［Python数据分析］新股破板买入，赚钱几率如何？

接下来，假设破板买入后最长持股10天，这样我们只取妹纸股票之后10天的数据，这样数据比较整齐，也便于后续处理。如果数据不满10天，则跳过。

df=df.tail(11)['close']

>>> import tushare as ts 
>>> df=ts.get_hist_data('603737')
>>> 
>>> df=df[['open','close','p_change']]
>>> start_date=df[df['p_change']<9.8].tail(1).index[0]
>>> df=df[df.index>=start_date]
>>> df=df.tail(11)['close']
>>> df
date
2016-07-15    100.90
2016-07-14     99.73
2016-07-13     98.87
2016-07-12    100.51
2016-07-11     99.38
2016-07-08    110.47
2016-07-07    113.71
2016-07-06    112.75
2016-07-05    115.63
2016-07-04    110.46
2016-07-01    111.82
Name: close, dtype: float64

通过上一步，我们取到包含破板当日以及之后十天的收盘价，将数据转换为numpy.array数据。

close_array=df.values

>>> close_array=df.values
>>> close_array
array([ 100.9 ,   99.73,   98.87,  100.51,   99.38,  110.47,  113.71,
        112.75,  115.63,  110.46,  111.82])

进一步进行处理，如果破板后某个交易日收盘价格高于破板当天收盘价，则将array中的值置为1，否则为0。

import tushare as ts
import numpy as np

df=ts.get_hist_data('603737')

df=df[['open','close','p_change']]
start_date=df[df['p_change']<9.8].tail(1).index[0]
df=df[df.index>=start_date]
df=df.tail(11)['close']

close_array=df.values
for i in range(1,11):
  if close_array[i]>close_array[0]:
    close_array[i]=1
  else:
    close_array[i]=0

close_array[0]=0

print (close_array)

输出结果：

localhost:~ shengtianhe$ python find.py
[ 0.  0.  0.  0.  0.  1.  1.  1.  1.  1.  1.]

一只股票的数据组织好了，接下来把所有新股的数据取出来，这里需要用到合并array的方法numpy.concatenate，另外代码里还有一些小地方的处理，搜了一圈才知道怎么做，比如如何返回值判断非空:

import tushare as ts
import numpy as np
import pandas as pd

df=ts.new_stocks()
df=df[df['issue_date']>'2016-06-01']
df=df[['code','name','issue_date']]

df=df[['code']]
stock_code=df.values

# print(stock_code)

i=0
df_matrix = None

for stock in stock_code:
  detail=ts.get_hist_data(stock_code[i][0])
  if detail is None:
    i=i+1
    continue  

  detail=detail[['open','close','p_change']]
  hasbreak=detail[detail['p_change']<9.8]

  if hasbreak.size==0:
    i=i+1
    continue

  start_date=hasbreak.tail(1).index[0]
  hasbreak=hasbreak[hasbreak.index>=start_date]
  hasbreak=hasbreak.tail(11)['close']

  if hasbreak.size <11:
    i=i+1
    continue

  close_array=hasbreak.values
  for day in range(1,hasbreak.size):
    if close_array[day]>close_array[0]:
      close_array[day]=1
    else:
      close_array[day]=0
  close_array[0]=0

  df_matrix_thisRound=pd.DataFrame(close_array)
  df_matrix_thisRound=df_matrix_thisRound.T

  if df_matrix is None:
    df_matrix=df_matrix_thisRound
  else:
    df_matrix=np.concatenate((df_matrix,df_matrix_thisRound.values))

  i=i+1

print(df_matrix)  

运行结果如下：

localhost:~ shengtianhe$ python proData.py 
[Getting data:]########[[ 0.  1.  1.  0.  1.  1.  1.  1.  1.  1.  1.]
 [ 0.  1.  0.  1.  0.  0.  1.  1.  1.  0.  0.]
 [ 0.  1.  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.  1.  1.  1.  1.  1.  1.  1.  0.  0.  0.]
 [ 0.  1.  1.  1.  1.  0.  1.  0.  1.  1.  1.]
 [ 0.  0.  0.  0.  0.  1.  1.  1.  1.  1.  1.]
 [ 0.  1.  1.  1.  1.  1.  0.  1.  1.  1.  1.]
 [ 0.  1.  1.  1.  1.  1.  0.  0.  0.  0.  0.]
 [ 0.  0.  0.  0.  0.  1.  1.  1.  1.  1.  1.]
 [ 0.  1.  1.  1.  1.  0.  0.  1.  1.  1.  1.]
 [ 0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]
 [ 0.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.]
 [ 0.  1.  0.  0.  0.  0.  0.  0.  0.  1.  1.]
 [ 0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  1.]
 [ 0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]
 [ 0.  0.  0.  0.  1.  1.  1.  1.  1.  1.  1.]
 [ 0.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.]
 [ 0.  0.  1.  1.  1.  1.  1.  1.  1.  0.  0.]
 [ 0.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.]
 [ 0.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.]
 [ 0.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.]
 [ 0.  0.  0.  0.  0.  0.  1.  1.  1.  1.  1.]
 [ 0.  1.  0.  0.  0.  0.  0.  1.  1.  1.  1.]
 [ 0.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.]
 [ 0.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.]
 [ 0.  1.  0.  1.  1.  1.  1.  1.  1.  1.  1.]
 [ 0.  0.  1.  1.  1.  1.  1.  1.  1.  1.  1.]
 [ 0.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.]
 [ 0.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.]
 [ 0.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.]
 [ 0.  0.  1.  1.  1.  1.  1.  1.  1.  1.  1.]
 [ 0.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.]
 [ 0.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.]
 [ 0.  1.  1.  1.  1.  1.  1.  1.  1.  1.  0.]
 [ 0.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.]
 [ 0.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.]
 [ 0.  0.  0.  1.  0.  0.  1.  1.  1.  1.  0.]
 [ 0.  1.  1.  1.  1.  1.  1.  0.  0.  0.  0.]
 [ 0.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.]
 [ 0.  0.  1.  1.  1.  1.  1.  1.  1.  1.  1.]
 [ 0.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.]
 [ 0.  0.  0.  1.  1.  1.  1.  1.  1.  1.  1.]
 [ 0.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.]
 [ 0.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.]
 [ 0.  0.  1.  1.  1.  1.  1.  1.  0.  1.  1.]
 [ 0.  1.  1.  1.  1.  0.  0.  0.  0.  0.  1.]
 [ 0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]
 [ 0.  0.  1.  1.  1.  1.  1.  0.  0.  0.  0.]
 [ 0.  1.  1.  0.  0.  1.  1.  0.  0.  0.  0.]
 [ 0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]
 [ 0.  0.  1.  1.  1.  1.  1.  1.  1.  1.  1.]
 [ 0.  1.  1.  0.  0.  1.  1.  1.  1.  0.  1.]
 [ 0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.]
 [ 0.  0.  1.  1.  1.  1.  1.  1.  1.  1.  1.]
 [ 0.  1.  1.  0.  0.  1.  1.  1.  1.  1.  1.]
 [ 0.  0.  0.  0.  1.  1.  1.  1.  1.  1.  1.]
 [ 0.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.]
 [ 0.  1.  1.  1.  0.  0.  0.  0.  0.  0.  0.]
 [ 0.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.]
 [ 0.  1.  0.  0.  0.  1.  1.  1.  1.  1.  0.]
 [ 0.  1.  0.  1.  1.  1.  1.  1.  1.  1.  1.]
 [ 0.  0.  1.  1.  1.  1.  1.  1.  1.  1.  1.]
 [ 0.  0.  1.  1.  1.  1.  1.  1.  1.  1.  1.]
 [ 0.  1.  1.  0.  1.  1.  1.  1.  1.  1.  1.]
 [ 0.  0.  0.  0.  0.  1.  1.  1.  1.  1.  1.]]

－－－－－

第四步：如何计算整体上每天盈利的概率。

这一步就相对简单了，直接求每一列的均值即可，数值上就代表当天盈利的概率了。

http://pandas.pydata.org/pandas-docs/version/0.17.0/generated/pandas.DataFrame.mean.html

加入代码如下：

mean=df_matrix.mean(0)
print(mean)

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