In [3]:

df = DataSource('bar1d_CN_STOCK_A').read(instruments=['600031.SHA'], start_date='2021-12-01', end_date='2021-12-31', adjust_type='pre')
df.tail()

Out[3]:

	turn	adjust_factor	instrument	date	open	volume	low	deal_number	close	high	amount
18	0.766817	49.292782	600031.SHA	2021-12-27	23.360001	65119869.0	23.190001	78715.0	23.510000	23.739998	1.531566e+09
19	1.044564	49.292782	600031.SHA	2021-12-28	23.579998	88706747.0	23.579998	107418.0	23.749998	24.000000	2.111211e+09
20	0.798631	49.292782	600031.SHA	2021-12-29	23.769999	67821577.0	23.330000	92780.0	23.350000	23.859999	1.593028e+09
21	0.875975	49.292782	600031.SHA	2021-12-30	23.219999	74389831.0	23.099998	92909.0	23.139997	23.299997	1.724204e+09
22	1.150565	49.292782	600031.SHA	2021-12-31	23.180000	97708670.0	22.719999	0.0	22.799999	23.269999	2.243586e+09

In [8]:

general_feature_train = M.general_feature_extractor.v7(
    instruments=['600031.SHA'],
    features=['close_0','open_0'],
    start_date='2021-12-01',
    end_date='2021-12-31',
    before_start_days=20
)
general_feature_train.data.read().sort_values('date', ascending=False)[['date','instrument','close_0','open_0']].head()

[2022-01-01 13:17:15.575470] INFO: moduleinvoker: general_feature_extractor.v7 开始运行..

[2022-01-01 13:17:15.595507] INFO: moduleinvoker: 命中缓存

[2022-01-01 13:17:15.597892] INFO: moduleinvoker: general_feature_extractor.v7 运行完成[0.022407s].

Out[8]:

	date	instrument	close_0	open_0
36	2021-12-31	600031.SHA	1123.875366	1142.606689
35	2021-12-30	600031.SHA	1140.634888	1144.578369
34	2021-12-29	600031.SHA	1150.986450	1171.689331
33	2021-12-28	600031.SHA	1170.703491	1162.323730
32	2021-12-27	600031.SHA	1158.873291	1151.479370

In [ ]: