.080897667, 0.081022173, 0.08075585686205862, 0.080842331, 0.080899209, 0.080934722105200613, 0.080738597, 0.081040144, 0.080920664642355561, 0.080840014, 0.080745853)
('2017-08-01 15:49:07.753908', 0.081, 0.080798368813899069, 0.080700286, 0.080993079, 0.080911795534375316, 0.080850087, 0.080932863, 0.080849377810956011, 0.08086019, 0.081022173, 0.080800038091678447, 0.080806792, 0.080899209, 0.080891149319967659, 0.080704294, 0.081040144, 0.080920664642355561, 0.080840535, 0.080745853)
('2017-08-01 15:49:18.178290', 0.081, 0.081035054903214457, 0.08077845, 0.081095986, 0.081137380329274464, 0.080946513, 0.081079334, 0.081127064475120969, 0.080959149, 0.081022173, 0.081296383177719964, 0.08090096, 0.081084922, 0.081060141526507562, 0.080792673, 0.081040144, 0.081060320827096938, 0.080953307, 0.081115216)
('2017-08-01 15:49:28.831512', 0.08081068, 0.080913138677013829, 0.080736794, 0.080880694, 0.080857466959447358, 0.080955073, 0.080983371, 0.080829042898242803, 0.080968484, 0.081022173, 0.081039686073161421, 0.080909513, 0.080899209, 0.081074847342121045, 0.080804393, 0.081040144, 0.080884059827922822, 0.08089976, 0.081115216)
('2017-08-01 15:49:39.326618', 0.081, 0.080775302542946806, 0.080738954, 0.080993079, 0.080852885959219176, 0.080904759, 0.080975957, 0.080832089289447392, 0.080917165, 0.081022173, 0.080872377257736161, 0.080861114, 0.080899209, 0.080935951152340424, 0.080758974, 0.081040144, 0.080846125081450088, 0.08089295, 0.081115216)
('2017-08-01 15:49:49.781005', 0.081, 0.081051520007446351, 0.080800846, 0.081095986, 0.081112592282375037, 0.080981508, 0.081097089, 0.081232422822695508, 0.080995284, 0.081022173, 0.081219915646902577, 0.080936246, 0.081122316, 0.081028140987098263, 0.080829576, 0.081040144, 0.081099510411849138, 0.080983818, 0.081115216)
('2017-08-01 15:50:00.236408', 0.081, 0.080851922312326532, 0.080797449, 0.080993079, 0.080850676860443907, 0.080976471, 0.08099667, 0.080842880525863164, 0.08099024, 0.081022173, 0.08101196455706651, 0.080928788, 0.080936596, 0.080927702715708358, 0.080822989, 0.081040144, 0.080910714055733093, 0.080924705, 0.081115216)
('2017-08-01 15:50:10.702029', 0.081, 0.081107943371983307, 0.080805831, 0.081095986, 0.081142989990335923, 0.080980323, 0.081097089, 0.081204841889929852, 0.080993615, 0.081022173, 0.081207827646045103, 0.080932625, 0.081122316, 0.081018204733366753, 0.080826566, 0.081040144, 0.08106509973787672, 0.080974251, 0.081115216)
('2017-08-01 15:50:21.059735', 0.081, 0.081047750238792987, 0.080845207, 0.081095986, 0.081156614504317978, 0.081036001, 0.081104502, 0.081238488610903742, 0.081053168, 0.081022173, 0.081328995988331459, 0.080991566, 0.081122316, 0.081116103060719341, 0.080882341, 0.081040144, 0.08112796113546622, 0.080993272, 0.081115216)
('2017-08-01 15:50:31.544232', 0.081, 0.080878481316150899, 0.080783181, 0.080993079, 0.080834849517934029, 0.080956958, 0.080983371, 0.080860148252760874, 0.080971189, 0.081022173, 0.080981209949878688, 0.080911055, 0.080936596, 0.080901135177959785, 0.080807455, 0.081040144, 0.08089103635370197, 0.08090128, 0.081115216)
('2017-08-01 15:50:42.066969', 0.081, 0.080761061341099005, 0.080763832, 0.080993079, 0.080834044863977439, 0.080931932, 0.080977947, 0.0808998115897019, 0.080944963, 0.081022173, 0.080892757414097155, 0.080887184, 0.080936596, 0.080826835390501006, 0.080785587, 0.081040144, 0.08087221430013565, 0.080927029, 0.081115216)
('2017-08-01 15:50:52.437749', 0.081, 0.081073477529755772, 0.080798484, 0.081095986, 0.081108098952313079, 0.080973148, 0.081097089, 0.081212130396677684, 0.080987841, 0.081022173, 0.081188666906816961, 0.080929399, 0.081122316, 0.081050940470938346, 0.08082334, 0.081040144, 0.081051990380807071, 0.080968618, 0.081115216)
('2017-08-01 15:51:02.814375', 0.081, 0.081072453369358102, 0.080849417, 0.081095986, 0.081175550402537461, 0.081046879, 0.081117794, 0.081287776336633186, 0.081064805, 0.081022173, 0.081366308456844019, 0.081003211, 0.081122316, 0.081083091076821004, 0.080893539, 0.081040144, 0.08112796113546622, 0.081007779, 0.081115216)
('2017-08-01 15:51:13.037529', 0.081, 0.081102341673175593, 0.080843002, 0.081095986, 0.081283823776831896, 0.081039838, 0.081059247, 0.081126009306034988, 0.081056401, 0.081022173, 0.081081222455252011, 0.080994695, 0.081039339, 0.080880649528354498, 0.080892064, 0.081037246, 0.081060587692250052, 0.081018843, 0.081101865)
('2017-08-01 15:51:23.363799', 0.08081201, 0.081021224491133428, 0.080746718, 0.080983609, 0.081271214272570919, 0.080967911, 0.081047207, 0.081122507898478691, 0.08098039, 0.081022173, 0.081146384498150911, 0.08091931, 0.081039339, 0.081041451664875627, 0.0808153, 0.081037246, 0.081061527230881752, 0.08096011, 0.081101865)
('2017-08-01 15:51:33.702977', 0.08119995, 0.081050518368020227, 0.080864444, 0.081095986, 0.081255592675254784, 0.081007108, 0.081049189, 0.081155054828487894, 0.081025802, 0.081022173, 0.081101904444407333, 0.080964856, 0.081039339, 0.081017694614029162, 0.080862671, 0.081037246, 0.081057314892343579, 0.081016459, 0.081101865)
('2017-08-01 15:51:44.114142', 0.08119996, 0.08106551746650463, 0.080862336, 0.081095986, 0.081251808819434571, 0.081005536, 0.081049189, 0.081160201995234077, 0.081021972, 0.081022173, 0.081170688090368801, 0.080966003, 0.081039339, 0.080914730212355152, 0.080863781, 0.081037246, 0.080997756548262606, 0.081019193, 0.081101865)
('2017-08-01 15:51:54.352740', 0.08119915, 0.081112689888745704, 0.080853082, 0.081095986, 0.081255414848658808, 0.080982335, 0.08109612, 0.081180024427496195, 0.080993943, 0.081022173, 0.081327194266761646, 0.080935419, 0.081118628, 0.081025567816057908, 0.080832466, 0.081037246, 0.081090208933406599, 0.080984034, 0.081101865)
('2017-08-01 15:52:04.701748', 0.081, 0.081075424019380254, 0.080832101, 0.081095986, 0.081230294776421352, 0.08101856, 0.081117794, 0.081232441579570033, 0.081034712, 0.081022173, 0.081351805014791029, 0.080975331, 0.081118628, 0.081063520845172529, 0.080869749, 0.081037246, 0.081086427975470721, 0.08099962, 0.081101865)
('2017-08-01 15:52:15.634027', 0.081, 0.081072096571050647, 0.08087004, 0.081095986, 0.081122465738268576, 0.081072979, 0.081119783, 0.081192808681968556, 0.081093043, 0.081022173, 0.081343652396912464, 0.081033505, 0.081118628, 0.081011045350472516, 0.080929615, 0.081037246, 0.081110991863016429, 0.081056766, 0.081101865)
('2017-08-01 15:52:26.050596', 0.081, 0.081091041817002829, 0.080870323, 0.081095986, 0.081149439096046744, 0.081073783, 0.081119783, 0.081185103941091907, 0.081093341, 0.081008799, 0.081308426336202, 0.08103238, 0.081114084, 0.081058331436668837, 0.080926955, 0.081029303, 0.081085217980218255, 0.081057854, 0.081101865)
('2017-08-01 15:52:36.456498', 0.081, 0.08106560017781117, 0.080855794, 0.081095986, 0.08111890644018252, 0.081051834, 0.081119783, 0.081194523276194766, 0.081069596, 0.081008799, 0.081303437526463046, 0.081008419, 0.081114084, 0.081032287392477795, 0.080902867, 0.081029303, 0.081088001440857788, 0.081025988, 0.081101865)
('2017-08-01 15:52:46.835403', 0.081, 0.081068098270446559, 0.080855139, 0.081095986, 0.08111890644018252, 0.081051409, 0.081119783, 0.081194523276194766, 0.081068672, 0.081008799, 0.081236518210784633, 0.08100611, 0.081114084, 0.081003497567937829, 0.080900922, 0.081029303, 0.081088001440857788, 0.081024237, 0.081101865)
('2017-08-01 15:52:57.230591', 0.081, 0.081080248046961359, 0.080860838, 0.081095986, 0.08116826393655939, 0.081058651, 0.081119783, 0.081167076926472848, 0.081079297, 0.081008799, 0.081294893536833585, 0.081016734, 0.081114084, 0.081036769228404387, 0.080911547, 0.081029303, 0.08115643309317748, 0.081031539, 0.081101865)
('2017-08-01 15:53:08.140413', 0.08081201, 0.081036846872024212, 0.080816776, 0.080983609, 0.081109155501292829, 0.081059486, 0.081119783, 0.081161998407954142, 0.081078403, 0.081008799, 0.081145793052995963, 0.081014879, 0.081114084, 0.081027748855858908, 0.080909386, 0.081029303, 0.081150195763737928, 0.081031792, 0.081101865)
('2017-08-01 15:53:18.497694', 0.0811, 0.081047258407443493, 0.08087524, 0.080993079, 0.081097208722437836, 0.081046656, 0.081081249, 0.081057841948174905, 0.081064217, 0.081008799, 0.081165523913605639, 0.081003346, 0.081007652, 0.080965062044545663, 0.080899529, 0.081029303, 0.080980574489963056, 0.081023782, 0.081101865)
('2017-08-01 15:53:28.913223', 0.0811, 0.08101590758190011, 0.080869354, 0.080993079, 0.081141160772209286, 0.081036754, 0.081073456, 0.081015799620240886, 0.081053317, 0.081008799, 0.081196498093391967, 0.080989562, 0.081007652, 0.080987554471583068, 0.080886401, 0.081029303, 0.081217450481988196, 0.081012018, 0.081101865)
('2017-08-01 15:53:39.283551', 0.08081201, 0.080817049407639271, 0.080754749, 0.080880694, 0.080861639476629346, 0.080976963, 0.080990866, 0.080812159637921932, 0.08099363, 0.081008799, 0.080904050315267692, 0.080933869, 0.08092837, 0.081003490540729431, 0.080836594, 0.081029303, 0.081044113740145182, 0.080948502, 0.081101865)
('2017-08-01 15:53:49.641162', 0.08081201, 0.080781474133646294, 0.080730006, 0.080880694, 0.080883452021036667, 0.080942482, 0.08098162, 0.080832712990143538, 0.08095555, 0.081008799, 0.08089178128988124, 0.080897495, 0.081007652, 0.080885449960539285, 0.080799982, 0.081029303, 0.08090027339569375, 0.080949448, 0.081101865)
('2017-08-01 15:54:00.044691', 0.08081201, 0.080806688779124308, 0.080727212, 0.080880694, 0.080875088825990885, 0.080939732, 0.080983639, 0.080864328448091188, 0.080951601, 0.081008799, 0.080868403788132773, 0.080891043, 0.08092837, 0.080937780018833241, 0.080793761, 0.081029303, 0.080948258438833631, 0.080920659, 0.081101865)
('2017-08-01 15:54:10.406792', 0.081, 0.080774870503362101, 0.080758356, 0.080993079, 0.080884982446587914, 0.080920868, 0.08097358, 0.08086799273523658, 0.080934651, 0.081008799, 0.080933539784411104, 0.080875777, 0.08092837, 0.080959337416571409, 0.080779165, 0.081029303, 0.08098857070688982, 0.080926999, 0.081101865)
('2017-08-01 15:54:20.749292', 0.081, 0.080775025673508513, 0.080759026, 0.080993079, 0.080887584830706113, 0.080924444, 0.08097358, 0.080903947592495071, 0.080935642, 0.081008799, 0.080883210614559317, 0.080876552, 0.08092837, 0.081021294879004224, 0.080780089, 0.081029303, 0.081083930298612855, 0.080927245, 0.081101865)
('2017-08-01 15:54:31.124993', 0.081, 0.080789666618533082, 0.080759026, 0.080993079, 0.080865358134857832, 0.080921158, 0.08097358, 0.080867636514402735, 0.080931611, 0.081008799, 0.080921905440741279, 0.080871135, 0.08092837, 0.080949645125430889, 0.08077506, 0.081029303, 0.080928117607634589, 0.08092469, 0.081101865)
('2017-08-01 15:54:41.479860', 0.08081201, 0.080784987210803474, 0.080710806, 0.080880694, 0.080892750698978624, 0.080913074, 0.080971591, 0.080916909533756104, 0.08092121, 0.081008799, 0.080888438887364092, 0.080860913, 0.08092837, 0.081009942868724621, 0.080763474, 0.081029303, 0.080997207142932903, 0.080908477, 0.081101865)
('2017-08-01 15:54:51.830075', 0.08081201, 0.080794503620921027, 0.080709219, 0.080880694, 0.080892750698978624, 0.080913752, 0.080963664, 0.080888014846507539, 0.08092279, 0.081008799, 0.080923713105958944, 0.080862798, 0.08092837, 0.081009942868724621, 0.080765195, 0.081029303, 0.080997207142932903, 0.080908522, 0.081101865)
('2017-08-01 15:55:02.180287', 0.08081201, 0.080834097943798269, 0.080689453, 0.080880694, 0.08087755483099042, 0.080884449, 0.080963664, 0.080831837338713464, 0.080890186, 0.081008799, 0.080807683021063392, 0.080830656, 0.08092837, 0.081038332029893009, 0.080732882, 0.081029303, 0.081215293263568022, 0.080864675, 0.081101865)
('2017-08-01 15:55:12.613377', 0.081, 0.080764259757187151, 0.08074002, 0.080993079, 0.080885171520375124, 0.080893427, 0.080976084, 0.080962686241325965, 0.080899291, 0.081008799, 0.080982485978469576, 0.080840535, 0.080899261, 0.081022813208796826, 0.080743641, 0.081029303, 0.081254179041937255, 0.08088246, 0.081101865)
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('2017-08-01 16:03:30.385925', 0.08055019, 0.080537147403820661, 0.080385663, 0.080563791, 0.080583359992265485, 0.08056476, 0.080593795, 0.080541027935230311, 0.080554403, 0.08043085, 0.080523152159043307, 0.08050248, 0.080513135, 0.080610045990187076, 0.080414578, 0.08059489, 0.08075252453121369, 0.080533408, 0.080545522)
('2017-08-01 16:03:41.255109', 0.08054002, 0.080545387751481221, 0.080389403, 0.080563791, 0.080569742244124429, 0.080576114, 0.080598444, 0.080515175481325266, 0.080566399, 0.08043085, 0.080510445571090522, 0.080514364, 0.080513135, 0.080610045990187076, 0.080426648, 0.08059489, 0.080763306951372182, 0.080548979, 0.080545522)
('2017-08-01 16:03:51.660971', 0.0807, 0.080583690287009943, 0.08045014, 0.080645904, 0.080671340414510295, 0.080605753, 0.080644645, 0.080642318584751183, 0.080598064, 0.080612339, 0.080545674931541411, 0.080547258, 0.08055488, 0.080982889985333928, 0.080457233, 0.08059489, 0.080859384101043721, 0.080582649, 0.080732495)
('2017-08-01 16:04:01.886508', 0.08054002, 0.080571073397873297, 0.080409482, 0.080563791, 0.080512194313302282, 0.080601811, 0.080574013, 0.080551158598571634, 0.080593958, 0.08043085, 0.080518508719429283, 0.080543377, 0.080500931, 0.08062473458608789, 0.080454104, 0.08059489, 0.080949836271975203, 0.080577783, 0.080545522)
('2017-08-01 16:04:12.226184', 0.08051, 0.080549750176364024, 0.080367602, 0.080528617, 0.080509870218863258, 0.080552168, 0.080552869, 0.080550993833059262, 0.080539346, 0.08043085, 0.080495858205163459, 0.08048787, 0.080488794, 0.08056331502825001, 0.0803992, 0.08059489, 0.080786023655768049, 0.080516644, 0.080545522)
('2017-08-01 16:04:22.700350', 0.08051, 0.080486592690860143, 0.080362916, 0.080528617, 0.08056098958380975, 0.08054892, 0.080593795, 0.080574495225039705, 0.080539204, 0.08043085, 0.080567774377723514, 0.080489747, 0.080488794, 0.080537231725156602, 0.08040119, 0.08059489, 0.080769769159233604, 0.080520101, 0.080545522)
('2017-08-01 16:04:32.951359', 0.08051, 0.08050395576573835, 0.080337577, 0.080537058, 0.080510372072800582, 0.080518872, 0.080499426, 0.080464146181453811, 0.080506511, 0.08043085, 0.08039507447691692, 0.080456719, 0.080476776, 0.080441144110402138, 0.080369093, 0.0803857, 0.080256066367341644, 0.08047954, 0.080338538)
('2017-08-01 16:04:43.311250', 0.0804, 0.080488201103178614, 0.08027906, 0.080519773, 0.080465625483208902, 0.080472499, 0.080478854, 0.080432693658141985, 0.080456011, 0.08043085, 0.080382712510689541, 0.080409169, 0.080447786, 0.080295556242315352, 0.080321975, 0.0803857, 0.08032152600432968, 0.080449671, 0.080338538)
('2017-08-01 16:04:53.674908', 0.08051, 0.080480193092856589, 0.080307335, 0.080528617, 0.080481045117164834, 0.080476396, 0.080478854, 0.080439855744263239, 0.080462068, 0.08043085, 0.080367149692249035, 0.080414951, 0.080447786, 0.080295556242315352, 0.08032769, 0.0803857, 0.080303568724716237, 0.080453612, 0.080338538)
('2017-08-01 16:05:04.318791', 0.08051, 0.080470267027726458, 0.08030428, 0.080528617, 0.080465037696706446, 0.080470577, 0.080478854, 0.080436381879611316, 0.080455907, 0.08043085, 0.080360614224185598, 0.080408104, 0.080447786, 0.080283063758295192, 0.080322199, 0.0803857, 0.080267751512029556, 0.080449894, 0.080338538)
('2017-08-01 16:05:14.683414', 0.0803738, 0.080432529649837881, 0.080279849, 0.08040233, 0.080460875899462686, 0.080479652, 0.08046928, 0.08043813907838028, 0.080465928, 0.08043085, 0.080376370811483064, 0.080420889, 0.080447786, 0.080299299082687542, 0.080336355, 0.0803857, 0.080364298275367632, 0.080469355, 0.080338538)
('2017-08-01 16:05:25.114395', 0.0803, 0.080246811546726596, 0.080200322, 0.080319382, 0.080325121368807526, 0.080401592, 0.080369644, 0.080256181711956406, 0.080384716, 0.080221087, 0.080238948361741017, 0.080342643, 0.080275849, 0.080212888665348481, 0.080259576, 0.080342971, 0.080194356266635042, 0.080378599, 0.080338538)
('2017-08-01 16:05:36.142456', 0.08028, 0.080263499004962463, 0.080147393, 0.080282539, 0.080176491788951393, 0.080339164, 0.080305085, 0.080184340786101593, 0.080319278, 0.080221087, 0.080134292438746937, 0.080278955, 0.080243297, 0.080233775629243442, 0.080196574, 0.080292359, 0.080179748188145411, 0.080321923, 0.080252409)
('2017-08-01 16:05:46.564810', 0.08028, 0.080251463743472182, 0.080140434, 0.080274127, 0.080176138930097951, 0.08032842, 0.080299944, 0.080148100709072717, 0.0803058, 0.080221087, 0.080172021016961137, 0.08026506, 0.080243297, 0.080211400118314208, 0.080183133, 0.080292359, 0.080194288693413968, 0.080316946, 0.080252409)
('2017-08-01 16:05:56.815655', 0.08028, 0.080229340163913906, 0.08013247, 0.080274127, 0.080183987762506834, 0.080316134, 0.080299944, 0.080141101448663279, 0.080293313, 0.080221087, 0.080158977266627685, 0.080254398, 0.080218934, 0.080211744999918635, 0.080171987, 0.080292359, 0.080199749068627715, 0.08029677, 0.080252409)
('2017-08-01 16:06:07.123486', 0.0802, 0.080172771264331241, 0.080079995, 0.080274127, 0.080197809526608446, 0.080274634, 0.080235593, 0.08014864557864898, 0.08024998, 0.080133282, 0.080232314429928453, 0.080213584, 0.080209047, 0.080228427180893666, 0.080132313, 0.080172755, 0.080185940309917561, 0.080245048, 0.080252409)
('2017-08-01 16:06:17.496770', 0.0802, 0.080126545814769218, 0.080050215, 0.080237977, 0.080188781286502808, 0.080236785, 0.080215633, 0.080123790704472717, 0.080210075, 0.080133282, 0.080271897749080931, 0.080177106, 0.080209047, 0.080264531566213909, 0.080095842, 0.080172755, 0.080185940309917561, 0.080230318, 0.080252409)
('2017-08-01 16:06:27.873955', 0.0802, 0.080147722361776252, 0.080053553, 0.080237977, 0.08023299819979246, 0.080240287, 0.080255732, 0.08017371144368271, 0.080216609, 0.080221087, 0.080186204611914169, 0.080183811, 0.080218934, 0.08017135680178511, 0.08010298, 0.080292359, 0.080214491208254982, 0.080236182, 0.080252409)
('2017-08-01 16:06:39.728654', 0.08028, 0.080127066946448375, 0.080081448, 0.080274127, 0.08021689767230307, 0.080254503, 0.080275692, 0.080188299094207272, 0.080230191, 0.080221087, 0.080213913779726337, 0.080196224, 0.080218934, 0.080108514689749333, 0.080114037, 0.080292359, 0.080203943162251906, 0.080247447, 0.080252409)
('2017-08-01 16:06:50.186416', 0.08, 0.079880326929521864, 0.079938591, 0.080097228, 0.079901000241713163, 0.080149218, 0.080094218, 0.079920271901317119, 0.080120489, 0.079989269, 0.079902427957611946, 0.080091663, 0.080027401, 0.079981227435825392, 0.080013022, 0.080100417, 0.079823285289569917, 0.080121607, 0.080000155)
('2017-08-01 16:07:00.541077', 0.08, 0.079957551023154938, 0.079866238, 0.080037147, 0.07990419398583494, 0.080061682, 0.080030188, 0.079946242787742705, 0.080025814, 0.079982303, 0.079829948019444194, 0.079997741, 0.079987213, 0.079885566443326325, 0.079919025, 0.080006108, 0.079674010184984087, 0.080052152, 0.080000155)
('2017-08-01 16:07:11.308980', 0.08, 0.080160939651386126, 0.07991688, 0.080063164, 0.080041850067487777, 0.080123603, 0.080110103, 0.079802558359420489, 0.080091625, 0.080065198, 0.079870444379748676, 0.080061525, 0.080052018, 0.079873363191270016, 0.079979599, 0.080062591, 0.079646775919271967, 0.080124997, 0.080000155)
('2017-08-01 16:07:22.625122', 0.0802809, 0.080127851103658207, 0.080077641, 0.080238275, 0.080189551151353136, 0.080242418, 0.080269836, 0.080157939421784952, 0.080217429, 0.080214113, 0.080094572103807049, 0.080182567, 0.080154739, 0.079990638205050424, 0.080097415, 0.080215551, 0.080209667306508456, 0.08026161, 0.080314718)
Significant Delta: -0.0071264062484649796. Prediction position: 17. Current: 0.08024320274561504. Prediction: 0.07967135708417185
('2017-08-01 16:07:32.990449', 0.0803, 0.080243202745615042, 0.080062121, 0.080221519, 0.080149072431700261, 0.080222547, 0.080238536, 0.080030595986477715, 0.08019577, 0.080101281, 0.079993515178046343, 0.080161825, 0.080100492, 0.079865574965980526, 0.080076456, 0.080174409, 0.079671357084171848, 0.080161847, 0.080062471)
('2017-08-01 16:07:43.437859', 0.0803, 0.080339132055233506, 0.080088601, 0.080311649, 0.080283768212218568, 0.080250949, 0.080323108, 0.080186361099007147, 0.080223031, 0.08043085, 0.080173592381451039, 0.080185905, 0.080290794, 0.080014931526712699, 0.080096342, 0.08025828, 0.080203063619869244, 0.080251552, 0.080314718)
('2017-08-01 16:07:53.802622', 0.0803, 0.08030252403185377, 0.080144905, 0.080347799, 0.080421786004190213, 0.080327503, 0.080387309, 0.080330677720596641, 0.08030466, 0.08043085, 0.080226884820154803, 0.080262549, 0.080362923, 0.08032642643099755, 0.080171883, 0.0803857, 0.080185524406811717, 0.080272913, 0.080338538)
('2017-08-01 16:08:04.225274', 0.0803, 0.080328615295488501, 0.080149047, 0.080354109, 0.080402146416304526, 0.080333069, 0.080387309, 0.080350730368635395, 0.080312215, 0.08043085, 0.080241360559576688, 0.080273412, 0.080371201, 0.080274644722124797, 0.080183856, 0.0803857, 0.080268630233622101, 0.080287978, 0.080338538)
('2017-08-01 16:08:14.444638', 0.0803, 0.080270855349155026, 0.080051154, 0.080220193, 0.080239595737555069, 0.080210134, 0.080270469, 0.080216985779556993, 0.080181636, 0.080133282, 0.080188899106281977, 0.080145925, 0.080242455, 0.080005200373320082, 0.080056578, 0.080223359, 0.079954002259837856, 0.080122992, 0.080181725)
('2017-08-01 16:08:24.832169', 0.0803, 0.080329635788324055, 0.079995982, 0.080184057, 0.080286543707882116, 0.080133207, 0.080211572, 0.08025315407466882, 0.08010114, 0.080133282, 0.080079851152493595, 0.080068484, 0.080170333, 0.079950250082252788, 0.079981126, 0.080095939, 0.07995836380228627, 0.080103099, 0.080157906)
('2017-08-01 16:08:35.525605', 0.0804, 0.080319433319250053, 0.080045789, 0.08019384, 0.080312296183685336, 0.080163606, 0.080211572, 0.080257195528379788, 0.080137476, 0.080133282, 0.080091212601535094, 0.080106765, 0.080170333, 0.079927591729965899, 0.080022477, 0.080095939, 0.080009918589099685, 0.080154411, 0.080157906)
('2017-08-01 16:08:45.906732', 0.0804, 0.080282047081487506, 0.080078863, 0.080251254, 0.080160431936789803, 0.080213666, 0.080211572, 0.079897080505604831, 0.080189787, 0.080198206, 0.080146981065769313, 0.080156088, 0.080212176, 0.07999407785399211, 0.08007215, 0.080138668, 0.08009296182454087, 0.080221877, 0.080157906)
('2017-08-01 16:08:56.178750', 0.08051, 0.080320683737802337, 0.080133744, 0.080350958, 0.080161789912869208, 0.080244914, 0.080321431, 0.080008921368526775, 0.080219455, 0.080198206, 0.080347609197577677, 0.08018408, 0.080259807, 0.080387937784679495, 0.080096483, 0.080171771, 0.080368169214931603, 0.080247074, 0.080338538)
('2017-08-01 16:09:06.407060', 0.08051, 0.080363251004713226, 0.080238231, 0.080489114, 0.08035107109425485, 0.080384515, 0.080440141, 0.080380715345464426, 0.080367431, 0.08043085, 0.080408531643137562, 0.080327153, 0.080412343, 0.080257427323047692, 0.080239885, 0.0803857, 0.080447262626770238, 0.080409713, 0.080338538)
('2017-08-01 16:09:16.743740', 0.0804, 0.080506794462575401, 0.080254756, 0.080516413, 0.080473697609828504, 0.080445625, 0.080469057, 0.080469583148790338, 0.080432884, 0.08043085, 0.080406563730350378, 0.080391869, 0.080460817, 0.080289918345548217, 0.080302291, 0.0803857, 0.080432767304719696, 0.080410682, 0.080338538)
