hpc-montecarlo
Google Cloud Datalab notebook for analysis of montecarlo stock portfolio tutorial. See URL for detailed tutorial.
In this notebook, we will load the simulation data from bigquery, then do some simple analysis. The first step is to load the bigquery python package into the notebook, and then connect to bigquery to extract the aggregate portfolio data.
In [6]:
%load_ext google.cloud.bigquery
The google.cloud.bigquery extension is already loaded. To reload it, use:
%reload_ext google.cloud.bigquery
In [141]:
%%bigquery df
SELECT *
FROM `montecarlo_outputs.portfolio`
Out[141]:
f0_
f1_
f2_
f3_
f4_
f5_
f6_
f7_
f8_
f9_
...
f242_
f243_
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...
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2955.03
2942.320585
2954.344317
2968.016812
3044.836416
3073.761342
3126.127654
3091.656130
3071.986693
3087.338850
...
17226.233036
17084.188883
17223.277174
17056.163846
17137.168669
17764.823763
17677.814386
17844.795921
18244.604156
18161.140219
991
2955.03
2984.263437
2995.946160
3048.776791
3094.794712
3098.478524
3113.820955
3094.024230
3107.622284
3104.552660
...
20023.606253
19869.640984
20569.375882
20619.247649
20843.781382
20991.579427
20626.362769
21455.432950
21463.869477
21775.578864
992
2955.03
2992.526359
3052.627035
3097.224282
3062.907823
3140.580380
3220.264515
3222.937163
3210.547219
3243.020601
...
35059.987704
36165.067489
36967.294630
38182.030200
39403.549211
38872.110356
40309.437255
40283.602976
40876.000230
39912.067572
993
2955.03
2986.343620
3028.023620
3020.305295
3033.370487
3094.012208
3054.888753
3071.798838
3092.265860
3074.650807
...
33626.680431
33886.515261
34511.658718
35236.445975
35435.869529
35417.487195
36746.274713
38036.898945
38502.772881
37764.025190
994
2955.03
2934.406227
2946.312366
2991.433435
3030.662270
3083.824713
3129.687373
3148.151258
3173.160321
3103.053792
...
27362.966781
27744.820951
28853.510680
28762.350361
29981.690200
29864.646558
29316.290455
28755.896323
29099.703272
29195.676417
995
2955.03
2926.255646
2968.959642
2900.490130
2915.941364
2869.378360
2907.049147
2860.396941
2927.998232
2948.079837
...
31253.695858
29777.493934
29248.445192
29591.802144
29061.488802
29186.105190
30719.035599
31456.978505
32260.752104
32265.643702
996
2955.03
2927.752961
2905.991646
2923.388865
2906.162248
3011.776902
2981.144173
3031.681106
3032.911316
3042.139000
...
27324.920780
27429.598618
27833.656684
27628.908933
28726.185036
31415.377987
31953.410752
32410.332140
32258.646779
34130.812020
997
2955.03
2907.457076
2864.184707
2864.929438
2848.121691
2863.446728
2865.338678
2864.657028
2866.242991
2849.620496
...
17610.789092
17840.249592
18053.681093
18399.176048
18023.899206
17862.971193
18122.868930
18545.832714
18868.594302
19252.093010
998
2955.03
2926.236979
2988.800943
2992.622886
2968.751512
2967.529441
3014.691205
3036.258020
3053.508677
3040.491655
...
24785.688756
25365.372972
26078.705510
26300.453442
26736.487294
27129.101952
27378.624882
26399.079635
27469.832849
26655.755731
999
2955.03
3009.801850
2977.471767
2936.135747
2956.611400
2980.979875
3037.274024
3092.244949
3115.247853
3149.359561
...
24236.354571
24185.309742
24495.748587
25624.733130
26332.542953
26306.473748
26383.627091
27271.397237
28268.842099
27847.730453
1000 rows × 252 columns
Now df contains portfolio as a pandas dataframe. Each row n represents a different simulation, and each column m represents the value of the portfolio m days into the simulation. Column 0 is the value of the portfolio before simulation, column 1 is the value after 1 day of simulation, and so on.
We can plot the divergence of the simulations over time and see the spread.
In [105]:
df.T.plot(legend=False)
Out[105]:
<matplotlib.axes._subplots.AxesSubplot at 0x7fe555ef6978>
/usr/local/envs/py3env/lib/python3.5/site-packages/matplotlib/font_manager.py:1320: UserWarning: findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans
(prop.get_family(), self.defaultFamily[fontext]))
In [165]:
import matplotlib.pyplot as plt
mean = df.mean().reset_index().iloc[:,-1]
std = df.std().reset_index().iloc[:,-1]
plt.errorbar(mean.index, mean, xerr=0.5, yerr=2*std, linestyle='-')
plt.show()
While this shows the general progression, we might be interested in the spread of values at the end of the simulations, day 252. Looking at this day in particular, we plat a histogram of the values.
In [63]:
df.iloc[:,-1].hist(bins=100)
Out[63]:
<matplotlib.axes._subplots.AxesSubplot at 0x7f931da89e48>
In [64]:
df.iloc[:,-1].describe()
Out[64]:
count 1000.000000
mean 27958.274051
std 9295.381959
min 9446.052887
25% 21106.335794
50% 26438.180608
75% 33112.044098
max 74756.152636
Name: f251_, dtype: float64
We can also load the individual stock simulations as opposed to the aggregate portfolio valuation. Loading the data into a second data frame, we can then take a look at each of the FANG stocks to see the progression over the 1000 simulations.
In [13]:
%%bigquery df2
SELECT *
FROM `montecarlo_outputs.vartable`
Out[13]:
string_field_0
int64_field_1
int64_field_2
double_field_3
double_field_4
double_field_5
double_field_6
double_field_7
double_field_8
double_field_9
...
double_field_246
double_field_247
double_field_248
double_field_249
double_field_250
double_field_251
double_field_252
double_field_253
double_field_254
double_field_255
0
FB
2410
0
152.19
144.905846
144.951172
142.749252
143.269165
142.594690
146.821937
...
89.606392
89.177785
92.721928
94.522026
91.451523
91.042981
94.177538
94.206427
91.200201
93.657338
1
FB
2410
1
152.19
152.425043
157.150582
157.506708
159.662221
161.801032
153.262579
...
60.808219
59.418792
58.933333
58.820547
57.606162
56.208928
54.603471
54.851866
53.232080
54.959868
2
FB
2410
2
152.19
151.677866
151.489566
152.819220
146.971852
148.378325
145.464370
...
196.188171
208.039583
209.094241
200.698370
203.292706
202.000613
193.434698
193.202570
189.096496
195.342309
3
FB
2410
3
152.19
150.445516
151.905515
150.441929
145.128322
149.626819
150.603106
...
83.293668
81.075901
79.486812
78.081918
79.386557
77.282347
79.287460
79.242839
79.246359
81.212200
4
FB
2410
4
152.19
145.485561
139.690635
138.331711
131.535753
129.032230
126.768496
...
51.211104
52.797385
51.371034
50.198716
49.938387
49.508197
48.624708
48.893717
48.093533
49.037524
5
FB
2410
5
152.19
150.926539
148.605474
150.917185
150.151479
149.199300
144.801924
...
69.347886
72.130507
72.205711
71.709342
73.033451
71.271345
69.843855
68.242521
67.288115
66.658413
6
FB
2410
6
152.19
145.659339
139.744537
139.062105
137.884794
141.655337
142.420351
...
82.911678
82.424948
84.519843
87.036998
86.054190
86.999170
88.731084
89.976490
88.976558
87.168492
7
FB
2410
7
152.19
149.709912
147.067371
145.081747
141.231673
138.993851
139.123350
...
177.767643
177.998859
173.648577
178.770341
174.177270
173.650900
170.394817
166.693643
172.527401
176.288891
8
FB
2410
8
152.19
158.881968
163.116787
159.661271
158.777866
164.299003
162.532402
...
118.122238
118.383675
121.651653
123.973182
126.149386
125.075431
126.814592
128.474181
126.028583
123.222751
9
FB
2410
9
152.19
147.680457
144.597714
143.011350
147.301939
146.468620
148.665525
...
77.096506
77.445335
77.671710
79.268693
78.967390
78.212094
77.827591
76.468421
77.587922
78.685890
10
FB
2410
10
152.19
147.236465
146.492540
142.981883
143.855610
149.609327
147.223572
...
70.432954
70.856623
71.965696
71.202508
72.222757
71.658664
69.919457
70.878551
69.371294
67.638804
11
FB
2410
11
152.19
155.921120
157.384621
154.634323
153.567112
155.168963
161.993068
...
110.435208
112.271033
113.619455
111.640161
109.205649
106.921261
107.492813
106.056660
106.185429
110.338856
12
FB
2410
12
152.19
148.388471
153.269795
157.574019
153.813486
148.238001
145.340732
...
76.904138
78.038147
79.811565
81.257851
81.121715
78.709399
82.327782
79.882510
80.314215
81.993288
13
FB
2410
13
152.19
150.272355
147.559696
143.201464
144.494025
142.649552
138.144664
...
76.026886
73.949976
73.702787
73.954957
73.369020
70.357322
67.970136
65.707866
66.104469
67.780220
14
FB
2410
14
152.19
147.482706
144.407998
147.297376
144.896649
142.437309
141.200672
...
47.360698
48.182876
50.078308
50.449088
51.241328
51.054741
50.555576
49.483474
49.959267
49.830932
15
FB
2410
15
152.19
151.246911
151.021456
152.690052
154.298993
154.143613
151.361528
...
124.963197
125.181307
126.500980
125.412868
127.585479
122.928744
124.123103
126.123480
129.772033
132.771861
16
FB
2410
16
152.19
155.738197
156.257891
158.198159
155.170974
149.563235
146.845680
...
92.159044
92.879475
92.404149
89.401473
91.884015
90.607711
91.475860
89.070110
84.476073
84.264844
17
FB
2410
17
152.19
153.970172
152.667072
156.171460
161.859306
157.157533
163.604572
...
124.144461
118.478897
114.529561
116.070275
115.224503
116.188505
115.871314
119.255483
115.389734
116.077695
18
FB
2410
18
152.19
153.404224
152.224329
148.393127
147.285438
151.376233
154.184843
...
92.983364
91.746209
91.840536
91.841263
92.595173
94.383514
95.221800
95.340481
92.291290
93.007549
19
FB
2410
19
152.19
145.800368
142.537908
142.417156
141.088855
143.727688
140.888659
...
63.252958
63.110556
63.886230
65.386959
64.290072
62.914436
62.784062
62.518954
62.405691
60.881630
20
FB
2410
20
152.19
152.833904
150.935223
150.358230
157.259832
152.844648
149.480116
...
80.515427
80.226483
78.419092
78.696261
79.173101
79.927047
80.145645
79.799507
81.560383
83.338307
21
FB
2410
21
152.19
152.515679
153.045833
157.193878
158.393077
165.185964
167.339219
...
87.201103
88.044515
91.998203
90.659132
90.938938
88.964557
90.141183
92.124667
91.171060
91.554965
22
FB
2410
22
152.19
153.359932
155.027263
151.164221
147.340910
148.404972
143.424526
...
99.946374
99.715417
98.773756
97.061172
96.184174
97.358438
97.339731
96.426987
95.682482
99.429415
23
FB
2410
23
152.19
151.464880
152.254210
156.236956
158.063597
159.289065
158.248291
...
106.302507
106.221468
104.181468
105.714676
108.133002
104.939538
105.488548
104.059445
103.913015
104.724577
24
FB
2410
24
152.19
155.432739
157.660377
160.588204
159.569804
155.147650
160.075594
...
82.409735
77.564064
77.444252
76.082021
76.482537
77.066720
76.930037
77.823213
79.722594
77.729430
25
FB
2410
25
152.19
153.511589
151.383835
152.032698
150.689130
147.009632
148.869567
...
94.627657
92.531969
93.372732
90.613618
88.414560
87.001523
88.433073
86.988349
86.875075
86.097537
26
FB
2410
26
152.19
150.116182
145.934654
142.178890
144.742045
145.198294
143.189636
...
66.790587
65.075422
63.251936
65.190079
65.501520
67.439780
66.871307
67.788949
67.976895
68.825342
27
FB
2410
27
152.19
153.083615
158.903122
158.332020
158.195446
161.647545
161.126182
...
71.385631
72.439665
70.368490
70.358074
70.507406
70.689234
71.861957
72.182803
72.768353
71.901615
28
FB
2410
28
152.19
152.718125
152.206901
148.963180
149.636026
146.810504
148.654407
...
80.784922
81.678452
78.520253
76.455253
73.057848
72.259153
72.100099
74.620167
75.562488
76.991252
29
FB
2410
29
152.19
154.677047
152.931325
148.838620
147.538428
149.355035
147.290230
...
42.826243
42.774531
41.796562
41.742561
42.211818
43.745637
45.377420
45.477676
45.704991
43.525000
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
3970
AMZN
5010
970
1497.05
1482.836053
1509.764923
1508.216133
1519.565884
1508.587130
1527.439921
...
6226.833863
6282.799888
6214.781888
6110.858316
6164.529101
5987.164536
5943.419950
6002.678721
6057.265555
5902.534440
3971
AMZN
5010
971
1497.05
1512.435344
1557.531153
1542.318435
1576.722997
1561.394625
1573.637540
...
5072.392169
5145.701276
4989.308775
4901.745484
4737.524627
4641.744624
4595.887812
4582.826889
4607.825089
4562.342968
3972
AMZN
5010
972
1497.05
1512.075042
1507.182469
1540.304385
1556.509658
1583.875797
1573.053114
...
7632.860193
7638.667270
7732.640106
7672.790815
7609.983004
7786.958936
7779.961904
7409.397800
7653.847450
7681.856955
3973
AMZN
5010
973
1497.05
1493.000144
1524.241616
1529.171383
1535.985024
1492.854969
1495.925356
...
5870.000977
5837.787807
5743.788441
5876.388876
6067.568168
6114.800384
5955.235481
5904.670472
5945.791956
6022.634860
3974
AMZN
5010
974
1497.05
1539.421866
1551.317244
1519.861713
1550.414956
1586.691684
1598.746245
...
5685.647353
5665.187827
5727.064974
5772.326425
5689.062690
5618.298396
5603.665603
5693.967760
5684.021752
5605.545476
3975
AMZN
5010
975
1497.05
1510.662756
1497.902058
1503.793731
1474.967670
1456.396078
1451.362453
...
5764.208783
5811.694361
5981.601029
5954.847271
5936.387951
5962.454878
5922.133363
6036.039583
6220.775510
6528.896048
3976
AMZN
5010
976
1497.05
1454.318493
1467.361326
1484.037723
1491.690601
1536.624813
1511.028730
...
9264.662831
9422.668068
9358.698214
9495.773199
9387.330487
9671.171419
9843.290287
10051.334982
10059.778014
10174.996176
3977
AMZN
5010
977
1497.05
1513.502801
1483.895895
1495.706590
1495.264124
1513.862608
1492.041147
...
6719.904639
6853.989028
7076.785970
6961.735429
6730.830849
6742.893341
6739.287910
6735.790437
6902.715942
6879.423229
3978
AMZN
5010
978
1497.05
1504.243564
1456.410393
1487.704856
1473.837242
1482.076928
1465.511036
...
4140.465859
4135.783464
4188.989092
4256.258015
4317.901859
4366.660499
4357.048014
4363.122605
4407.657931
4330.725593
3979
AMZN
5010
979
1497.05
1498.192066
1501.168278
1529.148656
1528.512451
1502.901741
1521.217580
...
7469.161335
7685.661107
7911.720247
7929.996249
8313.086424
8322.900071
8373.759624
8209.931462
8149.489180
7946.122830
3980
AMZN
5010
980
1497.05
1508.642516
1527.912559
1530.400764
1537.146530
1522.249278
1568.987887
...
8452.071511
8402.265522
8200.918064
8194.117252
8111.146905
8190.971809
8184.681335
8382.449908
8226.577739
8491.475502
3981
AMZN
5010
981
1497.05
1509.416270
1548.336619
1557.033915
1619.978862
1666.192080
1651.265185
...
5307.901044
5657.561444
5648.777081
5688.960054
5727.584867
5796.888734
5893.420793
5942.655002
6038.151551
6203.198161
3982
AMZN
5010
982
1497.05
1520.717641
1515.951512
1542.694699
1514.311093
1491.168642
1502.535310
...
7769.663022
8000.011961
8128.146873
8457.441657
8177.244022
8178.817173
8308.324082
8352.265858
8363.428294
8604.817959
3983
AMZN
5010
983
1497.05
1513.972903
1514.263953
1557.245069
1579.797815
1589.698985
1607.412725
...
10019.541166
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4000 rows × 256 columns
In [67]:
df2[df2.string_field_0 == 'FB'][1:10].T[3:].plot(legend=False)
Out[67]:
<matplotlib.axes._subplots.AxesSubplot at 0x7f9310f88c18>
In case you get a lot of font warnings for matplotlib, use this to ignore them.
In [107]:
import warnings
warnings.filterwarnings("ignore")
Content source: GoogleCloudPlatform/hpc-monte-carlo
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