相似K线的定量¶

1.设置样本池,并获取股票代码

In [24]:

indexcode = '000016.SH'
startdate = '20140101'
enddate = '20190201'
stocklist = get_index_stocks(indexcode,enddate)
stocklist

Out[24]:

['600196.SH',
 '600519.SH',
 '600036.SH',
 '601857.SH',
 '600050.SH',
 '601186.SH',
 '600029.SH',
 '601989.SH',
 '601166.SH',
 '601939.SH',
 '600309.SH',
 '600703.SH',
 '600000.SH',
 '600048.SH',
 '601088.SH',
 '601398.SH',
 '600887.SH',
 '600585.SH',
 '600690.SH',
 '601318.SH',
 '600276.SH',
 '600028.SH',
 '601818.SH',
 '601688.SH',
 '601888.SH',
 '601766.SH',
 '603993.SH',
 '601229.SH',
 '600340.SH',
 '600606.SH',
 '601138.SH',
 '600030.SH',
 '601169.SH',
 '600019.SH',
 '601668.SH',
 '600547.SH',
 '601988.SH',
 '603259.SH',
 '601328.SH',
 '601336.SH',
 '601390.SH',
 '601800.SH',
 '601360.SH',
 '600016.SH',
 '601628.SH',
 '601211.SH',
 '601006.SH',
 '600104.SH',
 '601601.SH',
 '601288.SH']

2.获取样本池股票的近四年的开高低收成交量

In [25]:

data = get_price(stocklist,startdate,enddate,'1d',['open','high','low','close'],is_panel =1)
#收盘价
closedf = data['close'].fillna(0)
opendf = data['open'].fillna(0)
highdf = data['high'].fillna(0)
lowdf = data['low'].fillna(0)
lowdf

Out[25]:

