python盘整之路

#以今天的上线浮动12%,看是否盘整
df1['c_up_12per'] =  df1['close'].shift(14)*1.12
df1['c_down_12per'] =  df1['close'].shift(14)*0.88
df1['c_shitf1_nei_flag'] = np.where((df1['close'].shift(1)> df1['c_down_12per']) & (df1['close'].shift(1)< df1['c_up_12per']),1,0)
df1['c_shitf1_down_flag'] = np.where(df1['close'].shift(1)< df1['c_down_12per'],1,0)
df1['c_shitf1_up_flag'] = np.where(df1['close'].shift(1)> df1['c_up_12per'],1,0)
df1['c_shitf2_nei_flag'] = np.where((df1['close'].shift(2)> df1['c_down_12per']) & (df1['close'].shift(2)< df1['c_up_12per']),1,0)
df1['c_shitf2_down_flag'] = np.where(df1['close'].shift(2)< df1['c_down_12per'],1,0)
df1['c_shitf2_up_flag'] = np.where(df1['close'].shift(2)> df1['c_up_12per'],1,0)
df1['c_shitf3_nei_flag'] = np.where((df1['close'].shift(3)> df1['c_down_12per']) & (df1['close'].shift(3)< df1['c_up_12per']),1,0)
df1['c_shitf3_down_flag'] = np.where(df1['close'].shift(3)< df1['c_down_12per'],1,0)
df1['c_shitf3_up_flag'] = np.where(df1['close'].shift(3)> df1['c_up_12per'],1,0)
df1['c_shitf4_nei_flag'] = np.where((df1['close'].shift(4)> df1['c_down_12per']) & (df1['close'].shift(4)< df1['c_up_12per']),1,0)
df1['c_shitf4_down_flag'] = np.where(df1['close'].shift(4)< df1['c_down_12per'],1,0)
df1['c_shitf4_up_flag'] = np.where(df1['close'].shift(4)> df1['c_up_12per'],1,0)
df1['c_shitf5_nei_flag'] = np.where((df1['close'].shift(5)> df1['c_down_12per']) & (df1['close'].shift(5)< df1['c_up_12per']),1,0)
df1['c_shitf5_down_flag'] = np.where(df1['close'].shift(5)< df1['c_down_12per'],1,0)
df1['c_shitf5_up_flag'] = np.where(df1['close'].shift(5)> df1['c_up_12per'],1,0)
df1['c_shitf6_nei_flag'] = np.where((df1['close'].shift(6)> df1['c_down_12per']) & (df1['close'].shift(6)< df1['c_up_12per']),1,0)
df1['c_shitf6_down_flag'] = np.where(df1['close'].shift(6)< df1['c_down_12per'],1,0)
df1['c_shitf6_up_flag'] = np.where(df1['close'].shift(6)> df1['c_up_12per'],1,0)
df1['c_shitf7_nei_flag'] = np.where((df1['close'].shift(7)> df1['c_down_12per']) & (df1['close'].shift(7)< df1['c_up_12per']),1,0)
df1['c_shitf7_down_flag'] = np.where(df1['close'].shift(7)< df1['c_down_12per'],1,0)
df1['c_shitf7_up_flag'] = np.where(df1['close'].shift(7)> df1['c_up_12per'],1,0)
df1['c_shitf8_nei_flag'] = np.where((df1['close'].shift(8)> df1['c_down_12per']) & (df1['close'].shift(8)< df1['c_up_12per']),1,0)
df1['c_shitf8_down_flag'] = np.where(df1['close'].shift(8)< df1['c_down_12per'],1,0)
df1['c_shitf8_up_flag'] = np.where(df1['close'].shift(8)> df1['c_up_12per'],1,0)
df1['c_shitf9_nei_flag'] = np.where((df1['close'].shift(9)> df1['c_down_12per']) & (df1['close'].shift(9)< df1['c_up_12per']),1,0)
df1['c_shitf9_down_flag'] = np.where(df1['close'].shift(9)< df1['c_down_12per'],1,0)
df1['c_shitf9_up_flag'] = np.where(df1['close'].shift(9)> df1['c_up_12per'],1,0)
df1['c_shitf10_nei_flag'] = np.where((df1['close'].shift(10)> df1['c_down_12per']) & (df1['close'].shift(10)< df1['c_up_12per']),1,0)
df1['c_shitf10_down_flag'] = np.where(df1['close'].shift(10)< df1['c_down_12per'],1,0)
df1['c_shitf10_up_flag'] = np.where(df1['close'].shift(10)> df1['c_up_12per'],1,0)
df1['c_shitf11_nei_flag'] = np.where((df1['close'].shift(11)> df1['c_down_12per']) & (df1['close'].shift(11)< df1['c_up_12per']),1,0)
df1['c_shitf11_down_flag'] = np.where(df1['close'].shift(11)< df1['c_down_12per'],1,0)
df1['c_shitf11_up_flag'] = np.where(df1['close'].shift(11)> df1['c_up_12per'],1,0)
df1['c_shitf12_nei_flag'] = np.where((df1['close'].shift(12)> df1['c_down_12per']) & (df1['close'].shift(12)< df1['c_up_12per']),1,0)
df1['c_shitf12_down_flag'] = np.where(df1['close'].shift(12)< df1['c_down_12per'],1,0)
df1['c_shitf12_up_flag'] = np.where(df1['close'].shift(12)> df1['c_up_12per'],1,0)
df1['c_shitf13_nei_flag'] = np.where((df1['close'].shift(13)> df1['c_down_12per']) & (df1['close'].shift(13)< df1['c_up_12per']),1,0)
df1['c_shitf13_down_flag'] = np.where(df1['close'].shift(13)< df1['c_down_12per'],1,0)
df1['c_shitf13_up_flag'] = np.where(df1['close'].shift(13)> df1['c_up_12per'],1,0)
df1['c_shitf0_nei_flag'] = np.where((df1['close']> df1['c_down_12per']) & (df1['close']< df1['c_up_12per']),1,0)
df1['c_shitf0_down_flag'] = np.where(df1['close']< df1['c_down_12per'],1,0)
df1['c_shitf0_up_flag'] = np.where(df1['close']> df1['c_up_12per'],1,0)

df1['pan_nei_14count'] = df1['c_shitf1_nei_flag'] +df1['c_shitf2_nei_flag'] +df1['c_shitf3_nei_flag'] +df1['c_shitf4_nei_flag'] +df1['c_shitf5_nei_flag']
+df1['c_shitf6_nei_flag'] +df1['c_shitf7_nei_flag'] +df1['c_shitf8_nei_flag'] +df1['c_shitf9_nei_flag'] +df1['c_shitf10_nei_flag'] +df1['c_shitf11_nei_flag'] 
+df1['c_shitf12_nei_flag'] +df1['c_shitf13_nei_flag'] 

df1['pan_up_14count'] = df1['c_shitf1_up_flag'] +df1['c_shitf2_up_flag'] +df1['c_shitf3_up_flag'] +df1['c_shitf4_up_flag'] +df1['c_shitf5_up_flag']
+df1['c_shitf6_up_flag'] +df1['c_shitf7_up_flag'] +df1['c_shitf8_up_flag'] +df1['c_shitf9_up_flag'] +df1['c_shitf10_up_flag'] +df1['c_shitf11_up_flag'] 
+df1['c_shitf12_up_flag'] +df1['c_shitf13_up_flag'] 

df1['pan_down_14count'] = df1['c_shitf1_down_flag'] +df1['c_shitf2_down_flag'] +df1['c_shitf3_down_flag'] +df1['c_shitf4_down_flag'] +df1['c_shitf5_down_flag']
+df1['c_shitf6_down_flag'] +df1['c_shitf7_down_flag'] +df1['c_shitf8_down_flag'] +df1['c_shitf9_down_flag'] +df1['c_shitf10_down_flag'] +df1['c_shitf11_down_flag'] 
+df1['c_shitf12_down_flag'] +df1['c_shitf13_down_flag'] 
 

<think>好的,我现在需要回答用户的问题:如何在Python中使用多行注释或快捷方式注释掉整段代码。首先,我得回忆一下Python中的注释方法。 根据用户提供的引用,特别是引用[2]和引用[3],Python有两种注释方式,单行和多行。单行用#,多行可以用三个单引号或双引号包裹,不过官方建议的多行注释其实还是用#,因为三引号实际上是字符串,不是真正的注释。不过在实际使用中,很多人用三引号来作为多行注释,尤其是文档字符串。 然后用户提到快捷方式。根据引用[1]和引用[4],在像PyCharm或Spyder这样的IDE中,可以用快捷键Ctrl+/来注释选中的多行。不过要注意,有些键盘布局可能需要不同的组合,比如芬兰语键盘可能需要其他方式,但一般来说Ctrl+/是通用的。 接下来需要整理步骤:单行注释用#,多行可以每行加#或者用三引号(但要注意可能被当作字符串)。快捷方式则是选中代码后按Ctrl+/,取消注释也是同样操作。 另外,要指出三引号作为注释的注意事项,比如如果出现在代码中,可能会被当作字符串,导致错误。所以推荐使用多个#或者IDE的快捷键。用户可能遇到的问题包括不同IDE的快捷键差异,以及三引号的使用场景限制。 最后生成相关问题,要涵盖注释方法、区别、注意事项和工具支持等方面。确保回答结构清晰,步骤明确,引用正确来源。</think>在Python中注释整段代码可通过以下方法实现: ### 一、单行注释扩展法 1. 选中需要注释的多行代码 2. 使用快捷键`Ctrl + /`(Windows/Linux)或`Command + /`(Mac)实现批量注释 3. 每行首部自动添加`#`符号,例如: ```python # print("注释1") # print("注释2") ``` ### 二、多行字符串法 使用三引号包裹代码段(本质是创建未赋值的字符串对象): ```python ''' print("注释3") print("注释4") ''' ``` 注意:这种方法在函数/类内部使用时可能影响语法结构[^3] ### 三、IDE批量操作 在Spyder/Jupyter/PyCharm等开发环境中: 1. 选中多行代码 2. 使用`Ctrl + /`快捷键注释/取消注释 3. 支持嵌套注释的智能处理[^4] ### 四、注意事项 1. 三引号注释在代码中间使用时可能引发缩进错误 2. 多行字符串不能包含未匹配的三引号 3. 官方推荐使用`#`进行正式注释[^2] 相关问题
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