［Python数据分析］新股破板买入，赚钱几率如何？
发布时间：2016-11-09 11:53:14 所属栏目：教程 来源：站长网
导读：副标题#e# 这是本人一直比较好奇的问题，网上没搜到，最近在看python数据分析，正好自己动手做一下试试。作者对于python是零基础，需要从头学起。 在写本文时，作者也没有完成这个小分析目标，边学边做吧。 ＝＝＝＝＝＝＝＝＝＝＝＝＝＝＝＝＝＝＝＝＝＝＝
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运行index和columns，果然date是index： 
>>> df.columns
Index(['open', 'high', 'close', 'low', 'volume', 'price_change', 'p_change',
'ma5', 'ma10', 'ma20', 'v_ma5', 'v_ma10', 'v_ma20', 'turnover'],
dtype='object')
>>> df.index
Index(['2016-10-28', '2016-10-27', '2016-10-26', '2016-10-25', '2016-10-24',
'2016-10-21', '2016-10-20', '2016-10-19', '2016-10-18', '2016-10-17',
'2016-10-14', '2016-10-13', '2016-10-12', '2016-10-11', '2016-10-10',
'2016-09-30', '2016-09-29', '2016-09-28', '2016-09-27', '2016-09-26',
'2016-09-23', '2016-09-22', '2016-09-21', '2016-09-20', '2016-09-19',
'2016-09-14', '2016-09-13', '2016-09-12', '2016-09-09', '2016-09-08',
'2016-09-07', '2016-09-06', '2016-09-05', '2016-09-02', '2016-09-01',
'2016-08-31', '2016-08-30', '2016-08-29', '2016-08-26', '2016-08-25',
'2016-08-24', '2016-08-23', '2016-08-22', '2016-08-19', '2016-08-18',
'2016-08-17', '2016-08-16', '2016-08-15', '2016-08-12', '2016-08-11',
'2016-08-10', '2016-08-09', '2016-08-08', '2016-08-05', '2016-08-04',
'2016-08-03', '2016-08-02', '2016-08-01', '2016-07-29', '2016-07-28',
'2016-07-27', '2016-07-26', '2016-07-25', '2016-07-22', '2016-07-21',
'2016-07-20', '2016-07-19', '2016-07-18', '2016-07-15', '2016-07-14',
'2016-07-13', '2016-07-12', '2016-07-11', '2016-07-08', '2016-07-07',
'2016-07-06', '2016-07-05', '2016-07-04', '2016-07-01', '2016-06-30',
'2016-06-29', '2016-06-28', '2016-06-27', '2016-06-24', '2016-06-23',
'2016-06-22', '2016-06-21', '2016-06-20', '2016-06-17', '2016-06-16',
'2016-06-15', '2016-06-14', '2016-06-13', '2016-06-08', '2016-06-07',
'2016-06-06', '2016-06-03'],
dtype='object', name='date')
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所以选取列的语句应该是： df=df[['open','close','p_change']] 结果如下：
>>> df=df[['open','close','p_change']]
>>> df
open close p_change
date
2016-10-28 82.50 81.53 -0.69
2016-10-27 82.30 82.19 0.29
2016-10-26 82.04 81.99 -0.13
2016-10-25 82.68 82.09 -1.07
2016-10-24 78.98 83.00 4.97
2016-10-21 79.19 79.08 -0.21
2016-10-20 78.50 79.25 0.95
2016-10-19 80.60 78.49 -1.59
2016-10-18 77.72 79.77 2.26
2016-10-17 78.60 78.01 -1.35
2016-10-14 79.42 79.00 -0.18
2016-10-13 78.85 79.15 0.23
2016-10-12 77.17 78.95 1.15
2016-10-11 77.95 78.07 0.06
2016-10-10 72.93 78.03 7.01
2016-09-30 73.08 72.90 -0.20
2016-09-29 73.18 73.46 0.07
2016-09-28 73.25 73.37 0.14
2016-09-27 72.02 73.30 1.08
2016-09-26 76.24 72.51 -4.94
2016-09-23 78.18 76.31 -2.04
2016-09-22 79.10 77.90 -0.99
2016-09-21 79.10 78.67 -1.21
2016-09-20 81.60 79.64 -1.33
2016-09-19 80.56 80.71 0.15
2016-09-14 81.80 80.57 -4.13
2016-09-13 86.20 83.99 -2.54
2016-09-12 82.50 86.19 1.83
2016-09-09 83.78 84.66 1.14
2016-09-08 82.50 83.71 1.09
... ... ... ...
2016-07-18 100.00 97.17 -3.68
2016-07-15 100.50 100.90 1.18
2016-07-14 98.00 99.73 0.88
2016-07-13 99.00 98.87 -1.64
2016-07-12 96.96 100.51 1.14
2016-07-11 110.00 99.38 -10.00
2016-07-08 111.51 110.47 -2.86
2016-07-07 111.12 113.71 0.85
2016-07-06 114.00 112.75 -2.53
2016-07-05 110.11 115.63 4.76
2016-07-04 111.89 110.46 -1.21
2016-07-01 111.00 111.82 -3.67
2016-06-30 111.00 116.08 10.00
2016-06-29 105.53 105.53 10.00
2016-06-28 95.94 95.94 10.00
2016-06-27 87.22 87.22 10.00
2016-06-24 79.29 79.29 10.00
2016-06-23 72.08 72.08 9.99
2016-06-22 65.53 65.53 10.01
2016-06-21 59.57 59.57 10.01
2016-06-20 54.15 54.15 9.99
2016-06-17 49.23 49.23 10.01
2016-06-16 44.75 44.75 10.01
2016-06-15 40.68 40.68 10.01
2016-06-14 36.98 36.98 9.99
2016-06-13 33.62 33.62 10.01
2016-06-08 30.56 30.56 10.01
2016-06-07 27.78 27.78 10.02
2016-06-06 25.25 25.25 10.02
2016-06-03 22.95 22.95 43.98
[97 rows x 3 columns]
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现在我们已经取得了过去半年新上市的股票和他们上市后的数据。 －－－－－ 第三步：如何筛选出破板后三十天的数据，并汇总。 （编辑：应用网_阳江站长网） 【声明】本站内容均来自网络，其相关言论仅代表作者个人观点，不代表本站立场。若无意侵犯到您的权利，请及时与联系站长删除相关内容! |