	600000.SH	600016.SH	600019.SH	600028.SH	600029.SH	600030.SH	600036.SH	600048.SH	600050.SH	600104.SH	...	601766.SH	601800.SH	601818.SH	601857.SH	601888.SH	601939.SH	601988.SH	601989.SH	603259.SH	603993.SH
2014-01-02	4.88	4.47	3.00	3.03	2.36	10.80	7.11	3.94	3.00	6.45	...	4.24	3.02	1.80	6.84	15.49	2.63	1.67	5.48	0.00	1.84
2014-01-03	4.74	4.39	2.99	2.94	2.31	10.52	6.84	3.88	2.95	5.96	...	4.13	2.97	1.77	6.81	15.25	2.58	1.64	5.36	0.00	1.83
2014-01-06	4.78	4.26	2.79	2.89	2.23	10.18	6.76	3.60	2.87	5.64	...	3.93	2.84	1.71	6.72	14.76	2.52	1.58	5.03	0.00	1.74
2014-01-07	4.69	4.18	2.86	2.98	2.22	10.29	6.72	3.55	2.87	5.57	...	3.91	2.85	1.72	6.89	14.69	2.55	1.57	5.01	0.00	1.74
2014-01-08	4.76	4.19	2.72	2.95	2.22	10.28	6.90	3.48	2.82	5.80	...	3.67	2.84	1.72	6.83	14.45	2.52	1.57	5.00	0.00	1.71
2014-01-09	4.79	4.21	2.77	2.95	2.21	10.13	7.05	3.56	2.82	5.66	...	3.76	2.83	1.71	6.79	14.41	2.50	1.57	5.01	0.00	1.66
2014-01-10	4.85	4.19	2.73	2.98	2.19	9.82	7.05	3.58	2.78	5.55	...	3.75	2.82	1.70	6.71	14.53	2.50	1.57	4.81	0.00	1.64
2014-01-13	4.92	4.19	2.76	2.98	2.19	9.68	7.17	3.44	2.78	5.53	...	3.74	2.82	1.70	6.71	14.85	2.51	1.58	4.89	0.00	1.66
2014-01-14	4.86	4.20	2.84	3.04	2.20	9.69	7.09	3.46	2.79	5.61	...	3.71	2.83	1.71	6.71	14.98	2.52	1.58	4.72	0.00	1.72
2014-01-15	4.79	4.20	2.85	3.13	2.21	9.66	6.97	3.45	2.79	5.68	...	3.74	2.83	1.70	6.78	15.35	2.51	1.58	4.75	0.00	1.68
2014-01-16	4.80	4.17	2.85	3.19	2.21	9.71	6.96	3.45	2.80	5.61	...	3.70	2.83	1.70	6.74	15.52	2.47	1.57	4.71	0.00	1.68
2014-01-17	4.73	4.06	2.68	3.18	2.20	9.67	6.85	3.44	2.78	5.26	...	3.58	2.79	1.66	6.72	15.14	2.44	1.56	4.63	0.00	1.69
2014-01-20	4.71	3.95	2.71	3.09	2.20	9.78	6.86	3.42	2.76	5.03	...	3.51	2.76	1.63	6.82	14.91	2.46	1.53	4.68	0.00	1.65
2014-01-21	4.75	3.97	2.71	3.15	2.21	9.87	6.92	3.49	2.77	5.18	...	3.55	2.80	1.64	6.81	15.30	2.46	1.54	4.69	0.00	1.65
2014-01-22	4.80	4.00	2.73	3.18	2.25	10.05	6.92	3.56	2.79	5.33	...	3.61	2.82	1.65	6.82	15.55	2.48	1.55	4.73	0.00	1.68
2014-01-23	4.83	4.04	2.75	3.13	2.28	10.15	6.93	3.77	2.81	5.69	...	3.79	2.86	1.65	6.82	15.81	2.47	1.56	4.98	0.00	1.71
2014-01-24	4.78	4.00	2.74	3.14	2.28	10.14	6.83	3.76	2.79	5.65	...	3.82	2.85	1.63	6.80	15.97	2.46	1.55	5.04	0.00	1.70
2014-01-27	4.75	3.96	2.74	3.08	2.28	9.84	6.72	3.82	2.78	5.60	...	3.68	2.83	1.63	6.72	15.63	2.45	1.54	5.07	0.00	1.73
2014-01-28	4.77	4.06	2.75	3.07	2.29	9.83	6.78	3.83	2.79	5.66	...	3.68	2.80	1.64	6.74	15.71	2.47	1.55	5.07	0.00	1.72
2014-01-29	4.82	4.12	2.76	3.06	2.28	9.87	6.88	3.78	2.82	5.62	...	3.60	2.84	1.66	6.74	15.12	2.49	1.57	5.08	0.00	1.72
