In [1]:
import pandas.io.data as web
import datetime
import random
import math
C:\Anaconda2\lib\site-packages\pandas\io\data.py:33: FutureWarning:
The pandas.io.data module is moved to a separate package (pandas-datareader) and will be removed from pandas in a future version.
After installing the pandas-datareader package (https://github.com/pydata/pandas-datareader), you can change the import ``from pandas.io import data, wb`` to ``from pandas_datareader import data, wb``.
FutureWarning)
In [2]:
start = datetime.datetime(2012, 1, 1)
end = datetime.datetime(2016, 5, 10)
stock = "TSLA"
In [3]:
gs = web.DataReader(stock, "yahoo", start, end)
In [4]:
gs.info()
<class 'pandas.core.frame.DataFrame'>
DatetimeIndex: 1095 entries, 2012-01-03 to 2016-05-10
Data columns (total 6 columns):
Open 1095 non-null float64
High 1095 non-null float64
Low 1095 non-null float64
Close 1095 non-null float64
Volume 1095 non-null int64
Adj Close 1095 non-null float64
dtypes: float64(5), int64(1)
memory usage: 59.9 KB
In [7]:
prices = [float(x) for x in gs['Adj Close']]
In [8]:
gs
Out[8]:
Open
High
Low
Close
Volume
Adj Close
Date
2012-01-03
28.940001
29.500000
27.650000
28.080000
928100
28.080000
2012-01-04
28.209999
28.670000
27.500000
27.709999
630100
27.709999
2012-01-05
27.760000
27.930000
26.850000
27.120001
1005500
27.120001
2012-01-06
27.200001
27.790001
26.410000
26.910000
986300
26.910000
2012-01-09
27.000000
27.490000
26.120001
27.250000
897000
27.250000
2012-01-10
27.440001
27.760000
27.250000
27.620001
671800
27.620001
2012-01-11
27.620001
28.379999
27.299999
28.230000
672300
28.230000
2012-01-12
28.480000
28.620001
27.809999
28.250000
729300
28.250000
2012-01-13
28.400000
28.500000
22.639999
22.790001
5500400
22.790001
2012-01-17
26.620001
27.340000
26.410000
26.600000
4651600
26.600000
2012-01-18
26.690001
26.879999
26.250000
26.809999
1260200
26.809999
2012-01-19
27.190001
27.740000
26.610001
26.760000
1246300
26.760000
2012-01-20
26.900000
27.000000
26.400000
26.600000
662300
26.600000
2012-01-23
26.809999
27.209999
26.600000
26.770000
594600
26.770000
2012-01-24
26.629999
27.680000
26.440001
27.420000
858000
27.420000
2012-01-25
27.270000
28.010000
27.049999
27.969999
611200
27.969999
2012-01-26
28.070000
29.580000
28.000000
28.940001
1271100
28.940001
2012-01-27
28.500000
29.719999
28.500000
29.330000
748400
29.330000
2012-01-30
29.490000
29.610001
28.530001
29.570000
729000
29.570000
2012-01-31
29.900000
30.000000
28.870001
29.070000
956400
29.070000
2012-02-01
29.070000
29.700001
29.000000
29.580000
523200
29.580000
2012-02-02
29.719999
30.879999
29.610001
30.250000
805700
30.250000
2012-02-03
30.410000
31.330000
30.250000
31.150000
764500
31.150000
2012-02-06
31.100000
31.900000
31.049999
31.799999
652100
31.799999
2012-02-07
31.799999
31.799999
30.820000
31.600000
1021600
31.600000
2012-02-08
31.600000
32.009998
31.290001
31.930000
623700
31.930000
2012-02-09
32.000000
32.900002
31.430000
32.580002
1277100
32.580002
2012-02-10
32.259998
32.270000
29.840000
31.100000
1874200
31.100000
2012-02-13
31.549999
32.060001
30.900000
31.490000
1157900
31.490000
2012-02-14
31.740000
33.790001
31.400000
33.169998
1810800
33.169998
...
...
...
...
...
...
...
2016-03-30
235.089996
235.500000
226.500000
226.889999
4019700
226.889999
2016-03-31
229.339996
237.419998
225.009995
229.770004
7956700
229.770004
2016-04-01
244.830002
247.899994
233.250000
237.589996
15962000
237.589996
2016-04-04
249.119995
252.119995
243.639999
246.990005
13396500
246.990005
2016-04-05
240.500000
256.559998
240.000000
255.470001
9915200
255.470001
2016-04-06
253.970001
267.739990
253.449997
265.420013
11679900
265.420013
2016-04-07
266.450012
269.339996
254.509995
257.200012
8836800
257.200012
2016-04-08
260.500000
260.820007
248.020004
250.070007
7332200
250.070007
2016-04-11
251.000000
258.989990
245.300003
249.919998
9153900
249.919998
2016-04-12
249.500000
251.800003
243.630005
247.820007
5750800
247.820007
2016-04-13
248.509995
255.500000
247.330002
254.529999
4917900
254.529999
2016-04-14
253.000000
256.839996
251.050003
251.860001
4120000
251.860001
2016-04-15
251.309998
254.600006
249.119995
254.509995
3742000
254.509995
2016-04-18
252.229996
258.309998
251.660004
253.880005
4261800
253.880005
2016-04-19
253.119995
254.369995
241.250000
247.369995
6349400
247.369995
2016-04-20
246.259995
253.660004
241.500000
249.970001
5190800
249.970001
2016-04-21
248.990005
250.899994
246.910004
248.289993
2754900
248.289993
2016-04-22
248.889999
254.000000
245.710007
253.750000
3774600
253.750000
2016-04-25
253.009995
257.380005
250.759995
251.820007
3664300
251.820007
2016-04-26
252.050003
255.729996
249.389999
253.740005
3212500
253.740005
2016-04-27
252.750000
255.000000
249.399994
251.470001
3190700
251.470001
2016-04-28
249.850006
253.429993
247.440002
247.710007
2509500
247.710007
2016-04-29
248.139999
248.429993
237.809998
240.759995
5388700
240.759995
2016-05-02
241.500000
243.190002
234.820007
241.800003
3836400
241.800003
2016-05-03
237.360001
238.910004
231.619995
232.320007
4289200
232.320007
2016-05-04
230.289993
234.460007
220.399994
222.559998
8262500
222.559998
2016-05-05
228.460007
228.639999
209.789993
211.529999
11235800
211.529999
2016-05-06
210.869995
216.369995
208.110001
214.929993
5681100
214.929993
2016-05-09
215.720001
216.149994
206.800003
208.919998
4758100
208.919998
2016-05-10
207.550003
209.470001
205.000000
208.690002
4065200
208.690002
1095 rows × 6 columns
In [9]:
import random
def getDiffValue(lastDayValue, marginDayValue):
return float(marginDayValue - lastDayValue)/marginDayValue
def selectSampleIndex(listSize, sampleSize, maxSeqLen):
if listSize <= 0 or sampleSize <= 0 or listSize < sampleSize:
return []
resSample = set()
while len(resSample) < sampleSize:
pick = random.randint(0, listSize-maxSeqLen)
resSample.add(pick)
res = list(resSample)
res.sort()
return res
In [11]:
maxSeqLen = 20
marginDays = 5
minGain = 0.05
numSamples = 200
In [12]:
resIdx = selectSampleIndex(len(gs), numSamples, maxSeqLen)
resIdx[0:10], resIdx[-10:] #see first and last 10 index
Out[12]:
([4, 13, 22, 26, 42, 45, 47, 50, 51, 54],
[1030, 1035, 1036, 1052, 1053, 1055, 1061, 1065, 1072, 1074])
In [16]:
outfilename = 'tesla_data.csv'
outfile = open(outfilename,'w')
#print header
header = 'day'+',day'.join([`num+1` for num in xrange(maxSeqLen-marginDays)])+',isBullish'+'\n'
outfile.write(header)
for idx in resIdx:
isBullish = 0
diff = getDiffValue(prices[idx+(maxSeqLen-marginDays)-1], prices[idx+maxSeqLen])
if diff > minGain:
isBullish = 1
elif diff < (-1*minGain):
isBullish = -1
else:
isBullish = 0
#print idx, tsla_prices[idx:idx+10], tsla_prices[idx+15], good, diff
#print tsla_prices[idx:idx+maxSeqLen-5], tsla_prices[idx+maxSeqLen], isBullish
result = prices[idx:idx+maxSeqLen-marginDays]
result.append(isBullish)
trainset = ",".join([`price` for price in result])
outfile.write(trainset)
outfile.write('\n')
outfile.close()
