上证指数预测之python建模与动态时间扭曲

继续学习人民邮电出版的《机器学习实践指南》，第7章股市预测，模型直接拿来用于上证指数，动态时间扭曲建模，结果跟教程预测标普500差别很大。
文中涉及pandas_profiling模块，图像是嵌入式，Markdown没有显示结果。

本文仅用于python数据分析之DataFrame操作学习！

一 、指数搜索

import tushare as ts
ts.get_index()

	code	name	change	open	preclose	close	high	low	volume	amount
0	000001	上证指数	1.61	2727.0186	2702.1296	2745.6182	2751.8964	2702.4933	252019507	2815.8337
1	000002	Ａ股指数	1.61	2857.6618	2831.5502	2877.1556	2883.7260	2831.9092	251741036	2814.3846
2	000003	Ｂ股指数	1.06	220.9384	220.0086	222.3326	223.2548	220.5027	278471	1.4491
3	000008	综合指数	1.73	2544.9532	2522.4297	2565.9899	2574.0928	2528.0644	53368275	585.2588
4	000009	上证380	1.46	4579.1702	4537.5197	4603.5710	4609.5472	4528.2879	55041282	629.8522
5	000010	上证180	1.88	7734.6028	7654.7969	7798.7930	7821.4258	7661.7982	83125699	1155.2277
6	000011	基金指数	0.91	6009.5347	5985.2705	6039.5412	6040.9660	5964.3047	153023459	407.7303
7	000012	国债指数	0.03	180.8213	180.8046	180.8513	180.8523	180.8106	400973	4.0256
8	000016	上证50	2.28	2597.9793	2569.7891	2628.4161	2636.1406	2580.3573	37231436	618.8246
9	000017	新综指	1.61	2304.1910	2283.1352	2319.9088	2325.2075	2283.4227	249192522	2677.9100
10	000300	沪深300	1.79	3629.5126	3589.0926	3653.2239	3663.9517	3588.7766	139603000	2112.2307
11	000905	中证500	1.19	5217.4880	5157.8235	5219.2809	5225.0268	5129.0733	127513289	1315.1903
12	399001	深证成指	1.30	10150.7840	10019.8580	10150.1250	10174.5090	9964.2860	38004697447	4354.5853
13	399002	深成指R	1.30	12453.4930	12292.8670	12452.6860	12482.6010	12224.6880	15836815504	2267.2686
14	399003	成份Ｂ指	1.57	4664.6670	4648.4650	4721.5380	4721.5380	4619.9620	10341618	0.7069
15	399004	深证100R	1.54	5590.8520	5518.6460	5603.5730	5620.6970	5501.8790	5399802857	993.9201
16	399005	中小板指	0.71	6681.1970	6588.8830	6635.7860	6699.7590	6525.1590	17873990979	1922.1422
17	399006	创业板指	1.06	1923.9600	1894.9430	1915.0460	1928.8790	1878.8490	9944795691	1473.2099
18	399008	中小300	0.79	1287.6060	1270.6290	1280.6110	1290.9450	1259.3420	9323739585	1237.4965
19	399100	新 指 数	1.33	7766.5520	7673.6800	7775.6440	7784.8760	7640.9580	37295220442	4316.5081
20	399101	中小板综	1.06	9720.5630	9601.0040	9702.4630	9740.9030	9546.9760	17873990979	1922.1422
21	399106	深证综指	1.28	1702.9490	1682.9300	1704.4620	1707.0660	1675.4420	38004697447	4354.5853
22	399107	深证Ａ指	1.28	1781.9020	1760.9240	1783.4680	1786.2040	1753.0940	37985716774	4353.5633
23	399108	深证Ｂ指	0.87	821.9270	819.7140	826.8650	827.0460	814.0530	18980673	1.0220
24	399333	中小板R	0.71	7521.6910	7417.7640	7470.5670	7542.5880	7346.0230	3718392677	695.0080
25	399606	创业板R	1.06	2029.7650	1999.1520	2020.3610	2034.9540	1982.1730	2292605286	496.8369

二、导入上证指数数据

import pandas as pd
import numpy as np
#import pandas_datareader as pdr
cons = ts.get_apis()#建立连接

#start_date = pd.to_datetime('2010-01-01')
#stop_date = pd.to_datetime('2020-03-12')
shx = ts.bar('000001',conn=cons,asset='INDEX',start_date='2019-01-01',end_date="")
shx = shx.dropna()#删除所有null行
shx.sort_values(['datetime'],inplace=True)#日期顺序
shx

	code	open	close	high	low	vol	amount	p_change
datetime
2019-01-03	000001	2461.78	2464.36	2488.48	2455.93	1243975.0	1.069228e+11	-0.04
2019-