这个在券商QMT运行后，打印出来的怎么会有2种不同的形式

import statsmodels.formula.api as smf import statistics import numpy as np import pandas as pd

class G(): pass

g = G()

def init(ContextInfo): print('测试') g.gpc=['002601.SZ'] g.gpc.append('000300.SH')

def handlebar(ContextInfo):

global mean_turnover
dtime = ContextInfo.get_market_data_ex(['close'], stock_code =\
g.gpc,period = '1d',start_time='20230325',end_time='20230928', count=-1,\
dividend_type = 'follow',fill_data=True,subscribe=True)
if len(dtime['000300.SH'])>65:
    for i in dtime.keys():
        return_signal(dtime, 'close',i)
return

def return_signal(data:dict,field:str,code:str):

_index=data[list(data.keys())[0]].index.tolist()
_columns=list(data.keys())
df=pd.DataFrame(index=_index,columns=_columns)
for i in _columns:
   df[i]=data[i][field]
df1=data[code][-61:-1]
df=df.rename(columns={'000300.SH':'SH'})

df=df.rename(columns={code:'SZ'})
test_df = df.iloc[-61:-1]
test_df = (test_df/test_df.iloc[0]).apply(np.log) 
test_df['idd'] = np.array(range(60))/252.0
test_df=pd.DataFrame(test_df)
print(test_df.columns)
return

上面的代码运行后，如下图，不是应该只显示['SZ''SH''IDD']怎么还会打印出['002601.SZ']这一项

统一下格式发吧，现在看着眼疼，看代码是不应该有002601，那有没有可能不是这里打印的

Willows 发表于 2024-1-17 11:18
统一下格式发吧，现在看着眼疼，看代码是不应该有002601，那有没有可能不是这里打印的 ...

这个是代码，辛苦您看一下

1. # -*- coding: gbk -*-
   
2. #import statsmodels.api as sm
   
3. import statsmodels.formula.api as smf
   
4. import statistics
   
5. import numpy as np
   
6. import pandas as pd
   
7. import matplotlib.pyplot as plt
   
8. import sys,os
   
9. from pylab import mpl
   
10. 
    
11. class G():
    
12.         pass
    
13. 
    
14. g = G()
    
15. 
    
16. g.r = 0.04
    
17. g.dtt = 1/255.0
    
18. 
    
19. g.code_dict = {}
    
20. g.date_dict = {}
    
21. 
    
22. g.SHORT_POINT = 1.25
    
23. g.COVER_POINT = 0.75
    
24. g.SELL_POINT = -0.5
    
25. g.BUY_POINT = -1.25
    
26. 
    
27. g.SUPER_SHORT = u"超卖区域"
    
28. g.SHORTING = u"卖方力量"
    
29. g.XUWU = u"中间区域"
    
30. g.BUYING = u"买方力量"
    
31. g.SUPER_BUY = u"超买区域"
    
32. 
    
33. g.SHORT = "SHORT"
    
34. g.COVER = "COVER"
    
35. g.SELL = "SELL"
    
36. g.BUY = "BUY"
    
37. 
    
38. g.DLONG = "DLONG"
    
39. g.DSHORT = "DSHORT"
    
40. g.DNOPE = "DNOPE"
    
41. 
    
42. def init(ContextInfo):
    
43.         print('测试')
    
44.         g.gpc=['002601.SZ']
    
45.         g.gpc.append('000300.SH')
    
46.         g.weight=[0.1]*10
    
47.         g.capital=1000000
    
48.         g.accountID='309919124048'
    
49.         commissionList=[0,0.001,0.0002,0.0002,0,5]
    
50.         ContextInfo.set_commission(0,commissionList)
    
51.         ContextInfo.set_universe(g.gpc)
    
52. def handlebar(ContextInfo):
    
53. 
    
54.         global mean_turnover
    
55.         dtime = ContextInfo.get_market_data_ex(['close'], stock_code =\
    
56.         g.gpc,period = '1d',start_time='20230325',end_time='20230928', count=-1,\
    
57.         dividend_type = 'follow',fill_data=True,subscribe=True)
    
58.         if len(dtime['000300.SH'])>65:
    
59.                 for i in dtime.keys():
    
60.                         return_signal(dtime, 'close',i)
    
61.         return
    
62.        
    
63. def return_signal(data:dict,field:str,code:str):
    
64.         """i是第几天，code是哪只票，返回信号和分数"""
    
65.         _index=data[list(data.keys())[0]].index.tolist()
    
66.         _columns=list(data.keys())
    
67.         df=pd.DataFrame(index=_index,columns=_columns)
    
68.         for i in _columns:
    
69.                 df[i]=data[i][field]
    
70.         df=df.rename(columns={'000300.SH':'SH'})
    
71.         df=df.rename(columns={code:'SZ'})
    
72.         test_df = df.iloc[-61:-1]
    
73.         test_df = (test_df/test_df.iloc[0]).apply(np.log)
    
74.         test_df['idd'] = np.array(range(60))/252.0
    
75.         test_df=pd.DataFrame(test_df)
    
76.         print(test_df.columns)
    
77.         return
    
78.         result=smf.ols('code~SH+idd',test_df).fit()#
    
79.         print(result.summary())
    
80.         return
    

复制代码