【Python】监督学习-上证指数预测涨跌-SVM
本实例来源于 MOOC_Python机器学习应用_第二周有监督学习_分类_上证指数预测涨跌.
由于下载不了课程所附的源数据，我采取了另一种渠道（调用tushare数据）来获取上证指数及个股数据。
附课程链接：https://www.icourse163.org/course/BIT-1001872001
核函数为默认rbf的svm模型跑出的结果是
svm classifier accuacy:[0.5635980323260716, 0.517217146872804, 0.5130007027406887, 0.5221363316936051, 0.5460295151089248]
与课程所说的0.53相差不大，算是结束了。只是，说好的预测呢。。。上周五收盘9月4日3355，那下周一9月7日，开盘是涨是跌？（/捂脸）
额，先不管这个（下周一上证指数是涨是跌）。
如果把证券市场（细说就股票市场）当做一个行业，在了解了基本的机器学习知识之后，如果要融会贯通并加以应用，应当进一步把这个行业内的“业务知识”理解多一些，再根据业务逻辑来选取较佳的模型和算法，我想是更佳。
本话题（金融股票）的下一步：《财务报表分析》和《蜡烛图方法》。
另一话题（金融风控）的下一步：把知友多次呼唤我补写的<信贷风控评分卡模型>下篇磨叽出来。
链接 https://zhuanlan.zhihu.com/p/67031799
本篇：结束于2020.9.5 周六
import pandas as pd
import numpy as np
from sklearn import svm
# from sklearn import cross_validation
# ImportError: cannot import name 'cross_validation' from 'sklearn' (D:\ProgramData\Anaconda3\lib\site-packages\sklearn\__init__.py)
# from sklearn import gam_cross_validation
# sklean.cross_validation模块找不到的解决方式：https://blog.csdn.net/u011573853/article/details/97638898
# 因为该模块在0.18版本中被弃用，支持所有重构的类和函数都被移动到的model_selection模块中了
from sklearn.model_selection import train_test_split
import datetime
import os
import tushare as ts
print(ts.__version__) # 查看tushare当前版本
import warnings
warnings.filterwarnings('ignore')
# 设置调用 tushare 的 token
# 这个token，可上tushare官网注册获取。我这里就先码掉。
tushare_token = '1b4c5a07cb9ce8d3261388ace6cxxxxxxxxxxx'
pro = ts.pro_api(tushare_token)
timeperiod = -1000 # datetime.timedelta调用的股票数据时间段参数
# 获取指数每日行情 (tushare 新接口 index_daily)
indexdaily_code = '000001.SH'
indexdaily = pro.index_daily(ts_code = indexdaily_code)
indexdaily_path = path +'/指数每日行情'+indexdaily_code+str(datetime.date.today())+'.xlsx'
indexdaily.to_excel(indexdaily_path, index=False)
# #或者按日期取
# df = pro.index_daily(ts_code='399300.SZ', start_date='20180101', end_date='20181010')
print(indexdaily.head())
data = indexdaily
print(data.shape[0])
# 进行列名变更，以匹配课程样例代码中的自定义函数
# [u'收盘价',u'最高价',u'最低价',u'开盘价',u'成交量']
print("原列名:",data.columns.values)
colNameDict = {
'open':'开盘价',
'high':'最高价',
'close':'收盘价',
'low':'最低价',
'vol':'成交量'} #将‘源数据列名’改为‘新列名’
data.rename(columns = colNameDict,inplace=True)
print("现列名:",data.columns.values)
data.head()
ts_code trade_date close open high low \
0 000001.SH 20200904 3355.3666 3336.4076 3360.1061 3328.5518
1 000001.SH 20200903 3384.9806 3404.0319 3425.6294 3374.2634
2 000001.SH 20200902 3404.8017 3420.4693 3421.3959 3377.2111
3 000001.SH 20200901 3410.6068 3389.7424 3410.6068 3381.7108
4 000001.SH 20200831 3395.6775 3416.5497 3442.7363 3395.4675
pre_close change pct_chg vol amount
0 3384.9806 -29.6140 -0.8749 221636550.0 308179657.6
1 3404.8017 -19.8211 -0.5822 255346279.0 350706563.9
2 3410.6068 -5.8051 -0.1702 261546319.0 345638982.3
3 3395.6775 14.9293 0.4397 246999249.0 326850955.3
4 3403.8066 -8.1291 -0.2388 323473890.0 436930125.4
7265
原列名: ['ts_code' 'trade_date' 'close' 'open' 'high' 'low' 'pre_close' 'change'
'pct_chg' 'vol' 'amount']
现列名: ['ts_code' 'trade_date' '收盘价' '开盘价' '最高价' '最低价' 'pre_close' 'change'
'pct_chg' '成交量' 'amount']
| ts_code | trade_date | 收盘价 | 开盘价 | 最高价 | 最低价 | pre_close | change | pct_chg | 成交量 | amount | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 000001.SH | 20200904 | 3355.3666 | 3336.4076 | 3360.1061 | 3328.5518 | 3384.9806 | -29.6140 | -0.8749 | 221636550.0 | 308179657.6 |
| 1 | 000001.SH | 20200903 | 3384.9806 | 3404.0319 | 3425.6294 | 3374.2634 | 3404.8017 | -19.8211 | -0.5822 | 255346279.0 | 350706563.9 |
| 2 | 000001.SH | 20200902 | 3404.8017 | 3420.4693 | 3421.3959 | 3377.2111 | 3410.6068 | -5.8051 | -0.1702 | 261546319.0 | 345638982.3 |
| 3 | 000001.SH | 20200901 | 3410.6068 | 3389.7424 | 3410.6068 | 3381.7108 | 3395.6775 | 14.9293 | 0.4397 | 246999249.0 | 326850955.3 |
| 4 | 000001.SH | 20200831 | 3395.6775 | 3416.5497 | 3442.7363 | 3395.4675 | 3403.8066 | -8.1291 | -0.2388 | 323473890.0 | 436930125.4 |
# 建模
data.sort_index(0,ascending=False,inplace=True)
dayfeature=150
featurenum=5*dayfeature
x=np.zeros((data.shape[0]-dayfeature,featurenum+1))
y=np.zeros((data.shape[0]-dayfeature))
for i in range(0,data.shape[0]-dayfeature):
x[i,0:featurenum]=np.array(data[i:i+dayfeature][[u'收盘价',u'最高价',u'最低价',u'开盘价',u'成交量']]).reshape((1,featurenum))
x[i,featurenum]=data.ix[i+dayfeature][u'开盘价']
for i in range(0,data.shape[0]-dayfeature):
if data.ix[i+dayfeature][u'收盘价']>=data.ix[i+dayfeature][u'开盘价']:
y[i]=1
else:
y[i]=0
clf=svm.SVC(kernel='rbf')
result = []
for i in range(5):
# x_train, x_test, y_train, y_test = cross_validation.train_test_split(x, y, test_size = 0.2)
x_train, x_test, y_train, y_test = train_test_split(x, y, test_size = 0.2)
clf.fit(x_train, y_train)
result.append(np.mean(y_test == clf.predict(x_test)))
print("svm classifier accuacy:")
print(result)
svm classifier accuacy:
[0.5635980323260716, 0.517217146872804, 0.5130007027406887, 0.5221363316936051, 0.5460295151089248]
上面是机器学习svm预测上证指数涨跌的函数代码，已全。
接下来，来补充熟悉tushare，以及将上面的函数拆解开一步步理解。如有需要，可阅；如无需要，可弃。
# 获取中核科技 [000777] 和上证指数[sh000001]过去150天的数据
# zhonghe = ts.get_hist_data('000777',start='2020-07-02',end='2020-07-08')
zhonghe = ts.get_hist_data('000777',
start=(datetime.date.today()+datetime.timedelta(days=timeperiod)).strftime("%Y-%m-%d"),
end=datetime.date.today().strftime("%Y-%m-%d"))
zhonghe.head()
| open | high | close | low | volume | price_change | p_change | ma5 | ma10 | ma20 | v_ma5 | v_ma10 | v_ma20 | turnover | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| date | ||||||||||||||
| 2020-09-04 | 14.20 | 14.49 | 14.31 | 14.10 | 87811.31 | -0.56 | -3.77 | 14.842 | 14.572 | 15.226 | 129616.31 | 108027.91 | 189154.32 | 2.29 |
| 2020-09-03 | 15.57 | 15.63 | 14.87 | 14.81 | 197450.16 | -0.26 | -1.72 | 14.834 | 14.638 | 15.336 | 124897.35 | 107117.60 | 211265.45 | 5.15 |