('2017-08-01 16:09:27.087316', 0.0804, 0.080506794462575401, 0.080254629, 0.080516413, 0.080505753189712315, 0.080444366, 0.080469057, 0.080473947027336998, 0.08043132, 0.08043085, 0.08041009508293448, 0.080389835, 0.08042343, 0.080286055065760614, 0.080300808, 0.0803857, 0.08045229155230299, 0.080410145, 0.080338538)
('2017-08-01 16:09:37.832748', 0.0804, 0.080474963302151592, 0.080226958, 0.080514006, 0.080541749017327108, 0.080406226, 0.080469057, 0.08040567924724891, 0.080389433, 0.08043085, 0.080465796654256336, 0.08034835, 0.080395795, 0.080352624395576125, 0.08025559, 0.0803857, 0.080477976974921761, 0.080345199, 0.080338538)
('2017-08-01 16:09:48.380255', 0.0804, 0.080509998872989624, 0.080223776, 0.080514006, 0.080500327119398737, 0.080405258, 0.080469057, 0.080451492113828446, 0.080388851, 0.08043085, 0.080484693339527805, 0.080347821, 0.080395795, 0.080353165126978487, 0.080256283, 0.0803857, 0.080469157425787655, 0.080349699, 0.080338538)
('2017-08-01 16:09:58.917927', 0.0804, 0.08047613418724707, 0.080273464, 0.080582976, 0.080530730135569939, 0.080465533, 0.080609627, 0.080598947694626111, 0.080452591, 0.080612339, 0.080588535681328025, 0.080407724, 0.0804739, 0.080562657121174608, 0.080314405, 0.08059489, 0.080604855850680932, 0.080421969, 0.080732495)
('2017-08-01 16:10:09.269055', 0.0807, 0.080548553258551772, 0.080391586, 0.080641218, 0.080492919964309212, 0.080529712, 0.080646701, 0.080613634324675318, 0.080521099, 0.080612339, 0.080567780717792928, 0.080474727, 0.080530763, 0.08059103537447232, 0.080381088, 0.08059489, 0.080658815539825832, 0.080476835, 0.080732495)
('2017-08-01 16:10:19.615875', 0.08051, 0.080453122048774472, 0.080267169, 0.080414869, 0.080248887682347961, 0.080431826, 0.080411099, 0.0801634620792578, 0.080420665, 0.080198206, 0.080472864573684924, 0.08037813, 0.080348544, 0.080358627548318129, 0.080291562, 0.080266081, 0.080356535935429449, 0.080368124, 0.080338538)
('2017-08-01 16:10:30.100669', 0.08051, 0.080356019094741826, 0.080203921, 0.080370285, 0.080224555958538918, 0.08035098, 0.080338232, 0.080157070623153706, 0.080335304, 0.080198206, 0.080460840277645579, 0.080298401, 0.080307417, 0.080392324278085667, 0.080216311, 0.080266081, 0.080297145864083638, 0.080375157, 0.080338538)
('2017-08-01 16:10:40.658146', 0.08051, 0.080375803156438663, 0.08018785, 0.080346897, 0.080120333423768009, 0.080331996, 0.08031743, 0.080141751655399412, 0.080314793, 0.080198206, 0.080439234188215858, 0.08027789, 0.080271542, 0.080404406676224699, 0.08019603, 0.080266081, 0.080423351582446623, 0.080353037, 0.080338538)
('2017-08-01 16:10:51.045643', 0.08041, 0.08035706130401496, 0.080152147, 0.080321133, 0.080128008619131788, 0.080315858, 0.08031743, 0.08017082723159509, 0.080297954, 0.080198206, 0.080306737388131308, 0.080261156, 0.080262646, 0.080313673683670483, 0.080179065, 0.080266081, 0.080421711611248461, 0.080331847, 0.080338538)
('2017-08-01 16:11:01.572848', 0.08041, 0.080408063434249458, 0.080166601, 0.080321133, 0.08025874719855336, 0.080331795, 0.080374904, 0.080281629681801678, 0.080314755, 0.080286011, 0.080217805835788178, 0.080280893, 0.08027254, 0.080337538945218612, 0.080197848, 0.0803857, 0.080423420884386962, 0.080352433, 0.080338538)
('2017-08-01 16:11:11.932387', 0.08069997, 0.080350983963824507, 0.080290698, 0.08049836, 0.080486548345042389, 0.080402844, 0.080480784, 0.080446065844793052, 0.080391645, 0.080467492, 0.080385354624195424, 0.080357566, 0.080332741, 0.080393323427612656, 0.080272548, 0.0803857, 0.080425944578794969, 0.08042299, 0.080338538)
('2017-08-01 16:11:22.277767', 0.08069997, 0.080686465654140394, 0.080436721, 0.080642544, 0.080676357394879231, 0.08058729, 0.080697536, 0.080729291460942237, 0.080586158, 0.080612339, 0.080769097783778165, 0.080545075, 0.080588423, 0.080948503936171951, 0.08045657, 0.08059489, 0.081031947013316299, 0.08065439, 0.080732495)
('2017-08-01 16:11:32.496178', 0.0802, 0.080272191366853651, 0.080217965, 0.080347523, 0.080236704071578485, 0.080468267, 0.080337115, 0.080173994711799201, 0.08046414, 0.080133282, 0.080432149106675718, 0.080427594, 0.080298804, 0.080341647746147785, 0.080348596, 0.08020585, 0.080295589183795618, 0.080403522, 0.080276221)
('2017-08-01 16:11:42.933572', 0.0802, 0.080168992692229157, 0.080027752, 0.080237977, 0.080252334715060256, 0.080213234, 0.080215633, 0.080131593470506093, 0.080189794, 0.080133282, 0.080250921631497524, 0.080159917, 0.080209047, 0.080245091709559277, 0.080077998, 0.080172755, 0.080250501284995349, 0.080199435, 0.080252409)
('2017-08-01 16:11:53.211294', 0.0802, 0.080187512082670651, 0.080027573, 0.080237977, 0.080247265772337095, 0.080211729, 0.080215633, 0.080108266407334427, 0.08019013, 0.080133282, 0.080194174305938185, 0.080161639, 0.080209047, 0.080245091709559277, 0.080081254, 0.080172755, 0.080261827198498004, 0.08020702, 0.080252409)
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('2017-08-01 16:20:07.152596', 0.0806599, 0.080562802452431775, 0.080395788, 0.080625847, 0.080616322600308613, 0.080547802, 0.08057975, 0.080663325685354864, 0.080550082, 0.080612339, 0.0806448268674297, 0.08051227, 0.080483131, 0.080674914729161093, 0.080427878, 0.08059489, 0.080819833917865819, 0.08055304, 0.080545522)
('2017-08-01 16:20:18.051527', 0.0806599, 0.080588851605044518, 0.080424272, 0.080634281, 0.080640797697503902, 0.080589578, 0.080597907, 0.08064491537085372, 0.080593891, 0.080612339, 0.080709204529024858, 0.080555104, 0.080486864, 0.080626024763205795, 0.080468059, 0.08059489, 0.080939146035743467, 0.08058957, 0.080545522)
('2017-08-01 16:20:28.285667', 0.0806599, 0.080594896560573642, 0.080424324, 0.080634281, 0.080611678999858424, 0.080589317, 0.080620348, 0.080673678626081491, 0.080592528, 0.080612339, 0.080659845105419237, 0.080552831, 0.080486864, 0.080649330315808829, 0.080465525, 0.08059489, 0.080939146035743467, 0.08058697, 0.080545522)
('2017-08-01 16:20:38.624418', 0.08081, 0.080696794674917277, 0.080485195, 0.080634281, 0.080692958566493694, 0.080623567, 0.080662981, 0.080798591652667062, 0.080628455, 0.080612339, 0.08079875240255191, 0.080588713, 0.080544695, 0.081113630247151194, 0.080499388, 0.08059489, 0.080870505232422785, 0.080627523, 0.080732495)
('2017-08-01 16:20:48.983653', 0.0805001, 0.080741475176551933, 0.080426574, 0.080594413, 0.080684287147634468, 0.08065328, 0.080668464, 0.080806967628157908, 0.080658652, 0.080612339, 0.080815172395273902, 0.080615833, 0.080582075, 0.081088305414932213, 0.080523022, 0.08059489, 0.080968895144578321, 0.080651514, 0.080732495)
('2017-08-01 16:20:59.383407', 0.0805001, 0.080710586026182807, 0.080413409, 0.080594413, 0.080682720454514453, 0.080634192, 0.080668464, 0.080797416932973543, 0.080637492, 0.080612339, 0.08073581349896912, 0.080595389, 0.080566004, 0.080838240513854881, 0.080502108, 0.08059489, 0.08079332094981638, 0.080622099, 0.080732495)
('2017-08-01 16:21:09.715405', 0.0805001, 0.080728390212969223, 0.080411941, 0.080601536, 0.080639785465894662, 0.080635086, 0.0806747, 0.080793416444501609, 0.080637902, 0.080612339, 0.08073581349896912, 0.080596611, 0.080580048, 0.080819902581521677, 0.080502748, 0.08059489, 0.080816016123761852, 0.080621719, 0.080732495)
('2017-08-01 16:21:22.112751', 0.080505, 0.080747330296737779, 0.080413416, 0.080601536, 0.080676617847197485, 0.080634184, 0.0806747, 0.080799829339283105, 0.080636725, 0.080612339, 0.080777612720429492, 0.080594786, 0.080580048, 0.080819902581521677, 0.080501482, 0.08059489, 0.080777166195266675, 0.080620684, 0.080732495)
('2017-08-01 16:21:36.978287', 0.0808099, 0.080717685686121435, 0.080527827, 0.080634281, 0.080816856129412237, 0.080685303, 0.080737375, 0.080882632796285339, 0.080691673, 0.080612339, 0.080849185921037847, 0.080650017, 0.080640756, 0.081071377007473994, 0.080558814, 0.08059489, 0.080930229060201336, 0.080692761, 0.080732495)
('2017-08-01 16:21:47.919206', 0.0808099, 0.08074580379518348, 0.080551669, 0.080673911, 0.080807574412722738, 0.080715299, 0.080748931, 0.080882632796285339, 0.08072421, 0.080612339, 0.080867126164694048, 0.080681875, 0.080640756, 0.081080545487723199, 0.080590829, 0.08059489, 0.08095277892448878, 0.080696404, 0.080732495)
('2017-08-01 16:21:58.289491', 0.08081, 0.080704334233386993, 0.080527097, 0.080673911, 0.080720894648017849, 0.080681525, 0.080678359, 0.080817497410046027, 0.080688454, 0.080612339, 0.080868369602921777, 0.080646507, 0.080582075, 0.081045851582481621, 0.080557905, 0.08059489, 0.080970844681384893, 0.080655843, 0.080732495)
('2017-08-01 16:22:08.732339', 0.08081, 0.080758348913136602, 0.080518521, 0.080645755, 0.080714183079524868, 0.080666952, 0.080666803, 0.08078684239376599, 0.080673046, 0.080612339, 0.08083442901428653, 0.080628864, 0.080544695, 0.081073862250890152, 0.080542713, 0.08059489, 0.08101165980856373, 0.080678396, 0.080732495)
('2017-08-01 16:22:19.358924', 0.08081, 0.080758348913136602, 0.080519535, 0.080645755, 0.080714183079524868, 0.080669135, 0.080666803, 0.080812983435807575, 0.080676608, 0.080612339, 0.080786079325235099, 0.080632925, 0.080544695, 0.081073862250890152, 0.080546767, 0.08059489, 0.081125730692598735, 0.080678709, 0.080732495)
('2017-08-01 16:22:29.780901', 0.08081, 0.080820270542881878, 0.08057338, 0.080830857, 0.080878488162915818, 0.08073812, 0.080808073, 0.080866374512009467, 0.080745779, 0.080938354, 0.080926870912608223, 0.080695704, 0.080751024, 0.080935479264248614, 0.080606326, 0.080897555, 0.081280917560628552, 0.080757514, 0.081101865)
('2017-08-01 16:22:40.164779', 0.08081, 0.080697864860977978, 0.080578171, 0.080708832, 0.080738020387520648, 0.080750555, 0.08071585, 0.080818124801295105, 0.080759771, 0.080612339, 0.080746268388079634, 0.080709279, 0.08055874, 0.081018646000743946, 0.080619328, 0.08059489, 0.080903920870702153, 0.080704398, 0.080732495)
('2017-08-01 16:22:50.559265', 0.08019865, 0.080740038408431053, 0.080445282, 0.080661654, 0.08078930753221579, 0.08075463, 0.080804683, 0.080902932736344843, 0.080764927, 0.080938354, 0.080681815573760848, 0.080717959, 0.080751024, 0.080938253862887069, 0.080628715, 0.080900766, 0.081098221921621, 0.080786668, 0.081112564)
('2017-08-01 16:23:00.787770', 0.08087136, 0.080791926752549981, 0.080677874, 0.08084964, 0.080802865609831714, 0.080857508, 0.080906205, 0.080889645360551821, 0.080871522, 0.080938354, 0.080659531064995293, 0.080822468, 0.080876932, 0.080902287020269595, 0.080730267, 0.081032507, 0.081284612799699493, 0.080843382, 0.081112564)
('2017-08-01 16:23:11.171004', 0.08097998, 0.080778459698031047, 0.08071737, 0.080962017, 0.080797714351619712, 0.080876119, 0.080925293, 0.080913413872034753, 0.080890864, 0.080938354, 0.080675986416884657, 0.080842935, 0.080876932, 0.080891676150045111, 0.080749549, 0.081032507, 0.081255762330144782, 0.080853879, 0.081112564)
('2017-08-01 16:23:21.524803', 0.08087136, 0.080770642645724519, 0.080687024, 0.08084964, 0.080777355670777554, 0.080865532, 0.080923267, 0.080891413110669885, 0.080881953, 0.080938354, 0.08061003276249154, 0.080833323, 0.080886491, 0.080921273542538891, 0.08074002, 0.081032507, 0.081110944889762282, 0.080851816, 0.081112564)
('2017-08-01 16:23:32.394892', 0.08087136, 0.080822192835329695, 0.080692567, 0.08084964, 0.080834351526095333, 0.080873966, 0.080933325, 0.080855491906083168, 0.080888629, 0.080938354, 0.080652420681549472, 0.080836803, 0.080886491, 0.080939180693196028, 0.080741711, 0.081032507, 0.081286150959634554, 0.080843404, 0.081112564)
Significant Delta: -0.008614438816476671. Prediction position: 17. Current: 0.08063870973385018. Prediction: 0.0799440525026083
('2017-08-01 16:23:43.362785', 0.08087136, 0.080638709733850178, 0.080530003, 0.080514088, 0.080347117719885425, 0.08066576, 0.08057607, 0.08039868714262198, 0.08067359, 0.080379695, 0.080286346585648025, 0.080630541, 0.080337077, 0.080319869957243023, 0.080547661, 0.08025635, 0.079944052502608304, 0.080600128, 0.080297314)
('2017-08-01 16:23:53.769860', 0.08, 0.080220217667246144, 0.080078825, 0.080084346, 0.079902125800658141, 0.080344379, 0.080164753, 0.079908780315714564, 0.08033669, 0.079989269, 0.079815653085609686, 0.080307476, 0.080054626, 0.079882221714147411, 0.080239214, 0.08004532, 0.079773274683254514, 0.080444157, 0.080058768)
('2017-08-01 16:24:04.106578', 0.08087131, 0.080510271843615741, 0.080279894, 0.080382824, 0.080224657662937543, 0.080332503, 0.080363654, 0.080173175722216217, 0.080320299, 0.080372721, 0.080186826997536223, 0.080294125, 0.080307752, 0.080064286507078697, 0.080216482, 0.080144778, 0.080286865149695852, 0.080406979, 0.080379687)
('2017-08-01 16:24:14.517971', 0.0800001, 0.080185726967823528, 0.080151759, 0.080189846, 0.080250234418514615, 0.080443233, 0.08035095, 0.079921053301802741, 0.080438465, 0.080198206, 0.080366122645542271, 0.080407627, 0.080286354, 0.080137920629748657, 0.080331542, 0.080177888, 0.080438540867159014, 0.080549553, 0.08021652)
('2017-08-01 16:24:24.919847', 0.0800001, 0.080173760124021914, 0.080112122, 0.080190815, 0.080214855446397934, 0.080390491, 0.080302224, 0.079960186580209688, 0.080384888, 0.080198206, 0.080387377767664844, 0.080358811, 0.080274224, 0.08016218568698194, 0.080286212, 0.080177888, 0.080509574990351537, 0.080500647, 0.08021652)
('2017-08-01 16:24:35.363404', 0.08049001, 0.080632826927496667, 0.080364451, 0.080578186, 0.080670738653938701, 0.080563769, 0.080589682, 0.080677195449253114, 0.080566406, 0.080612339, 0.080641445743686174, 0.080534123, 0.080468334, 0.08095447359138204, 0.080452465, 0.080506682, 0.080995421786748192, 0.080700621, 0.080767304)
('2017-08-01 16:24:45.709294', 0.08049001, 0.080358836491974464, 0.080342859, 0.080341004, 0.080253007950903205, 0.080548048, 0.08039593, 0.080079859281778978, 0.080548994, 0.080198206, 0.080467210034496445, 0.08051499, 0.080363818, 0.080135840717624307, 0.080439121, 0.080272198, 0.080559512633960939, 0.080571912, 0.08021652)
('2017-08-01 16:24:56.081347', 0.08049001, 0.080363181197728936, 0.080224611, 0.080296427, 0.080275508794658265, 0.080390669, 0.080307819, 0.079978334032925202, 0.080384009, 0.08021158, 0.08041134437823734, 0.080356605, 0.080278769, 0.08017709628909625, 0.080281235, 0.080185831, 0.080509574990351537, 0.080494016, 0.08021652)
('2017-08-01 16:25:06.306843', 0.08049001, 0.080374331072642274, 0.080246225, 0.080481581, 0.08064639024635134, 0.080406412, 0.080468677, 0.080379508766084254, 0.080397964, 0.080444224, 0.080690510701643334, 0.080366552, 0.080378823, 0.080744802274407348, 0.080278888, 0.080514632, 0.080452196889743463, 0.080468699, 0.080580324)
('2017-08-01 16:25:17.034670', 0.08086984, 0.08071110682876162, 0.080404848, 0.08064767, 0.080750016047527046, 0.080492228, 0.080614105, 0.080785525315663451, 0.080483258, 0.080625713, 0.080768018216727078, 0.080442257, 0.08051265, 0.080867972009786751, 0.080346726, 0.080606043, 0.080548285676761575, 0.08043173, 0.080753945)
('2017-08-01 16:25:27.430920', 0.08049001, 0.080624327615442998, 0.080424234, 0.080626279, 0.080679911638032856, 0.080638632, 0.080645882, 0.080689367969207088, 0.080642082, 0.080625713, 0.080706903388463605, 0.080599532, 0.080530815, 0.080801919605699338, 0.0805071, 0.080606043, 0.080672992475382746, 0.080631748, 0.080753945)
('2017-08-01 16:25:37.793114', 0.08049001, 0.080381995969349043, 0.080258273, 0.08038982, 0.080221631145140304, 0.08042673, 0.080386072, 0.08028335392265383, 0.080420576, 0.08021158, 0.080320061967145515, 0.080383115, 0.080340385, 0.080193139406270597, 0.080296554, 0.08028014, 0.080402740911335965, 0.080369204, 0.08021652)
('2017-08-01 16:25:48.151458', 0.08049001, 0.080331256187928662, 0.080140606, 0.080345243, 0.080235495228710735, 0.080267563, 0.080304585, 0.080226584927001049, 0.08025191, 0.08021158, 0.080310010153536004, 0.080222227, 0.080250785, 0.080217449810009084, 0.080137931, 0.080185831, 0.080437557603997831, 0.080291271, 0.08021652)
('2017-08-01 16:25:58.551512', 0.08049001, 0.080334112571918373, 0.080196522, 0.080359176, 0.080332921098705365, 0.080344081, 0.080304585, 0.080244452693462379, 0.080334306, 0.08021158, 0.080397482911534535, 0.080306463, 0.080275081, 0.08023564717772097, 0.080230318, 0.080182932, 0.080437557603997831, 0.080420397, 0.080203168)
('2017-08-01 16:26:08.920395', 0.08049001, 0.080521961701914987, 0.080267929, 0.08053419, 0.080669921709142903, 0.080418833, 0.080592044, 0.080616175054050082, 0.080406599, 0.080625713, 0.080558414798441891, 0.080368645, 0.080444895, 0.080741076911741261, 0.080279872, 0.080514632, 0.080590880041066001, 0.080475219, 0.080767304)
('2017-08-01 16:26:19.274373', 0.08031103, 0.080456604209833499, 0.080306649, 0.080460191, 0.080264629243026783, 0.080539592, 0.080599286, 0.080479090501625092, 0.080537878, 0.080625713, 0.080529778840156219, 0.080496989, 0.080522478, 0.080654153358252698, 0.080406599, 0.080399752, 0.080636170156170306, 0.080504626, 0.080560319)
('2017-08-01 16:26:29.627895', 0.0808, 0.080551385505358175, 0.080430865, 0.080510221, 0.080331990041653548, 0.080553897, 0.080574267, 0.080457370679017148, 0.080555685, 0.080612339, 0.080524294469217841, 0.080516815, 0.080516919, 0.080595072330883005, 0.080431029, 0.080391809, 0.080752329171139489, 0.080587327, 0.080560319)
('2017-08-01 16:26:39.993367', 0.0808, 0.080632929718248827, 0.080457002, 0.080521703, 0.080328390144967121, 0.080587246, 0.080574267, 0.080549190028564241, 0.080592237, 0.080612339, 0.080561647423796068, 0.08055418, 0.080516919, 0.08058966962396108, 0.08047016, 0.080391809, 0.080757744720473812, 0.080638506, 0.080560319)
('2017-08-01 16:26:50.943317', 0.08080001, 0.080678111715225875, 0.080546938, 0.080818206, 0.080828014316447275, 0.080702722, 0.080775037, 0.080704123284019755, 0.080711491, 0.080951735, 0.080862400156146597, 0.080668502, 0.080739819, 0.080803620285688563, 0.080578193, 0.080911614, 0.080916397972030651, 0.080774359, 0.080767304)
('2017-08-01 16:27:01.403762', 0.08080001, 0.080643162918428227, 0.080450274, 0.08048559, 0.080308288004958386, 0.080582641, 0.08059106, 0.080448193555966843, 0.080583878, 0.080625713, 0.080599090620203248, 0.080541037, 0.080480732, 0.080785509180564249, 0.080448858, 0.080399752, 0.080587855397507568, 0.080485828, 0.080373347)
('2017-08-01 16:27:11.697242', 0.08085, 0.080609347869137093, 0.08042863, 0.080632381, 0.080664714244590346, 0.080532186, 0.080616839, 0.080713736360406754, 0.080529168, 0.080612339, 0.080581293749608179, 0.080487214, 0.08050096, 0.080762140576189592, 0.080393814, 0.080600999, 0.080604092568810501, 0.08052744, 0.080767304)
('2017-08-01 16:27:22.060495', 0.08085, 0.080722142728792592, 0.080528535, 0.080781385, 0.080881711072782872, 0.080669582, 0.080816954, 0.080784290075904458, 0.080672294, 0.080951735, 0.080948861409648881, 0.080625139, 0.080754876, 0.080978980685966775, 0.080526546, 0.080911614, 0.081099510621772897, 0.080649801, 0.080767304)
('2017-08-01 16:27:32.577005', 0.08051103, 0.080680101862709172, 0.080487341, 0.080641583, 0.080731743264122996, 0.080734998, 0.080706097, 0.080746144083056026, 0.080743544, 0.080625713, 0.080810832150448431, 0.080698811, 0.080558792, 0.081000350522543202, 0.080608554, 0.080606043, 0.080952297427457506, 0.080695897, 0.080753945)
('2017-08-01 16:27:42.953164', 0.08085, 0.080721846277770043, 0.080564566, 0.080824241, 0.080809076311091202, 0.080720432, 0.080801859, 0.080675678866193881, 0.080726959, 0.080951735, 0.080859635192055038, 0.080682203, 0.08075273, 0.080857090507539184, 0.080588326, 0.080911614, 0.080764447319018523, 0.080736041, 0.080767304)
('2017-08-01 16:27:53.297254', 0.08088, 0.080799052683927119, 0.080627181, 0.08086165, 0.080835008209636461, 0.080793314, 0.080879301, 0.080827804574130357, 0.080801867, 0.080951735, 0.080901060555837567, 0.080753021, 0.080874957, 0.080787389477432192, 0.080658227, 0.080908716, 0.080833905125468147, 0.080757618, 0.081123315)
('2017-08-01 16:28:06.558000', 0.08097998, 0.080773597545333517, 0.080681063, 0.080974028, 0.080775670255836776, 0.080832928, 0.080930494, 0.080846967511556234, 0.080843203, 0.080951735, 0.080853900414594906, 0.080791861, 0.080878645, 0.080827014179516252, 0.080695309, 0.081043355, 0.080847290929867158, 0.080786586, 0.081136666)
('2017-08-01 16:28:16.966482', 0.08088001, 0.080882363786162606, 0.080711372, 0.080865011, 0.080860375354651529, 0.080903091, 0.080939077, 0.080881769559758718, 0.080918051, 0.080951735, 0.080932462263007446, 0.080866769, 0.080894716, 0.080822272882213428, 0.080774024, 0.081043355, 0.080875372236121859, 0.080892228, 0.081136666)
('2017-08-01 16:28:27.163781', 0.08097996, 0.080793697821953794, 0.080683179, 0.080962554, 0.080791669757168741, 0.080833569, 0.080930494, 0.080843976156986561, 0.080841504, 0.080951735, 0.080802538892023976, 0.080787599, 0.080878645, 0.080743171195016811, 0.080691233, 0.081043355, 0.08074773156985432, 0.080783993, 0.081136666)
('2017-08-01 16:28:37.508588', 0.08097996, 0.08078271589199025, 0.080710433, 0.080974028, 0.080858714662714312, 0.080871657, 0.080933884, 0.080913805312477535, 0.080883175, 0.080951735, 0.080853333903247906, 0.080829993, 0.080894716, 0.080832526197155946, 0.080736421, 0.081043355, 0.080879813114733967, 0.080845527, 0.081136666)
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('2017-08-01 17:08:54.046640', 0.08052011, 0.080684585627232519, 0.08052101, 0.080638662, 0.080687068094995679, 0.080749966, 0.08075089, 0.080882251261432794, 0.080752745, 0.080612339, 0.080871241265212071, 0.080700763, 0.080731593, 0.080924485997710724, 0.080618836, 0.080729842, 0.080823360080266055, 0.080795668, 0.080753945)
('2017-08-01 17:09:04.390811', 0.08052011, 0.080761161463544312, 0.080534562, 0.080638662, 0.080804919123834043, 0.08077237, 0.080762446, 0.080908024503495907, 0.080776677, 0.080612339, 0.080892401295322477, 0.080722347, 0.080731593, 0.080994922153274473, 0.080636062, 0.080729842, 0.08074090397678968, 0.080798447, 0.080753945)
('2017-08-01 17:09:14.833514', 0.08098202, 0.08088871225688142, 0.080641404, 0.080790915, 0.08080221281976567, 0.08076901, 0.080762446, 0.080908024503495907, 0.080772005, 0.080612339, 0.080897389063809902, 0.080718912, 0.080731593, 0.080998525924904197, 0.080634594, 0.080729842, 0.080863603400463197, 0.080797546, 0.080753945)
('2017-08-01 17:09:25.956023', 0.0812, 0.081288243565080312, 0.080855936, 0.0812217, 0.081250256169575125, 0.080972791, 0.081179433, 0.081187888260144184, 0.080983363, 0.081376888, 0.081431414551065645, 0.080923513, 0.081254348, 0.081271868514928305, 0.080830641, 0.081386589, 0.081326178007930336, 0.081037097, 0.081169359)
('2017-08-01 17:09:36.318813', 0.08139117, 0.081228721414318014, 0.081004463, 0.081235498, 0.081171620131548564, 0.081120364, 0.081179842, 0.081137780791430081, 0.081142068, 0.081345551, 0.081159651167591204, 0.081071854, 0.081158042, 0.081283415061389211, 0.080975808, 0.081386589, 0.081248494569717819, 0.081071645, 0.081127629)
('2017-08-01 17:09:46.691471', 0.08139117, 0.08113775753207185, 0.080948241, 0.081139661, 0.081180410833149558, 0.081042111, 0.081135109, 0.081023344776115239, 0.081058465, 0.081006706, 0.081176347219795914, 0.08099322, 0.081123643, 0.081279396235226453, 0.08090014, 0.081032507, 0.081142695075434326, 0.081026547, 0.081123315)
('2017-08-01 17:09:57.161931', 0.081, 0.081121810769345729, 0.080861837, 0.08108601, 0.081257663540228536, 0.081058785, 0.081129715, 0.081243799323751448, 0.081078775, 0.081006706, 0.081239093093624412, 0.081015378, 0.081123643, 0.081378652162332185, 0.080925748, 0.081032507, 0.081103789963137593, 0.08106637, 0.081123315)
('2017-08-01 17:10:07.383576', 0.08139115, 0.081255848477307352, 0.080952927, 0.081235498, 0.081203397209370975, 0.081051372, 0.081161536, 0.081103105523700827, 0.081068583, 0.081356019, 0.081146164877458876, 0.081005052, 0.081187636, 0.081400683745411179, 0.080910563, 0.081386589, 0.08123891187208826, 0.081036232, 0.081169359)
('2017-08-01 17:10:17.743308', 0.08139114, 0.081239904946703342, 0.080981195, 0.081235498, 0.081122122467527549, 0.081088431, 0.081184871, 0.081102561010979804, 0.081105977, 0.081358105, 0.08109728291913372, 0.081039406, 0.081187636, 0.081492041271562857, 0.080944426, 0.081386589, 0.081457883668777598, 0.081081375, 0.081169359)
('2017-08-01 17:10:28.154356', 0.08139113, 0.081220085619109039, 0.080981493, 0.081235498, 0.08110029398743572, 0.081090085, 0.081184871, 0.081062935740545947, 0.081108734, 0.081356019, 0.081131615783031658, 0.081043318, 0.081187636, 0.081517794477563674, 0.080948234, 0.081386589, 0.081457909183233937, 0.081083909, 0.081169359)
('2017-08-01 17:10:38.515326', 0.08100001, 0.081316771704035937, 0.080890641, 0.081181847, 0.081116425577141951, 0.081091523, 0.081184871, 0.081087012682501264, 0.081111588, 0.081356019, 0.081183264335653441, 0.081044853, 0.081187636, 0.081389732338802506, 0.080950305, 0.081386589, 0.081191304563919375, 0.081081711, 0.081169359)
('2017-08-01 17:10:48.887769', 0.08100001, 0.081252424802068859, 0.08095511, 0.081181847, 0.081282346124041907, 0.081184968, 0.081211083, 0.081223502003051132, 0.081211731, 0.081358105, 0.081147291853766221, 0.081144206, 0.081187636, 0.081232745189929512, 0.081054166, 0.081386589, 0.081185826016800036, 0.081232384, 0.081169359)
('2017-08-01 17:10:59.234424', 0.08100001, 0.081286537122349373, 0.080941848, 0.081181847, 0.081309150205426561, 0.081167206, 0.081211083, 0.081192912554526098, 0.081191309, 0.081358105, 0.081197781889027904, 0.081127554, 0.081187636, 0.08124265790472722, 0.081034899, 0.081386589, 0.081163032759961756, 0.081202231, 0.081169359)
('2017-08-01 17:11:09.618013', 0.08138989, 0.081284253691723665, 0.081022114, 0.081235498, 0.081275105877302412, 0.081150249, 0.081209414, 0.081196989978134182, 0.081172682, 0.081358105, 0.081192868722850239, 0.081105478, 0.081187636, 0.081253043608043501, 0.081012189, 0.081386589, 0.081176300734351517, 0.081174493, 0.081169359)
('2017-08-01 17:11:20.083290', 0.08138989, 0.081160408773016307, 0.081017099, 0.081235498, 0.081365771769455503, 0.081143148, 0.081209414, 0.081161744477286579, 0.081168287, 0.081356019, 0.081183369050851426, 0.081103742, 0.081158534, 0.081615262517213266, 0.081013128, 0.081386589, 0.081351007526732882, 0.081165977, 0.081169359)
('2017-08-01 17:11:31.524030', 0.08138987, 0.08123179880361947, 0.081030913, 0.081235498, 0.081327145689155431, 0.08116129, 0.081192091, 0.081054836190430635, 0.081187934, 0.081356019, 0.081182540096290098, 0.081125103, 0.081158534, 0.081593059981595356, 0.081035368, 0.081386589, 0.081437716654618311, 0.081196301, 0.081169359)
('2017-08-01 17:11:41.867235', 0.08138987, 0.08119859306842496, 0.081036285, 0.081235498, 0.081368105855238279, 0.081167109, 0.081208207, 0.081112572126936386, 0.081195168, 0.081356019, 0.081228068244661708, 0.081131779, 0.081158534, 0.081574236718944176, 0.081042022, 0.081386589, 0.081487806386032882, 0.081203558, 0.081169359)
('2017-08-01 17:11:52.439531', 0.08138987, 0.081168657065533167, 0.081021294, 0.081139661, 0.081212249691701696, 0.081149943, 0.08118704, 0.081184228196504296, 0.081176177, 0.081008799, 0.081210040297254801, 0.08111278, 0.081158534, 0.08155600925720298, 0.081022426, 0.081386589, 0.081269652360086236, 0.081182949, 0.081169359)
('2017-08-01 17:12:03.350347', 0.08100006, 0.081100282256756223, 0.080916032, 0.08108601, 0.081146757294540336, 0.081131019, 0.081163697, 0.081168133102665047, 0.081156127, 0.081008799, 0.081202742360344818, 0.08109393, 0.081158534, 0.081564565332787997, 0.081004679, 0.081386589, 0.081292512777649775, 0.081161894, 0.081169359)
('2017-08-01 17:12:13.712896', 0.08128984, 0.081134269660713229, 0.081004933, 0.081223771, 0.081233812546223444, 0.081152596, 0.081217088, 0.081137848266497503, 0.08117868, 0.081029668, 0.081317324118116832, 0.081114992, 0.081225239, 0.081540245502004172, 0.081024326, 0.081386589, 0.081310327692482504, 0.081187367, 0.081169359)
('2017-08-01 17:12:24.101564', 0.08110001, 0.081003375515546497, 0.080969565, 0.081170119, 0.081220055704228897, 0.081168756, 0.081217088, 0.081156748354768382, 0.081195638, 0.081029668, 0.081204239947469337, 0.081130758, 0.081225239, 0.081564416942045637, 0.081041306, 0.081386589, 0.081503467782347058, 0.081185415, 0.081169359)
('2017-08-01 17:12:34.694614', 0.08110001, 0.080986416767174926, 0.080969989, 0.081170119, 0.08118649993888466, 0.081168994, 0.081217088, 0.081176441233040908, 0.081194617, 0.081029668, 0.081213124480963447, 0.08112698, 0.081225239, 0.081512109620132098, 0.081035316, 0.081386589, 0.08151999749458512, 0.081185505, 0.081169359)
('2017-08-01 17:12:45.049222', 0.08110001, 0.081006963517695443, 0.080969773, 0.081170119, 0.08124134710212598, 0.081168972, 0.081217088, 0.081174095061373769, 0.08119709, 0.081029668, 0.081273044231968947, 0.081132054, 0.081225239, 0.081407257004811412, 0.081041127, 0.081386589, 0.081533487940257748, 0.081183627, 0.081169359)
('2017-08-01 17:12:56.661656', 0.08110001, 0.080986416767174926, 0.080970503, 0.081170119, 0.081204189373356134, 0.081169941, 0.081217088, 0.081176441233040908, 0.081196517, 0.081029668, 0.081221906917198869, 0.08113008, 0.081225239, 0.081541246884367091, 0.081039153, 0.081386589, 0.081233583854082386, 0.081184611, 0.081169359)
('2017-08-01 17:13:09.081954', 0.08110001, 0.081082371713184803, 0.080975197, 0.081170119, 0.081164488040105162, 0.081170104, 0.081217088, 0.081176441233040908, 0.0811968, 0.081029668, 0.081179409172133232, 0.081130095, 0.081225239, 0.081545603744819631, 0.081038781, 0.081386589, 0.081572765596794827, 0.081185237, 0.081169359)
('2017-08-01 17:13:19.448193', 0.08138999, 0.081226368120565079, 0.081053622, 0.081319608, 0.081329880564161655, 0.081188321, 0.081240416, 0.081228620271558846, 0.08121717, 0.081378981, 0.08149645534583598, 0.081152163, 0.081225239, 0.081562486793769345, 0.081058703, 0.081386589, 0.081507528218986894, 0.08120212, 0.081169359)
('2017-08-01 17:13:29.719878', 0.08138999, 0.081214173517874091, 0.081048086, 0.081223771, 0.081246146024467031, 0.081179187, 0.081238762, 0.081178983861943554, 0.081206247, 0.081029668, 0.08128366741790112, 0.081138358, 0.081225239, 0.081597871077219317, 0.081045873, 0.081386589, 0.081477615378502191, 0.081191532, 0.081169359)
('2017-08-01 17:13:39.990818', 0.08129, 0.081336040827607758, 0.081062756, 0.081329532, 0.081357830163473213, 0.081228442, 0.081303939, 0.081242505414719376, 0.081257321, 0.081378981, 0.081411036422252808, 0.081188202, 0.081225239, 0.081509728413709695, 0.081092983, 0.081386589, 0.081408136882461138, 0.081254721, 0.081169359)
('2017-08-01 17:13:50.391845', 0.08138998, 0.081321540533042885, 0.081143267, 0.08134684, 0.081357784033813121, 0.08130347, 0.081376672, 0.081443942813120257, 0.081334785, 0.081378981, 0.081510165288294884, 0.081262924, 0.081298597, 0.081495680142139468, 0.081164196, 0.081386589, 0.081332727630712309, 0.081300884, 0.081169359)
('2017-08-01 17:14:00.784711', 0.08138998, 0.081322514411283145, 0.081158757, 0.08134684, 0.081341799341665591, 0.081325941, 0.081376672, 0.081434971921671456, 0.081358105, 0.081378981, 0.081474349532120793, 0.081285357, 0.081298597, 0.081563243378986808, 0.081185386, 0.081386589, 0.081381392344296505, 0.081301816, 0.081169359)
('2017-08-01 17:14:11.133683', 0.08138998, 0.081322514411283145, 0.081156544, 0.08134684, 0.081320702499034281, 0.081322826, 0.081376672, 0.081421698885642185, 0.081355058, 0.081378981, 0.081420664362187894, 0.081282623, 0.081298597, 0.081519322694320578, 0.081182651, 0.081386589, 0.08131124863606419, 0.081297405, 0.081169359)
('2017-08-01 17:14:21.438815', 0.08110102, 0.081181799317937231, 0.081038274, 0.081265956, 0.081316036243783529, 0.081257336, 0.081267513, 0.081204078118770187, 0.081287295, 0.081378981, 0.081464605790296726, 0.081217416, 0.081225239, 0.081586627530959244, 0.081122339, 0.081386589, 0.081474348968938445, 0.081225604, 0.081169359)
('2017-08-01 17:14:31.804986', 0.08138998, 0.08123923577315266, 0.081067458, 0.081319608, 0.081310833665720242, 0.081205197, 0.081259929, 0.081164071197716112, 0.08123295, 0.081378981, 0.081474718532307089, 0.081165344, 0.081225239, 0.081588918279988681, 0.081071593, 0.081386589, 0.08147639164417525, 0.081224933, 0.081169359)
('2017-08-01 17:14:42.302712', 0.08138498, 0.081294347296866631, 0.081072889, 0.081319608, 0.081283858073714327, 0.081213847, 0.08126159, 0.08114394269232654, 0.081241474, 0.081378981, 0.081271152235542141, 0.081173941, 0.081225239, 0.081427503151033379, 0.081080809, 0.081386589, 0.081412345822942656, 0.081240632, 0.081169359)
('2017-08-01 17:14:52.654026', 0.08138498, 0.081263416013011083, 0.081067808, 0.081319608, 0.081310824556504568, 0.081206396, 0.081259929, 0.081199183708197051, 0.081233718, 0.081378981, 0.081471226230375018, 0.081166923, 0.081225239, 0.081681809448076029, 0.081072584, 0.081386589, 0.081569569861709199, 0.081228659, 0.081169359)
('2017-08-01 17:15:02.882223', 0.08110106, 0.081171277585307705, 0.081018761, 0.081265956, 0.081308342377663753, 0.081233479, 0.08126159, 0.081200558216292393, 0.081263103, 0.081378981, 0.081260749360252307, 0.081197865, 0.081225239, 0.081640622801802273, 0.081105061, 0.081386589, 0.081572794188256845, 0.081273079, 0.081169359)
('2017-08-01 17:15:13.580057', 0.08138998, 0.081271142158029955, 0.081092715, 0.081319608, 0.081326808756402497, 0.081244648, 0.081264473, 0.08120570267086448, 0.081274264, 0.081378981, 0.081321602822380004, 0.081206739, 0.081225239, 0.081651843772924781, 0.081113517, 0.081386589, 0.081597072039039695, 0.081287496, 0.081169359)
('2017-08-01 17:15:24.587930', 0.08110121, 0.081177727912680103, 0.081021681, 0.081265956, 0.081304530109221312, 0.081238337, 0.081264473, 0.081215810099702843, 0.081266671, 0.081378981, 0.081449835571785034, 0.081197351, 0.081225239, 0.081597480828898381, 0.081106313, 0.081386589, 0.081624640273040783, 0.081280947, 0.081169359)
('2017-08-01 17:15:34.961145', 0.08110121, 0.081173077420032991, 0.081020102, 0.081265956, 0.081329966130836734, 0.081237711, 0.081264473, 0.081215810099702843, 0.081266001, 0.081378981, 0.081459255384128168, 0.08119797, 0.081225239, 0.08159298444437367, 0.081106313, 0.081386589, 0.081628251593744261, 0.081279501, 0.081169359)
('2017-08-01 17:15:45.293566', 0.0811, 0.081138589648901832, 0.081001543, 0.081265956, 0.081349607538051052, 0.081210189, 0.081264473, 0.081185589335214289, 0.081236191, 0.081378981, 0.081524700751575016, 0.081166521, 0.081225239, 0.081593214792356428, 0.081073426, 0.081386589, 0.081620917538420976, 0.081233144, 0.081169359)
('2017-08-01 17:15:55.643217', 0.0811, 0.081123529128408117, 0.08100348, 0.081258833, 0.081350668102342594, 0.081209615, 0.081264473, 0.081173588598674587, 0.081236295, 0.081378981, 0.081447871542077888, 0.081167378, 0.081211194, 0.081626324004676093, 0.081074305, 0.081386589, 0.081624852428044986, 0.081235684, 0.081169359)
Significant Delta: 0.0073742496040689175. Prediction position: 14. Current: 0.08112352912840812. Prediction: 0.08172175428096395
Significant Delta: 0.0074227456395448066. Prediction position: 17. Current: 0.08112352912840812. Prediction: 0.0817256884505105
('2017-08-01 17:16:05.995687', 0.08110001, 0.081123529128408117, 0.081002787, 0.081264876, 0.081309151755085976, 0.081211716, 0.081264473, 0.081181824375586145, 0.081238866, 0.081378981, 0.081455772809827409, 0.081171684, 0.081211194, 0.081721754280963954, 0.081079386, 0.081386589, 0.081725688450510495, 0.081237361, 0.081169359)
Significant Delta: 0.0074232868990106995. Prediction position: 14. Current: 0.08112352912840812. Prediction: 0.08172573235938854
Significant Delta: 0.0072498398330461036. Prediction position: 17. Current: 0.08112352912840812. Prediction: 0.08171166172128053
('2017-08-01 17:16:16.328928', 0.08110001, 0.081123529128408117, 0.081004836, 0.081265956, 0.08134965062269571, 0.081212476, 0.08126159, 0.081215875882431968, 0.081240736, 0.081378981, 0.081426717931336212, 0.081174999, 0.081225239, 0.081725732359388542, 0.081082925, 0.081386589, 0.081711661721280526, 0.081241317, 0.081169359)
('2017-08-01 17:16:26.676508', 0.08138788, 0.081224387809492649, 0.081072561, 0.081319608, 0.08134965062269571, 0.081212297, 0.08126159, 0.081205746207688001, 0.081240594, 0.081378981, 0.081426717931336212, 0.081174776, 0.081225239, 0.081740119592428301, 0.081082821, 0.081386589, 0.081711661721280526, 0.081241235, 0.081169359)
('2017-08-01 17:16:36.888804', 0.08138788, 0.081224387809492649, 0.081071943, 0.081319608, 0.081333742186213226, 0.081211656, 0.08126159, 0.081210858047764053, 0.081239589, 0.081378981, 0.081452564311273845, 0.081174411, 0.081225239, 0.081740119592428301, 0.081080981, 0.081386589, 0.081711661721280526, 0.081239998, 0.081169359)
('2017-08-01 17:16:47.273144', 0.08121211, 0.081277230431891639, 0.081048802, 0.081275888, 0.081265929518841443, 0.081234179, 0.081313588, 0.081249464586600761, 0.08126273, 0.081378981, 0.081478669136716669, 0.081196859, 0.081225239, 0.081736862557566195, 0.081102014, 0.081386589, 0.08169439202399606, 0.081266746, 0.081169359)
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('2017-08-01 17:29:12.272609', 0.08130003, 0.081346990413243031, 0.081202991, 0.081406631, 0.081554139597433309, 0.081414655, 0.081493989, 0.081408749482323087, 0.081459299, 0.081633292, 0.081413934291023782, 0.081393085, 0.081365854, 0.08155802717789129, 0.081288449, 0.08150132, 0.081520247920884428, 0.081439905, 0.081555769)
('2017-08-01 17:29:22.524741', 0.08130003, 0.081353156239605914, 0.081213146, 0.081406631, 0.081549253753011239, 0.081423849, 0.081517458, 0.081445674344944513, 0.081471413, 0.081633292, 0.081428668000518262, 0.081407309, 0.081429146, 0.081586291449572412, 0.081303954, 0.08150132, 0.081374603704848947, 0.08145643, 0.081555769)
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('2017-08-01 17:29:44.492746', 0.0816, 0.081520982919720889, 0.081282742, 0.081515513, 0.081510459395533044, 0.081419222, 0.081517458, 0.081445674344944513, 0.081465125, 0.081633292, 0.081447882622897497, 0.081399694, 0.081429146, 0.081581663984635691, 0.081296377, 0.08150132, 0.081372480419387647, 0.081450984, 0.081555769)
('2017-08-01 17:29:54.759731', 0.08130003, 0.081350340387760373, 0.081206046, 0.081406631, 0.081505459500803581, 0.081417724, 0.081517458, 0.081470504836480218, 0.081460744, 0.081633292, 0.081447882622897497, 0.081392698, 0.081429146, 0.081563990908774156, 0.081287257, 0.08150132, 0.081372480419387647, 0.081441306, 0.081555769)
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('2017-08-01 17:30:26.290303', 0.08155, 0.081447225156556752, 0.081259139, 0.08142022, 0.081491751010857538, 0.081416436, 0.08148133, 0.081366998287351069, 0.08146061, 0.081378981, 0.081408762274754493, 0.081392884, 0.081365854, 0.081520060036075148, 0.081288129, 0.081391566, 0.081360014499760297, 0.081414588, 0.081555769)
('2017-08-01 17:30:36.562088', 0.08155, 0.081421105780653327, 0.081246659, 0.081433699, 0.0814874779841257, 0.081399195, 0.081493989, 0.0813149968137441, 0.08144287, 0.081378981, 0.08133967034458077, 0.08137618, 0.081365854, 0.081599639545092223, 0.081273034, 0.081391566, 0.081528766358634611, 0.081419475, 0.081555769)
('2017-08-01 17:30:46.969606', 0.08130004, 0.081337789486299578, 0.081190988, 0.081408896, 0.081502594124243155, 0.081402265, 0.081493989, 0.08134296689605687, 0.081446007, 0.081378981, 0.081325112969349814, 0.081380568, 0.081365854, 0.081541866280741837, 0.081276514, 0.081391566, 0.081489221316633026, 0.081420854, 0.081555769)
('2017-08-01 17:30:57.395119', 0.08130005, 0.081337789486299578, 0.081191003, 0.081408896, 0.0814874779841257, 0.081399053, 0.081493989, 0.0813149968137441, 0.081442833, 0.081378981, 0.08133967034458077, 0.081376337, 0.081365854, 0.081581966469230688, 0.081273139, 0.081391566, 0.081508876501599284, 0.081419267, 0.081555769)
('2017-08-01 17:31:08.284529', 0.08154999, 0.081424902313977046, 0.081252202, 0.081433699, 0.0814874779841257, 0.081400611, 0.081493989, 0.081304992416747032, 0.081446603, 0.081378981, 0.081292799967737681, 0.081382222, 0.081365854, 0.0815143377415639, 0.081280082, 0.081391566, 0.081541260802429544, 0.081426181, 0.081555769)
('2017-08-01 17:31:18.549889', 0.08154999, 0.081417401658405067, 0.081250064, 0.081433699, 0.081502227208268344, 0.081401557, 0.081493989, 0.081373913659762875, 0.081444688, 0.081378981, 0.081299878812304965, 0.081378393, 0.081439219, 0.081565332341032387, 0.081274256, 0.081391566, 0.081542416405097323, 0.081420116, 0.081555769)
('2017-08-01 17:31:30.035371', 0.08154999, 0.081441344066490035, 0.081254907, 0.081433699, 0.0814874779841257, 0.081403106, 0.081493989, 0.081413127713926614, 0.081447929, 0.081378981, 0.081290371257164165, 0.081381381, 0.081439219, 0.08156492096113703, 0.081279688, 0.081391566, 0.081528766358634611, 0.08142665, 0.081555769)
('2017-08-01 17:31:40.308534', 0.08154999, 0.081458551029002846, 0.081251867, 0.08142022, 0.081458163598707833, 0.0814026, 0.08148133, 0.081467638686623808, 0.081447914, 0.081378981, 0.08138609745887411, 0.081380583, 0.081439219, 0.081535716347429274, 0.081280075, 0.081391566, 0.081528485741340709, 0.081425309, 0.081555769)
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('2017-08-01 17:32:01.051770', 0.08154999, 0.081452360009226163, 0.081287242, 0.08142022, 0.08149690907686305, 0.081451744, 0.081490681, 0.081470467299981489, 0.081499003, 0.081400745, 0.081383427145357803, 0.081431627, 0.081439219, 0.081297423886653883, 0.081326507, 0.081391566, 0.081390337105158694, 0.081425965, 0.081555769)
('2017-08-01 17:32:11.386867', 0.08155, 0.081430805491986766, 0.081253022, 0.08142022, 0.081476380650774213, 0.081406198, 0.08148133, 0.081467826746779257, 0.081451312, 0.081378981, 0.081337141292138301, 0.081386313, 0.081439219, 0.081509632611561716, 0.081281729, 0.081391566, 0.081429834770214551, 0.081423782, 0.081555769)
('2017-08-01 17:32:21.779984', 0.08159999, 0.081485523116462177, 0.081273466, 0.081502028, 0.081438694415826601, 0.08141578, 0.08148133, 0.081498475794511305, 0.081461735, 0.081633292, 0.081399357340637565, 0.081393927, 0.081439219, 0.081376728040900626, 0.08129023, 0.08150132, 0.081440269126520137, 0.081433915, 0.081555769)
('2017-08-01 17:32:32.145722', 0.08155, 0.081411546716445585, 0.081253424, 0.08142022, 0.081480434261414922, 0.081405066, 0.081504807, 0.081455656357924081, 0.08145012, 0.081378981, 0.081342623437809361, 0.081381328, 0.081502512, 0.081419363578753234, 0.081277981, 0.081391566, 0.081371601429533827, 0.0814225, 0.081555769)
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('2017-08-01 17:34:27.130475', 0.08099999, 0.08091085656725025, 0.080811463, 0.080983095, 0.080858653171088299, 0.080985487, 0.081012018, 0.080939026759923038, 0.081008784, 0.081008799, 0.080895099739739768, 0.080952354, 0.080900162, 0.08079434724293233, 0.080859803, 0.081032507, 0.081130355865583939, 0.081019409, 0.080753945)
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('2017-08-01 17:34:47.616665', 0.081, 0.08103637774753597, 0.080825619, 0.080983095, 0.080997308959298658, 0.081002638, 0.081044756, 0.080890801618515112, 0.0810275, 0.081008799, 0.08095297180586114, 0.080972791, 0.080979452, 0.080843576090165389, 0.080882184, 0.081032507, 0.081208416588615551, 0.08105734, 0.081123315)
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('2017-08-01 17:39:11.524836', 0.081189, 0.081186366687666725, 0.080972627, 0.081170119, 0.0812821818914852, 0.081138879, 0.081208542, 0.081380334775804114, 0.08115755, 0.081043042, 0.08133589896658254, 0.081080839, 0.081169471, 0.081099324330665287, 0.080977328, 0.081043355, 0.081338413906773294, 0.081090249, 0.081136666)
('2017-08-01 17:39:21.916838', 0.081189, 0.081389010494785863, 0.081002921, 0.081265956, 0.08121736167712551, 0.081181966, 0.081316106, 0.081385333196201004, 0.081202239, 0.081392355, 0.08156159404298681, 0.081123829, 0.081250452, 0.081251104698304794, 0.081015065, 0.081397429, 0.081581866704776934, 0.081132241, 0.081182711)
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('2017-08-01 18:13:17.811762', 0.08049993, 0.080349518157196237, 0.080182195, 0.080370687, 0.080300689491011032, 0.080324776, 0.080404572, 0.080384616031700926, 0.080306612, 0.080299385, 0.080366769833416632, 0.0802674, 0.080318451, 0.080297605933443067, 0.080175586, 0.080366656, 0.080288106753489713, 0.080303743, 0.080349527)
('2017-08-01 18:13:28.524031', 0.08049995, 0.080438133211808954, 0.080198415, 0.080387607, 0.080263164270943904, 0.080345258, 0.080418661, 0.080329897199877778, 0.08032728, 0.080299385, 0.08040570519660184, 0.080286704, 0.080343373, 0.080314511747014081, 0.080194958, 0.080399752, 0.080283211048993899, 0.080328435, 0.080373347)
('2017-08-01 18:13:38.779764', 0.08049995, 0.080314012629804851, 0.080166303, 0.080307029, 0.0802433463806633, 0.080304436, 0.080360174, 0.080162809930657233, 0.080284059, 0.080234461, 0.08059585275718692, 0.080244012, 0.080271527, 0.080432830825301296, 0.080152981, 0.08035703, 0.080692925237785637, 0.080277838, 0.080373347)
('2017-08-01 18:13:49.199791', 0.08032992, 0.080323463625058975, 0.080113314, 0.080290116, 0.080221229838168567, 0.080288075, 0.080328241, 0.080147032384143585, 0.080267675, 0.080234461, 0.080334137610190245, 0.080231436, 0.080236897, 0.080424077389905854, 0.080140412, 0.08032392, 0.080444816254549481, 0.080253631, 0.080349527)
('2017-08-01 18:13:59.438854', 0.08021, 0.080249353953046498, 0.08010345, 0.080272563, 0.080251688827997117, 0.080313474, 0.080338039, 0.08020366992954113, 0.080296211, 0.080234461, 0.080363615439641295, 0.080260061, 0.080252632, 0.080434571390264234, 0.080172442, 0.08032392, 0.080464124435204329, 0.08029975, 0.080349527)
('2017-08-01 18:14:09.870493', 0.08021, 0.080238033466147288, 0.080105916, 0.080272563, 0.080233321675542174, 0.080313668, 0.080338039, 0.080214063106203798, 0.080296844, 0.080234461, 0.08034079224682085, 0.080260396, 0.080245011, 0.080174741260774757, 0.080173478, 0.08032392, 0.080165063861579205, 0.080302253, 0.080349527)
('2017-08-01 18:14:20.222037', 0.08021, 0.080241707229247797, 0.080081359, 0.080271468, 0.080221229838168567, 0.080278926, 0.080328241, 0.080193239015183354, 0.080258131, 0.080234461, 0.080311954792034068, 0.080222026, 0.080246605, 0.080387681824642496, 0.080132149, 0.08032392, 0.080456079405866326, 0.080245845, 0.080349527)
('2017-08-01 18:14:30.626748', 0.08032992, 0.080337456788836206, 0.08012931, 0.08029151, 0.080282581115410007, 0.080303341, 0.080358379, 0.080230476881522045, 0.080283076, 0.080379307, 0.080281416925403151, 0.080245174, 0.080352716, 0.08044517905054327, 0.080154911, 0.08032392, 0.080428994950575539, 0.080280013, 0.080349527)
('2017-08-01 18:14:40.864916', 0.08032992, 0.080401256760471648, 0.08015088, 0.080292597, 0.08033216383574783, 0.080333292, 0.080368176, 0.080241248789998179, 0.080315515, 0.080379307, 0.08028268153588923, 0.080276944, 0.080358744, 0.080458387460184772, 0.080186978, 0.08032392, 0.080428994950575539, 0.080299221, 0.080349527)
('2017-08-01 18:14:51.274366', 0.08032992, 0.080401256760471648, 0.080151029, 0.080292597, 0.080316299717838019, 0.080335058, 0.080368176, 0.080248803994135962, 0.080318622, 0.080379307, 0.080285040881755018, 0.08028125, 0.080358744, 0.080463106795848227, 0.080191582, 0.08032392, 0.080428994950575539, 0.080299661, 0.080349527)
('2017-08-01 18:15:01.667506', 0.08032992, 0.080387276399418409, 0.080179185, 0.080370985, 0.080549628834664877, 0.080367491, 0.080421597, 0.080522906997237173, 0.080351591, 0.080444224, 0.080398006019077914, 0.080310985, 0.080410913, 0.080338626896411955, 0.080219068, 0.080366656, 0.080302717601952581, 0.080333054, 0.080349527)
('2017-08-01 18:15:12.459453', 0.08049993, 0.080521627598422366, 0.080253944, 0.080387898, 0.080551093964414525, 0.080411024, 0.080456205, 0.080452264331330278, 0.080397733, 0.080444224, 0.080488935283852078, 0.080355242, 0.080443464, 0.080487710935845239, 0.080261953, 0.080399752, 0.080519315400935626, 0.080366775, 0.080373347)
('2017-08-01 18:15:22.815812', 0.08021, 0.080342609229064721, 0.080137305, 0.080344483, 0.080311186239821961, 0.080353461, 0.080362849, 0.080468624713885883, 0.080336809, 0.080299385, 0.080311473327319652, 0.080297485, 0.08029189, 0.080429664948994645, 0.080205075, 0.08035703, 0.080395432031373587, 0.080298088, 0.080373347)
('2017-08-01 18:15:33.768040', 0.08021, 0.080258545243055465, 0.080093451, 0.080271468, 0.080212361698253132, 0.080292389, 0.080346085, 0.080149130448939496, 0.08027225, 0.080234461, 0.080225616242750167, 0.08023335, 0.080246605, 0.080397166590216285, 0.080144323, 0.08032392, 0.080391059469012899, 0.080261692, 0.080349527)
('2017-08-01 18:15:44.306348', 0.0801009, 0.080244243462104098, 0.080025926, 0.080190472, 0.080133692893711597, 0.080239438, 0.080233932, 0.080116259813379873, 0.080216832, 0.080146655, 0.080170005898443059, 0.08018025, 0.080236703, 0.080092896303325362, 0.080093317, 0.080186807, 0.080122221363522789, 0.080199905, 0.080287211)
('2017-08-01 18:15:55.161172', 0.08021, 0.080133555198501003, 0.080025896, 0.080226913, 0.080185177159697502, 0.080206171, 0.080208831, 0.080078593354302424, 0.080181725, 0.080146655, 0.080122507620798977, 0.080147035, 0.080188237, 0.080028718955161449, 0.08006148, 0.080186807, 0.080106507866940679, 0.080189675, 0.080287211)
('2017-08-01 18:16:05.537471', 0.08053, 0.080394224977458295, 0.080168568, 0.080333889, 0.08032945203529035, 0.08029104, 0.080358714, 0.080433276994872085, 0.080271006, 0.080299385, 0.080350799113349775, 0.080232754, 0.08026395, 0.080331584386787627, 0.080143481, 0.080366656, 0.080316339697771966, 0.080288656, 0.080349527)
('2017-08-01 18:16:15.765447', 0.08053, 0.080347662275810638, 0.080208808, 0.080395356, 0.080257668723326958, 0.080345072, 0.08035139, 0.080240220365621728, 0.080328234, 0.08021158, 0.080457900460560797, 0.080288097, 0.080327459, 0.080294412793796538, 0.080198653, 0.08028014, 0.080188511249396577, 0.080325589, 0.080373347)
('2017-08-01 18:16:26.584704', 0.08053, 0.080381630691764433, 0.080209479, 0.080360308, 0.080237776741642619, 0.080347389, 0.080341227, 0.080216923194502091, 0.080331653, 0.08021158, 0.080410339463763927, 0.080292337, 0.080285013, 0.080294412793796538, 0.080202244, 0.08028014, 0.080198510908699061, 0.080358371, 0.080373347)
('2017-08-01 18:16:36.970376', 0.0801, 0.080447776135018342, 0.080104388, 0.08025182, 0.080242938448723017, 0.080348112, 0.080341227, 0.080194337927661799, 0.080333196, 0.08021158, 0.080430791524329484, 0.080292173, 0.080285013, 0.080278733627890689, 0.080201805, 0.08028014, 0.080194213273064807, 0.080356613, 0.080373347)
('2017-08-01 18:16:47.353735', 0.0801, 0.080407031759229863, 0.080088273, 0.080250725, 0.080233423430039835, 0.080320135, 0.08033143, 0.080226848528276967, 0.080301307, 0.08021158, 0.080447415230950114, 0.080262907, 0.080278985, 0.080294412793796538, 0.080169782, 0.08028014, 0.080184213613762309, 0.080313548, 0.080373347)
('2017-08-01 18:16:57.940835', 0.0801, 0.080419440093690522, 0.080086507, 0.080250725, 0.080233423430039835, 0.080316573, 0.08033143, 0.080218752413170252, 0.080297559, 0.08021158, 0.080436339004311352, 0.080259018, 0.080278985, 0.080294281335787809, 0.080165029, 0.08028014, 0.080184213613762309, 0.080307938, 0.080373347)
('2017-08-01 18:17:08.343074', 0.0801, 0.08043749624889128, 0.080079265, 0.080244407, 0.080244833292944809, 0.080305912, 0.080371536, 0.080348215365873346, 0.080284938, 0.080299385, 0.080350317715866731, 0.080246054, 0.08028888, 0.080241716133872737, 0.080149814, 0.080399752, 0.080137749448578682, 0.080286562, 0.080373347)
('2017-08-01 18:17:18.721404', 0.08052997, 0.080368303043557979, 0.080187276, 0.080389045, 0.080229512480269202, 0.080314279, 0.080391496, 0.080321657268805538, 0.080293894, 0.080299385, 0.080403402600146556, 0.080254957, 0.080337346, 0.080241716133872737, 0.080159605, 0.080399752, 0.080162726874206419, 0.080287211, 0.080373347)
('2017-08-01 18:17:29.097675', 0.08052997, 0.080426160318408171, 0.080280654, 0.08051528, 0.08057806609312898, 0.080433957, 0.080530711, 0.080553302236738206, 0.080420211, 0.080444224, 0.08048477655271194, 0.080379151, 0.080457829, 0.080538161866026567, 0.080284439, 0.080606043, 0.08081910695734601, 0.080449991, 0.080566972)
('2017-08-01 18:17:39.436474', 0.08015001, 0.080439601608260483, 0.080171131, 0.080286883, 0.080255013011152176, 0.080413051, 0.080422916, 0.080285743575626109, 0.080398597, 0.080299385, 0.080435240799189317, 0.080356866, 0.080354638, 0.080333159699405618, 0.080264471, 0.080396853, 0.080202741620445647, 0.080332175, 0.080359988)
('2017-08-01 18:17:50.028633', 0.08015001, 0.080471032701863107, 0.080119796, 0.080286883, 0.080228431636364061, 0.080341533, 0.080401294, 0.08029054285017044, 0.080322862, 0.080299385, 0.080388282791104881, 0.080282182, 0.080343373, 0.080204270296950625, 0.08018931, 0.080399752, 0.080162726874206419, 0.080327541, 0.080373347)
('2017-08-01 18:18:03.630152', 0.08015001, 0.080471032701863107, 0.080119863, 0.080286883, 0.08023437093523024, 0.080342337, 0.080401294, 0.08029054285017044, 0.08032506, 0.080299385, 0.080375487426912293, 0.080285899, 0.080343373, 0.080239992253713852, 0.080192998, 0.080399752, 0.080162726874206419, 0.080331065, 0.080373347)
('2017-08-01 18:18:14.028748', 0.08053, 0.080338390177386462, 0.080184989, 0.080320545, 0.080187747566753342, 0.080307849, 0.080330931, 0.08002238058926818, 0.080288872, 0.08017958, 0.080149565194379477, 0.080251686, 0.080226608, 0.080172313781917512, 0.080161698, 0.0802643, 0.079842075516374469, 0.080295071, 0.080121092)
Significant Delta: -0.007866555639039553. Prediction position: 17. Current: 0.08048284948968054. Prediction: 0.07984972667618152
('2017-08-01 18:18:24.395212', 0.08079997, 0.080482849489680541, 0.08026883, 0.08039324, 0.080302975606850568, 0.080331221, 0.080429621, 0.080208621546711337, 0.080313817, 0.080393068, 0.080127903283777765, 0.080275953, 0.080244221, 0.080062777602263019, 0.080184177, 0.080169983, 0.079849726676181523, 0.080354869, 0.080308065)
Significant Delta: -0.0076589919661965225. Prediction position: 17. Current: 0.08045825643741877. Prediction: 0.0798420272977504
('2017-08-01 18:18:34.773407', 0.08079997, 0.080458256437418774, 0.080307685, 0.080401674, 0.080317658594482005, 0.080384433, 0.080482878, 0.080191119074273842, 0.080370367, 0.080393068, 0.080216327072339305, 0.080330953, 0.08025635, 0.079976801910585177, 0.08023829, 0.080169983, 0.079842027297750404, 0.080418915, 0.080308065)
('2017-08-01 18:18:45.189260', 0.08079997, 0.080416518226791675, 0.080336168, 0.080470473, 0.080383823207344593, 0.080418564, 0.080553241, 0.08024834249707323, 0.080407776, 0.080467492, 0.08039016692544726, 0.080365576, 0.080359921, 0.080196621362034287, 0.080270745, 0.080297492, 0.080280229822217705, 0.080455892, 0.080560319)
('2017-08-01 18:18:55.391896', 0.08000008, 0.080225144130498804, 0.080169417, 0.080289468, 0.0803683805059731, 0.080450304, 0.080484666, 0.080323898283726228, 0.080441736, 0.080379695, 0.080311516624677851, 0.080398604, 0.0803077, 0.080073927235702322, 0.080307767, 0.080272198, 0.080291474160141277, 0.080473877, 0.080403499)
('2017-08-01 18:19:05.640523', 0.08000008, 0.080152066528844643, 0.080119401, 0.080253318, 0.080317658594482005, 0.080383353, 0.0804919, 0.080191119074273842, 0.08036644, 0.080393068, 0.080334765134049899, 0.080326766, 0.08025267, 0.080172876147007022, 0.080234192, 0.080182932, 0.080219145859607086, 0.080412462, 0.080390148)
('2017-08-01 18:19:16.009009', 0.08000008, 0.080348126297249162, 0.080127537, 0.080322117, 0.080410978846165479, 0.080394514, 0.080514252, 0.080259555208445243, 0.080378667, 0.080393068, 0.080414966010026373, 0.080338761, 0.080345497, 0.080263881743554838, 0.080245189, 0.080182932, 0.080228875241943609, 0.080424272, 0.080390148)
('2017-08-01 18:19:26.629409', 0.0805, 0.080554318839456865, 0.080327891, 0.08054059, 0.080566398670349801, 0.080501087, 0.080612652, 0.080603205464596137, 0.080490522, 0.080625713, 0.080597139336433241, 0.080447823, 0.080473527, 0.080786673177806564, 0.080349468, 0.080606043, 0.08070328031668618, 0.080542982, 0.080753945)
('2017-08-01 18:19:39.317268', 0.0805, 0.08063325285199148, 0.080381982, 0.080575407, 0.080588561680761131, 0.080572695, 0.080642849, 0.080654410892045197, 0.080565237, 0.080625713, 0.080687247186998337, 0.080518782, 0.080539092, 0.080828816323127842, 0.080418728, 0.080606043, 0.08070328031668618, 0.080520011, 0.080753945)
('2017-08-01 18:19:49.682641', 0.08079992, 0.080601540633484819, 0.080467992, 0.0806356, 0.080561585912196379, 0.080595568, 0.080642849, 0.080640879083725137, 0.080588832, 0.080625713, 0.080693076436735067, 0.080540627, 0.080542773, 0.08082415492192449, 0.080440186, 0.080608949, 0.080698971386809898, 0.080548599, 0.080767304)
('2017-08-01 18:20:00.074007', 0.08079992, 0.080601540633484819, 0.080466561, 0.0806356, 0.080556703743970781, 0.080593914, 0.080655262, 0.080621484276725677, 0.080587178, 0.080625713, 0.08069218464422008, 0.080538817, 0.080542773, 0.080861454895418405, 0.080439329, 0.080608949, 0.080694636222734192, 0.080548264, 0.080767304)
('2017-08-01 18:20:10.479191', 0.08079992, 0.080596063389995892, 0.080476858, 0.080636919, 0.080558162016772683, 0.080608569, 0.080642849, 0.080650622985614581, 0.080602609, 0.080625713, 0.080651679004274099, 0.080554284, 0.080550991, 0.08089042052170177, 0.080455609, 0.080606043, 0.080705437320342452, 0.080573536, 0.080753945)
('2017-08-01 18:20:20.995940', 0.0805, 0.080547439409705282, 0.080398113, 0.080586888, 0.080550816493410768, 0.080598012, 0.080673195, 0.080690613286614185, 0.080591924, 0.080612339, 0.080708682025597375, 0.080543585, 0.080539942, 0.080807350184215199, 0.080442362, 0.080598101, 0.080651358922999641, 0.080555238, 0.080753945)
('2017-08-01 18:20:31.398278', 0.08079992, 0.080590401965126249, 0.08046779, 0.0806356, 0.08054598708101722, 0.080596164, 0.080647357, 0.080639421982331219, 0.080590077, 0.080612339, 0.080692856980348754, 0.080541752, 0.08052589, 0.080882768498523619, 0.080441818, 0.080598101, 0.080709536510784635, 0.080554701, 0.080753945)
('2017-08-01 18:20:41.753987', 0.0805, 0.080547439409705282, 0.08040221, 0.080586888, 0.08054738195316892, 0.080607243, 0.080644511, 0.080615578979061298, 0.080601819, 0.080612339, 0.080673855388983168, 0.080552615, 0.08052589, 0.080793942938091487, 0.080450989, 0.080598101, 0.08070004862662114, 0.080565453, 0.080753945)
('2017-08-01 18:20:52.133510', 0.0805, 0.080638803181327504, 0.080430113, 0.080588207, 0.0805750532497123, 0.080646515, 0.080642849, 0.080673700969384837, 0.080644593, 0.080612339, 0.080787801216722854, 0.080596812, 0.080553874, 0.081118516605625468, 0.08050026, 0.080598101, 0.081155670290375431, 0.080633558, 0.080753945)
('2017-08-01 18:21:03.870218', 0.0805, 0.080668594465593199, 0.080429874, 0.080588207, 0.080578930294196993, 0.08064729, 0.080647215, 0.080680038959387856, 0.080644518, 0.080625713, 0.080744490074340702, 0.080596007, 0.080567077, 0.081118516605625468, 0.080498092, 0.080606043, 0.081172426761170743, 0.080632739, 0.080753945)
('2017-08-01 18:21:14.316737', 0.0805, 0.080706120176845947, 0.080436379, 0.080588207, 0.080611895326327329, 0.080647804, 0.080642849, 0.080665988962059842, 0.080647625, 0.080625713, 0.080804345528220384, 0.080601968, 0.080567077, 0.081185305204931654, 0.08050596, 0.080606043, 0.081161732516693827, 0.080636971, 0.080753945)
('2017-08-01 18:21:24.567140', 0.08011, 0.080432397203694134, 0.080245361, 0.080300756, 0.080441062444355518, 0.080523729, 0.080462523, 0.080397967936875994, 0.080518737, 0.080379695, 0.08037979015953077, 0.080476806, 0.080384091, 0.080315323963935481, 0.080387831, 0.080269299, 0.080466412472504453, 0.08049421, 0.080359988)
('2017-08-01 18:21:34.855494', 0.08011, 0.080334315977590984, 0.080161586, 0.080264613, 0.080461914441792404, 0.08041244, 0.080379218, 0.080361464662453197, 0.080400229, 0.080379695, 0.080394256388657909, 0.080361076, 0.080342971, 0.080317121454517082, 0.080274694, 0.080269299, 0.080302144936148734, 0.080451749, 0.080359988)
('2017-08-01 18:21:45.207368', 0.08011, 0.08034123837049291, 0.080194041, 0.080332451, 0.080476285988670487, 0.080462389, 0.080465339, 0.080427102049702312, 0.080454193, 0.080379695, 0.080400230613559748, 0.08041583, 0.080384716, 0.080260811812190108, 0.080325916, 0.080269299, 0.080322687625369552, 0.080502756, 0.080546968)
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('2017-08-01 18:29:43.955449', 0.080999, 0.080947721650574275, 0.08075887, 0.080975622, 0.080930565619394892, 0.080931619, 0.080990948, 0.080946943911368849, 0.080947064, 0.080938354, 0.08095679549721925, 0.080897562, 0.080965407, 0.08091784470141096, 0.080793373, 0.081032507, 0.081170064575922407, 0.08092884, 0.081123315)
('2017-08-01 18:29:54.348162', 0.081, 0.081005436349867635, 0.080789872, 0.080976248, 0.08094839393532019, 0.08096879, 0.081025302, 0.081076593978156503, 0.080986135, 0.081008799, 0.080977669043529849, 0.080936812, 0.080965407, 0.081051055700753799, 0.080830976, 0.081032507, 0.081156269815017085, 0.080929704, 0.081123315)
('2017-08-01 18:30:04.740056', 0.0809999, 0.081034943676370402, 0.080788814, 0.080980249, 0.080905650089285228, 0.080971487, 0.081008933, 0.080966466138607085, 0.080989845, 0.080938354, 0.080987592084072135, 0.08094012, 0.080965407, 0.081105015012180981, 0.080835529, 0.081032507, 0.08108539449353297, 0.080934018, 0.081123315)
('2017-08-01 18:30:16.174287', 0.08099988, 0.080864457190492636, 0.080756634, 0.080975622, 0.08087673163844393, 0.080931127, 0.08094047, 0.080839580073735387, 0.080947466, 0.080938354, 0.080885752665659089, 0.080900408, 0.080886118, 0.080964177139577403, 0.080798194, 0.081032507, 0.08117302229323764, 0.080886245, 0.081123315)
('2017-08-01 18:30:26.734810', 0.080999, 0.080816293970927919, 0.080727257, 0.080976248, 0.080871847623349091, 0.080897503, 0.080925927, 0.080744647903649353, 0.080914125, 0.080938354, 0.080760234145103221, 0.080868885, 0.080886118, 0.080712444232677122, 0.080767259, 0.081032507, 0.08091778941642648, 0.080889255, 0.081123315)
('2017-08-01 18:30:36.955283', 0.080999, 0.080823017499445871, 0.080730297, 0.080976248, 0.080871847623349091, 0.080903031, 0.080939583, 0.080798272398748081, 0.080920286, 0.080938354, 0.080799546666955629, 0.080874696, 0.080886118, 0.080721934459656627, 0.08077082, 0.081032507, 0.080960862869476527, 0.080894008, 0.081123315)
('2017-08-01 18:30:47.371001', 0.080999, 0.080823017499445871, 0.080728464, 0.080977395, 0.08086555885749247, 0.080896884, 0.080939583, 0.080825596427443649, 0.080912434, 0.080938354, 0.080799546666955629, 0.080864549, 0.080886118, 0.080701073570468615, 0.08076147, 0.081032507, 0.080951163442044088, 0.080885783, 0.081123315)
('2017-08-01 18:30:57.738315', 0.080999, 0.080879185012732252, 0.080745198, 0.080977395, 0.080853601701388791, 0.080917358, 0.080947623, 0.080881711579845972, 0.080934212, 0.080938354, 0.080921707487197431, 0.080885552, 0.080965407, 0.080686830837583504, 0.080781996, 0.081032507, 0.08083279430320657, 0.080911644, 0.081123315)
('2017-08-01 18:31:07.979330', 0.08118999, 0.080930726636648839, 0.080800086, 0.080977395, 0.080833114749323864, 0.08093442, 0.080968723, 0.080917049369709143, 0.080953464, 0.081008799, 0.080817269160946489, 0.080904797, 0.080886118, 0.080638631849917652, 0.080803648, 0.081032507, 0.080938227973984034, 0.080919296, 0.081123315)
('2017-08-01 18:31:18.234226', 0.081, 0.081072933972570232, 0.080804661, 0.081084929, 0.081096593843054746, 0.080997363, 0.081092335, 0.081033269141886757, 0.081018157, 0.081008799, 0.081148441256314985, 0.080967121, 0.081071839, 0.081036124396488621, 0.080859922, 0.081032507, 0.080973590734350759, 0.081007406, 0.081123315)
('2017-08-01 18:31:28.630624', 0.081, 0.081081719494049936, 0.080856778, 0.081084929, 0.081178954097823705, 0.081066817, 0.081112795, 0.081086049741084631, 0.081091262, 0.081008799, 0.081299712106550667, 0.081037857, 0.081071839, 0.081030549894997542, 0.080929138, 0.081032507, 0.080907953885309969, 0.081046551, 0.081123315)
('2017-08-01 18:31:39.544094', 0.081, 0.081081623877015743, 0.080848798, 0.081084929, 0.08115759993740454, 0.081050463, 0.081109911, 0.081100609711249438, 0.081077158, 0.081008799, 0.081312439022573554, 0.081026137, 0.081071839, 0.081080224101963694, 0.080918424, 0.081032507, 0.081018184935295987, 0.081026956, 0.081123315)
('2017-08-01 18:31:50.172880', 0.08118798, 0.081131836370704369, 0.08089342, 0.081084929, 0.081138295617024556, 0.081046209, 0.081109911, 0.08109268443329358, 0.081072509, 0.081008799, 0.081291989664397601, 0.081019938, 0.081071839, 0.081030549894997542, 0.080914125, 0.081032507, 0.081018184935295987, 0.081023619, 0.081123315)
('2017-08-01 18:32:00.435843', 0.08118798, 0.081096404431221333, 0.080924913, 0.081169039, 0.081255137020741469, 0.081085227, 0.081201486, 0.081268945286927366, 0.081112511, 0.081029668, 0.081338003221367888, 0.081059352, 0.081138536, 0.080982424810031414, 0.080951817, 0.081032507, 0.081010906847243755, 0.081070453, 0.081123315)
('2017-08-01 18:32:10.795474', 0.08118999, 0.081100369031655989, 0.08095035, 0.081169039, 0.081273070293796976, 0.081119373, 0.081201486, 0.081277052471529762, 0.081146657, 0.081029668, 0.081323364677926932, 0.081090987, 0.081138536, 0.081030326839178449, 0.080982104, 0.081032507, 0.081034034784252695, 0.081070229, 0.081123315)
('2017-08-01 18:32:21.217566', 0.08118999, 0.081179839749604313, 0.08095184, 0.081169039, 0.081240149826148522, 0.08113002, 0.08120437, 0.081257172391102778, 0.081157394, 0.081029668, 0.081312546703366406, 0.081102669, 0.081138536, 0.081092912402723372, 0.080992952, 0.081032507, 0.081028338617624457, 0.081081517, 0.081123315)
('2017-08-01 18:32:31.681426', 0.08118799, 0.081216927970395744, 0.080942921, 0.081169039, 0.081245175556932184, 0.081114575, 0.081197143, 0.081257172391102778, 0.081139654, 0.081029668, 0.081238932366053049, 0.081084974, 0.081138536, 0.081022955961977283, 0.080976009, 0.081032507, 0.080849812981618183, 0.081065103, 0.081123315)
('2017-08-01 18:32:42.183396', 0.08118799, 0.081207639929741629, 0.080969602, 0.081169039, 0.081262405525039452, 0.081151269, 0.081199132, 0.081245505427748846, 0.081179522, 0.081029668, 0.081270783848735553, 0.081124656, 0.081138536, 0.081010755108206794, 0.081018098, 0.081032507, 0.080942962101955909, 0.081120737, 0.081123315)
('2017-08-01 18:32:55.138754', 0.0812, 0.081149685115081388, 0.080974951, 0.081169039, 0.08129175774612088, 0.081155501, 0.081209011, 0.081245417334561768, 0.081184715, 0.081029668, 0.081322048548310377, 0.081131727, 0.081138536, 0.081082976017052819, 0.081025742, 0.081032507, 0.080966894532375502, 0.081124954, 0.081123315)
('2017-08-01 18:33:05.350552', 0.0812, 0.081158368545648174, 0.080978297, 0.081169039, 0.081211010727097702, 0.08115381, 0.081211165, 0.081224612830976636, 0.081183091, 0.081029668, 0.081255076300208118, 0.081130624, 0.081138536, 0.081149800647381545, 0.081026077, 0.081032507, 0.080984342268613368, 0.081126459, 0.081123315)
('2017-08-01 18:33:15.739569', 0.08118798, 0.081158942085144445, 0.08097595, 0.081169039, 0.081231894849211034, 0.081153452, 0.081211165, 0.081224612830976636, 0.081182256, 0.081029668, 0.081255076300208118, 0.081129067, 0.081138536, 0.081101830090653901, 0.081024252, 0.081032507, 0.080984342268613368, 0.081124231, 0.081123315)
('2017-08-01 18:33:26.567078', 0.0812, 0.081167498936114565, 0.080988362, 0.081169039, 0.081251468459893575, 0.08117035, 0.081201293, 0.081176020117045083, 0.081198767, 0.081029668, 0.081292223470036409, 0.081141919, 0.081138536, 0.081134818408720985, 0.081035696, 0.081032507, 0.080987664381697619, 0.08114659, 0.081123315)
('2017-08-01 18:33:36.992480', 0.08118798, 0.081172867944149713, 0.080990121, 0.081170119, 0.081252523774541882, 0.081180073, 0.081210971, 0.081184302355867141, 0.081210025, 0.081029668, 0.08137022106678371, 0.081155695, 0.081152588, 0.081173675671531789, 0.081046864, 0.081032507, 0.081030005834904939, 0.081156172, 0.081123315)
('2017-08-01 18:33:47.299133', 0.08118798, 0.081165602768856357, 0.080987804, 0.081170119, 0.081258233872558094, 0.081181966, 0.081210971, 0.081190657596612051, 0.081212342, 0.081029668, 0.081201229208105283, 0.081159398, 0.081152588, 0.081100265259785642, 0.081049815, 0.081032507, 0.080996512310812327, 0.081157587, 0.081123315)
---------------------------------------------------------------------------
KeyboardInterrupt Traceback (most recent call last)
<ipython-input-66-6f5ef49d9412> in <module>()
25 print(tuple)
26
---> 27 time.sleep(10)
28
29
KeyboardInterrupt:
In [67]:
list(predictions)
Out[67]:
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...]
In [68]:
predictions_2 = predictions
In [69]:
# predictions_1 = predictions
In [70]:
# len(predictions_0)
In [71]:
# pr_sorted_0 = sorted(list(predictions_0))
In [72]:
df_1 = pd.DataFrame(sorted(list(predictions_2)), columns=['ts_utc', 'last', 'predicted_mdl_rf_1',
'predicted_mdl_rnn_1', 'predicted_mdl_xgb_1',
'predicted_mdl_rf_2', 'predicted_mdl_rnn_2', 'predicted_mdl_xgb_2',
'predicted_mdl_rf_3', 'predicted_mdl_rnn_3', 'predicted_mdl_xgb_3',
'predicted_mdl_rf_4', 'predicted_mdl_rnn_4', 'predicted_mdl_xgb_4',
'predicted_mdl_rf_5', 'predicted_mdl_rnn_5', 'predicted_mdl_xgb_5',
'predicted_mdl_rf_6', 'predicted_mdl_rnn_6', 'predicted_mdl_xgb_6'])
In [73]:
# df_0.to_csv('../data/allticker_bittrex_prediction_{}_{}_{}_fileslb_{}_datalb_{}_g0.csv.gz'.format(
# pair, n_columns_original_data, yesterday.strftime('%Y%m%d'), days_back_file*-1, datapoints_back), index=False, compression='gzip')
df_1.to_csv('../data/allticker_bittrex_prediction_{}_{}_{}_fileslb_{}_datalb_{}_xgb8.csv.gz'.format(
pair, n_columns_original_data, yesterday.strftime('%Y%m%d'), days_back_file*-1, datapoints_back), index=False, compression='gzip')
In [ ]: