import numpy as np import pandas as pd import matplotlib.pyplot as plt %matplotlib inline

使用pandas的內建函數DataReader從雅虎財經網站讀取股價數據

import tushare as ts pingan = ts.get_k_data('601318',start='2011-01-01') pingan.head() dateopenclosehighlowvolumecode01234

2011-01-04	24.984	25.011	25.314	24.830	245626.0	601318
2011-01-05	24.870	24.110	24.962	24.083	427554.0	601318
2011-01-06	24.145	23.112	24.145	22.611	947078.0	601318
2011-01-07	23.279	23.297	23.842	22.765	659013.0	601318
2011-01-10	23.248	22.866	23.688	22.809	282919.0	601318

pingan.tail() dateopenclosehighlowvolumecode19992000200120022003

2019-04-03	78.48	79.86	80.15	78.37	760219.0	601318
2019-04-04	80.08	80.20	80.96	79.25	795626.0	601318
2019-04-08	80.70	80.59	82.25	80.02	916122.0	601318
2019-04-09	80.40	81.10	82.00	80.37	601737.0	601318
2019-04-10	80.80	82.03	82.11	79.66	783620.0	601318

pingan['date'] = pd.to_datetime(pingan.date) pingan.set_index('date',inplace = True) pingan.index DatetimeIndex(['2011-01-04', '2011-01-05', '2011-01-06', '2011-01-07','2011-01-10', '2011-01-11', '2011-01-12', '2011-01-13','2011-01-14', '2011-01-17',...'2019-03-27', '2019-03-28', '2019-03-29', '2019-04-01','2019-04-02', '2019-04-03', '2019-04-04', '2019-04-08','2019-04-09', '2019-04-10'],dtype='datetime64[ns]', name='date', length=2004, freq=None) pingan['close'].plot(figsize=(12,8)) <matplotlib.axes._subplots.AxesSubplot at 0x2360fa68e10>

g_yue = pingan['close'].resample('M').ohlc() g_yue openhighlowclosedate2011-01-312011-02-282011-03-312011-04-302011-05-312011-06-302011-07-312011-08-312011-09-302011-10-312011-11-302011-12-312012-01-312012-02-292012-03-312012-04-302012-05-312012-06-302012-07-312012-08-312012-09-302012-10-312012-11-302012-12-312013-01-312013-02-282013-03-312013-04-302013-05-312013-06-30...2016-11-302016-12-312017-01-312017-02-282017-03-312017-04-302017-05-312017-06-302017-07-312017-08-312017-09-302017-10-312017-11-302017-12-312018-01-312018-02-282018-03-312018-04-302018-05-312018-06-302018-07-312018-08-312018-09-302018-10-312018-11-302018-12-312019-01-312019-02-282019-03-312019-04-30

25.011	25.011	21.249	21.842
21.952	22.980	21.477	21.868
22.084	23.512	21.433	21.736
22.514	24.123	22.514	22.954
23.103	23.103	20.972	21.407
21.310	21.310	19.495	21.213
21.156	21.609	19.534	19.724
19.906	19.906	17.867	18.053
18.231	18.294	14.935	14.935
15.082	17.226	15.082	17.208
17.449	18.036	15.687	15.687
16.541	16.901	14.935	15.322
15.082	17.395	15.060	17.057
16.643	18.556	16.643	18.160
18.040	18.352	16.123	16.274
16.799	18.405	16.750	18.111
18.761	19.166	18.085	18.636
18.761	20.518	18.094	20.349
20.509	20.572	19.493	19.793
20.227	20.227	17.193	17.381
17.829	18.833	17.560	18.833
18.613	19.018	17.091	17.423
17.661	17.661	15.865	16.382
16.449	20.338	16.449	20.338
21.155	21.653	19.952	21.653
22.799	23.701	20.194	21.137
20.634	20.634	18.245	18.757
18.407	19.265	17.877	17.877
17.976	18.717	17.374	17.920
17.929	17.929	15.558	15.726
...	...	...	...
33.393	35.031	32.964	34.602
34.688	35.269	33.012	33.745
33.983	34.774	33.536	34.774
33.926	35.069	33.926	34.507
34.440	35.250	33.907	35.250
35.336	36.155	33.821	36.155
36.107	43.327	35.488	42.927
43.784	47.584	42.546	47.251
46.613	52.003	45.641	50.087
52.812	54.940	47.738	53.948
54.083	54.685	51.567	52.616
53.199	62.525	53.199	62.525
62.292	76.203	62.185	67.926
66.158	72.774	66.158	67.985
70.520	76.514	68.014	72.939
73.328	73.843	62.593	65.828
66.965	71.977	63.448	63.448
62.816	66.304	59.154	59.154
59.591	62.272	58.921	60.358
59.882	63.990	56.267	58.000
54.673	61.545	54.673	60.990
59.109	62.842	56.020	62.357
62.109	68.500	60.600	68.500
64.780	67.930	61.850	63.630
64.300	67.350	62.640	63.490
64.990	64.990	56.100	56.100
55.180	62.980	55.180	62.980
63.210	73.000	63.210	70.010
72.390	77.100	68.700	77.100
78.600	82.030	78.600	82.030

100 rows × 4 columns

pingan['close'].loc['2017'].plot(figsize=(12,8)) <matplotlib.axes._subplots.AxesSubplot at 0x23612ba5e10>

shouyi = (pingan['close'][-1] - pingan['close'][0])/pingan['close'][0] shouyi 2.2797569069609374 y_num = pingan.index[-1].year - pingan.index[0].year y_num 8 shouyi**(1/y_num) 1.1085009445718548 pingan['close'].to_period('A') date 2011 25.011 2011 24.110 2011 23.112 2011 23.297 2011 22.866 2011 22.905 2011 23.288 2011 23.283 2011 22.844 2011 21.908 2011 21.974 2011 22.273 2011 21.438 2011 21.714 2011 21.249 2011 21.315 2011 21.504 2011 21.943 2011 21.710 2011 21.842 2011 21.952 2011 21.930 2011 22.224 2011 21.961 2011 22.980 2011 22.695 2011 22.629 2011 22.528 2011 22.492 2011 22.659... 2019 69.980 2019 70.010 2019 72.390 2019 72.580 2019 72.070 2019 72.160 2019 70.290 2019 68.700 2019 68.790 2019 69.250 2019 71.540 2019 72.800 2019 73.850 2019 75.000 2019 75.350 2019 76.550 2019 75.800 2019 74.720 2019 72.290 2019 72.690 2019 74.220 2019 73.400 2019 77.100 2019 78.600 2019 78.960 2019 79.860 2019 80.200 2019 80.590 2019 81.100 2019 82.030 Freq: A-DEC, Name: close, Length: 2004, dtype: float64 pingan['close'].to_period('A').groupby(level = 0).first().plot() <matplotlib.axes._subplots.AxesSubplot at 0x23612bdc748>

pingan['42d'] = pingan['close'].rolling(window=42).mean() pingan['250d'] = pingan['close'].rolling(window=250).mean() pingan[['close','42d','250d']].plot(figsize=(12,8),color = ['y','b','r']) <matplotlib.axes._subplots.AxesSubplot at 0x2360fa38550>

pingan['duishu'] = np.log(pingan['close']/pingan['close'].shift(1)) #shifting 指的是沿著時間軸將數據前移或后移。 pingan[['close','duishu']].plot(subplots = True,figsize=(12,12)) array([<matplotlib.axes._subplots.AxesSubplot object at 0x0000023612FB0CF8>,<matplotlib.axes._subplots.AxesSubplot object at 0x000002361301E978>],dtype=object)

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