In [1]:
from sys import path
path.append('/home/bingnan/ecworkspace/HFT1')
In [2]:
import matplotlib.pyplot as plt
import seaborn as sns
In [3]:
%matplotlib inline
In [4]:
from init import *
In [7]:
x0_prop.iloc[32:]
Out[7]:
rsq_in
rsq_out
slope_P
slope
const_P
const
corr with Y
mean
std
min
25%
50%
75%
max
x32
0.012593
0.004479
0.000000e+00
0.098536
1.563721e-04
-0.002278
0.094170
0.005331
0.516706
-2.026272
-3.207682e-01
0.004422
3.341454e-01
1.981084
x33
0.037485
0.024064
0.000000e+00
0.111786
1.624675e-08
-0.003360
0.176089
0.013353
0.756463
-1.000000
-8.000000e-01
0.100000
9.000000e-01
1.000000
x34
0.054657
0.034020
0.000000e+00
0.148977
2.921350e-07
-0.003023
0.211363
0.005916
0.681527
-1.000000
-6.500000e-01
0.000000
6.666667e-01
1.000000
x35
0.056139
0.034573
0.000000e+00
0.158412
4.814137e-06
-0.002693
0.213798
0.002897
0.649677
-1.000000
-6.000000e-01
0.000000
6.000000e-01
1.000000
x36
0.006972
0.015099
0.000000e+00
1.660526
4.251162e-03
-0.001727
0.104972
-0.000010
0.028097
-0.531218
-1.312341e-02
0.000000
1.337027e-02
0.491824
x37
0.007539
0.010985
0.000000e+00
1.293470
5.466588e-03
-0.001678
0.097439
-0.000133
0.030172
-0.819886
-1.371007e-02
0.000000
1.381232e-02
0.456009
x38
0.013086
0.015601
0.000000e+00
1.816383
4.194454e-03
-0.001724
0.120450
0.000015
0.028221
-0.586053
-1.441199e-02
0.000000
1.446215e-02
0.364065
x39
0.014025
0.014344
0.000000e+00
2.741665
2.797050e-04
-0.002187
0.119286
0.000170
0.020478
-0.227473
-1.038361e-02
0.000000
1.047093e-02
0.324944
x40
0.010363
0.014523
0.000000e+00
2.408771
2.541182e-04
-0.002206
0.112021
0.000096
0.019996
-0.288067
-9.662981e-03
0.000000
9.612246e-03
0.306738
x41
0.010877
0.015577
0.000000e+00
2.145162
1.440139e-03
-0.001921
0.115319
0.000079
0.023440
-0.445933
-1.205707e-02
0.000000
1.208543e-02
0.343716
x42
0.001084
0.001637
3.006864e-125
0.063534
4.505705e-03
-0.001721
0.038397
0.002384
0.221671
-1.212832
-1.318046e-01
0.003954
1.384451e-01
1.138966
x43
0.003796
0.002481
0.000000e+00
0.252876
3.454921e-03
-0.001769
0.056615
0.001647
0.111206
-0.738249
-6.010550e-02
0.000624
6.452087e-02
0.802034
x44
0.002922
0.003283
0.000000e+00
0.104812
4.222059e-04
-0.002135
0.056048
0.007355
0.247733
-1.365750
-1.402246e-01
0.007971
1.572438e-01
1.412043
x45
0.004384
0.004284
0.000000e+00
0.108536
6.183663e-04
-0.002071
0.066516
0.004282
0.261694
-1.339704
-1.574684e-01
0.005359
1.697673e-01
1.268719
x46
0.000554
0.002849
6.972247e-65
0.062691
5.447910e-04
-0.002097
0.046762
0.005244
0.190200
-1.107632
-9.547786e-02
0.006735
1.090240e-01
1.115071
x47
0.001207
0.001758
3.365239e-139
0.045301
4.219238e-03
-0.001734
0.039955
0.002446
0.327621
-1.448128
-1.971294e-01
0.003433
2.035180e-01
1.494480
x48
0.003236
0.003195
0.000000e+00
0.149624
3.405888e-03
-0.001773
0.057154
0.001683
0.173333
-1.177285
-8.605302e-02
0.000000
9.031007e-02
1.182892
x49
0.002829
0.003496
0.000000e+00
0.069968
9.567692e-04
-0.002000
0.056658
0.007279
0.359988
-1.643423
-2.089041e-01
0.007022
2.259238e-01
1.716797
x50
0.003707
0.004364
0.000000e+00
0.068587
1.291578e-03
-0.001947
0.064548
0.004213
0.379452
-1.600814
-2.318389e-01
0.005940
2.420133e-01
1.601556
x51
0.000396
0.002147
5.990529e-47
0.036182
1.271207e-03
-0.001954
0.040979
0.005262
0.280778
-1.351381
-1.411197e-01
0.003868
1.539879e-01
1.439747
x52
0.015780
0.009973
0.000000e+00
1.775136
3.594354e-03
-0.001751
0.114030
-0.000015
0.030728
-0.669406
-5.555669e-03
0.000000
5.504655e-03
0.381391
x53
0.020334
0.013679
0.000000e+00
6.319268
5.187640e-03
-0.001677
0.131005
-0.000025
0.009505
-0.159443
-5.908440e-03
0.000000
5.883632e-03
0.100136
x54
0.001812
-0.001907
5.057587e-208
0.120247
3.227178e-03
-0.001784
0.017994
0.000757
0.136164
-0.982159
-6.752076e-02
0.000615
7.047668e-02
0.970479
x55
0.002279
0.001779
3.287589e-261
-0.046948
3.012538e-03
-0.001796
-0.045715
-0.000393
0.483335
-3.664548
0.000000e+00
0.000000
0.000000e+00
4.001388
x56
0.010004
0.007300
0.000000e+00
-0.135979
2.074905e-03
-0.001857
-0.093692
-0.000055
0.348561
-6.465667
-2.273737e-13
0.000000
2.273737e-13
7.159021
x57
0.010267
0.010534
0.000000e+00
-0.099494
1.534660e-03
-0.001911
-0.102227
0.000238
0.491779
-7.278310
-4.547474e-13
0.000000
6.821210e-13
7.147906
x58
0.021352
0.009614
0.000000e+00
-0.310760
3.067238e-15
-0.004736
-0.125622
-0.008492
0.205834
-0.533333
-1.333333e-01
0.000000
1.333333e-01
0.533333
x59
0.015737
0.005635
0.000000e+00
-0.343559
1.211072e-16
-0.004990
-0.104851
-0.008334
0.159596
-0.500000
-1.000000e-01
0.000000
1.000000e-01
0.500000
x60
0.023344
0.013053
0.000000e+00
-0.205115
1.266251e-10
-0.003855
-0.135739
-0.009042
0.327733
-0.600000
-2.000000e-01
0.000000
2.000000e-01
0.600000
x61
0.005880
0.001320
0.000000e+00
-0.285581
3.124004e-06
-0.002820
-0.061891
-0.003151
0.120707
-0.561393
-7.919910e-02
-0.004759
7.154116e-02
0.609120
x62
0.006381
-0.003041
0.000000e+00
0.155431
9.138644e-04
-0.002004
0.050382
0.002213
0.234073
-1.342820
-1.311930e-01
0.004871
1.393951e-01
1.201704
x63
0.011538
0.002213
0.000000e+00
0.104184
1.346315e-03
-0.001932
0.085859
0.002469
0.472604
-2.300416
-2.644439e-01
0.000000
2.723456e-01
2.061524
x64
0.003077
-0.001823
0.000000e+00
0.135369
1.090501e-03
-0.001977
0.033553
0.002199
0.186246
-1.045232
-1.052631e-01
0.004644
1.133190e-01
1.070161
x65
0.006100
0.011983
0.000000e+00
2.414170
5.307385e-03
-0.001685
0.095014
-0.000019
0.017879
-0.239056
-8.596676e-03
0.000000
8.706528e-03
0.233725
x66
0.007501
0.009123
0.000000e+00
2.018742
8.510263e-03
-0.001589
0.091926
-0.000150
0.019132
-0.430980
-9.492595e-03
0.000000
9.427441e-03
0.340267
x67
0.011284
0.012977
0.000000e+00
2.636956
4.996012e-03
-0.001692
0.110822
0.000009
0.017892
-0.294545
-9.203905e-03
0.000000
9.276418e-03
0.233266
x68
0.013171
0.012066
0.000000e+00
4.229836
1.110893e-04
-0.002328
0.112625
0.000143
0.012812
-0.131110
-6.728199e-03
0.000000
6.819887e-03
0.240946
x69
0.008807
0.011324
0.000000e+00
3.419984
9.739654e-05
-0.002352
0.100755
0.000081
0.012853
-0.137953
-6.609845e-03
0.000000
6.577795e-03
0.233424
x70
0.010373
0.012423
0.000000e+00
3.304626
1.303462e-03
-0.001939
0.106919
0.000054
0.014837
-0.242700
-7.855872e-03
0.000000
7.828223e-03
0.235795
x71
0.005105
0.007440
0.000000e+00
-1.094911
3.908556e-04
-0.002145
-0.079721
-0.000159
0.031134
-0.361702
0.000000e+00
0.000000
0.000000e+00
0.250000
x72
0.006143
0.007556
0.000000e+00
-3.009261
1.415605e-04
-0.002300
-0.083019
-0.000086
0.012678
-0.237460
-6.508649e-03
0.000000
6.558969e-03
0.149889
x73
0.008162
0.008182
0.000000e+00
-0.077270
7.717035e-04
-0.002030
-0.090872
0.001333
0.524056
-0.991667
-3.750000e-01
0.000000
3.833333e-01
0.991667
x74
0.010983
0.007544
0.000000e+00
-0.147436
1.175300e-03
-0.001956
-0.096922
-0.000347
0.330196
-0.950000
0.000000e+00
0.000000
0.000000e+00
0.950000
x75
0.009496
0.009951
0.000000e+00
-0.097513
9.872748e-04
-0.001988
-0.098996
0.000232
0.455041
-0.983333
-2.000000e-01
0.000000
2.000000e-01
0.983333
x76
0.037044
0.024034
0.000000e+00
0.111544
1.056480e-07
-0.003163
0.175400
0.011560
0.753917
-1.000000
-8.000000e-01
0.100000
8.000000e-01
1.000000
x77
0.052336
0.030184
0.000000e+00
0.151946
2.897205e-05
-0.002468
0.204056
0.002146
0.653816
-1.000000
-6.000000e-01
0.000000
6.000000e-01
1.000000
x78
0.050408
0.029962
0.000000e+00
0.136487
4.788693e-07
-0.002975
0.201318
0.007192
0.715330
-1.000000
-7.333333e-01
0.033333
7.333333e-01
1.000000
x79
0.052589
0.031439
0.000000e+00
0.145412
8.123284e-07
-0.002911
0.205832
0.005300
0.684711
-1.000000
-6.666667e-01
0.000000
6.666667e-01
1.000000
In [9]:
Myols(x0['x34'], yin, yout)
OLS Regression Results
==============================================================================
Dep. Variable: y R-squared: 0.055
Model: OLS Adj. R-squared: 0.055
Method: Least Squares F-statistic: 3.020e+04
Date: Fri, 29 Jul 2016 Prob (F-statistic): 0.00
Time: 02:07:10 Log-Likelihood: -2.9550e+05
No. Observations: 522387 AIC: 5.910e+05
Df Residuals: 522385 BIC: 5.910e+05
Df Model: 1
==============================================================================
coef std err t P>|t| [95.0% Conf. Int.]
------------------------------------------------------------------------------
const -0.0030 0.001 -5.129 0.000 -0.004 -0.002
x34 0.1490 0.001 173.790 0.000 0.147 0.151
==============================================================================
Omnibus: 13867.586 Durbin-Watson: 0.120
Prob(Omnibus): 0.000 Jarque-Bera (JB): 34769.001
Skew: -0.023 Prob(JB): 0.00
Kurtosis: 4.263 Cond. No. 1.45
==============================================================================
outsample Rsquare: 0.034020
Out[9]:
<statsmodels.regression.linear_model.RegressionResultsWrapper at 0x7f0afd742850>
In [10]:
sns.distplot(x0['x34'])
Out[10]:
<matplotlib.axes.AxesSubplot at 0x7f0afd74ef90>
In [12]:
x34 = x0['x34'].copy()
In [15]:
xx = np.sign(x34) * np.log(np.abs(x34) + 10000)
In [16]:
sns.distplot(xx)
Out[16]:
<matplotlib.axes.AxesSubplot at 0x7f0afd264a10>
In [ ]:
Content source: brillliantz/Quantitative_Finance
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