In [14]:
prices
Out[14]:
[28.08,
27.709999,
27.120001000000002,
26.91,
27.25,
27.620001000000002,
28.23,
28.25,
22.790001,
26.6,
26.809998999999998,
26.76,
26.6,
26.77,
27.42,
27.969998999999998,
28.940001000000002,
29.33,
29.57,
29.07,
29.58,
30.25,
31.15,
31.799999,
31.6,
31.93,
32.580002,
31.1,
31.49,
33.169998,
33.599998,
34.18,
34.970001,
34.5,
34.220001,
34.529999,
33.75,
33.619999,
33.810001,
33.41,
34.41,
34.040001000000004,
33.77,
33.110001000000004,
33.119999,
33.07,
34.740002000000004,
36.009997999999996,
36.09,
35.290001000000004,
35.0,
35.32,
34.98,
34.959998999999996,
35.150002,
34.400002,
34.080002,
37.400002,
37.939999,
37.849998,
37.330002,
37.240002000000004,
36.580002,
38.009997999999996,
35.0,
34.48,
33.150002,
32.459998999999996,
33.09,
33.439999,
33.59,
32.25,
32.240002000000004,
32.66,
33.16,
33.16,
31.940001000000002,
31.82,
32.91,
33.490002000000004,
33.34,
33.130001,
33.779999,
33.939999,
32.459998999999996,
31.83,
32.470001,
30.190001000000002,
30.059998999999998,
32.959998999999996,
32.25,
30.059998999999998,
29.43,
29.18,
28.57,
27.559998999999998,
28.77,
30.799999,
31.02,
30.280001000000002,
29.809998999999998,
31.690001000000002,
30.41,
29.5,
28.15,
27.879998999999998,
27.91,
29.219998999999998,
28.93,
30.08,
29.120001000000002,
29.66,
29.77,
29.389999,
29.91,
31.84,
32.09,
33.779999,
32.189999,
33.790001000000004,
33.110001000000004,
31.610001,
31.959999,
31.41,
31.290001,
30.4,
30.66,
31.23,
30.99,
31.49,
31.27,
31.51,
32.700001,
34.25,
35.959998999999996,
33.349998,
32.150002,
32.27,
31.790001,
30.66,
29.84,
28.950001,
28.129998999999998,
29.51,
27.35,
27.42,
26.25,
26.1,
27.27,
28.27,
30.25,
29.09,
29.41,
29.940001000000002,
31.17,
29.42,
29.4,
30.299999,
30.01,
29.51,
29.110001,
29.950001,
30.73,
29.5,
28.32,
28.690001000000002,
28.41,
28.41,
28.52,
28.139999,
27.940001000000002,
28.549999,
29.35,
27.370001000000002,
27.799999,
28.280001000000002,
29.48,
30.389999,
32.540001000000004,
31.34,
31.049999,
30.9,
30.02,
30.66,
27.66,
27.540001,
28.49,
29.280001000000002,
29.16,
29.799999,
29.299999,
29.4,
28.889999,
29.25,
28.370001000000002,
28.4,
28.32,
27.639999,
27.33,
28.059998999999998,
28.82,
28.040001,
27.74,
27.85,
28.389999,
27.42,
27.52,
27.379998999999998,
28.129998999999998,
29.25,
28.92,
31.5,
31.15,
31.540001,
31.309998999999998,
30.32,
31.07,
31.610001,
31.379998999999998,
30.82,
31.84,
32.919998,
33.0,
32.470001,
32.130001,
32.27,
32.150002,
33.23,
33.689999,
33.82,
34.619999,
33.900002,
33.709998999999996,
33.900002,
34.169998,
34.57,
35.279999,
35.259997999999996,
33.610001000000004,
33.810001,
34.400002,
34.59,
34.610001000000004,
34.43,
34.0,
34.279999,
33.59,
33.689999,
33.220001,
33.869999,
35.360001000000004,
34.77,
34.400002,
34.34,
33.68,
33.639998999999996,
33.529999,
32.91,
33.259997999999996,
33.900002,
34.099998,
34.380001,
34.52,
35.189999,
36.0,
36.990002000000004,
36.98,
38.029999,
37.950001,
37.52,
37.509997999999996,
38.299999,
37.740002000000004,
38.130001,
39.169998,
39.48,
39.240002000000004,
38.419998,
37.889998999999996,
38.450001,
38.27,
37.040001000000004,
39.279999,
38.540001000000004,
35.16,
36.110001000000004,
34.380001,
34.43,
35.099998,
34.830002,
34.650002,
35.580002,
36.650002,
37.689999,
38.23,
38.470001,
39.099998,
39.119999,
38.98,
36.849998,
35.290001000000004,
35.150002,
35.080002,
35.950001,
36.009997999999996,
36.619999,
37.529999,
37.860001000000004,
38.16,
37.889998999999996,
43.93,
44.34,
41.099998,
42.009997999999996,
41.369999,
41.830002,
40.5,
41.860001000000004,
43.59,
43.75,
43.299999,
45.59,
45.450001,
46.970001,
47.830002,
50.189999,
51.009997999999996,
50.43,
52.0,
51.200001,
54.939999,
53.990002000000004,
53.279999,
54.110001000000004,
54.549999,
59.5,
55.509997999999996,
55.790001000000004,
69.400002,
76.760002,
87.800003,
83.239998,
84.839996,
92.25,
91.5,
89.940002,
87.589996,
87.239998,
92.730003,
97.08000200000001,
110.33000200000001,
104.629997,
104.949997,
97.760002,
92.589996,
94.839996,
95.370003,
97.349998,
102.040001,
100.050003,
94.470001,
97.730003,
98.18,
100.300003,
102.199997,
103.389999,
104.68,
100.650002,
99.550003,
101.489998,
102.400002,
105.720001,
109.25,
107.360001,
117.18,
117.82,
115.239998,
120.089996,
121.610001,
123.449997,
122.269997,
125.610001,
129.899994,
127.260002,
109.050003,
120.25,
119.029999,
119.68,
122.43,
122.739998,
121.699997,
124.07,
129.389999,
134.619995,
131.740005,
134.279999,
135.550003,
138.0,
144.679993,
142.149994,
134.229996,
153.479996,
153.0,
147.380005,
145.429993,
139.360001,
139.669998,
142.0,
144.899994,
149.580002,
147.860001,
157.100006,
161.83999599999999,
164.220001,
167.009995,
166.449997,
166.059998,
169.0,
168.940002,
170.619995,
169.929993,
166.970001,
160.699997,
166.369995,
163.520004,
164.929993,
165.53999299999998,
166.580002,
166.229996,
166.220001,
177.919998,
183.389999,
181.110001,
182.330002,
185.240005,
188.639999,
190.899994,
193.369995,
193.0,
180.949997,
173.309998,
180.979996,
183.070007,
174.729996,
168.779999,
172.929993,
178.699997,
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183.940002,
183.559998,
182.800003,
183.399994,
172.600006,
171.53999299999998,
164.5,
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162.860001,
164.470001,
159.220001,
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126.089996,
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142.190002,
139.649994,
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164.130005,
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178.559998,
181.5,
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181.41000400000001,
177.110001,
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201.350006,
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182.259995,
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210.24000499999997,
207.770004,
204.699997,
204.940002,
203.990005,
206.899994,
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In [25]:
gs
Out[25]:
Open
High
Low
Close
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Date
2012-01-03
652.939982
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2016-03-30
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2016-04-20
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2016-04-22
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2016-04-25
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2016-04-26
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2016-04-27
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2016-05-02
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2016-05-03
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2016-05-04
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2016-05-05
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2016-05-06
712.200012
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2016-05-09
726.700012
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2016-05-10
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1095 rows × 6 columns
In [36]:
'day'+',day'.join([`num+1` for num in xrange(maxSeqLen)])
Out[36]:
'day1,day2,day3,day4,day5,day6,day7,day8,day9,day10,day11,day12,day13,day14,day15,day16,day17,day18,day19,day20,day21,day22,day23,day24,day25'
In [30]:
%alias_magic --line whereami pwd
Created `%whereami` as an alias for `%pwd`.
In [32]:
%pwd
Out[32]:
u'D:\\Work\\data\\stockprices'
In [ ]:
Content source: finetea/stockprices
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