In [1]:
import tushare as ts
In [3]:
cons = ts.get_apis()
1、获取股票行情
In [11]:
#股票日线行情
df = ts.bar('600000', conn=cons, freq='D', start_date='2016-01-01', end_date='')
df.head(5)
Out[11]:
code
open
close
high
low
vol
amount
datetime
2017-10-27
600000
12.85
12.85
12.94
12.82
326738.0
420972320.0
2017-10-26
600000
12.87
12.82
12.88
12.78
252751.0
324110144.0
2017-10-25
600000
12.86
12.90
12.94
12.82
207438.0
267109744.0
2017-10-24
600000
12.84
12.86
12.95
12.82
272360.0
350864960.0
2017-10-23
600000
13.02
12.84
13.03
12.83
315638.0
406941728.0
2、股票因子数据
In [9]:
#带因子的行情
df = ts.bar('600000', conn=cons, start_date='2016-01-01', end_date='', ma=[5, 10, 20], factors=['vr', 'tor'])
df.head(5)
Out[9]:
code
open
close
high
low
vol
amount
tor
vr
ma5
ma10
ma20
datetime
2017-10-27
600000
12.85
12.85
12.94
12.82
326738.0
420972320.0
0.12
1.34
12.85
12.95
12.95
2017-10-26
600000
12.87
12.82
12.88
12.78
252751.0
324110144.0
0.09
0.84
12.89
12.96
12.95
2017-10-25
600000
12.86
12.90
12.94
12.82
207438.0
267109744.0
0.07
0.59
12.95
12.98
12.96
2017-10-24
600000
12.84
12.86
12.95
12.82
272360.0
350864960.0
0.10
0.84
12.98
13.00
12.96
2017-10-23
600000
13.02
12.84
13.03
12.83
315638.0
406941728.0
0.11
1.03
13.01
13.02
12.96
3、复权数据
In [13]:
#复权行情, adj=qfq(前复权)， hfq（后复权），默认None不复权
df = ts.bar('600000', conn=cons, adj='qfq', start_date='2016-01-01', end_date='')
df.head(5)
Out[13]:
code
open
close
high
low
vol
amount
datetime
2017-10-27
600000
12.85
12.85
12.94
12.82
326738.0
420972320.0
2017-10-26
600000
12.87
12.82
12.88
12.78
252751.0
324110144.0
2017-10-25
600000
12.86
12.90
12.94
12.82
207438.0
267109744.0
2017-10-24
600000
12.84
12.86
12.95
12.82
272360.0
350864960.0
2017-10-23
600000
13.02
12.84
13.03
12.83
315638.0
406941728.0
4、分钟和其它频度数据
In [17]:
#分钟数据, 设置freq参数，分别为1min/5min/15min/30min/60min，D(日)/W(周)/M(月)/Q(季)/Y(年)
df = ts.bar('600000', conn=cons, freq='1min', start_date='2016-01-01', end_date='')
df.head(15)
Out[17]:
code
open
close
high
low
vol
amount
datetime
2017-10-27 15:00:00
600000
12.86
12.85
12.87
12.85
4622.0
5941414.0
2017-10-27 14:59:00
600000
12.86
12.87
12.87
12.85
7610.0
9782857.0
2017-10-27 14:58:00
600000
12.87
12.86
12.87
12.85
1603.0
2061478.0
2017-10-27 14:57:00
600000
12.86
12.86
12.88
12.85
2667.0
3432980.0
2017-10-27 14:56:00
600000
12.86
12.86
12.87
12.86
2712.0
3488104.0
2017-10-27 14:55:00
600000
12.86
12.86
12.87
12.86
689.0
886152.0
2017-10-27 14:54:00
600000
12.86
12.86
12.87
12.85
1613.0
2073555.0
2017-10-27 14:53:00
600000
12.86
12.88
12.88
12.86
1362.0
1753090.0
2017-10-27 14:52:00
600000
12.85
12.86
12.87
12.85
1847.0
2375993.0
2017-10-27 14:51:00
600000
12.86
12.85
12.87
12.85
502.0
645482.0
2017-10-27 14:50:00
600000
12.86
12.86
12.87
12.86
923.0
1187002.0
2017-10-27 14:49:00
600000
12.86
12.86
12.87
12.86
1123.0
1445024.0
2017-10-27 14:48:00
600000
12.86
12.86
12.87
12.86
649.0
834725.0
2017-10-27 14:47:00
600000
12.86
12.86
12.86
12.86
361.0
464246.0
2017-10-27 14:46:00
600000
12.86
12.86
12.87
12.86
193.0
248291.0
指数行情
In [27]:
#指数日行情, 设置asset='INDEX'
df = ts.bar('000300', conn=cons, asset='INDEX', start_date='2016-01-01', end_date='')
df.head(5)
Out[27]:
code
open
close
high
low
vol
amount
datetime
2017-10-27
000300
3992.55
4021.97
4024.46
3991.35
1266728.0
1.529748e+11
2017-10-26
000300
3980.05
3993.58
4013.60
3973.55
1298954.0
1.704886e+11
2017-10-25
000300
3958.17
3976.95
3978.09
3954.00
882054.0
1.068203e+11
2017-10-24
000300
3926.23
3959.40
3959.50
3922.03
930581.0
1.201921e+11
2017-10-23
000300
3930.89
3930.80
3936.27
3920.66
814849.0
1.002809e+11
5、港股数据
In [14]:
#港股数据, 设置asset='X'
df = ts.bar('00981', conn=cons, asset='X', start_date='2016-01-01', end_date='')
df.head(5)
Out[14]:
code
open
close
high
low
vol
datetime
2017-10-27
00981
10.800000
11.040001
11.200001
10.780001
613583
2017-10-26
00981
10.840000
10.760000
11.140000
10.700001
387966
2017-10-25
00981
10.820001
10.840000
10.980000
10.680000
258851
2017-10-24
00981
10.680000
10.700001
11.080001
10.620001
485034
2017-10-23
00981
10.500001
10.800000
11.160001
10.500001
1361470
6、期货数据
In [18]:
#期货数据, 设置asset='X'
df = ts.bar('CU1801', conn=cons, asset='X', start_date='2016-01-01', end_date='')
df.head(5)
Out[18]:
code
open
close
high
low
vol
avg_price
position
datetime
2017-10-30
CU1801
53600.0
53730.0
53770.0
53250.0
57106
53463.21875
132312
2017-10-27
CU1801
54700.0
53970.0
54880.0
53850.0
120542
54340.00000
129300
2017-10-26
CU1801
54880.0
54780.0
55130.0
54650.0
90966
54870.00000
129132
2017-10-25
CU1801
55570.0
54570.0
55650.0
54200.0
125144
54880.00000
128514
2017-10-24
CU1801
54700.0
55520.0
55880.0
54630.0
108282
55360.00000
133424
7、美股数据
In [20]:
#美股数据, 设置asset='X'
df = ts.bar('BABA', conn=cons, asset='X', start_date='2016-01-01', end_date='')
df.head(5)
Out[20]:
code
open
close
high
low
vol
datetime
2017-10-27
BABA
173.190002
176.149994
177.000000
171.110001
19676631
2017-10-26
BABA
170.619995
170.320007
171.449997
168.580002
13447225
2017-10-25
BABA
174.690002
170.220001
175.440002
169.300003
18093552
2017-10-24
BABA
174.000000
173.699997
175.979996
173.259995
11937219
2017-10-23
BABA
177.800003
173.130005
178.009995
173.050003
17590402
8,股票tick
In [23]:
#股票tick,type:买卖方向，0-买入 1-卖出 2-集合竞价成交
df = ts.tick('600000', conn=cons, date='2017-10-26')
df.head(20)
Out[23]:
datetime
price
vol
type
0
2017-10-26 09:30
12.87
1108
2
1
2017-10-26 09:30
12.88
302
0
2
2017-10-26 09:30
12.86
1759
1
3
2017-10-26 09:30
12.86
24
1
4
2017-10-26 09:30
12.86
26
1
5
2017-10-26 09:30
12.85
60
1
6
2017-10-26 09:30
12.85
141
1
7
2017-10-26 09:30
12.84
405
1
8
2017-10-26 09:30
12.84
148
1
9
2017-10-26 09:30
12.84
60
1
10
2017-10-26 09:30
12.85
76
1
11
2017-10-26 09:30
12.84
303
1
12
2017-10-26 09:30
12.85
10
1
13
2017-10-26 09:30
12.84
60
1
14
2017-10-26 09:30
12.85
5
1
15
2017-10-26 09:30
12.84
11
1
16
2017-10-26 09:30
12.84
58
1
17
2017-10-26 09:30
12.84
120
1
18
2017-10-26 09:31
12.84
7
1
19
2017-10-26 09:31
12.84
10
1
9,期货tick
In [26]:
#期货tick,type:买卖方向，0:开仓 1:多开 -1:空开
df = ts.tick('CU1801', conn=cons, asset='X', date='2017-10-25')
df.head(20)
Out[26]:
date
price
vol
oi_change
type
0
2017-10-25 20:59:00
55570.0
26
18
开仓
1
2017-10-25 21:00:00
55600.0
28
6
多开
2
2017-10-25 21:00:01
55620.0
26
-6
空平
3
2017-10-25 21:00:01
55610.0
78
0
空换
4
2017-10-25 21:00:02
55610.0
50
0
多换
5
2017-10-25 21:00:02
55580.0
2
0
空换
6
2017-10-25 21:00:03
55580.0
10
0
空换
7
2017-10-25 21:00:03
55600.0
20
12
多开
8
2017-10-25 21:00:04
55600.0
12
10
空开
9
2017-10-25 21:00:04
55600.0
4
0
空换
10
2017-10-25 21:00:05
55590.0
6
0
空换
11
2017-10-25 21:00:05
55590.0
2
2
双开
12
2017-10-25 21:00:06
55580.0
10
2
空开
13
2017-10-25 21:00:07
55580.0
2
0
空换
14
2017-10-25 21:00:08
55580.0
2
0
多换
15
2017-10-25 21:00:09
55580.0
4
-2
多平
16
2017-10-25 21:00:09
55590.0
8
0
多换
17
2017-10-25 21:00:10
55580.0
10
0
多换
18
2017-10-25 21:00:10
55580.0
4
4
双开
19
2017-10-25 21:00:11
55580.0
10
0
空换
In [32]:
#沪/深港通每日资金流向(南向/北向资金)
df = ts.moneyflow_hsgt()
df.sort_values('date', ascending=False)
Out[32]:
date
ggt_ss
ggt_sz
hgt
sgt
north_money
south_money
697
2017-10-27
2949.0
1147.0
127.00
215.00
342.00
4096.0
696
2017-10-26
2057.0
1170.0
1445.83
241.66
1687.49
3227.0
695
2017-10-25
1202.0
614.0
287.37
328.13
615.50
1816.0
694
2017-10-24
1039.0
682.0
-2207.21
-943.46
-3150.67
1721.0
693
2017-10-23
1233.0
1051.0
-1183.63
-190.19
-1373.82
2284.0
692
2017-10-20
833.0
504.0
-126.00
91.00
-35.00
1337.0
691
2017-10-19
1809.0
673.0
-94.61
167.70
73.09
2482.0
690
2017-10-18
1464.0
736.0
-650.93
223.47
-427.46
2200.0
689
2017-10-17
867.0
762.0
218.55
1592.31
1810.86
1629.0
688
2017-10-16
1518.0
860.0
363.99
1208.51
1572.50
2378.0
687
2017-10-13
2170.0
1079.0
-674.00
1084.00
410.00
3249.0
686
2017-10-12
1400.0
1126.0
-407.39
893.54
486.15
2526.0
685
2017-10-11
1775.0
1225.0
597.67
1147.25
1744.92
3000.0
684
2017-10-10
1518.0
777.0
2465.29
1754.27
4219.56
2295.0
683
2017-10-09
1240.0
813.0
4714.84
2968.41
7683.25
2053.0
682
2017-09-29
NaN
NaN
455.00
778.00
1233.00
0.0
681
2017-09-28
NaN
NaN
-158.17
-138.14
-296.31
0.0
680
2017-09-27
903.0
732.0
-142.22
876.26
734.04
1635.0
679
2017-09-26
780.0
1026.0
-101.16
211.66
110.50
1806.0
678
2017-09-25
948.0
595.0
-1834.49
157.41
-1677.08
1543.0
677
2017-09-22
1165.0
804.0
-440.00
1050.00
610.00
1969.0
676
2017-09-21
1597.0
821.0
878.64
405.50
1284.14
2418.0
675
2017-09-20
1074.0
1036.0
985.18
1157.62
2142.80
2110.0
674
2017-09-19
1432.0
560.0
-88.11
330.59
242.48
1992.0
673
2017-09-18
574.0
786.0
2588.74
1905.98
4494.72
1360.0
672
2017-09-15
-157.0
801.0
203.00
-99.00
104.00
644.0
671
2017-09-14
1138.0
706.0
-235.47
387.50
152.03
1844.0
670
2017-09-13
1057.0
1241.0
-281.86
634.62
352.76
2298.0
669
2017-09-12
1472.0
692.0
1821.45
750.05
2571.50
2164.0
668
2017-09-11
752.0
291.0
1818.64
309.94
2128.58
1043.0
...
...
...
...
...
...
...
...
29
2014-12-30
NaN
NaN
-630.00
NaN
-630.00
0.0
28
2014-12-29
NaN
NaN
750.00
NaN
750.00
0.0
27
2014-12-24
292.0
NaN
NaN
NaN
0.00
292.0
26
2014-12-23
516.0
NaN
653.00
NaN
653.00
516.0
25
2014-12-22
368.0
NaN
1428.00
NaN
1428.00
368.0
24
2014-12-19
248.0
NaN
1071.00
NaN
1071.00
248.0
23
2014-12-18
398.0
NaN
1908.00
NaN
1908.00
398.0
22
2014-12-17
330.0
NaN
648.00
NaN
648.00
330.0
21
2014-12-16
393.0
NaN
1318.00
NaN
1318.00
393.0
20
2014-12-15
196.0
NaN
1068.00
NaN
1068.00
196.0
19
2014-12-12
311.0
NaN
1862.00
NaN
1862.00
311.0
18
2014-12-11
483.0
NaN
1074.00
NaN
1074.00
483.0
17
2014-12-10
391.0
NaN
1941.00
NaN
1941.00
391.0
16
2014-12-09
763.0
NaN
1159.00
NaN
1159.00
763.0
15
2014-12-08
525.0
NaN
1677.00
NaN
1677.00
525.0
14
2014-12-05
1431.0
NaN
2567.00
NaN
2567.00
1431.0
13
2014-12-04
415.0
NaN
3521.00
NaN
3521.00
415.0
12
2014-12-03
457.0
NaN
3861.00
NaN
3861.00
457.0
11
2014-12-02
299.0
NaN
3311.00
NaN
3311.00
299.0
10
2014-12-01
281.0
NaN
1398.00
NaN
1398.00
281.0
9
2014-11-28
293.0
NaN
2345.00
NaN
2345.00
293.0
8
2014-11-27
175.0
NaN
2990.00
NaN
2990.00
175.0
7
2014-11-26
213.0
NaN
3295.00
NaN
3295.00
213.0
6
2014-11-25
155.0
NaN
2853.00
NaN
2853.00
155.0
5
2014-11-24
141.0
NaN
6956.00
NaN
6956.00
141.0
4
2014-11-21
186.0
NaN
2341.00
NaN
2341.00
186.0
3
2014-11-20
196.0
NaN
2276.00
NaN
2276.00
196.0
2
2014-11-19
253.0
NaN
2612.00
NaN
2612.00
253.0
1
2014-11-18
800.0
NaN
4845.00
NaN
4845.00
800.0
0
2014-11-17
1768.0
NaN
13000.00
NaN
13000.00
1768.0
698 rows × 7 columns