2014-01-30	4.78	4.23	2.78	3.04	2.27	9.86	6.76	3.66	2.79	5.61	...	3.47	2.82	1.65	6.74	15.05	2.49	1.59	5.04	0.00	1.70
2014-02-07	4.73	4.14	2.76	2.99	2.24	9.66	6.68	3.58	2.79	5.28	...	3.39	2.81	1.64	6.66	15.00	2.46	1.56	5.02	0.00	1.67
2014-02-10	4.79	4.19	2.78	3.05	2.27	9.85	6.79	3.76	2.83	5.69	...	3.51	2.85	1.65	6.71	14.68	2.48	1.57	5.32	0.00	1.72
2014-02-11	4.84	4.24	2.80	3.07	2.30	10.04	6.77	3.77	2.83	6.06	...	3.72	2.89	1.67	6.74	15.80	2.48	1.58	5.49	0.00	1.75
2014-02-12	4.99	4.46	2.82	3.09	2.32	10.18	6.92	3.83	2.86	5.98	...	3.72	2.93	1.72	6.76	17.05	2.53	1.61	5.44	0.00	1.76
2014-02-13	4.97	4.50	2.82	3.09	2.32	10.08	6.87	3.71	2.86	5.97	...	3.63	2.92	1.69	6.76	16.91	2.53	1.61	5.29	0.00	1.77
2014-02-14	5.04	4.67	2.81	3.08	2.33	10.12	6.89	3.66	2.86	5.99	...	3.62	2.92	1.71	6.74	16.95	2.54	1.61	5.29	0.00	1.76
2014-02-17	5.02	4.57	2.83	3.10	2.34	10.15	6.91	3.60	2.87	6.11	...	3.68	2.95	1.70	6.76	17.00	2.55	1.62	5.37	0.00	1.79
2014-02-18	4.85	4.50	2.80	3.10	2.28	9.76	6.74	3.53	2.88	6.06	...	3.62	2.90	1.67	6.77	17.13	2.51	1.61	5.43	0.00	1.78
2014-02-19	4.82	4.52	2.80	3.09	2.28	9.62	6.72	3.50	2.88	6.09	...	3.59	2.89	1.67	6.73	17.18	2.51	1.61	5.38	0.00	1.76
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
2018-12-20	10.11	5.73	6.57	5.57	6.90	16.20	25.65	12.21	5.12	24.87	...	9.01	11.77	3.68	7.38	58.56	6.33	3.52	4.13	75.71	4.01
2018-12-21	10.00	5.67	6.55	5.51	6.73	15.89	25.25	11.53	5.08	25.08	...	9.01	11.72	3.62	7.35	57.10	6.20	3.52	4.06	74.00	3.91
2018-12-24	9.86	5.66	6.57	5.56	6.75	15.88	24.92	11.61	5.11	25.21	...	9.07	11.60	3.62	7.35	58.00	6.23	3.55	4.06	74.60	3.92
2018-12-25	9.60	5.58	6.31	5.55	6.75	16.01	24.77	11.67	5.06	25.49	...	8.81	10.99	3.57	7.20	58.60	6.20	3.56	4.12	74.00	3.76
2018-12-26	9.64	5.63	6.32	5.60	6.80	16.01	24.85	11.62	5.13	25.96	...	8.94	11.19	3.62	7.24	57.35	6.25	3.60	4.17	74.98	3.84
2018-12-27	9.66	5.63	6.39	5.23	6.60	16.01	24.82	11.75	5.11	25.97	...	8.81	11.15	3.64	7.15	58.67	6.28	3.63	4.21	74.75	3.75
2018-12-28	9.71	5.65	6.43	4.99	6.57	16.01	24.91	11.70	5.11	26.05	...	8.84	11.17	3.65	7.14	58.78	6.30	3.61	4.23	74.27	3.74
2019-01-02	9.58	5.61	6.30	4.96	6.51	16.01	24.40	11.52	5.12	24.95	...	8.70	10.98	3.62	7.12	58.90	6.23	3.53	4.23	71.55	3.65
2019-01-03	9.66	5.62	6.33	4.96	6.58	16.01	24.38	11.61	5.13	24.76	...	8.76	11.02	3.65	7.13	56.50	6.22	3.52	4.24	71.06	3.67
2019-01-04	9.70	5.63	6.36	5.00	6.55	16.01	24.65	11.75	5.06	24.31	...	8.89	10.83	3.71	7.17	55.73	6.19	3.52	4.30	70.48	3.67
2019-01-07	9.92	5.74	6.53	5.12	6.81	16.01	25.29	12.06	5.20	24.60	...	9.22	11.34	3.83	7.28	56.89	6.31	3.55	4.47	71.38	3.83
2019-01-08	9.91	5.73	6.58	5.17	6.86	16.01	25.16	11.72	5.23	24.23	...	9.19	11.32	3.80	7.30	57.74	6.29	3.52	4.51	71.02	3.85
2019-01-09	9.98	5.76	6.68	5.29	6.89	16.01	25.22	11.92	5.26	24.98	...	9.16	11.27	3.83	7.32	59.55	6.34	3.54	4.46	71.53	3.83
2019-01-10	9.92	5.71	6.67	5.29	6.92	16.75	25.47	11.94	5.21	24.78	...	9.01	11.12	3.83	7.34	59.80	6.33	3.51	4.42	71.82	3.80
2019-01-11	9.96	5.74	6.70	5.28	7.09	16.74	25.75	11.90	5.27	24.89	...	8.95	11.07	3.84	7.34	59.77	6.30	3.52	4.43	71.30	3.80
2019-01-14	10.01	5.72	6.69	5.28	7.15	16.97	26.22	11.65	5.30	24.80	...	8.98	11.02	3.85	7.30	58.46	6.29	3.49	4.45	71.55	3.80
2019-01-15	10.05	5.74	6.74	5.30	7.17	17.01	26.30	11.23	5.32	24.79	...	8.98	11.02	3.87	7.31	58.80	6.28	3.50	4.47	71.30	3.80
2019-01-16	10.07	5.76	6.76	5.33	7.09	17.48	26.61	11.50	5.35	25.11	...	8.98	10.93	3.90	7.33	60.00	6.36	3.50	4.38	71.83	3.81
2019-01-17	10.07	5.75	6.76	5.34	7.10	17.51	26.43	11.72	5.31	25.02	...	8.91	10.87	3.90	7.30	60.45	6.37	3.51	4.37	72.80	3.78
2019-01-18	10.22	5.76	6.77	5.36	7.12	17.51	26.46	11.75	5.31	25.00	...	8.92	10.90	3.91	7.28	57.00	6.47	3.53	4.38	73.40	3.81
2019-01-21	10.30	5.81	6.94	5.43	7.11	17.69	27.03	11.85	5.36	25.45	...	8.93	11.05	3.92	7.36	59.28	6.63	3.56	4.41	75.15	3.91
2019-01-22	10.23	5.78	6.95	5.34	6.96	17.37	26.76	11.95	5.34	25.30	...	8.86	10.83	3.86	7.30	58.39	6.56	3.55	4.39	75.56	3.85
2019-01-23	10.25	5.79	6.81	5.34	6.98	17.36	26.92	11.87	5.32	25.18	...	8.69	10.75	3.88	7.26	58.50	6.60	3.55	4.38	75.30	3.83
2019-01-24	10.27	5.80	6.76	5.35	6.91	17.52	27.18	11.73	5.39	24.94	...	8.38	10.53	3.87	7.22	53.31	6.63	3.56	4.35	75.37	3.83
2019-01-25	10.38	5.83	6.88	5.46	6.95	17.89	27.85	11.93	5.38	25.59	...	8.43	10.58	3.91	7.26	55.06	6.72	3.59	4.41	76.21	3.83
2019-01-28	10.39	5.85	6.82	5.53	7.08	17.82	28.50	12.15	5.33	25.99	...	8.65	10.64	3.92	7.26	54.67	6.75	3.61	4.38	73.73	3.83
2019-01-29	10.38	5.85	6.80	5.49	6.94	17.82	28.47	12.14	5.26	25.73	...	8.61	10.44	3.93	7.21	54.50	6.71	3.60	4.30	72.88	3.62
2019-01-30	10.47	5.90	6.88	5.58	7.09	17.91	28.32	12.48	5.25	25.92	...	8.60	10.56	3.97	7.24	54.01	6.79	3.62	4.31	74.43	3.73
2019-01-31	10.51	5.91	6.94	5.63	7.05	17.99	28.45	12.64	5.26	26.10	...	8.48	10.59	3.99	7.25	53.35	6.90	3.63	4.29	73.86	3.78
2019-02-01	10.62	5.90	6.98	5.70	6.99	18.42	28.88	12.56	5.30	26.25	...	8.50	10.63	4.03	7.28	53.50	6.97	3.65	4.30	76.00	3.83

