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This topic created in 2002 days ago, the information mentioned may be changed or developed.
'''
def fuc_compare(df, i):
try:
if (df.loc[i+1].h > df.loc[i].h
and df.loc[i+1].l > (df.loc[i].l + (df.loc[i].h - df.loc[i].l)/2)):
df.loc['deal',i+1] = 1
return
if (df.loc[i+1].l < df.loc[i].l
and df.loc[i+1].h < (df.loc[i].h - (df.loc[i].h - df.loc[i].l)/2)):
df.loc['deal',i+1] = -1
return
except:
print('^^^^^^^^^error^^^^^^^^^^^^')
return
i=0 while i < len(df_temp): fuc_compare(df_temp, i) i = i+1
'''
Supplement 1 · Nov 2, 2020
'''
while i < len(df_temp): fuc_compare(df_temp, i) i = i+1 '''
Supplement 2 · Nov 2, 2020
i=0
while i < len(df_temp):
fuc_compare(df_temp, i)
i = i+1
while i < len(df_temp):
fuc_compare(df_temp, i)
i = i+1
Supplement 3 · Nov 2, 2020
最优的办法是 shift 值然后增加一列进行向量化运算
 |
1
yellowtail OP 不知道为啥代码格式总不对，还在上班（ zf 编外摸鱼发帖），先不改了。。
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2
yellowtail OP 其实也就是 pandas 中需要跨行取值的问题，如果用 for i,n in df.iterrows():会快一些但是不知道，程序运行时间主要是花在哪一块... 如果 loc 操作相对于 for 循环不算高时间消耗的话
也许有其他更好的办法.. |
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