数据的批量完整下载方式代码

https://github.com/rainx/pytdx/issues/21 via yutiansut

看到有issue提出不知道如果下载完整的数据,关于数据位置指针的使用,给一个示例代码:

from pytdx.hq import  TdxHq_API
api=TdxHq_API()

with api.connect():
     data=[]

      for i in range(10):
              data+=api.get_security_bars(9,0,'000001',(9-i)*800,800)
print(api.to_df(data))

open	close	high	low	vol	amount	year	month	day	hour	minute	datetime
27.70	27.80	27.90	27.60	1270.0	3.530600e+06	1991	12	23	15	0	1991-12-23 15:00
27.90	29.05	29.30	27.00	1050.0	3.050250e+06	1991	12	24	15	0	1991-12-24 15:00
29.15	29.30	30.00	29.10	2269.0	6.648170e+06	1991	12	25	15	0	1991-12-25 15:00
29.30	28.00	29.30	28.00	1918.0	5.370400e+06	1991	12	26	15	0	1991-12-26 15:00
28.00	28.45	28.50	28.00	2105.0	5.988725e+06	1991	12	27	15	0	1991-12-27 15:00
28.40	29.25	29.30	28.40	1116.0	3.264300e+06	1991	12	28	15	0	1991-12-28 15:00
29.30	28.80	29.40	28.80	1059.0	3.049920e+06	1991	12	30	15	0	1991-12-30 15:00
29.15	29.15	29.40	29.00	1886.0	5.497690e+06	1992	1	2	15	0	1992-01-02 15:00
29.20	29.10	29.30	29.00	2212.0	6.436920e+06	1992	1	3	15	0	1992-01-03 15:00
29.10	29.70	29.80	29.05	2634.0	7.822980e+06	1992	1	6	15	0	1992-01-06 15:00
29.70	29.90	30.20	29.55	2931.0	8.763690e+06	1992	1	7	15	0	1992-01-07 15:00
29.80	29.65	30.20	29.55	1928.0	5.716520e+06	1992	1	8	15	0	1992-01-08 15:00
29.70	29.70	29.80	29.30	1535.0	4.558950e+06	1992	1	9	15	0	1992-01-09 15:00
29.65	29.80	29.80	29.50	1525.0	4.544500e+06	1992	1	10	15	0	1992-01-10 15:00
29.80	29.65	29.80	29.60	1353.0	4.011645e+06	1992	1	13	15	0	1992-01-13 15:00
29.65	29.20	29.65	29.00	1711.0	4.996120e+06	1992	1	14	15	0	1992-01-14 15:00
29.20	29.10	29.40	29.00	2205.0	6.416550e+06	1992	1	15	15	0	1992-01-15 15:00
29.00	28.90	29.00	28.55	1268.0	3.664520e+06	1992	1	16	15	0	1992-01-16 15:00
28.90	28.80	28.90	28.35	1143.0	3.291840e+06	1992	1	17	15	0	1992-01-17 15:00
28.80	29.45	29.50	28.80	1085.0	3.195325e+06	1992	1	20	15	0	1992-01-20 15:00
29.50	29.30	29.80	29.20	1576.0	4.617680e+06	1992	1	21	15	0	1992-01-21 15:00
29.25	28.95	29.30	28.80	1592.0	4.608840e+06	1992	1	22	15	0	1992-01-22 15:00
28.95	29.20	29.30	28.95	1477.0	4.312840e+06	1992	1	23	15	0	1992-01-23 15:00
29.25	29.05	29.70	29.05	2327.0	6.759935e+06	1992	1	24	15	0	1992-01-24 15:00
29.55	29.40	29.60	29.30	2571.0	7.558740e+06	1992	1	27	15	0	1992-01-27 15:00
29.40	29.50	29.50	29.35	2336.0	6.891200e+06	1992	1	28	15	0	1992-01-28 15:00
29.50	30.00	30.05	29.50	2127.0	6.381000e+06	1992	1	29	15	0	1992-01-29 15:00
29.95	30.20	30.35	29.95	1735.0	5.239700e+06	1992	1	30	15	0	1992-01-30 15:00
31.15	32.00	32.00	31.15	2228.0	7.129600e+06	1992	2	1	15	0	1992-02-01 15:00
32.00	32.32	32.70	31.90	1735.0	5.607520e+06	1992	2	2	15	0	1992-02-02 15:00
...	...	...	...	...	...	...	...	...	...	...	...
9.30	9.36	9.39	9.27	546016.0	5.091620e+08	2017	6	27	15	0	2017-06-27 15:00
9.35	9.43	9.49	9.33	1168796.0	1.102438e+09	2017	6	28	15	0	2017-06-28 15:00
9.43	9.43	9.45	9.37	488804.0	4.598104e+08	2017	6	29	15	0	2017-06-29 15:00
9.40	9.39	9.43	9.31	499633.0	4.680035e+08	2017	6	30	15	0	2017-06-30 15:00
9.40	9.40	9.43	9.34	388349.0	3.644659e+08	2017	7	3	15	0	2017-07-03 15:00
9.40	9.34	9.41	9.30	488362.0	4.565770e+08	2017	7	4	15	0	2017-07-04 15:00
9.29	9.37	9.38	9.27	567720.0	5.292941e+08	2017	7	5	15	0	2017-07-05 15:00
9.36	9.40	9.41	9.31	738911.0	6.913872e+08	2017	7	6	15	0	2017-07-06 15:00
9.37	9.47	9.48	9.34	760369.0	7.170844e+08	2017	7	7	15	0	2017-07-07 15:00
9.45	9.59	9.66	9.44	1360815.0	1.303090e+09	2017	7	10	15	0	2017-07-10 15:00
9.61	10.25	10.46	9.61	3812086.0	3.842010e+09	2017	7	11	15	0	2017-07-11 15:00
10.27	10.34	10.58	10.20	2998844.0	3.113681e+09	2017	7	12	15	0	2017-07-12 15:00
10.30	10.90	10.90	10.24	2994534.0	3.180145e+09	2017	7	13	15	0	2017-07-13 15:00
10.81	10.90	10.94	10.66	1722570.0	1.864449e+09	2017	7	14	15	0	2017-07-14 15:00
10.95	10.81	11.33	10.72	3273123.0	3.608692e+09	2017	7	17	15	0	2017-07-17 15:00
10.75	11.05	11.14	10.62	2349431.0	2.558434e+09	2017	7	18	15	0	2017-07-18 15:00
10.99	11.09	11.19	10.88	1933075.0	2.131336e+09	2017	7	19	15	0	2017-07-19 15:00
11.08	10.97	11.22	10.91	1537338.0	1.695061e+09	2017	7	20	15	0	2017-07-20 15:00
10.83	10.89	10.95	10.69	1501020.0	1.625416e+09	2017	7	21	15	0	2017-07-21 15:00
10.82	10.95	11.06	10.73	1692664.0	1.846887e+09	2017	7	24	15	0	2017-07-24 15:00
10.98	11.00	11.27	10.95	1954768.0	2.172115e+09	2017	7	25	15	0	2017-07-25 15:00
10.92	10.74	11.18	10.66	1697412.0	1.846282e+09	2017	7	26	15	0	2017-07-26 15:00
10.72	10.59	10.77	10.53	1194490.0	1.273889e+09	2017	7	27	15	0	2017-07-27 15:00
10.61	10.74	10.81	10.58	819195.0	8.777693e+08	2017	7	28	15	0	2017-07-28 15:00
10.80	10.67	10.82	10.45	1575864.0	1.671814e+09	2017	7	31	15	0	2017-07-31 15:00
10.64	11.04	11.08	10.60	2035709.0	2.222888e+09	2017	8	1	15	0	2017-08-01 15:00
11.05	11.15	11.34	10.96	2062069.0	2.307727e+09	2017	8	2	15	0	2017-08-02 15:00
11.14	11.01	11.22	10.97	984219.0	1.090954e+09	2017	8	3	15	0	2017-08-03 15:00
11.00	11.17	11.29	10.93	1353951.0	1.511390e+09	2017	8	4	15	0	2017-08-04 15:00
11.06	11.00	11.17	10.90	860644.0	9.469757e+08	2017	8	7	15	0	2017-08-07 15:00

这么做的原因很简单,改变指针的位置

分别是

0-799

800-1599

1600-2399

....

依次

然后需要注意的是 0代表的是今天的指针 ,所以 指针获取方式要翻过来写 首先是 7200-7999的数据

最后才是0-799的数据

基本上 日线级别 8000条足够覆盖了

分钟线,小时线要长一点

封装成函数

from pytdx.hq import  TdxHq_API
api=TdxHq_API()


def get_all_day_data():
   with api.connect():
        data=[]

        for i in range(10):
              data+=api.get_security_bars(9,0,'000001',(9-i)*800,800)
    print(api.to_df(data))