日期	股票代码	名称	收盘价	最高价	最低价	开盘价	前收盘	涨跌额	涨跌幅	换手率	成交量	成交金额	总市值	流通市值
2022/6/27	'600519	贵州茅台	2010.55	2049.94	2000.3	2019.94	2009.01	1.54	0.0767	0.3193	4011517	8124448900	2.53E+12	2.53E+12
2022/6/24	'600519	贵州茅台	2009.01	2020	1965	1970	1957.1	51.91	2.6524	0.3155	3963465	7921199792	2.52E+12	2.52E+12
2022/6/23	'600519	贵州茅台	1957.1	1965.04	1940	1942.7	1936	21.1	1.0899	0.2137	2684352	5239860443	2.46E+12	2.46E+12
2022/6/22	'600519	贵州茅台	1936	1958	1932	1955	1945.74	-9.74	-0.5006	0.1564	1964665	3813775294	2.43E+12	2.43E+12
2022/6/21	'600519	贵州茅台	1945.74	1966.99	1928	1949	1942.02	3.72	0.1916	0.1888	2371702	4617805127	2.44E+12	2.44E+12
2022/6/20	'600519	贵州茅台	1942.02	1970	1930	1950	1951	-8.98	-0.4603	0.2784	3497478	6802792459	2.44E+12	2.44E+12
2022/6/17	'600519	贵州茅台	1951	1952	1878.09	1878.09	1877	74	3.9425	0.4023	5054161	9749530916	2.45E+12	2.45E+12
2022/6/16	'600519	贵州茅台	1877	1907.63	1875.33	1894.59	1875.1	1.9	0.1013	0.214	2688670	5087605391	2.36E+12	2.36E+12
2022/6/15	'600519	贵州茅台	1875.1	1905	1862.99	1870	1871	4.1	0.2191	0.268	3366362	6354869100	2.36E+12	2.36E+12
2022/6/14	'600519	贵州茅台	1871	1875.42	1832	1834	1856	15	0.8082	0.2342	2941623	5467949348	2.35E+12	2.35E+12
2022/6/13	'600519	贵州茅台	1856	1892	1848.08	1890	1900.6	-44.6	-2.3466	0.2926	3675518	6847248995	2.33E+12	2.33E+12
2022/6/10	'600519	贵州茅台	1900.6	1907	1835	1845.01	1853	47.6	2.5688	0.3769	4734462	8882462598	2.39E+12	2.39E+12
2022/6/9	'600519	贵州茅台	1853	1888.35	1849	1872	1865.6	-12.6	-0.6754	0.2096	2632902	4897066622	2.33E+12	2.33E+12
2022/6/8	'600519	贵州茅台	1865.6	1882	1825	1825	1817.9	47.7	2.6239	0.3531	4435381	8236953846	2.34E+12	2.34E+12
2022/6/7	'600519	贵州茅台	1817.9	1825	1770.31	1784.14	1788	29.9	1.6723	0.279	3504859	6356031009	2.28E+12	2.28E+12
2022/6/6	'600519	贵州茅台	1788	1795	1758	1790	1786	2	0.112	0.2925	3674126	6535329352	2.25E+12	2.25E+12
2022/6/2	'600519	贵州茅台	1786	1795.8	1780	1787.97	1788.25	-2.25	-0.1258	0.1347	1691473	3019718032	2.24E+12	2.24E+12
2022/6/1	'600519	贵州茅台	1788.25	1814.78	1779	1802	1804.03	-15.78	-0.8747	0.1732	2176001	3897858999	2.25E+12	2.25E+12
2022/5/31	'600519	贵州茅台	1804.03	1814.9	1766.98	1774.77	1778.41	25.62	1.4406	0.3244	4075082	7329201058	2.27E+12	2.27E+12
2022/5/30	'600519	贵州茅台	1778.41	1790.55	1766	1766	1755.16	23.25	1.3247	0.2744	3446569	6135631304	2.23E+12	2.23E+12

# 2、数据集预处理

# 2.1、数据集切分

training_set 
 [2019.94 1970.   1942.7  ...   26.07   25.92   26.5 ]
test_set 
 [26.5   0.   25.69 25.6  26.3  25.92 26.   26.24 26.48 26.   25.8  25.8
 25.98 25.78 26.05 26.13 27.2  26.75 26.95 26.7  26.22 26.08 26.03 26.25
 26.5  26.6  27.11 27.1  27.45 26.97 26.79 27.5  27.91 27.78 27.6  27.9
 27.68 27.7  28.   28.15 28.12 28.36 27.98 28.4  28.68 28.97 28.8  28.99
 28.75 29.11 29.01 29.   29.46 30.   30.3  30.35 30.52 30.63 30.4  30.45
 30.56 30.55 30.89 30.73 31.15 31.15 31.   31.   30.59 30.79 30.5  30.98
 30.98 30.7  30.8  31.21 31.42 31.43 31.32 31.44 31.3  31.28 31.52 31.68
 32.2  32.5  32.61 36.3  36.45 36.68 36.37 36.05 35.95 35.68 36.01 35.99
 35.63 36.12 36.18 36.18 36.06 36.68 36.75 36.8  37.08 36.7  36.9  37.28
 39.04 35.   34.98 34.9  34.7  34.55 34.9  35.1  34.8  34.75 35.   34.8
 34.38 34.5  34.9  34.9  35.   34.88 35.21 35.2  35.   35.01 35.88 35.1
 35.54 34.99 34.89 35.25 35.68 35.4  35.57 36.05 36.   36.31 36.48 36.2
 35.5  35.1  35.5  36.19 36.   36.39 37.   38.5  37.88 38.46 37.62 37.49
 37.43 37.   37.3  37.78 36.97 37.02 37.61 37.16 38.   38.01 38.15 38.7
 38.49 38.92 39.3  38.8  38.1  38.12 38.02 38.11 38.31 39.45 39.69 38.55
 38.2  38.8  38.06 37.35 37.95 38.   37.85 37.99 37.6  37.18 37.86 37.93
 37.18 37.5  36.   35.6  35.2  37.   37.24 37.36 36.65 35.8  36.3  34.8
 36.2  36.48 35.98 35.7  37.01 36.98 36.5  37.   37.15 38.72 37.67 37.3
 37.22 36.54 36.45 35.99 34.7  35.9  35.9  35.48 35.11 35.02 35.61 35.6
 36.   36.   36.1  35.9  37.   36.25 35.35 34.83 35.01 35.05 34.58 35.
 35.01 35.22 35.48 35.2  34.15 36.2  33.65 33.64 33.28 34.4  33.7  33.35
 35.   34.8  35.   35.28 35.05 35.   35.25 34.88 34.7  35.7  36.78 36.
 33.3  34.   34.2  34.79 35.13 35.9  35.9  36.01 37.3  36.6  37.   36.9
 36.08 36.11 36.28 36.06 36.28 36.9  36.3  35.88 36.08 36.01 36.01 35.33
 36.8  35.4  36.5  37.35 37.61 37.01 37.2  37.15 36.28 36.98 34.99 34.51]

# 2.2、数据维度转换

进行MinMaxScaler之前，需要将数据从(4754,)→(4754, 1)

before reshape <class 'numpy.ndarray'> (4752,) (300,)
after reshape <class 'numpy.ndarray'> (4752, 1) (300, 1)

# 2.3、训练集、测试集进行MinMax归一化

# 2.4、依次构建train、test的时序性数据集矩阵

# (1)、for循环构建train时序性数据集矩阵

# 提取训练集中连续X_num=60天的开盘价，作为输入特征x_train；以第61天的数据作为label，for循环共构建4752-300-60=4392组数据

	0	1	2	3	4	5	6	7	8	9	10	11	12	13	14	15	16	17	18	19	20	21	22	23	24	25	26	27	28	29	30	31	32	33	34	35	36	37	38	39	40	41	42	43	44	45	46	47	48	49	50	51	52	53	54	55	56	57	58	59
0	0.78050835	0.761211447	0.750662679	0.755415421	0.75309701	0.753483412	0.725697262	0.732072891	0.722571272	0.708660809	0.730299307	0.712915092	0.723344075	0.705183193	0.689394818	0.691659132	0.690874736	0.696295953	0.685774233	0.68238549	0.680063215	0.680453481	0.682103417	0.690113525	0.695523149	0.680063215	0.674271053	0.685345327	0.683884729	0.694363944	0.687795114	0.677362267	0.678135071	0.664611009	0.687795114	0.701315312	0.707115202	0.709433612	0.692818337	0.68281826	0.658169692	0.676203062	0.681226285	0.691736412	0.696176168	0.695523149	0.688231748	0.696373233	0.690886328	0.682771892	0.668088625	0.683931097	0.683158293	0.679294276	0.677362267	0.668451843	0.664611009	0.656110171	0.642006507	0.627902843
1	0.761211447	0.750662679	0.755415421	0.75309701	0.753483412	0.725697262	0.732072891	0.722571272	0.708660809	0.730299307	0.712915092	0.723344075	0.705183193	0.689394818	0.691659132	0.690874736	0.696295953	0.685774233	0.68238549	0.680063215	0.680453481	0.682103417	0.690113525	0.695523149	0.680063215	0.674271053	0.685345327	0.683884729	0.694363944	0.687795114	0.677362267	0.678135071	0.664611009	0.687795114	0.701315312	0.707115202	0.709433612	0.692818337	0.68281826	0.658169692	0.676203062	0.681226285	0.691736412	0.696176168	0.695523149	0.688231748	0.696373233	0.690886328	0.682771892	0.668088625	0.683931097	0.683158293	0.679294276	0.677362267	0.668451843	0.664611009	0.656110171	0.642006507	0.627902843	0.661519795
2	0.750662679	0.755415421	0.75309701	0.753483412	0.725697262	0.732072891	0.722571272	0.708660809	0.730299307	0.712915092	0.723344075	0.705183193	0.689394818	0.691659132	0.690874736	0.696295953	0.685774233	0.68238549	0.680063215	0.680453481	0.682103417	0.690113525	0.695523149	0.680063215	0.674271053	0.685345327	0.683884729	0.694363944	0.687795114	0.677362267	0.678135071	0.664611009	0.687795114	0.701315312	0.707115202	0.709433612	0.692818337	0.68281826	0.658169692	0.676203062	0.681226285	0.691736412	0.696176168	0.695523149	0.688231748	0.696373233	0.690886328	0.682771892	0.668088625	0.683931097	0.683158293	0.679294276	0.677362267	0.668451843	0.664611009	0.656110171	0.642006507	0.627902843	0.661519795	0.671566241
3	0.755415421	0.75309701	0.753483412	0.725697262	0.732072891	0.722571272	0.708660809	0.730299307	0.712915092	0.723344075	0.705183193	0.689394818	0.691659132	0.690874736	0.696295953	0.685774233	0.68238549	0.680063215	0.680453481	0.682103417	0.690113525	0.695523149	0.680063215	0.674271053	0.685345327	0.683884729	0.694363944	0.687795114	0.677362267	0.678135071	0.664611009	0.687795114	0.701315312	0.707115202	0.709433612	0.692818337	0.68281826	0.658169692	0.676203062	0.681226285	0.691736412	0.696176168	0.695523149	0.688231748	0.696373233	0.690886328	0.682771892	0.668088625	0.683931097	0.683158293	0.679294276	0.677362267	0.668451843	0.664611009	0.656110171	0.642006507	0.627902843	0.661519795	0.671566241	0.658814983
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