| 2020-09-02 | 15.14 | 15.17 | 15.13 | 14.70 | 98608.90 | 0.12 | 0.80 | 14.702 | 14.652 | 15.343 | 99849.76 | 99770.59 | 219753.29 | 2.57 |
| 2020-09-01 | 14.90 | 15.26 | 15.01 | 14.80 | 112804.44 | 0.12 | 0.81 | 14.466 | 14.669 | 15.283 | 104791.19 | 110236.09 | 223397.63 | 2.94 |
| 2020-08-31 | 14.28 | 15.13 | 14.89 | 14.27 | 151406.73 | 0.62 | 4.34 | 14.338 | 14.791 | 15.203 | 101797.45 | 124469.83 | 223753.36 | 3.95 |
# 获取中核科技 [000777] 和上证指数[sh000001]过去150天的数据
data0 = ts.get_hist_data('sh000001',
start=(datetime.date.today()+datetime.timedelta(days=timeperiod)).strftime("%Y-%m-%d"),
end=datetime.date.today().strftime("%Y-%m-%d"))
data0.head()
| open | high | close | low | volume | price_change | p_change | ma5 | ma10 | ma20 | v_ma5 | v_ma10 | v_ma20 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| date | |||||||||||||
| 2020-09-04 | 3336.41 | 3360.11 | 3355.37 | 3328.55 | 2216365.50 | -29.61 | -0.88 | 3390.288 | 3379.432 | 3377.828 | 2618004.60 | 2655219.50 | 3144904.75 |
| 2020-09-03 | 3404.03 | 3425.63 | 3384.98 | 3374.26 | 2553462.75 | -19.82 | -0.58 | 3399.976 | 3381.963 | 3377.762 | 2717382.60 | 2721071.30 | 3236051.29 |
| 2020-09-02 | 3420.47 | 3421.40 | 3404.80 | 3377.21 | 2615463.25 | -5.81 | -0.17 | 3393.002 | 3379.855 | 3377.836 | 2680888.75 | 2801326.00 | 3316029.40 |
| 2020-09-01 | 3389.74 | 3410.61 | 3410.61 | 3381.71 | 2469992.50 | 14.93 | 0.44 | 3377.990 | 3380.188 | 3376.474 | 2746698.15 | 2945501.90 | 3378167.75 |
| 2020-08-31 | 3416.55 | 3442.74 | 3395.68 | 3395.47 | 3234739.00 | -8.13 | -0.24 | 3370.584 | 3384.236 | 3374.528 | 2806926.50 | 3079231.93 | 3475832.40 |
# 获取中核科技 [000777] 和上证指数[sh000001]实时数据
df = ts.get_realtime_quotes(['sh000001','000777','000001'])
df[['code','name','price','bid','ask','volume','amount','time']]
| code | name | price | bid | ask | volume | amount | time | |
|---|---|---|---|---|---|---|---|---|
| 0 | 000001 | 上证指数 | 3355.3666 | 0 | 0 | 221636550 | 308179657649 | 15:02:16 |
| 1 | 000777 | 中核科技 | 14.310 | 14.310 | 14.320 | 8781131 | 125404027.230 | 15:00:03 |
| 2 | 000001 | 平安银行 | 14.960 | 14.960 | 14.970 | 90988999 | 1353550808.280 | 15:00:03 |
# 设置token，看看例子
pro = ts.pro_api(tushare_token)
df1 = pro.daily(ts_code = '000001',
start_date = (datetime.date.today()+datetime.timedelta(days=timeperiod)).strftime("%Y-%m-%d"),
end_date = datetime.date.today().strftime("%Y-%m-%d"))
print(df1.head())
print('\n新接口pro，对代码的识别有了新的要求，000001不能返回平安银行数据，得000001.SZ才行，如上，如下。\n')
df2 = pro.daily(ts_code='000001.SZ',
start_date = (datetime.date.today()+datetime.timedelta(days=timeperiod)).strftime("%Y-%m-%d"),
end_date = datetime.date.today().strftime("%Y-%m-%d"))
# 尝试按日期降序排序
# df2.sort_index(0,ascending=True,inplace=True) # 升序，不成功
# df2.sort_index(0,ascending=False,inplace=True) # 降序，可能本来就是降序
# df2['trade_date'] = df2['trade_date'].apply(lambda x: x.values())
# 报错 AttributeError: 'str' object has no attribute 'values'
# 标准化日期，获取时间的“年、月、日” (亲测自定义函数change_date(s)可行)
def change_date(s):
s = datetime.datetime.strptime(s, "%Y%m%d") # 把日期标准化，转化结果如：20150104 => 2015-01-04 00:00:00
s = str(s) # 上一步把date转化为了时间格式，因此要把date转回str格式
return s[:10] # 只获取年月日，即“位置10”之前的字符串
df2['trade_date'] = df2['trade_date'].map(change_date) # 用change_date函数处理列表中date这一列，如把“20150104”转化为“2015-01-04”
df2.sort_values(by='trade_date',axis=0,ascending=True,inplace=True) # 从后面print(df2.head())验证升序成功
# 尝试用另一种方式获取：标准化日期，获取时间的“年、月、日”（亲测提示df.sort_values和df2.head语法错误，可能是因为lambda x: x.strftime("%Y-%m-%d")没转换成功）
# df2['trade_date'] = df2['trade_date'].apply(lambda x: x.strftime("%Y-%m-%d")
# df2.sort_values(by='trade_date',axis=0,ascending=True,inplace=True)
# 报错 SyntaxError: invalid syntax
print(df2.head())
Empty DataFrame
Columns: [ts_code, trade_date, open, high, low, close, pre_close, change, pct_chg, vol, amount]
Index: []
新接口pro，对代码的识别有了新的要求，000001不能返回平安银行数据，得000001.SZ才行，如上，如下。
ts_code trade_date open high low close pre_close change \
730 000001.SZ 2017-01-03 9.11 9.18 9.09 9.16 9.10 0.06
729 000001.SZ 2017-01-04 9.15 9.18 9.14 9.16 9.16 0.00
728 000001.SZ 2017-01-05 9.17 9.18 9.15 9.17 9.16 0.01
727 000001.SZ 2017-01-06 9.17 9.17 9.11 9.13 9.17 -0.04
726 000001.SZ 2017-01-09 9.13 9.17 9.11 9.15 9.13 0.02
pct_chg vol amount
730 0.66 459840.49 420595.176
729 0.00 449329.53 411503.444
728 0.11 344372.91 315769.693
727 -0.44 358154.20 327176.433
726 0.22 361081.57 329994.604
'''
这部分代码是尝试调用tushare的一些数据，并生成Excel到根目录方便查阅。与本次预测无太大关系，现先注释掉。
'''
# # 查询当前所有正常上市交易的股票列表
# alldata1 = pro.stock_basic(exchange='', list_status='L', fields='ts_code,symbol,name,area,industry,list_date')
# # 导出Excel到当前目录：所有正常上市交易的股票列表
# path = os.path.abspath('.')
# alldata_path = path +'/当前所有正常上市交易的股票列表'+str(datetime.date.today())+'.xlsx'
# alldata1.to_excel(alldata_path, index=False)
# alldata1.head(10)
# # 另一种方式 查询当前所有正常上市交易的股票列表
# # alldata2 = pro.query('stock_basic', exchange='', list_status='L', fields='ts_code,symbol,name,area,industry,list_date')
# # alldata2.head(10)
# # 获取指数基础信息及
# indexbasic1 = pro.index_basic(market='SW')
# indexbasic1_path = path +'/指数基础信息列表_SW'+str(datetime.date.today())+'.xlsx'
# indexbasic1.to_excel(indexbasic1_path, index=False)
# print(indexbasic1.head())
# indexbasic2 = pro.index_basic()
# indexbasic2_path = path +'/指数基础信息列表_默认SSE'+str(datetime.date.today())+'.xlsx'
# indexbasic2.to_excel(indexbasic2_path, index=False)
# print(indexbasic2.head())
# # 获取指数每日行情
# # indexdaily = pro.index_daily(ts_code='399300.SZ')
# indexdaily_code = '000001.SH'
# indexdaily = pro.index_daily(ts_code = indexdaily_code)
# indexdaily_path = path +'/指数每日行情'+indexdaily_code+str(datetime.date.today())+'.xlsx'
# indexdaily.to_excel(indexdaily_path, index=False)
# # #或者按日期取
# # df = pro.index_daily(ts_code='399300.SZ', start_date='20180101', end_date='20181010')
# print(indexdaily.head())
# # 获取沪股通、深股通成分数据
# #获取沪股通成分
# shcf = pro.hs_const(hs_type='SH')
# shcf_path = path +'/沪股通成分数据'+str(datetime.date.today())+'.xlsx'
# shcf.to_excel(shcf_path, index=False)
# print(shcf.head())
# #获取深股通成分
# szcf = pro.hs_const(hs_type='SZ')
# szcf_path = path +'/深股通成分数据'+str(datetime.date.today())+'.xlsx'
# szcf.to_excel(szcf_path, index=False)
# print(szcf.head())
'\n这部分代码是尝试调用tushare的一些数据，并生成Excel到根目录方便查阅。与本次预测无太大关系，现先注释掉。\n'
# 把tushare 调用到的数据，进行列名变更，以匹配课程样例代码中的自定义函数
# [u'收盘价',u'最高价',u'最低价',u'开盘价',u'成交量']
print("原列名:",data0.columns.values)
colNameDict = {
'open':'收盘价',
'high':'最高价',
'close':'最低价',
'low':'开盘价',
'volume':'成交量'} #将‘源数据列名’改为‘新列名’
data0.rename(columns = colNameDict,inplace=True)
print("现列名:",data0.columns.values)
data0.head()
原列名: ['open' 'high' 'close' 'low' 'volume' 'price_change' 'p_change' 'ma5'
'ma10' 'ma20' 'v_ma5' 'v_ma10' 'v_ma20']
现列名: ['收盘价' '最高价' '最低价' '开盘价' '成交量' 'price_change' 'p_change' 'ma5' 'ma10'
'ma20' 'v_ma5' 'v_ma10' 'v_ma20']
| 收盘价 | 最高价 | 最低价 | 开盘价 | 成交量 | price_change | p_change | ma5 | ma10 | ma20 | v_ma5 | v_ma10 | v_ma20 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| date | |||||||||||||
| 2020-09-04 | 3336.41 | 3360.11 | 3355.37 | 3328.55 | 2216365.50 | -29.61 | -0.88 | 3390.288 | 3379.432 | 3377.828 | 2618004.60 | 2655219.50 | 3144904.75 |
| 2020-09-03 | 3404.03 | 3425.63 | 3384.98 | 3374.26 | 2553462.75 | -19.82 | -0.58 | 3399.976 | 3381.963 | 3377.762 | 2717382.60 | 2721071.30 | 3236051.29 |
| 2020-09-02 | 3420.47 | 3421.40 | 3404.80 | 3377.21 | 2615463.25 | -5.81 | -0.17 | 3393.002 | 3379.855 | 3377.836 | 2680888.75 | 2801326.00 | 3316029.40 |
| 2020-09-01 | 3389.74 | 3410.61 | 3410.61 | 3381.71 | 2469992.50 | 14.93 | 0.44 | 3377.990 | 3380.188 | 3376.474 | 2746698.15 | 2945501.90 | 3378167.75 |
| 2020-08-31 | 3416.55 | 3442.74 | 3395.68 | 3395.47 | 3234739.00 | -8.13 | -0.24 | 3370.584 | 3384.236 | 3374.528 | 2806926.50 | 3079231.93 | 3475832.40 |
data0.sort_index(0,ascending=True,inplace=True)
data0.head()
| 收盘价 | 最高价 | 最低价 | 开盘价 | 成交量 | price_change | p_change | ma5 | ma10 | ma20 | v_ma5 | v_ma10 | v_ma20 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| date | |||||||||||||
| 2018-03-07 | 3288.86 | 3308.41 | 3271.67 | 3264.76 | 1686650.25 | -17.97 | -0.55 | 3271.670 | 3271.670 | 3271.670 | 1686650.25 | 1686650.25 | 1686650.25 |
| 2018-03-08 | 3268.35 | 3289.50 | 3288.41 | 3261.55 | 1498275.25 | 16.74 | 0.51 | 3280.040 | 3280.040 | 3280.040 | 1592462.75 | 1592462.75 | 1592462.75 |
| 2018-03-09 | 3291.43 | 3309.72 | 3307.17 | 3283.56 | 1684245.12 | 18.76 | 0.57 | 3289.083 | 3289.083 | 3289.083 | 1623056.87 | 1623056.87 | 1623056.87 |
| 2018-03-12 | 3319.21 | 3333.56 | 3326.70 | 3313.56 | 2065324.38 | 19.53 | 0.59 | 3298.488 | 3298.488 | 3298.488 | 1733623.75 | 1733623.75 | 1733623.75 |
| 2018-03-13 | 3324.12 | 3333.88 | 3310.24 | 3307.38 | 1771143.50 | -16.46 | -0.49 | 3300.838 | 3300.838 | 3300.838 | 1741127.70 | 1741127.70 | 1741127.70 |
data0.shape[0]
611
课件的最后大块代码函数，一开始直接运行报错，所以我们接着前面data的数据集，一步步拆解看看
# data.sort_index(0,ascending=True,inplace=True)
# 由于调用的tushare数据排序与课程提供的不一致，所以这里排序用index降序。
data.sort_index(0,ascending=False,inplace=True)
dayfeature=150
featurenum=5*dayfeature
x=np.zeros((data.shape[0]-dayfeature,featurenum+1))
print(x.shape)
y=np.zeros((data.shape[0]-dayfeature))
print(y.shape)
print('\n看看x\n',x)
print('\n看看y\n',y)
(7115, 751)
(7115,)
看看x
[[0. 0. 0. ... 0. 0. 0.]
[0. 0. 0. ... 0. 0. 0.]
[0. 0. 0. ... 0. 0. 0.]
...
[0. 0. 0. ... 0. 0. 0.]
[0. 0. 0. ... 0. 0. 0.]
[0. 0. 0. ... 0. 0. 0.]]
看看y
[0. 0. 0. ... 0. 0. 0.]
# 拆解循环函数，赋值 i=1 看看情况
i = 1
# np.array(data[i:i+dayfeature])
data[i:i+dayfeature]
| ts_code | trade_date | 收盘价 | 开盘价 | 最高价 | 最低价 | pre_close | change | pct_chg | 成交量 | amount | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 7263 | 000001.SH | 19901220 | 104.39 | 104.30 | 104.39 | 99.98 | 99.98 | 4.41 | 4.4109 | 197.0 | 84.992 |
| 7262 | 000001.SH | 19901221 | 109.13 | 109.07 | 109.13 | 103.73 | 104.39 | 4.74 | 4.5407 | 28.0 | 16.096 |
| 7261 | 000001.SH | 19901224 | 114.55 | 113.57 | 114.55 | 109.13 | 109.13 | 5.42 | 4.9666 | 32.0 | 31.063 |
| 7260 | 000001.SH | 19901225 | 120.25 | 120.09 | 120.25 | 114.55 | 114.55 | 5.70 | 4.9760 | 15.0 | 6.510 |
| 7259 | 000001.SH | 19901226 | 125.27 | 125.27 | 125.27 | 120.25 | 120.25 | 5.02 | 4.1746 | 100.0 | 53.730 |
| 7258 | 000001.SH | 19901227 | 125.28 | 125.27 | 125.28 | 125.27 | 125.27 | 0.01 | 0.0080 | 66.0 | 104.644 |
| 7257 | 000001.SH | 19901228 | 126.45 | 126.39 | 126.45 | 125.28 | 125.28 | 1.17 | 0.9339 | 108.0 | 88.031 |
| 7256 | 000001.SH | 19901231 | 127.61 | 126.56 | 127.61 | 126.48 | 126.45 | 1.16 | 0.9174 | 78.0 | 60.030 |
| 7255 | 000001.SH | 19910102 | 128.84 | 127.61 | 128.84 | 127.61 | 127.61 | 1.23 | 0.9639 | 91.0 | 59.098 |
| 7254 | 000001.SH | 19910103 | 130.14 | 128.84 | 130.14 | 128.84 | 128.84 | 1.30 | 1.0090 | 141.0 | 93.918 |
| 7253 | 000001.SH | 19910104 | 131.44 | 131.27 | 131.44 | 130.14 | 130.14 | 1.30 | 0.9989 | 420.0 | 261.904 |
| 7252 | 000001.SH | 19910107 | 132.06 | 131.99 | 132.06 | 131.45 | 131.44 | 0.62 | 0.4717 | 217.0 | 141.737 |
| 7251 | 000001.SH | 19910108 | 132.68 | 132.62 | 132.68 | 132.06 | 132.06 | 0.62 | 0.4695 | 2926.0 | 1806.867 |
| 7250 | 000001.SH | 19910109 | 133.34 | 133.30 | 133.34 | 132.68 | 132.68 | 0.66 | 0.4974 | 5603.0 | 3228.719 |
| 7249 | 000001.SH | 19910110 | 133.97 | 133.93 | 133.97 | 133.34 | 133.34 | 0.63 | 0.4725 | 9990.0 | 5399.457 |
| 7248 | 000001.SH | 19910111 | 134.60 | 134.61 | 134.61 | 134.51 | 133.97 | 0.63 | 0.4703 | 13327.0 | 7115.732 |
| 7247 | 000001.SH | 19910114 | 134.67 | 134.11 | 135.19 | 134.11 | 134.60 | 0.07 | 0.0520 | 12530.0 | 6883.604 |
| 7246 | 000001.SH | 19910115 | 134.74 | 134.21 | 134.74 | 134.19 | 134.67 | 0.07 | 0.0520 | 1446.0 | 1010.364 |
| 7245 | 000001.SH | 19910116 | 134.24 | 134.19 | 134.74 | 134.14 | 134.74 | -0.50 | -0.3711 | 509.0 | 270.133 |
| 7244 | 000001.SH | 19910117 | 134.25 | 133.67 | 134.25 | 133.65 | 134.24 | 0.01 | 0.0074 | 658.0 | 334.238 |
| 7243 | 000001.SH | 19910118 | 134.24 | 133.70 | 134.25 | 133.67 | 134.25 | -0.01 | -0.0074 | 3004.0 | 1570.833 |
| 7242 | 000001.SH | 19910121 | 134.24 | 133.70 | 134.24 | 133.66 | 134.24 | 0.00 | 0.0000 | 2051.0 | 1029.305 |
| 7241 | 000001.SH | 19910122 | 133.72 | 133.72 | 134.24 | 133.66 | 134.24 | -0.52 | -0.3874 | 354.0 | 180.787 |
| 7240 | 000001.SH | 19910123 | 133.17 | 133.17 | 133.72 | 133.14 | 133.72 | -0.55 | -0.4113 | 1095.0 | 575.928 |
| 7239 | 000001.SH | 19910124 | 132.61 | 132.61 | 133.17 | 132.57 | 133.17 | -0.56 | -0.4205 | 1857.0 | 917.392 |
| 7238 | 000001.SH | 19910125 | 132.05 | 132.05 | 132.07 | 132.03 | 132.61 | -0.56 | -0.4223 | 3447.0 | 1722.246 |
| 7237 | 000001.SH | 19910128 | 131.46 | 131.46 | 131.55 | 131.46 | 132.05 | -0.59 | -0.4468 | 5107.0 | 2565.573 |
| 7236 | 000001.SH | 19910129 | 130.95 | 130.95 | 130.97 | 130.95 | 131.46 | -0.51 | -0.3880 | 1387.0 | 710.741 |
| 7235 | 000001.SH | 19910130 | 130.44 | 130.44 | 130.95 | 130.41 | 130.95 | -0.51 | -0.3895 | 527.0 | 260.701 |
| 7234 | 000001.SH | 19910131 | 129.97 | 129.93 | 130.46 | 129.93 | 130.44 | -0.47 | -0.3603 | 510.0 | 244.662 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 7143 | 000001.SH | 19910612 | 124.11 | 123.90 | 124.11 | 122.89 | 122.89 | 1.22 | 0.9928 | 1372.0 | 735.371 |
| 7142 | 000001.SH | 19910613 | 125.34 | 125.33 | 125.34 | 123.65 | 124.11 | 1.23 | 0.9911 | 20868.0 | 11563.763 |
| 7141 | 000001.SH | 19910614 | 124.53 | 124.47 | 126.34 | 124.47 | 125.34 | -0.81 | -0.6462 | 6633.0 | 3541.098 |
| 7140 | 000001.SH | 19910617 | 125.77 | 125.63 | 125.77 | 123.54 | 124.53 | 1.24 | 0.9957 | 6933.0 | 4056.679 |
| 7139 | 000001.SH | 19910618 | 127.03 | 127.03 | 127.03 | 126.67 | 125.77 | 1.26 | 1.0018 | 2030.0 | 1015.149 |
| 7138 | 000001.SH | 19910619 | 128.29 | 128.12 | 128.29 | 127.03 | 127.03 | 1.26 | 0.9919 | 1481.0 | 744.123 |
| 7137 | 000001.SH | 19910620 | 129.57 | 129.55 | 129.57 | 129.21 | 128.29 | 1.28 | 0.9977 | 2299.0 | 1321.261 |
| 7136 | 000001.SH | 19910621 | 130.86 | 130.72 | 130.86 | 128.81 | 129.57 | 1.29 | 0.9956 | 13613.0 | 7746.480 |
| 7135 | 000001.SH | 19910624 | 132.17 | 132.10 | 132.17 | 131.80 | 130.86 | 1.31 | 1.0011 | 1960.0 | 1657.788 |
| 7134 | 000001.SH | 19910625 | 133.49 | 133.48 | 133.49 | 132.17 | 132.17 | 1.32 | 0.9987 | 1624.0 | 1296.047 |
| 7133 | 000001.SH | 19910626 | 134.83 | 134.83 | 134.83 | 133.49 | 133.49 | 1.34 | 1.0038 | 3409.0 | 2149.983 |
| 7132 | 000001.SH | 19910627 | 136.19 | 136.04 | 136.19 | 134.83 | 134.83 | 1.36 | 1.0087 | 4497.0 | 2521.206 |
| 7131 | 000001.SH | 19910628 | 137.56 | 137.41 | 137.56 | 136.19 | 136.19 | 1.37 | 1.0059 | 4935.0 | 3032.950 |
| 7130 | 000001.SH | 19910701 | 136.85 | 136.64 | 138.62 | 136.56 | 137.56 | -0.71 | -0.5161 | 22940.0 | 12469.884 |
| 7129 | 000001.SH | 19910702 | 135.96 | 135.91 | 135.96 | 135.69 | 136.85 | -0.89 | -0.6503 | 2838.0 | 3794.100 |
| 7128 | 000001.SH | 19910703 | 135.27 | 135.28 | 135.96 | 134.98 | 135.96 | -0.69 | -0.5075 | 2715.0 | 1818.504 |
| 7127 | 000001.SH | 19910704 | 136.63 | 136.63 | 136.63 | 134.19 | 135.27 | 1.36 | 1.0054 | 13394.0 | 8095.138 |
| 7126 | 000001.SH | 19910705 | 135.96 | 136.01 | 137.68 | 135.90 | 136.63 | -0.67 | -0.4904 | 14540.0 | 9394.861 |
| 7125 | 000001.SH | 19910708 | 135.28 | 135.26 | 135.28 | 134.93 | 135.96 | -0.68 | -0.5001 | 5874.0 | 2925.933 |
| 7124 | 000001.SH | 19910709 | 134.64 | 136.56 | 136.57 | 134.31 | 135.28 | -0.64 | -0.4731 | 8442.0 | 4174.836 |
| 7123 | 000001.SH | 19910710 | 133.99 | 134.40 | 135.60 | 133.72 | 134.64 | -0.65 | -0.4828 | 6023.0 | 2894.591 |
| 7122 | 000001.SH | 19910711 | 133.38 | 133.22 | 133.99 | 133.13 | 133.99 | -0.61 | -0.4553 | 5073.0 | 2417.896 |
| 7121 | 000001.SH | 19910712 | 132.80 | 132.80 | 133.38 | 132.42 | 133.38 | -0.58 | -0.4348 | 3144.0 | 1484.090 |
| 7120 | 000001.SH | 19910715 | 133.14 | 133.90 | 134.10 | 131.87 | 132.80 | 0.34 | 0.2560 | 11938.0 | 5534.900 |
| 7119 | 000001.SH | 19910716 | 134.47 | 134.39 | 134.47 | 133.14 | 133.14 | 1.33 | 0.9989 | 2796.0 | 1328.502 |
| 7118 | 000001.SH | 19910717 | 135.81 | 135.81 | 135.81 | 135.39 | 134.47 | 1.34 | 0.9965 | 660.0 | 397.524 |
| 7117 | 000001.SH | 19910718 | 137.17 | 137.17 | 137.17 | 135.81 | 135.81 | 1.36 | 1.0014 | 847.0 | 464.416 |
| 7116 | 000001.SH | 19910719 | 136.70 | 137.66 | 138.54 | 136.66 | 137.17 | -0.47 | -0.3426 | 10823.0 | 5242.826 |
| 7115 | 000001.SH | 19910722 | 138.07 | 138.07 | 138.07 | 136.70 | 136.70 | 1.37 | 1.0022 | 2764.0 | 1423.205 |
| 7114 | 000001.SH | 19910723 | 139.39 | 139.35 | 139.39 | 138.07 | 138.07 | 1.32 | 0.9560 | 7241.0 | 3548.584 |
150 rows × 11 columns
# 一步步深入
data[i:i+dayfeature][[u'收盘价',u'最高价',u'最低价',u'开盘价',u'成交量']]
# 要去掉换行符 \ ，不然报错 SyntaxError: unexpected character after line continuation character
# 错误示例：data[i:i+dayfeature]\[[u'收盘价',u'最高价',u'最低价',u'开盘价',u'成交量']]
| 收盘价 | 最高价 | 最低价 | 开盘价 | 成交量 | |
|---|---|---|---|---|---|
| 7263 | 104.39 | 104.39 | 99.98 | 104.30 | 197.0 |
| 7262 | 109.13 | 109.13 | 103.73 | 109.07 | 28.0 |
| 7261 | 114.55 | 114.55 | 109.13 | 113.57 | 32.0 |
| 7260 | 120.25 | 120.25 | 114.55 | 120.09 | 15.0 |
| 7259 | 125.27 | 125.27 | 120.25 | 125.27 | 100.0 |
| 7258 | 125.28 | 125.28 | 125.27 | 125.27 | 66.0 |
| 7257 | 126.45 | 126.45 | 125.28 | 126.39 | 108.0 |
| 7256 | 127.61 | 127.61 | 126.48 | 126.56 | 78.0 |
| 7255 | 128.84 | 128.84 | 127.61 | 127.61 | 91.0 |
| 7254 | 130.14 | 130.14 | 128.84 | 128.84 | 141.0 |
| 7253 | 131.44 | 131.44 | 130.14 | 131.27 | 420.0 |
| 7252 | 132.06 | 132.06 | 131.45 | 131.99 | 217.0 |
| 7251 | 132.68 | 132.68 | 132.06 | 132.62 | 2926.0 |
| 7250 | 133.34 | 133.34 | 132.68 | 133.30 | 5603.0 |
| 7249 | 133.97 | 133.97 | 133.34 | 133.93 | 9990.0 |
| 7248 | 134.60 | 134.61 | 134.51 | 134.61 | 13327.0 |
| 7247 | 134.67 | 135.19 | 134.11 | 134.11 | 12530.0 |
| 7246 | 134.74 | 134.74 | 134.19 | 134.21 | 1446.0 |
| 7245 | 134.24 | 134.74 | 134.14 | 134.19 | 509.0 |
| 7244 | 134.25 | 134.25 | 133.65 | 133.67 | 658.0 |
| 7243 | 134.24 | 134.25 | 133.67 | 133.70 | 3004.0 |
| 7242 | 134.24 | 134.24 | 133.66 | 133.70 | 2051.0 |
| 7241 | 133.72 | 134.24 | 133.66 | 133.72 | 354.0 |
| 7240 | 133.17 | 133.72 | 133.14 | 133.17 | 1095.0 |
| 7239 | 132.61 | 133.17 | 132.57 | 132.61 | 1857.0 |
| 7238 | 132.05 | 132.07 | 132.03 | 132.05 | 3447.0 |
| 7237 | 131.46 | 131.55 | 131.46 | 131.46 | 5107.0 |
| 7236 | 130.95 | 130.97 | 130.95 | 130.95 | 1387.0 |
| 7235 | 130.44 | 130.95 | 130.41 | 130.44 | 527.0 |
| 7234 | 129.97 | 130.46 | 129.93 | 129.93 | 510.0 |
| ... | ... | ... | ... | ... | ... |
| 7143 | 124.11 | 124.11 | 122.89 | 123.90 | 1372.0 |
| 7142 | 125.34 | 125.34 | 123.65 | 125.33 | 20868.0 |
| 7141 | 124.53 | 126.34 | 124.47 | 124.47 | 6633.0 |
| 7140 | 125.77 | 125.77 | 123.54 | 125.63 | 6933.0 |
| 7139 | 127.03 | 127.03 | 126.67 | 127.03 | 2030.0 |
| 7138 | 128.29 | 128.29 | 127.03 | 128.12 | 1481.0 |
| 7137 | 129.57 | 129.57 | 129.21 | 129.55 | 2299.0 |
| 7136 | 130.86 | 130.86 | 128.81 | 130.72 | 13613.0 |
| 7135 | 132.17 | 132.17 | 131.80 | 132.10 | 1960.0 |
| 7134 | 133.49 | 133.49 | 132.17 | 133.48 | 1624.0 |
| 7133 | 134.83 | 134.83 | 133.49 | 134.83 | 3409.0 |
| 7132 | 136.19 | 136.19 | 134.83 | 136.04 | 4497.0 |
| 7131 | 137.56 | 137.56 | 136.19 | 137.41 | 4935.0 |
| 7130 | 136.85 | 138.62 | 136.56 | 136.64 | 22940.0 |
| 7129 | 135.96 | 135.96 | 135.69 | 135.91 | 2838.0 |
| 7128 | 135.27 | 135.96 | 134.98 | 135.28 | 2715.0 |
| 7127 | 136.63 | 136.63 | 134.19 | 136.63 | 13394.0 |
| 7126 | 135.96 | 137.68 | 135.90 | 136.01 | 14540.0 |
| 7125 | 135.28 | 135.28 | 134.93 | 135.26 | 5874.0 |
| 7124 | 134.64 | 136.57 | 134.31 | 136.56 | 8442.0 |
| 7123 | 133.99 | 135.60 | 133.72 | 134.40 | 6023.0 |
| 7122 | 133.38 | 133.99 | 133.13 | 133.22 | 5073.0 |
| 7121 | 132.80 | 133.38 | 132.42 | 132.80 | 3144.0 |
| 7120 | 133.14 | 134.10 | 131.87 | 133.90 | 11938.0 |
| 7119 | 134.47 | 134.47 | 133.14 | 134.39 | 2796.0 |
| 7118 | 135.81 | 135.81 | 135.39 | 135.81 | 660.0 |
| 7117 | 137.17 | 137.17 | 135.81 | 137.17 | 847.0 |
| 7116 | 136.70 | 138.54 | 136.66 | 137.66 | 10823.0 |
| 7115 | 138.07 | 138.07 | 136.70 | 138.07 | 2764.0 |
| 7114 | 139.39 | 139.39 | 138.07 | 139.35 | 7241.0 |
150 rows × 5 columns
# 变更格式为数组array,因为需要 reshape成一行存储 而 'DataFrame' object has no attribute 'reshape'
np.array(data[i:i+dayfeature][[u'收盘价',u'最高价',u'最低价',u'开盘价',u'成交量']])
array([[1.0439e+02, 1.0439e+02, 9.9980e+01, 1.0430e+02, 1.9700e+02],
[1.0913e+02, 1.0913e+02, 1.0373e+02, 1.0907e+02, 2.8000e+01],
[1.1455e+02, 1.1455e+02, 1.0913e+02, 1.1357e+02, 3.2000e+01],
[1.2025e+02, 1.2025e+02, 1.1455e+02, 1.2009e+02, 1.5000e+01],
[1.2527e+02, 1.2527e+02, 1.2025e+02, 1.2527e+02, 1.0000e+02],
[1.2528e+02, 1.2528e+02, 1.2527e+02, 1.2527e+02, 6.6000e+01],
[1.2645e+02, 1.2645e+02, 1.2528e+02, 1.2639e+02, 1.0800e+02],
[1.2761e+02, 1.2761e+02, 1.2648e+02, 1.2656e+02, 7.8000e+01],
[1.2884e+02, 1.2884e+02, 1.2761e+02, 1.2761e+02, 9.1000e+01],
[1.3014e+02, 1.3014e+02, 1.2884e+02, 1.2884e+02, 1.4100e+02],
[1.3144e+02, 1.3144e+02, 1.3014e+02, 1.3127e+02, 4.2000e+02],
[1.3206e+02, 1.3206e+02, 1.3145e+02, 1.3199e+02, 2.1700e+02],
[1.3268e+02, 1.3268e+02, 1.3206e+02, 1.3262e+02, 2.9260e+03],
[1.3334e+02, 1.3334e+02, 1.3268e+02, 1.3330e+02, 5.6030e+03],
[1.3397e+02, 1.3397e+02, 1.3334e+02, 1.3393e+02, 9.9900e+03],
[1.3460e+02, 1.3461e+02, 1.3451e+02, 1.3461e+02, 1.3327e+04],
[1.3467e+02, 1.3519e+02, 1.3411e+02, 1.3411e+02, 1.2530e+04],
[1.3474e+02, 1.3474e+02, 1.3419e+02, 1.3421e+02, 1.4460e+03],
[1.3424e+02, 1.3474e+02, 1.3414e+02, 1.3419e+02, 5.0900e+02],
[1.3425e+02, 1.3425e+02, 1.3365e+02, 1.3367e+02, 6.5800e+02],
[1.3424e+02, 1.3425e+02, 1.3367e+02, 1.3370e+02, 3.0040e+03],
[1.3424e+02, 1.3424e+02, 1.3366e+02, 1.3370e+02, 2.0510e+03],
[1.3372e+02, 1.3424e+02, 1.3366e+02, 1.3372e+02, 3.5400e+02],
[1.3317e+02, 1.3372e+02, 1.3314e+02, 1.3317e+02, 1.0950e+03],
[1.3261e+02, 1.3317e+02, 1.3257e+02, 1.3261e+02, 1.8570e+03],
[1.3205e+02, 1.3207e+02, 1.3203e+02, 1.3205e+02, 3.4470e+03],
[1.3146e+02, 1.3155e+02, 1.3146e+02, 1.3146e+02, 5.1070e+03],
[1.3095e+02, 1.3097e+02, 1.3095e+02, 1.3095e+02, 1.3870e+03],
[1.3044e+02, 1.3095e+02, 1.3041e+02, 1.3044e+02, 5.2700e+02],
[1.2997e+02, 1.3046e+02, 1.2993e+02, 1.2993e+02, 5.1000e+02],
[1.2951e+02, 1.2997e+02, 1.2945e+02, 1.2950e+02, 3.4500e+02],
[1.2905e+02, 1.2958e+02, 1.2905e+02, 1.2905e+02, 5.5300e+02],
[1.2858e+02, 1.2858e+02, 1.2853e+02, 1.2856e+02, 8.5620e+03],
[1.2914e+02, 1.2915e+02, 1.2806e+02, 1.2913e+02, 6.6410e+03],
[1.2979e+02, 1.2979e+02, 1.2914e+02, 1.2974e+02, 2.1330e+03],
[1.3038e+02, 1.3039e+02, 1.2979e+02, 1.3036e+02, 1.2340e+03],
[1.3097e+02, 1.3097e+02, 1.3039e+02, 1.3092e+02, 9.3170e+03],
[1.3135e+02, 1.3156e+02, 1.3097e+02, 1.3154e+02, 3.7740e+03],
[1.3192e+02, 1.3193e+02, 1.3135e+02, 1.3193e+02, 1.1520e+03],
[1.3253e+02, 1.3253e+02, 1.3230e+02, 1.3253e+02, 3.6240e+03],
[1.3313e+02, 1.3314e+02, 1.3308e+02, 1.3312e+02, 1.7480e+03],
[1.3367e+02, 1.3367e+02, 1.3313e+02, 1.3363e+02, 8.7600e+02],
[1.3428e+02, 1.3428e+02, 1.3367e+02, 1.3424e+02, 3.2200e+03],
[1.3487e+02, 1.3487e+02, 1.3428e+02, 1.3485e+02, 3.4270e+03],
[1.3440e+02, 1.3487e+02, 1.3433e+02, 1.3437e+02, 8.1000e+02],
[1.3393e+02, 1.3444e+02, 1.3390e+02, 1.3390e+02, 1.0940e+03],
[1.3347e+02, 1.3398e+02, 1.3344e+02, 1.3344e+02, 2.1400e+02],
[1.3301e+02, 1.3352e+02, 1.3298e+02, 1.3299e+02, 2.2580e+03],
[1.3253e+02, 1.3253e+02, 1.3247e+02, 1.3253e+02, 7.1240e+03],
[1.3199e+02, 1.3209e+02, 1.3198e+02, 1.3209e+02, 2.5180e+03],
[1.3146e+02, 1.3199e+02, 1.3141e+02, 1.3142e+02, 9.8200e+02],
[1.3094e+02, 1.3095e+02, 1.3089e+02, 1.3089e+02, 7.1900e+02],
[1.3041e+02, 1.3094e+02, 1.3041e+02, 1.3041e+02, 5.6800e+02],
[1.2989e+02, 1.3041e+02, 1.2984e+02, 1.2984e+02, 9.3300e+02],
[1.2931e+02, 1.2989e+02, 1.2930e+02, 1.2930e+02, 5.1400e+02],
[1.2877e+02, 1.2931e+02, 1.2873e+02, 1.2873e+02, 9.5700e+02],
[1.2615e+02, 1.2877e+02, 1.2613e+02, 1.2613e+02, 4.9800e+02],
[1.2563e+02, 1.2615e+02, 1.2561e+02, 1.2561e+02, 3.2600e+02],
[1.2517e+02, 1.2562e+02, 1.2508e+02, 1.2508e+02, 7.9000e+02],
[1.2471e+02, 1.2517e+02, 1.2469e+02, 1.2470e+02, 1.2500e+03],
[1.2422e+02, 1.2471e+02, 1.2422e+02, 1.2423e+02, 8.9200e+02],
[1.2366e+02, 1.2421e+02, 1.2363e+02, 1.2366e+02, 6.7800e+02],
[1.2312e+02, 1.2366e+02, 1.2312e+02, 1.2314e+02, 1.5100e+02],
[1.2262e+02, 1.2312e+02, 1.2262e+02, 1.2262e+02, 2.0700e+02],
[1.2212e+02, 1.2262e+02, 1.2210e+02, 1.2212e+02, 4.4300e+02],
[1.2162e+02, 1.2214e+02, 1.2162e+02, 1.2162e+02, 1.1640e+03],
[1.2112e+02, 1.2164e+02, 1.2112e+02, 1.2112e+02, 6.6800e+02],
[1.2061e+02, 1.2115e+02, 1.2061e+02, 1.2065e+02, 9.6100e+02],
[1.2019e+02, 1.2061e+02, 1.2011e+02, 1.2016e+02, 2.1850e+03],
[1.2073e+02, 1.2073e+02, 1.1968e+02, 1.2069e+02, 1.1999e+04],
[1.2121e+02, 1.2129e+02, 1.2073e+02, 1.2121e+02, 8.1600e+02],
[1.2171e+02, 1.2171e+02, 1.2121e+02, 1.2171e+02, 2.0630e+03],
[1.2172e+02, 1.2220e+02, 1.2124e+02, 1.2220e+02, 1.0517e+04],
[1.2154e+02, 1.2173e+02, 1.2103e+02, 1.2107e+02, 1.1589e+04],
[1.2109e+02, 1.2111e+02, 1.2105e+02, 1.2111e+02, 1.8620e+03],
[1.2062e+02, 1.2110e+02, 1.2060e+02, 1.2060e+02, 3.2000e+02],
[1.2016e+02, 1.2062e+02, 1.2009e+02, 1.2015e+02, 4.6600e+02],
[1.1964e+02, 1.2016e+02, 1.1962e+02, 1.1964e+02, 2.3900e+02],
[1.1921e+02, 1.1965e+02, 1.1915e+02, 1.1920e+02, 3.9900e+02],
[1.1877e+02, 1.1922e+02, 1.1872e+02, 1.1873e+02, 3.0100e+02],
[1.1834e+02, 1.1877e+02, 1.1834e+02, 1.1836e+02, 1.0500e+02],
[1.1790e+02, 1.1834e+02, 1.1789e+02, 1.1792e+02, 2.0100e+02],
[1.1745e+02, 1.1793e+02, 1.1745e+02, 1.1745e+02, 1.7800e+02],
[1.1708e+02, 1.1750e+02, 1.1705e+02, 1.1708e+02, 6.4600e+02],
[1.1663e+02, 1.1708e+02, 1.1658e+02, 1.1663e+02, 4.3000e+02],
[1.1619e+02, 1.1663e+02, 1.1615e+02, 1.1621e+02, 2.8200e+02],
[1.1579e+02, 1.1619e+02, 1.1570e+02, 1.1570e+02, 1.4800e+02],
[1.1536e+02, 1.1579e+02, 1.1533e+02, 1.1538e+02, 1.4950e+03],
[1.1556e+02, 1.1631e+02, 1.1452e+02, 1.1631e+02, 1.3354e+04],
[1.1475e+02, 1.1629e+02, 1.1475e+02, 1.1486e+02, 8.4110e+03],
[1.1394e+02, 1.1487e+02, 1.1389e+02, 1.1389e+02, 4.8500e+03],
[1.1316e+02, 1.1320e+02, 1.1303e+02, 1.1304e+02, 1.4650e+03],
[1.1241e+02, 1.1317e+02, 1.1230e+02, 1.1246e+02, 4.2300e+03],
[1.1161e+02, 1.1241e+02, 1.1157e+02, 1.1165e+02, 2.7640e+03],
[1.1082e+02, 1.1090e+02, 1.1080e+02, 1.1080e+02, 1.8750e+03],
[1.1003e+02, 1.1082e+02, 1.1000e+02, 1.1016e+02, 4.2300e+02],
[1.0929e+02, 1.1012e+02, 1.0929e+02, 1.0936e+02, 2.2660e+03],
[1.0853e+02, 1.0940e+02, 1.0853e+02, 1.0861e+02, 2.4900e+02],
[1.0784e+02, 1.0788e+02, 1.0774e+02, 1.0788e+02, 2.3500e+02],
[1.0719e+02, 1.0784e+02, 1.0706e+02, 1.0721e+02, 2.6700e+02],
[1.0657e+02, 1.0660e+02, 1.0642e+02, 1.0656e+02, 3.9000e+02],
[1.0577e+02, 1.0586e+02, 1.0569e+02, 1.0583e+02, 3.1080e+03],
[1.0675e+02, 1.0675e+02, 1.0496e+02, 1.0512e+02, 2.0192e+04],
[1.0782e+02, 1.0782e+02, 1.0671e+02, 1.0776e+02, 8.6300e+02],
[1.0881e+02, 1.0889e+02, 1.0862e+02, 1.0889e+02, 7.7100e+02],
[1.0825e+02, 1.0982e+02, 1.0811e+02, 1.0971e+02, 1.8677e+04],
[1.0908e+02, 1.0916e+02, 1.0735e+02, 1.0752e+02, 5.6090e+03],
[1.1008e+02, 1.1008e+02, 1.0982e+02, 1.1007e+02, 1.0240e+03],
[1.1066e+02, 1.1107e+02, 1.0947e+02, 1.1105e+02, 8.6190e+03],
[1.1168e+02, 1.1168e+02, 1.1136e+02, 1.1161e+02, 1.6300e+03],
[1.1275e+02, 1.1275e+02, 1.1167e+02, 1.1275e+02, 5.3000e+02],
[1.1382e+02, 1.1382e+02, 1.1277e+02, 1.1374e+02, 3.8800e+02],
[1.1483e+02, 1.1483e+02, 1.1384e+02, 1.1483e+02, 1.2110e+03],
[1.1597e+02, 1.1597e+02, 1.1489e+02, 1.1590e+02, 9.5400e+02],
[1.1712e+02, 1.1712e+02, 1.1683e+02, 1.1712e+02, 3.6030e+03],
[1.1817e+02, 1.1817e+02, 1.1714e+02, 1.1817e+02, 3.0800e+02],
[1.1935e+02, 1.1935e+02, 1.1817e+02, 1.1926e+02, 2.6600e+02],
[1.2047e+02, 1.2047e+02, 1.1935e+02, 1.2046e+02, 1.0000e+02],
[1.2167e+02, 1.2167e+02, 1.2047e+02, 1.2165e+02, 4.0000e+02],
[1.2289e+02, 1.2289e+02, 1.2167e+02, 1.2289e+02, 5.4300e+02],
[1.2411e+02, 1.2411e+02, 1.2289e+02, 1.2390e+02, 1.3720e+03],
[1.2534e+02, 1.2534e+02, 1.2365e+02, 1.2533e+02, 2.0868e+04],
[1.2453e+02, 1.2634e+02, 1.2447e+02, 1.2447e+02, 6.6330e+03],
[1.2577e+02, 1.2577e+02, 1.2354e+02, 1.2563e+02, 6.9330e+03],
[1.2703e+02, 1.2703e+02, 1.2667e+02, 1.2703e+02, 2.0300e+03],
[1.2829e+02, 1.2829e+02, 1.2703e+02, 1.2812e+02, 1.4810e+03],
[1.2957e+02, 1.2957e+02, 1.2921e+02, 1.2955e+02, 2.2990e+03],
[1.3086e+02, 1.3086e+02, 1.2881e+02, 1.3072e+02, 1.3613e+04],
[1.3217e+02, 1.3217e+02, 1.3180e+02, 1.3210e+02, 1.9600e+03],
[1.3349e+02, 1.3349e+02, 1.3217e+02, 1.3348e+02, 1.6240e+03],
[1.3483e+02, 1.3483e+02, 1.3349e+02, 1.3483e+02, 3.4090e+03],
[1.3619e+02, 1.3619e+02, 1.3483e+02, 1.3604e+02, 4.4970e+03],
[1.3756e+02, 1.3756e+02, 1.3619e+02, 1.3741e+02, 4.9350e+03],
[1.3685e+02, 1.3862e+02, 1.3656e+02, 1.3664e+02, 2.2940e+04],
[1.3596e+02, 1.3596e+02, 1.3569e+02, 1.3591e+02, 2.8380e+03],
[1.3527e+02, 1.3596e+02, 1.3498e+02, 1.3528e+02, 2.7150e+03],
[1.3663e+02, 1.3663e+02, 1.3419e+02, 1.3663e+02, 1.3394e+04],
[1.3596e+02, 1.3768e+02, 1.3590e+02, 1.3601e+02, 1.4540e+04],
[1.3528e+02, 1.3528e+02, 1.3493e+02, 1.3526e+02, 5.8740e+03],
[1.3464e+02, 1.3657e+02, 1.3431e+02, 1.3656e+02, 8.4420e+03],
[1.3399e+02, 1.3560e+02, 1.3372e+02, 1.3440e+02, 6.0230e+03],
[1.3338e+02, 1.3399e+02, 1.3313e+02, 1.3322e+02, 5.0730e+03],
[1.3280e+02, 1.3338e+02, 1.3242e+02, 1.3280e+02, 3.1440e+03],
[1.3314e+02, 1.3410e+02, 1.3187e+02, 1.3390e+02, 1.1938e+04],
[1.3447e+02, 1.3447e+02, 1.3314e+02, 1.3439e+02, 2.7960e+03],
[1.3581e+02, 1.3581e+02, 1.3539e+02, 1.3581e+02, 6.6000e+02],
[1.3717e+02, 1.3717e+02, 1.3581e+02, 1.3717e+02, 8.4700e+02],
[1.3670e+02, 1.3854e+02, 1.3666e+02, 1.3766e+02, 1.0823e+04],
[1.3807e+02, 1.3807e+02, 1.3670e+02, 1.3807e+02, 2.7640e+03],
[1.3939e+02, 1.3939e+02, 1.3807e+02, 1.3935e+02, 7.2410e+03]])
# 继续深入，只是，为啥要 reshape 呢？
# reshape 成一行内存储，后面是要干啥子？来，拭目以待。
np.array(data[i:i+dayfeature][[u'收盘价',u'最高价',u'最低价',u'开盘价',u'成交量']]).reshape((1,featurenum))
array([[1.0439e+02, 1.0439e+02, 9.9980e+01, 1.0430e+02, 1.9700e+02,
1.0913e+02, 1.0913e+02, 1.0373e+02, 1.0907e+02, 2.8000e+01,
1.1455e+02, 1.1455e+02, 1.0913e+02, 1.1357e+02, 3.2000e+01,
1.2025e+02, 1.2025e+02, 1.1455e+02, 1.2009e+02, 1.5000e+01,
1.2527e+02, 1.2527e+02, 1.2025e+02, 1.2527e+02, 1.0000e+02,
1.2528e+02, 1.2528e+02, 1.2527e+02, 1.2527e+02, 6.6000e+01,
1.2645e+02, 1.2645e+02, 1.2528e+02, 1.2639e+02, 1.0800e+02,
1.2761e+02, 1.2761e+02, 1.2648e+02, 1.2656e+02, 7.8000e+01,
1.2884e+02, 1.2884e+02, 1.2761e+02, 1.2761e+02, 9.1000e+01,
1.3014e+02, 1.3014e+02, 1.2884e+02, 1.2884e+02, 1.4100e+02,
1.3144e+02, 1.3144e+02, 1.3014e+02, 1.3127e+02, 4.2000e+02,
1.3206e+02, 1.3206e+02, 1.3145e+02, 1.3199e+02, 2.1700e+02,
1.3268e+02, 1.3268e+02, 1.3206e+02, 1.3262e+02, 2.9260e+03,
1.3334e+02, 1.3334e+02, 1.3268e+02, 1.3330e+02, 5.6030e+03,
1.3397e+02, 1.3397e+02, 1.3334e+02, 1.3393e+02, 9.9900e+03,
1.3460e+02, 1.3461e+02, 1.3451e+02, 1.3461e+02, 1.3327e+04,
1.3467e+02, 1.3519e+02, 1.3411e+02, 1.3411e+02, 1.2530e+04,
1.3474e+02, 1.3474e+02, 1.3419e+02, 1.3421e+02, 1.4460e+03,
1.3424e+02, 1.3474e+02, 1.3414e+02, 1.3419e+02, 5.0900e+02,
1.3425e+02, 1.3425e+02, 1.3365e+02, 1.3367e+02, 6.5800e+02,
1.3424e+02, 1.3425e+02, 1.3367e+02, 1.3370e+02, 3.0040e+03,
1.3424e+02, 1.3424e+02, 1.3366e+02, 1.3370e+02, 2.0510e+03,
1.3372e+02, 1.3424e+02, 1.3366e+02, 1.3372e+02, 3.5400e+02,
1.3317e+02, 1.3372e+02, 1.3314e+02, 1.3317e+02, 1.0950e+03,
1.3261e+02, 1.3317e+02, 1.3257e+02, 1.3261e+02, 1.8570e+03,
1.3205e+02, 1.3207e+02, 1.3203e+02, 1.3205e+02, 3.4470e+03,
1.3146e+02, 1.3155e+02, 1.3146e+02, 1.3146e+02, 5.1070e+03,
1.3095e+02, 1.3097e+02, 1.3095e+02, 1.3095e+02, 1.3870e+03,
1.3044e+02, 1.3095e+02, 1.3041e+02, 1.3044e+02, 5.2700e+02,
1.2997e+02, 1.3046e+02, 1.2993e+02, 1.2993e+02, 5.1000e+02,
1.2951e+02, 1.2997e+02, 1.2945e+02, 1.2950e+02, 3.4500e+02,
1.2905e+02, 1.2958e+02, 1.2905e+02, 1.2905e+02, 5.5300e+02,
1.2858e+02, 1.2858e+02, 1.2853e+02, 1.2856e+02, 8.5620e+03,
1.2914e+02, 1.2915e+02, 1.2806e+02, 1.2913e+02, 6.6410e+03,
1.2979e+02, 1.2979e+02, 1.2914e+02, 1.2974e+02, 2.1330e+03,
1.3038e+02, 1.3039e+02, 1.2979e+02, 1.3036e+02, 1.2340e+03,
1.3097e+02, 1.3097e+02, 1.3039e+02, 1.3092e+02, 9.3170e+03,
1.3135e+02, 1.3156e+02, 1.3097e+02, 1.3154e+02, 3.7740e+03,
1.3192e+02, 1.3193e+02, 1.3135e+02, 1.3193e+02, 1.1520e+03,
1.3253e+02, 1.3253e+02, 1.3230e+02, 1.3253e+02, 3.6240e+03,
1.3313e+02, 1.3314e+02, 1.3308e+02, 1.3312e+02, 1.7480e+03,
1.3367e+02, 1.3367e+02, 1.3313e+02, 1.3363e+02, 8.7600e+02,
1.3428e+02, 1.3428e+02, 1.3367e+02, 1.3424e+02, 3.2200e+03,
1.3487e+02, 1.3487e+02, 1.3428e+02, 1.3485e+02, 3.4270e+03,
1.3440e+02, 1.3487e+02, 1.3433e+02, 1.3437e+02, 8.1000e+02,
1.3393e+02, 1.3444e+02, 1.3390e+02, 1.3390e+02, 1.0940e+03,
1.3347e+02, 1.3398e+02, 1.3344e+02, 1.3344e+02, 2.1400e+02,
1.3301e+02, 1.3352e+02, 1.3298e+02, 1.3299e+02, 2.2580e+03,
1.3253e+02, 1.3253e+02, 1.3247e+02, 1.3253e+02, 7.1240e+03,
1.3199e+02, 1.3209e+02, 1.3198e+02, 1.3209e+02, 2.5180e+03,
1.3146e+02, 1.3199e+02, 1.3141e+02, 1.3142e+02, 9.8200e+02,
1.3094e+02, 1.3095e+02, 1.3089e+02, 1.3089e+02, 7.1900e+02,
1.3041e+02, 1.3094e+02, 1.3041e+02, 1.3041e+02, 5.6800e+02,
1.2989e+02, 1.3041e+02, 1.2984e+02, 1.2984e+02, 9.3300e+02,
1.2931e+02, 1.2989e+02, 1.2930e+02, 1.2930e+02, 5.1400e+02,
1.2877e+02, 1.2931e+02, 1.2873e+02, 1.2873e+02, 9.5700e+02,
1.2615e+02, 1.2877e+02, 1.2613e+02, 1.2613e+02, 4.9800e+02,
1.2563e+02, 1.2615e+02, 1.2561e+02, 1.2561e+02, 3.2600e+02,
1.2517e+02, 1.2562e+02, 1.2508e+02, 1.2508e+02, 7.9000e+02,
1.2471e+02, 1.2517e+02, 1.2469e+02, 1.2470e+02, 1.2500e+03,
1.2422e+02, 1.2471e+02, 1.2422e+02, 1.2423e+02, 8.9200e+02,
1.2366e+02, 1.2421e+02, 1.2363e+02, 1.2366e+02, 6.7800e+02,
1.2312e+02, 1.2366e+02, 1.2312e+02, 1.2314e+02, 1.5100e+02,
1.2262e+02, 1.2312e+02, 1.2262e+02, 1.2262e+02, 2.0700e+02,
1.2212e+02, 1.2262e+02, 1.2210e+02, 1.2212e+02, 4.4300e+02,
1.2162e+02, 1.2214e+02, 1.2162e+02, 1.2162e+02, 1.1640e+03,
1.2112e+02, 1.2164e+02, 1.2112e+02, 1.2112e+02, 6.6800e+02,
1.2061e+02, 1.2115e+02, 1.2061e+02, 1.2065e+02, 9.6100e+02,
1.2019e+02, 1.2061e+02, 1.2011e+02, 1.2016e+02, 2.1850e+03,
1.2073e+02, 1.2073e+02, 1.1968e+02, 1.2069e+02, 1.1999e+04,
1.2121e+02, 1.2129e+02, 1.2073e+02, 1.2121e+02, 8.1600e+02,
1.2171e+02, 1.2171e+02, 1.2121e+02, 1.2171e+02, 2.0630e+03,
1.2172e+02, 1.2220e+02, 1.2124e+02, 1.2220e+02, 1.0517e+04,
1.2154e+02, 1.2173e+02, 1.2103e+02, 1.2107e+02, 1.1589e+04,
1.2109e+02, 1.2111e+02, 1.2105e+02, 1.2111e+02, 1.8620e+03,
1.2062e+02, 1.2110e+02, 1.2060e+02, 1.2060e+02, 3.2000e+02,
1.2016e+02, 1.2062e+02, 1.2009e+02, 1.2015e+02, 4.6600e+02,
1.1964e+02, 1.2016e+02, 1.1962e+02, 1.1964e+02, 2.3900e+02,
1.1921e+02, 1.1965e+02, 1.1915e+02, 1.1920e+02, 3.9900e+02,
1.1877e+02, 1.1922e+02, 1.1872e+02, 1.1873e+02, 3.0100e+02,
1.1834e+02, 1.1877e+02, 1.1834e+02, 1.1836e+02, 1.0500e+02,
1.1790e+02, 1.1834e+02, 1.1789e+02, 1.1792e+02, 2.0100e+02,
1.1745e+02, 1.1793e+02, 1.1745e+02, 1.1745e+02, 1.7800e+02,
1.1708e+02, 1.1750e+02, 1.1705e+02, 1.1708e+02, 6.4600e+02,
1.1663e+02, 1.1708e+02, 1.1658e+02, 1.1663e+02, 4.3000e+02,
1.1619e+02, 1.1663e+02, 1.1615e+02, 1.1621e+02, 2.8200e+02,
1.1579e+02, 1.1619e+02, 1.1570e+02, 1.1570e+02, 1.4800e+02,
1.1536e+02, 1.1579e+02, 1.1533e+02, 1.1538e+02, 1.4950e+03,
1.1556e+02, 1.1631e+02, 1.1452e+02, 1.1631e+02, 1.3354e+04,
1.1475e+02, 1.1629e+02, 1.1475e+02, 1.1486e+02, 8.4110e+03,
1.1394e+02, 1.1487e+02, 1.1389e+02, 1.1389e+02, 4.8500e+03,
1.1316e+02, 1.1320e+02, 1.1303e+02, 1.1304e+02, 1.4650e+03,
1.1241e+02, 1.1317e+02, 1.1230e+02, 1.1246e+02, 4.2300e+03,
1.1161e+02, 1.1241e+02, 1.1157e+02, 1.1165e+02, 2.7640e+03,
1.1082e+02, 1.1090e+02, 1.1080e+02, 1.1080e+02, 1.8750e+03,
1.1003e+02, 1.1082e+02, 1.1000e+02, 1.1016e+02, 4.2300e+02,
1.0929e+02, 1.1012e+02, 1.0929e+02, 1.0936e+02, 2.2660e+03,
1.0853e+02, 1.0940e+02, 1.0853e+02, 1.0861e+02, 2.4900e+02,
1.0784e+02, 1.0788e+02, 1.0774e+02, 1.0788e+02, 2.3500e+02,
1.0719e+02, 1.0784e+02, 1.0706e+02, 1.0721e+02, 2.6700e+02,
1.0657e+02, 1.0660e+02, 1.0642e+02, 1.0656e+02, 3.9000e+02,
1.0577e+02, 1.0586e+02, 1.0569e+02, 1.0583e+02, 3.1080e+03,
1.0675e+02, 1.0675e+02, 1.0496e+02, 1.0512e+02, 2.0192e+04,
1.0782e+02, 1.0782e+02, 1.0671e+02, 1.0776e+02, 8.6300e+02,
1.0881e+02, 1.0889e+02, 1.0862e+02, 1.0889e+02, 7.7100e+02,
1.0825e+02, 1.0982e+02, 1.0811e+02, 1.0971e+02, 1.8677e+04,
1.0908e+02, 1.0916e+02, 1.0735e+02, 1.0752e+02, 5.6090e+03,
1.1008e+02, 1.1008e+02, 1.0982e+02, 1.1007e+02, 1.0240e+03,
1.1066e+02, 1.1107e+02, 1.0947e+02, 1.1105e+02, 8.6190e+03,
1.1168e+02, 1.1168e+02, 1.1136e+02, 1.1161e+02, 1.6300e+03,
1.1275e+02, 1.1275e+02, 1.1167e+02, 1.1275e+02, 5.3000e+02,
1.1382e+02, 1.1382e+02, 1.1277e+02, 1.1374e+02, 3.8800e+02,
1.1483e+02, 1.1483e+02, 1.1384e+02, 1.1483e+02, 1.2110e+03,
1.1597e+02, 1.1597e+02, 1.1489e+02, 1.1590e+02, 9.5400e+02,
1.1712e+02, 1.1712e+02, 1.1683e+02, 1.1712e+02, 3.6030e+03,
1.1817e+02, 1.1817e+02, 1.1714e+02, 1.1817e+02, 3.0800e+02,
1.1935e+02, 1.1935e+02, 1.1817e+02, 1.1926e+02, 2.6600e+02,
1.2047e+02, 1.2047e+02, 1.1935e+02, 1.2046e+02, 1.0000e+02,
1.2167e+02, 1.2167e+02, 1.2047e+02, 1.2165e+02, 4.0000e+02,
1.2289e+02, 1.2289e+02, 1.2167e+02, 1.2289e+02, 5.4300e+02,
1.2411e+02, 1.2411e+02, 1.2289e+02, 1.2390e+02, 1.3720e+03,
1.2534e+02, 1.2534e+02, 1.2365e+02, 1.2533e+02, 2.0868e+04,
1.2453e+02, 1.2634e+02, 1.2447e+02, 1.2447e+02, 6.6330e+03,
1.2577e+02, 1.2577e+02, 1.2354e+02, 1.2563e+02, 6.9330e+03,
1.2703e+02, 1.2703e+02, 1.2667e+02, 1.2703e+02, 2.0300e+03,
1.2829e+02, 1.2829e+02, 1.2703e+02, 1.2812e+02, 1.4810e+03,
1.2957e+02, 1.2957e+02, 1.2921e+02, 1.2955e+02, 2.2990e+03,
1.3086e+02, 1.3086e+02, 1.2881e+02, 1.3072e+02, 1.3613e+04,
1.3217e+02, 1.3217e+02, 1.3180e+02, 1.3210e+02, 1.9600e+03,
1.3349e+02, 1.3349e+02, 1.3217e+02, 1.3348e+02, 1.6240e+03,
1.3483e+02, 1.3483e+02, 1.3349e+02, 1.3483e+02, 3.4090e+03,
1.3619e+02, 1.3619e+02, 1.3483e+02, 1.3604e+02, 4.4970e+03,
1.3756e+02, 1.3756e+02, 1.3619e+02, 1.3741e+02, 4.9350e+03,
1.3685e+02, 1.3862e+02, 1.3656e+02, 1.3664e+02, 2.2940e+04,
1.3596e+02, 1.3596e+02, 1.3569e+02, 1.3591e+02, 2.8380e+03,
1.3527e+02, 1.3596e+02, 1.3498e+02, 1.3528e+02, 2.7150e+03,
1.3663e+02, 1.3663e+02, 1.3419e+02, 1.3663e+02, 1.3394e+04,
1.3596e+02, 1.3768e+02, 1.3590e+02, 1.3601e+02, 1.4540e+04,
1.3528e+02, 1.3528e+02, 1.3493e+02, 1.3526e+02, 5.8740e+03,
1.3464e+02, 1.3657e+02, 1.3431e+02, 1.3656e+02, 8.4420e+03,
1.3399e+02, 1.3560e+02, 1.3372e+02, 1.3440e+02, 6.0230e+03,
1.3338e+02, 1.3399e+02, 1.3313e+02, 1.3322e+02, 5.0730e+03,
1.3280e+02, 1.3338e+02, 1.3242e+02, 1.3280e+02, 3.1440e+03,
1.3314e+02, 1.3410e+02, 1.3187e+02, 1.3390e+02, 1.1938e+04,
1.3447e+02, 1.3447e+02, 1.3314e+02, 1.3439e+02, 2.7960e+03,
1.3581e+02, 1.3581e+02, 1.3539e+02, 1.3581e+02, 6.6000e+02,
1.3717e+02, 1.3717e+02, 1.3581e+02, 1.3717e+02, 8.4700e+02,
1.3670e+02, 1.3854e+02, 1.3666e+02, 1.3766e+02, 1.0823e+04,
1.3807e+02, 1.3807e+02, 1.3670e+02, 1.3807e+02, 2.7640e+03,
1.3939e+02, 1.3939e+02, 1.3807e+02, 1.3935e+02, 7.2410e+03]])
# 把上面reshape成一行的750列数据（150天*5个特征），赋值进一开始建的数据组x的第一行。
x[i,0:featurenum] = np.array(data[i:i+dayfeature][[u'收盘价',u'最高价',u'最低价',u'开盘价',u'成交量']]).reshape((1,featurenum))
x
array([[ 0. , 0. , 0. , ..., 0. , 0. , 0. ],
[ 104.39, 104.39, 99.98, ..., 139.35, 7241. , 0. ],
[ 0. , 0. , 0. , ..., 0. , 0. , 0. ],
...,
[ 0. , 0. , 0. , ..., 0. , 0. , 0. ],
[ 0. , 0. , 0. , ..., 0. , 0. , 0. ],
[ 0. , 0. , 0. , ..., 0. , 0. , 0. ]])
# 将150天周期的最新一日开盘价赋值到（150天*5个特征）的最后一列
x[i,featurenum]=data.ix[i+dayfeature][u'开盘价']
x
array([[ 0. , 0. , 0. , ..., 0. , 0. ,
0. ],
[ 104.39 , 104.39 , 99.98 , ..., 139.35 , 7241. ,
3085.7895],
[ 0. , 0. , 0. , ..., 0. , 0. ,
0. ],
...,
[ 0. , 0. , 0. , ..., 0. , 0. ,
0. ],
[ 0. , 0. , 0. , ..., 0. , 0. ,
0. ],
[ 0. , 0. , 0. , ..., 0. , 0. ,
0. ]])
y
array([0., 0., 0., ..., 0., 0., 0.])
for i in range(0,data.shape[0]-dayfeature):
if data.ix[i+dayfeature][u'收盘价']>=data.ix[i+dayfeature][u'开盘价']:
y[i]=1
else:
y[i]=0
y
array([1., 0., 1., ..., 1., 1., 1.])
print(np.sum(y==1))
print(data.shape[0]-dayfeature)
3832
7115
data.sort_index(0,ascending=True,inplace=True)
dayfeature=150
featurenum=5*dayfeature
x=np.zeros((data.shape[0]-dayfeature,featurenum+1))
y=np.zeros((data.shape[0]-dayfeature))
for i in range(0,data.shape[0]-dayfeature):
x[i,0:featurenum]=np.array(data[i:i+dayfeature] \
[[u'收盘价',u'最高价',u'最低价',u'开盘价',u'成交量']]).reshape((1,featurenum))
x[i,featurenum]=data.ix[i+dayfeature][u'开盘价']
for i in range(0,data.shape[0]-dayfeature):
if data.ix[i+dayfeature][u'收盘价']>=data.ix[i+dayfeature][u'开盘价']:
y[i]=1
else:
y[i]=0
clf=svm.SVC(kernel='rbf')
result = []
for i in range(5):
# x_train, x_test, y_train, y_test = \
# cross_validation.train_test_split(x, y, test_size = 0.2)
x_train, x_test, y_train, y_test = \
train_test_split(x, y, test_size = 0.2)
clf.fit(x_train, y_train)
result.append(np.mean(y_test == clf.predict(x_test)))
print("svm classifier accuacy:")
print(result)
svm classifier accuacy:
[0.5360896986685354, 0.5494043447792571, 0.5367904695164681, 0.5508058864751226, 0.5409950946040645]
结束。