［Python数据分析］新股破板买入，赚钱几率如何？

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')

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]

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_丽江站长网）

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