1243 rows × 50 columns

3.挑两段日级行情，长度为60个交易日

浦发银行(600000.SH)与中国石化(600028.SH)
2017-11-13至2018-02-05

In [26]:

stock = ['600000.SH','600028.SH']
trade = list(closedf.index.strftime('%Y%m%d'))
num = trade.index('20180205')
close = closedf[stock].iloc[num-59:num+1]
opens = opendf[stock].iloc[num-59:num+1]
high = highdf[stock].iloc[num-59:num+1]
low = lowdf[stock].iloc[num-59:num+1]
low

Out[26]:

	600000.SH	600028.SH
2017-11-13	12.53	5.49
2017-11-14	12.48	5.49
2017-11-15	12.45	5.37
2017-11-16	12.28	5.33
2017-11-17	12.28	5.33
2017-11-20	12.51	5.38
2017-11-21	12.65	5.35
2017-11-22	12.90	5.42
2017-11-23	12.81	5.54
2017-11-24	12.83	5.56
2017-11-27	12.71	5.52
2017-11-28	12.82	5.44
2017-11-29	12.71	5.44
2017-11-30	12.70	5.42
2017-12-01	12.71	5.40
2017-12-04	12.74	5.45
2017-12-05	12.81	5.41
2017-12-06	12.81	5.50
2017-12-07	12.81	5.45
2017-12-08	12.77	5.46
2017-12-11	12.75	5.44
2017-12-12	12.64	5.47
2017-12-13	12.53	5.44
2017-12-14	12.56	5.39
2017-12-15	12.51	5.34
2017-12-18	12.51	5.35
2017-12-19	12.52	5.36
2017-12-20	12.50	5.34
2017-12-21	12.50	5.37
2017-12-22	12.51	5.39
2017-12-25	12.46	5.43
2017-12-26	12.46	5.45
2017-12-27	12.43	5.52
2017-12-28	12.43	5.50
2017-12-29	12.41	5.54
2018-01-02	12.50	5.60
2018-01-03	12.56	5.78
2018-01-04	12.52	6.00
2018-01-05	12.52	6.29
2018-01-08	12.53	6.42
2018-01-09	12.56	6.37
2018-01-10	12.58	6.54
2018-01-11	12.77	6.45
2018-01-12	12.80	6.43
2018-01-15	12.75	6.54
2018-01-16	12.68	6.53
2018-01-17	12.79	6.40
2018-01-18	12.95	6.33
2018-01-19	13.08	6.38
2018-01-22	12.61	6.31
2018-01-23	12.58	6.32
2018-01-24	12.79	6.46
2018-01-25	13.26	6.66
2018-01-26	13.26	6.58
2018-01-29	13.22	6.80
2018-01-30	12.99	6.62
2018-01-31	12.88	6.46
2018-02-01	12.84	6.41
2018-02-02	12.91	6.51
2018-02-05	12.88	6.78

In [27]:

import numpy as np
import matplotlib.pyplot as plt 
from matplotlib.finance import candlestick2_ohlc
import datetime
for s in stock:
    open1=list(opens[s])
    high1=list(high[s])
    low1=list(low[s])
    close1=list(close[s])
    #画图
    fig,ax = plt.subplots(figsize = (10,6.18),facecolor='white')
    fig.subplots_adjust() 

    ticks = ax.set_xticks([0,60])
    labels = ax.set_xticklabels([list(opens.index.strftime('%Y%m%d'))[0],list(opens.index.strftime('%Y%m%d'))[-1]], fontsize=10) 

    plt.yticks()  
    plt.title("{} K线走势图".format(s),fontsize = 15)  
    plt.ylabel("股价",fontsize = 15)  
    candlestick2_ohlc(ax,open1,high1,low1,close1,width=0.6,colorup='red',colordown='green')

4.计算相似度

In [28]:

s1 = '600000.SH'
s2 = '600028.SH' 
corropen = round(np.corrcoef(opens[s1],opens[s2])[0][1],3)
corrhigh = round(np.corrcoef(high[s1],high[s2])[0][1],3)
corrlow = round(np.corrcoef(low[s1],low[s2])[0][1],3)
corrclose = round(np.corrcoef(close[s1],close[s2])[0][1],3)

print('开盘价的相似度：{}'.format(corropen))
print('最高价的相似度：{}'.format(corrhigh))
print('最低价价的相似度：{}'.format(corrlow))
print('收盘价的相似度：{}'.format(corrclose))

#综合值
T = (corropen+corrhigh+corrlow+corrclose)/4
T = round(T,2)
T

开盘价的相似度：0.534
最高价的相似度：0.582
最低价价的相似度：0.573
收盘价的相似度：0.607

Out[28]:

0.57

In [ ]: