In [1]:
#This file is use to check data integrity with pandas
#ipython notebook
In [2]:
import pandas as pd
import matplotlib
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
In [3]:
df_eur_usd = pd.read_csv("price_EUR_USD_D1_merge.csv", header=0, index_col=0)
In [4]:
df_eur_usd.columns
Out[4]:
Index([u'Currency_pair', u'Prediction_action', u'Day Open', u'Day Close', u'Day High', u'Day Low', u'Day Average', u'Momentum_3day', u'Momentum_4day', u'Momentum_5day', u'Momentum_8day', u'Momentum_9day', u'Momentum_10day', u'Roc_3day', u'Roc_4day', u'Roc_5day', u'Roc_8day', u'Roc_9day', u'Roc_10day', u'Fast_k_3day', u'Fast_d_3day', u'Fast_k_4day', u'Fast_d_4day', u'Fast_k_5day', u'Fast_d_5day', u'Fast_k_8day', u'Fast_d_8day', u'Fast_k_9day', u'Fast_d_9day', u'Fast_k_10day', u'Fast_d_10day', u'PROC_12day', u'PROC_13day', u'PROC_14day', u'PROC_15day', u'Weighted_Close_Price', u'WILLIAM_A_D', u'ADOSC_1day', u'ADOSC_2day', u'ADOSC_3day', u'ADOSC_4day', u'ADOSC_5day', u'EMA_12Day', u'EMA_26Day', u'MACD', u'CCI', u'BOLLINGER_BANDS_LOW', u'BOLLINGER_BANDS_HIGH', u'HEIKIN_ASHI_XCLOSE', u'HEIKIN_ASHI_XOPEN', u'HEIKIN_ASHI_XHIGH', u'HEIKIN_ASHI_XLOW', u'2DAY_HIGH', u'2DAY_LOW', u'1DAY_HIGH_LOW_AVG', u'2DAY_HIGH_LOW_AVG', u'High_slope_3day', u'High_slope_4day', u'High_slope_5day', u'High_slope_8day', u'High_slope_10day', u'High_slope_12day', u'High_slope_15day', u'High_slope_20day', u'High_slope_25day', u'High_slope_30day', u'Pips', u'Prediction_Pips', u'Volume', u'Active Hour', u'Active Hour Volume'], dtype='object')
In [5]:
df_eur_usd['Volume'].hist()
Out[5]:
<matplotlib.axes._subplots.AxesSubplot at 0x7f69c0618a10>
In [6]:
df_eur_usd.describe()
Out[6]:
Day Open
Day Close
Day High
Day Low
Day Average
Momentum_3day
Momentum_4day
Momentum_5day
Momentum_8day
Momentum_9day
...
High_slope_12day
High_slope_15day
High_slope_20day
High_slope_25day
High_slope_30day
Pips
Prediction_Pips
Volume
Active Hour
Active Hour Volume
count
3393.000000
3393.000000
3393.000000
3393.000000
3393.000000
3393.000000
3393.000000
3393.000000
3393.000000
3393.000000
...
3393.000000
3393.000000
3393.000000
3393.000000
3393.000000
3393.000000
3393.000000
3393.000000
3393.000000
3393.000000
mean
1.328421
1.328375
1.333709
1.323140
1.328355
-0.000212
-0.000280
-0.000345
-0.000538
-0.000605
...
-0.077097
-0.079106
-0.082205
-0.084662
-0.086529
-0.741615
-0.741615
38004.039493
14.066313
4248.521662
std
0.099314
0.099349
0.099534
0.098968
0.099243
0.013297
0.015269
0.017037
0.021381
0.022741
...
2.034391
1.873241
1.669560
1.529292
1.415316
61.739827
61.739827
31380.498988
4.919580
3918.906844
min
1.047040
1.048410
1.050580
1.046240
1.049250
-0.067170
-0.075150
-0.082550
-0.109460
-0.114780
...
-9.194600
-7.340900
-7.324000
-7.900000
-7.221400
-445.400000
-445.400000
1.000000
0.000000
1.000000
25%
1.271700
1.271620
1.276610
1.266550
1.271690
-0.007350
-0.009060
-0.010200
-0.012710
-0.014020
...
-1.283500
-1.155000
-1.042900
-0.915400
-0.837700
-32.800000
-32.800000
13688.000000
12.000000
1641.000000
50%
1.324220
1.324240
1.329550
1.318860
1.323790
-0.000070
0.000420
0.000200
0.000140
0.000220
...
-0.033100
-0.015900
-0.010500
-0.056200
-0.042600
0.900000
0.900000
35025.000000
14.000000
3282.000000
75%
1.381660
1.381690
1.386330
1.377300
1.381590
0.007450
0.008860
0.010190
0.012540
0.013160
...
1.165400
1.048400
0.910000
0.838100
0.798200
31.800000
31.800000
52246.000000
15.000000
5629.000000
max
1.597460
1.597420
1.603890
1.589760
1.595290
0.096120
0.103630
0.107460
0.149000
0.167320
...
14.770800
12.210600
8.039500
8.179400
6.195500
417.300000
417.300000
272386.000000
23.000000
53617.000000
8 rows × 69 columns
In [7]:
df_eur_usd['2015-07-01':'2015-07-10'][['Momentum_3day','Roc_3day','Momentum_4day','Roc_4day','Momentum_5day','Roc_5day','Momentum_8day','Roc_8day','Momentum_9day','Roc_9day','Momentum_10day','Roc_10day']]
Out[7]:
Momentum_3day
Roc_3day
Momentum_4day
Roc_4day
Momentum_5day
Roc_5day
Momentum_8day
Roc_8day
Momentum_9day
Roc_9day
Momentum_10day
Roc_10day
Date
2015-07-01
0.00621
0.01
-0.01362
-0.01
-0.01660
-0.01
-0.03083
-0.03
-0.03243
-0.03
-0.03129
-0.03
2015-07-02
-0.01327
-0.01
0.01110
0.01
-0.00873
-0.01
-0.00888
-0.01
-0.02594
-0.02
-0.02755
-0.02
2015-07-03
-0.00204
-0.00
-0.01049
-0.01
0.01388
0.01
-0.00922
-0.01
-0.00610
-0.01
-0.02316
-0.02
2015-07-05
-0.00422
-0.00
-0.01393
-0.01
-0.02238
-0.02
-0.02082
-0.02
-0.02111
-0.02
-0.01799
-0.02
2015-07-06
-0.00385
-0.00
0.00104
0.00
-0.00867
-0.01
-0.01258
-0.01
-0.01555
-0.01
-0.01585
-0.01
2015-07-07
-0.01061
-0.01
-0.00783
-0.01
-0.00294
-0.00
0.00327
0.00
-0.01656
-0.01
-0.01954
-0.02
2015-07-08
0.00764
0.01
-0.00425
-0.00
-0.00147
-0.00
-0.01474
-0.01
0.00963
0.01
-0.01020
-0.01
2015-07-09
0.00134
0.00
0.00660
0.01
-0.00529
-0.00
-0.00733
-0.01
-0.01578
-0.01
0.00859
0.01
2015-07-10
0.01476
0.01
0.01078
0.01
0.01604
0.01
0.01182
0.01
0.00211
0.00
-0.00634
-0.01
In [8]:
df_eur_usd['2005-02-01':'2005-02-10'][['Prediction_action','Day Average','Pips','Prediction_Pips']]
Out[8]:
Prediction_action
Day Average
Pips
Prediction_Pips
Date
2005-02-01
buy
1.30303
3.8
26.9
2005-02-02
sell
1.30572
26.9
-69.1
2005-02-03
sell
1.29881
-69.1
-48.6
2005-02-04
sell
1.29395
-48.6
-189.5
2005-02-05
buy
1.27500
-189.5
106.7
2005-02-06
sell
1.28567
106.7
-38.8
2005-02-07
sell
1.28178
-38.8
-54.5
2005-02-08
buy
1.27634
-54.5
19.0
2005-02-09
buy
1.27824
19.0
52.9
2005-02-10
buy
1.28353
52.9
33.0
In [9]:
df_eur_usd['2005-02-01':'2005-02-10'][['Fast_k_3day', 'Fast_d_3day','Fast_k_4day', 'Fast_d_4day','Fast_k_5day', 'Fast_d_5day','Fast_k_8day', 'Fast_d_8day','Fast_k_9day', 'Fast_d_9day','Fast_k_10day', 'Fast_d_10day']]
Out[9]:
Fast_k_3day
Fast_d_3day
Fast_k_4day
Fast_d_4day
Fast_k_5day
Fast_d_5day
Fast_k_8day
Fast_d_8day
Fast_k_9day
Fast_d_9day
Fast_k_10day
Fast_d_10day
Date
2005-02-01
67
50
47
47
50
50
56
55
58
57
61
58
2005-02-02
47
54
47
49
38
49
49
55
49
57
51
58
2005-02-03
18
44
18
38
18
35
15
40
15
41
15
43
2005-02-04
1
22
1
22
1
19
1
22
1
22
1
22
2005-02-05
0
6
0
6
0
6
0
5
0
5
0
5
2005-02-06
35
12
29
10
29
10
29
10
27
9
27
9
2005-02-07
9
14
9
13
8
12
8
12
8
11
7
11
2005-02-08
25
23
11
16
11
16
9
16
9
15
9
14
2005-02-09
54
29
54
25
24
14
21
13
21
13
21
12
2005-02-10
82
54
82
49
82
39
39
23
39
23
39
23
In [10]:
df_eur_usd['2015-07-01':'2015-07-10'][['PROC_12day','PROC_13day','PROC_14day','PROC_15day']]
Out[10]:
PROC_12day
PROC_13day
PROC_14day
PROC_15day
Date
2015-07-01
-0.03
-0.02
-0.02
-0.02
2015-07-02
-0.03
-0.02
-0.01
-0.02
2015-07-03
-0.02
-0.02
-0.02
-0.01
2015-07-05
-0.03
-0.03
-0.03
-0.03
2015-07-06
-0.03
-0.03
-0.03
-0.03
2015-07-07
-0.01
-0.03
-0.03
-0.03
2015-07-08
-0.01
-0.01
-0.02
-0.03
2015-07-09
-0.01
-0.01
-0.01
-0.03
2015-07-10
-0.00
-0.00
-0.00
-0.00
In [11]:
df_eur_usd['2015-07-01':'2015-07-10'][['WILLIAM_A_D']]
Out[11]:
WILLIAM_A_D
Date
2015-07-01
0.01635
2015-07-02
-0.00810
2015-07-03
-0.00935
2015-07-05
0.00171
2015-07-06
-0.00232
2015-07-07
0.00430
2015-07-08
0.00692
2015-07-09
0.00644
2015-07-10
0.00257
In [12]:
df_eur_usd['2015-07-01':'2015-07-10'][['ADOSC_1day','ADOSC_2day','ADOSC_3day','ADOSC_4day','ADOSC_5day']]
Out[12]:
ADOSC_1day
ADOSC_2day
ADOSC_3day
ADOSC_4day
ADOSC_5day
Date
2015-07-01
-34985
-33294
41620
-2280
-1161
2015-07-02
5274
-34985
-33294
41620
-2280
2015-07-03
17659
5274
-34985
-33294
41620
2015-07-05
-1614
17659
5274
-34985
-33294
2015-07-06
2107
-1614
17659
5274
-34985
2015-07-07
17096
2107
-1614
17659
5274
2015-07-08
29657
17096
2107
-1614
17659
2015-07-09
1398
29657
17096
2107
-1614
2015-07-10
11679
1398
29657
17096
2107
In [13]:
df_eur_usd['2015-07-01':'2015-07-10'][[ u'EMA_12Day', u'EMA_26Day', u'MACD']]
Out[13]:
EMA_12Day
EMA_26Day
MACD
Date
2015-07-01
1.11699
1.11847
-0.00148
2015-07-02
1.11570
1.11774
-0.00204
2015-07-03
1.11503
1.11727
-0.00224
2015-07-05
1.11264
1.11595
-0.00331
2015-07-06
1.11142
1.11512
-0.00370
2015-07-07
1.10978
1.11405
-0.00427
2015-07-08
1.10937
1.11354
-0.00417
2015-07-09
1.10887
1.11299
-0.00412
2015-07-10
1.10989
1.11318
-0.00329
In [14]:
df_eur_usd['2015-07-01':'2015-07-10'][[ u'CCI']]
Out[14]:
CCI
Date
2015-07-01
-140.53
2015-07-02
-121.53
2015-07-03
-96.04
2015-07-05
-148.89
2015-07-06
-102.69
2015-07-07
-120.76
2015-07-08
-78.18
2015-07-09
-63.71
2015-07-10
-8.07
In [15]:
df_eur_usd['2015-07-01':'2015-07-10'][[u'BOLLINGER_BANDS_LOW','Day Close','Day Average','BOLLINGER_BANDS_HIGH']]
Out[15]:
BOLLINGER_BANDS_LOW
Day Close
Day Average
BOLLINGER_BANDS_HIGH
Date
2015-07-01
1.10783
1.10370
1.11038
1.13986
2015-07-02
1.10581
1.10859
1.10823
1.13982
2015-07-03
1.10458
1.11137
1.10991
1.13909
2015-07-05
1.10136
1.09948
1.10017
1.13980
2015-07-06
1.09913
1.10474
1.10534
1.13982
2015-07-07
1.09647
1.10076
1.09773
1.14031
2015-07-08
1.09542
1.10712
1.10393
1.13932
2015-07-09
1.09408
1.10608
1.10483
1.13885
2015-07-10
1.09512
1.11552
1.11432
1.13574
In [16]:
df_eur_usd['2015-07-01':'2015-07-10'][[u'Day High', u'Day Low',u'2DAY_HIGH', u'2DAY_LOW', u'1DAY_HIGH_LOW_AVG', u'2DAY_HIGH_LOW_AVG']]
Out[16]:
Day High
Day Low
2DAY_HIGH
2DAY_LOW
1DAY_HIGH_LOW_AVG
2DAY_HIGH_LOW_AVG
Date
2015-07-01
1.11714
1.10324
1.12445
1.10324
1.66877
1.11402
2015-07-02
1.11217
1.10367
1.11714
1.10324
1.66400
1.10906
2015-07-03
1.11177
1.10652
1.11217
1.10367
1.66503
1.10853
2015-07-05
1.10393
1.09698
1.11177
1.09698
1.65242
1.10480
2015-07-06
1.10956
1.09940
1.10956
1.09698
1.65926
1.10247
2015-07-07
1.10524
1.09164
1.10956
1.09164
1.65106
1.10146
2015-07-08
1.10933
1.09743
1.10933
1.09164
1.65804
1.10091
2015-07-09
1.11253
1.09917
1.11253
1.09743
1.66212
1.10461
2015-07-10
1.12159
1.10550
1.12159
1.09917
1.67434
1.10970
In [17]:
df_eur_usd['2015-07-01':'2015-07-10'][[u'High_slope_3day', u'High_slope_4day', u'High_slope_5day', u'High_slope_8day', u'High_slope_10day', u'High_slope_12day', u'High_slope_15day', u'High_slope_20day', u'High_slope_25day', u'High_slope_30day']]
Out[17]:
High_slope_3day
High_slope_4day
High_slope_5day
High_slope_8day
High_slope_10day
High_slope_12day
High_slope_15day
High_slope_20day
High_slope_25day
High_slope_30day
Date
2015-07-01
3.8800
-0.965
-0.9433
-2.6550
-1.9632
-1.5288
-0.3931
-0.6450
-0.0892
0.7816
2015-07-02
-3.9200
2.108
-1.6342
-2.4933
-2.3614
-2.4204
-1.0800
-1.0681
-0.6290
0.4756
2015-07-03
-3.1688
-3.215
1.6908
-1.3028
-2.6609
-2.0746
-1.3263
-1.2805
-1.0092
0.3587
2015-07-05
-3.3037
-4.103
-3.9858
-2.0972
-2.7886
-2.6315
-2.0681
-1.3926
-0.9262
0.1606
2015-07-06
-0.6512
-1.517
-2.4808
-1.3789
-1.2668
-2.4215
-2.1294
-0.9576
-0.0602
0.3761
2015-07-07
-1.6325
-1.385
-1.9842
0.4017
-1.5968
-2.2588
-2.0937
-0.8664
-0.9788
-0.4589
2015-07-08
1.3488
-0.489
-0.4733
-2.0578
-1.1495
-1.0900
-1.8009
-0.9581
-0.9719
-0.6192
2015-07-09
0.7425
1.720
0.1267
-1.3239
0.9914
-0.7904
-1.7819
-0.9743
-1.0050
-0.8219
2015-07-10
4.0875
2.406
2.9433
0.4939
-0.5686
-0.0292
-0.8134
-0.7348
-0.4456
-0.2071
In [22]:
df_eur_usd['2015-07-01':'2015-07-10'][[u'Volume', u'Active Hour', u'Active Hour Volume']]
Out[22]:
Volume
Active Hour
Active Hour Volume
Date
2015-07-01
37437
9
4728
2015-07-02
33667
12
6855
2015-07-03
20838
8
2299
2015-07-05
5773
22
2377
2015-07-06
41557
7
4107
2015-07-07
50110
19
4466
2015-07-08
47132
7
4278
2015-07-09
41979
7
4067
2015-07-10
47575
9
5037
In [20]:
df_eur_usd[[u'Active Hour']].hist()
Out[20]:
array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f69996c4850>]], dtype=object)
In [ ]:
Content source: yuecong/autotrade
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