In [1]:
import pandas as pd
import os
In [ ]:
In [ ]:
In [ ]:
In [2]:
DB = []
path = '/Users/insuyu/GitHub/historical.data/yahoo.csv/'
nFiles = 0
for file in os.listdir(path):
if file.endswith(".csv"):
table = pd.read_csv(path+file)
code = file[:-4]
if (table['Adj Close'][0] > 5000) :
print (nFiles, code, 0.0, len(table.index))
DB.append([nFiles, code, 0.0, table]) #table['Adj Close'][0],
nFiles+=1
0 000030 0.0 3365
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2 000050 0.0 3894
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5 000087 0.0 808
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7 000105 0.0 1646
8 000120 0.0 3882
9 000140 0.0 3997
10 000145 0.0 1778
11 000150 0.0 3898
12 000155 0.0 3869
13 000157 0.0 3828
14 000180 0.0 3899
15 000210 0.0 3988
16 000215 0.0 1864
17 000225 0.0 1832
18 000270 0.0 3907
19 000300 0.0 3865
20 000327 0.0 1815
21 000370 0.0 3878
22 000480 0.0 1954
23 000500 0.0 1922
24 000540 0.0 3877
25 000545 0.0 1846
26 000547 0.0 1598
27 000590 0.0 3877
28 000640 0.0 1922
29 000650 0.0 3878
30 000660 0.0 4004
31 000670 0.0 3899
32 000680 0.0 3271
33 000720 0.0 3988
34 000725 0.0 1833
35 000760 0.0 3896
36 000810 0.0 3986
37 000815 0.0 1862
38 000830 0.0 4008
39 000835 0.0 1864
40 000850 0.0 3899
41 000880 0.0 3897
42 000885 0.0 1800
43 000950 0.0 3839
44 000990 0.0 3998
45 000995 0.0 1722
46 001040 0.0 4008
47 001045 0.0 3855
48 001060 0.0 1894
49 001065 0.0 1756
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51 001070 0.0 3899
52 001080 0.0 3882
53 001130 0.0 3898
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55 001200 0.0 3882
56 001210 0.0 3899
57 001260 0.0 3887
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60 001390 0.0 3877
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62 001440 0.0 3896
63 001460 0.0 3897
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66 001500 0.0 3899
67 001510 0.0 3893
68 001520 0.0 3890
69 001525 0.0 1750
70 001527 0.0 3752
71 001529 0.0 1709
72 001530 0.0 1909
73 001550 0.0 3882
74 001630 0.0 3877
75 001680 0.0 3886
76 001689 0.0 1444
77 001720 0.0 3898
78 001725 0.0 1775
79 001740 0.0 3997
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81 001750 0.0 3899
82 001755 0.0 1528
83 001770 0.0 3894
84 001790 0.0 3887
85 001795 0.0 1647
86 001799 0.0 771
87 001800 0.0 3899
88 002000 0.0 3998
89 002005 0.0 3814
90 002020 0.0 1891
91 002025 0.0 1784
92 002030 0.0 1943
93 002070 0.0 3882
94 002150 0.0 1271
95 002170 0.0 3894
96 002200 0.0 3894
97 002220 0.0 3866
98 002240 0.0 1929
99 002250 0.0 3998
100 002270 0.0 3899
101 002300 0.0 3899
102 002310 0.0 3899
103 002320 0.0 3998
104 002380 0.0 3998
105 002410 0.0 3885
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108 002600 0.0 3896
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110 002785 0.0 1810
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112 002840 0.0 3843
113 002870 0.0 3894
114 002900 0.0 3899
115 002960 0.0 3899
116 002990 0.0 3867
117 002995 0.0 1813
118 003000 0.0 1926
119 003030 0.0 1913
120 003060 0.0 3784
121 003070 0.0 3998
122 003075 0.0 1756
123 003080 0.0 3894
124 003090 0.0 3882
125 003120 0.0 3899
126 003200 0.0 3895
127 003230 0.0 3894
128 003240 0.0 3898
129 003300 0.0 3971
130 003410 0.0 4030
131 003415 0.0 1831
132 003419 0.0 3815
133 003450 0.0 3987
134 003460 0.0 1955
135 003465 0.0 1531
136 003470 0.0 3899
137 003475 0.0 1762
138 003490 0.0 4033
139 003495 0.0 1828
140 003530 0.0 3899
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142 003540 0.0 3988
143 003545 0.0 1867
144 003547 0.0 1929
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146 003555 0.0 1861
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148 003570 0.0 3876
149 003580 0.0 3999
150 003600 0.0 4033
151 003605 0.0 1817
152 003610 0.0 3899
153 003620 0.0 3920
154 003650 0.0 3899
155 003680 0.0 3899
156 003690 0.0 1951
157 003830 0.0 3897
158 003850 0.0 3889
159 003920 0.0 3898
160 003925 0.0 1622
161 003940 0.0 3933
162 003945 0.0 1398
163 003960 0.0 3827
164 004000 0.0 3998
165 004020 0.0 3998
166 004090 0.0 3899
167 004130 0.0 3934
168 004140 0.0 1917
169 004150 0.0 4033
170 004170 0.0 3871
171 004200 0.0 3899
172 004270 0.0 3896
173 004370 0.0 3877
174 004410 0.0 3862
175 004415 0.0 1809
176 004450 0.0 3869
177 004540 0.0 3878
178 004545 0.0 1850
179 004690 0.0 3993
180 004710 0.0 3998
181 004740 0.0 3885
182 004800 0.0 4033
183 004870 0.0 3837
184 004890 0.0 2545
185 004960 0.0 3876
186 004985 0.0 1827
187 004989 0.0 2270
188 004990 0.0 1954
189 005010 0.0 3899
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191 005180 0.0 3899
192 005300 0.0 3998
193 005305 0.0 1728
194 005320 0.0 3882
195 005380 0.0 4033
196 005385 0.0 1867
197 005387 0.0 1867
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200 005430 0.0 3997
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205 005680 0.0 3899
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214 005880 0.0 3866
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219 005950 0.0 3899
220 005965 0.0 1737
221 005980 0.0 3840
222 006040 0.0 3922
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224 006125 0.0 1864
225 006260 0.0 3998
226 006360 0.0 3995
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232 006650 0.0 3899
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236 006880 0.0 433
237 007070 0.0 849
238 007120 0.0 3899
239 007310 0.0 3997
240 007460 0.0 1926
241 007540 0.0 3898
242 007570 0.0 3890
243 007575 0.0 1840
244 007630 0.0 3831
245 007690 0.0 3899
246 007810 0.0 3899
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248 008060 0.0 3899
249 008250 0.0 3899
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251 008500 0.0 3893
252 008700 0.0 3868
253 008770 0.0 3898
254 008775 0.0 1856
255 008870 0.0 3881
256 008930 0.0 1929
257 009140 0.0 3995
258 009150 0.0 4003
259 009155 0.0 1864
260 009180 0.0 3899
261 009190 0.0 3898
262 009240 0.0 3345
263 009270 0.0 3879
264 009275 0.0 1829
265 009320 0.0 1876
266 009540 0.0 4002
267 009770 0.0 2220
268 009810 0.0 3836
269 009830 0.0 3999
270 009835 0.0 1862
271 010060 0.0 3999
272 010100 0.0 3898
273 010120 0.0 3998
274 010130 0.0 4025
275 010140 0.0 3998
276 010145 0.0 1829
277 010420 0.0 3857
278 010600 0.0 1912
279 010620 0.0 3988
280 010660 0.0 3899
281 010820 0.0 3860
282 010950 0.0 3991
283 010955 0.0 3869
284 011070 0.0 1787
285 011160 0.0 3976
286 011170 0.0 3998
287 011200 0.0 3993
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289 011230 0.0 3899
290 011330 0.0 3860
291 011390 0.0 3899
292 011720 0.0 3842
293 011760 0.0 4005
294 011785 0.0 1762
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296 011810 0.0 3872
297 011930 0.0 3878
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299 012320 0.0 3897
300 012330 0.0 3998
301 012450 0.0 3988
302 012510 0.0 3878
303 012600 0.0 3899
304 012630 0.0 3998
305 012690 0.0 3885
306 012750 0.0 3899
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308 013360 0.0 3888
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310 013520 0.0 3898
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313 014130 0.0 3899
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317 014680 0.0 3998
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324 015350 0.0 3899
325 015360 0.0 3899
326 015540 0.0 3786
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331 016590 0.0 3898
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335 017550 0.0 3857
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337 017800 0.0 3897
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341 017960 0.0 3899
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387 035000 0.0 3996
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395 037620 0.0 2386
396 037710 0.0 3378
397 039130 0.0 1926
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426 069620 0.0 1926
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428 069660 0.0 1776
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432 071090 0.0 3130
433 071320 0.0 1404
434 071840 0.0 849
435 071970 0.0 1584
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437 072710 0.0 3019
438 073240 0.0 2621
439 077970 0.0 2828
440 078000 0.0 2773
441 078930 0.0 2769
442 078935 0.0 1867
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444 079430 0.0 2443
445 079660 0.0 2733
446 079980 0.0 884
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450 084670 0.0 2527
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453 086790 0.0 3199
454 088350 0.0 1374
455 088790 0.0 2343
456 088980 0.0 1458
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460 091160 0.0 1870
461 091170 0.0 1870
462 091180 0.0 1870
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464 091220 0.0 2214
465 091230 0.0 2073
466 092220 0.0 2220
467 092230 0.0 2225
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471 094280 0.0 1970
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475 097230 0.0 2005
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477 097720 0.0 1639
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479 097950 0.0 1988
480 097955 0.0 1867
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482 099140 0.0 1870
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484 100840 0.0 1878
485 101280 0.0 1836
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487 102460 0.0 1316
488 102960 0.0 1769
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In [ ]:
In [ ]:
In [ ]:
In [35]:
DB[651]
Out[35]:
[651,
'196230',
0.0,
Date Open High Low Close Volume Adj Close \
0 2014-05-22 100085 100085 100080 100080 400 99190.64
1 2014-05-23 100090 100090 100090 100090 500 99200.55
2 2014-05-28 100105 100105 100105 100105 20000 99215.42
3 2014-05-29 100130 100130 100115 100120 44500 99230.28
4 2014-05-30 100110 100140 100110 100135 25400 99245.15
5 2014-06-03 100180 100180 100175 100175 148000 99284.79
6 2014-06-05 100185 100185 100185 100185 0 99294.70
7 2014-06-09 100190 100190 100185 100185 10000 99294.70
8 2014-06-10 100190 100200 100190 100200 0 99309.57
9 2014-06-12 100230 100230 100230 100230 0 99339.30
10 2014-06-13 100245 100245 100245 100245 20000 99354.17
11 2014-06-16 100250 100250 100250 100250 20000 99359.13
12 2014-06-17 100260 100260 100260 100260 0 99369.04
13 2014-06-18 100265 100265 100265 100265 0 99373.99
14 2014-06-19 100285 100285 100285 100285 98000 99393.82
15 2014-06-20 100290 100290 100290 100290 0 99398.77
16 2014-06-23 100300 100300 100300 100300 69900 99408.68
17 2014-06-24 100310 100310 100310 100310 0 99418.59
18 2014-06-25 100315 100315 100315 100315 0 99423.55
19 2014-06-26 100330 100330 100330 100330 0 99438.42
20 2014-06-27 100340 100340 100340 100340 0 99448.33
21 2014-06-30 100345 100345 100345 100345 500 99453.28
22 2014-07-01 100355 100355 100355 100355 0 99463.19
23 2014-07-02 100365 100365 100365 100365 15000 99473.10
24 2014-07-03 100400 100400 100400 100400 0 99507.79
25 2014-07-04 100405 100405 100405 100405 0 99512.75
26 2014-07-07 100410 100410 100410 100410 0 99517.70
27 2014-07-08 100415 100415 100415 100415 0 99522.66
28 2014-07-09 100425 100425 100425 100425 0 99532.57
29 2014-07-10 100450 100450 100450 100450 0 99557.35
.. ... ... ... ... ... ... ...
163 2015-05-08 101465 101465 101460 101460 9800 101460.00
164 2015-05-11 101480 101480 101480 101480 0 101480.00
165 2015-05-15 101510 101510 101505 101505 2600 101505.00
166 2015-05-26 101545 101545 101540 101540 3300 101540.00
167 2015-05-27 101550 101550 101545 101545 0 101545.00
168 2015-05-29 101575 101575 101570 101570 5700 101570.00
169 2015-06-02 101610 101610 101610 101610 0 101610.00
170 2015-06-04 101615 101615 101610 101610 1300 101610.00
171 2015-06-08 101615 101615 101615 101615 0 101615.00
172 2015-06-11 101670 101670 101670 101670 0 101670.00
173 2015-06-12 101665 101665 101665 101665 0 101665.00
174 2015-06-15 101700 101700 101695 101695 11900 101695.00
175 2015-06-16 101700 101700 101690 101690 19300 101690.00
176 2015-06-18 101715 101715 101710 101710 16200 101710.00
177 2015-06-22 101720 101720 101715 101715 76700 101715.00
178 2015-06-23 101710 101715 101710 101715 0 101715.00
179 2015-06-24 101715 101715 101715 101715 0 101715.00
180 2015-06-25 101730 101730 101725 101725 3000 101725.00
181 2015-06-26 101730 101730 101725 101725 1000 101725.00
182 2015-06-30 101745 101745 101740 101740 23100 101740.00
183 2015-07-01 101745 101750 101745 101750 2000 101750.00
184 2015-07-02 101755 101760 101755 101760 1000 101760.00
185 2015-07-06 101765 101765 101765 101765 100 101765.00
186 2015-07-07 101775 101780 101775 101775 10700 101775.00
187 2015-07-08 101785 101785 101785 101785 100 101785.00
188 2015-07-09 101800 101805 101800 101805 300 101805.00
189 2015-07-10 101805 101805 101805 101805 200 101805.00
190 2015-07-14 101815 101815 101815 101815 100 101815.00
191 2015-07-17 101835 101835 101835 101835 0 101835.00
192 2015-07-20 101835 101840 101835 101840 58500 101840.00
MA30 MA60 MA120 MA240
0 NaN NaN NaN NaN
1 NaN NaN NaN NaN
2 NaN NaN NaN NaN
3 NaN NaN NaN NaN
4 NaN NaN NaN NaN
5 NaN NaN NaN NaN
6 NaN NaN NaN NaN
7 NaN NaN NaN NaN
8 NaN NaN NaN NaN
9 NaN NaN NaN NaN
10 NaN NaN NaN NaN
11 NaN NaN NaN NaN
12 NaN NaN NaN NaN
13 NaN NaN NaN NaN
14 NaN NaN NaN NaN
15 NaN NaN NaN NaN
16 NaN NaN NaN NaN
17 NaN NaN NaN NaN
18 NaN NaN NaN NaN
19 NaN NaN NaN NaN
20 NaN NaN NaN NaN
21 NaN NaN NaN NaN
22 NaN NaN NaN NaN
23 NaN NaN NaN NaN
24 NaN NaN NaN NaN
25 NaN NaN NaN NaN
26 NaN NaN NaN NaN
27 NaN NaN NaN NaN
28 NaN NaN NaN NaN
29 99384.399333 NaN NaN NaN
.. ... ... ... ...
163 101281.000000 100994.304667 100591.496417 NaN
164 101297.333333 101009.747667 100605.866667 NaN
165 101314.000000 101025.524833 100620.362667 NaN
166 101331.000000 101041.885333 100635.109000 NaN
167 101347.500000 101058.329167 100649.607917 NaN
168 101363.500000 101075.024333 100664.273917 NaN
169 101379.166667 101092.221000 100679.149333 NaN
170 101394.000000 101109.500333 100693.859583 NaN
171 101408.166667 101126.532667 100708.446333 NaN
172 101423.333333 101144.316500 100723.450083 NaN
173 101438.000000 101161.934333 100738.205667 NaN
174 101453.500000 101179.969500 100753.170000 NaN
175 101468.500000 101197.756167 100767.927417 NaN
176 101483.500000 101215.793667 100782.727667 NaN
177 101498.333333 101233.666667 100797.528250 NaN
178 101512.333333 101250.666667 100812.328833 NaN
179 101526.333333 101267.333333 100827.005583 NaN
180 101539.333333 101284.166667 100841.063583 NaN
181 101551.500000 101300.666667 100854.956417 NaN
182 101564.000000 101317.416667 100868.891667 NaN
183 101576.000000 101333.583333 100882.662417 NaN
184 101588.333333 101349.583333 100896.351333 NaN
185 101600.333333 101365.500000 100910.081917 NaN
186 101612.166667 101381.083333 100923.771917 NaN
187 101624.333333 101397.083333 100937.462667 NaN
188 101637.333333 101413.000000 100951.237500 NaN
189 101649.500000 101428.750000 100964.929750 NaN
190 101662.000000 101444.500000 100978.457583 NaN
191 101675.166667 101460.083333 100992.028167 NaN
192 101687.833333 101475.666667 101005.475250 NaN
[193 rows x 11 columns]]
In [30]:
len(DB[0][3]['MA240'])
Out[30]:
3365
In [1]:
for file in range(len(DB)):
print (len(DB[100][3].index))
---------------------------------------------------------------------------
NameError Traceback (most recent call last)
<ipython-input-1-a340c7b9bccb> in <module>()
----> 1 for file in range(len(DB)):
2 print (len(DB[100][3].index))
NameError: name 'DB' is not defined
In [2]:
DB
---------------------------------------------------------------------------
NameError Traceback (most recent call last)
<ipython-input-2-3938971d2e66> in <module>()
----> 1 DB
NameError: name 'DB' is not defined
In [7]:
data = [[11, 12, 13, 14], [21, 22, 23, 24], [31,32,33,34]]
data
Out[7]:
[[11, 12, 13, 14], [21, 22, 23, 24], [31, 32, 33, 34]]
In [10]:
from xlwt import Workbook
book = Workbook()
sheet1 = book.add_sheet("Data Book1")
row = 0
for line in data:
col = 0
for datum in line:
sheet1.write(row, col, datum)
col += 1
row += 1
In [15]:
book.save("workbook.xlsx")
In [ ]:
In [ ]:
In [ ]:
In [ ]:
In [ ]:
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In [ ]:
Content source: 3WiseMen/python
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